From 9e5695d52952ecb6af998d748233e789546b15ff Mon Sep 17 00:00:00 2001
From: Eve-ning <dev_evening@hotmail.com>
Date: Mon, 23 Oct 2023 13:21:46 +0800
Subject: [PATCH 01/28] Writerside configuration

Added new configuration files necessary for the initialization of Writerside. These configurations include the setup for instance profiles, categories, variables, topics, and images.
---
 Writerside/c.list         |  6 ++++++
 Writerside/d.tree         | 12 ++++++++++++
 Writerside/v.list         |  5 +++++
 Writerside/writerside.cfg |  8 ++++++++
 4 files changed, 31 insertions(+)
 create mode 100644 Writerside/c.list
 create mode 100644 Writerside/d.tree
 create mode 100644 Writerside/v.list
 create mode 100644 Writerside/writerside.cfg

diff --git a/Writerside/c.list b/Writerside/c.list
new file mode 100644
index 00000000..c4c77a29
--- /dev/null
+++ b/Writerside/c.list
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE categories
+        SYSTEM "https://resources.jetbrains.com/writerside/1.0/categories.dtd">
+<categories>
+    <category id="wrs" name="Writerside documentation" order="1"/>
+</categories>
\ No newline at end of file
diff --git a/Writerside/d.tree b/Writerside/d.tree
new file mode 100644
index 00000000..7408f9e1
--- /dev/null
+++ b/Writerside/d.tree
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE instance-profile
+        SYSTEM "https://resources.jetbrains.com/writerside/1.0/product-profile.dtd">
+
+<instance-profile id="d"
+                 name="Documentation"
+                 start-page="Overview.md">
+
+    <toc-element topic="Overview.md"/>
+    <toc-element topic="starter-topic.md"/>
+    <toc-element topic="Getting-Started.md"/>
+</instance-profile>
\ No newline at end of file
diff --git a/Writerside/v.list b/Writerside/v.list
new file mode 100644
index 00000000..2d12cb39
--- /dev/null
+++ b/Writerside/v.list
@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE vars SYSTEM "https://resources.jetbrains.com/writerside/1.0/vars.dtd">
+<vars>
+    <var name="product" value="Writerside"/>
+</vars>
diff --git a/Writerside/writerside.cfg b/Writerside/writerside.cfg
new file mode 100644
index 00000000..e83c370c
--- /dev/null
+++ b/Writerside/writerside.cfg
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE ihp SYSTEM "https://resources.jetbrains.com/writerside/1.0/ihp.dtd">
+
+<ihp version="2.0">
+    <topics dir="topics" web-path="topics"/>
+    <images dir="images" web-path="images"/>
+    <instance src="d.tree" web-path="/d/" version="0.0.4"/>
+</ihp>
\ No newline at end of file

From 5d817912d8848a331edb2bdcbc543ad5f25550ba Mon Sep 17 00:00:00 2001
From: Eve-ning <dev_evening@hotmail.com>
Date: Mon, 23 Oct 2023 13:24:45 +0800
Subject: [PATCH 02/28] Add Overview for FRDC-ML in project docs, remove
 starter-topic

Added a new Overview section in the Writerside/topics documentation, outlining the purpose and usage of our FRDC-ML machine learning backbone. This will give new contributors a clear initial understanding of the project. Also, removed the starter-topic.md from the d.tree, as it was a placeholder and no longer needed.
---
 Writerside/d.tree             |  1 -
 Writerside/topics/Overview.md | 17 +++++++++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)
 create mode 100644 Writerside/topics/Overview.md

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 7408f9e1..72afc82a 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -7,6 +7,5 @@
                  start-page="Overview.md">
 
     <toc-element topic="Overview.md"/>
-    <toc-element topic="starter-topic.md"/>
     <toc-element topic="Getting-Started.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/Writerside/topics/Overview.md b/Writerside/topics/Overview.md
new file mode 100644
index 00000000..a90cd34c
--- /dev/null
+++ b/Writerside/topics/Overview.md
@@ -0,0 +1,17 @@
+# Overview
+
+Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for
+ecologists to automatically classify surveyed trees via an Unmanned Aerial
+Vehicle (UAV).
+
+This package, FRDC-ML is the Machine Learning backbone of this project, 
+a centralized repository of tools and model architectures to be used in the
+FRDC pipeline.
+
+[**Get started here**](Getting-Started.md)
+
+## Other Projects
+
+FRDC-UI
+: [The User Interface Repository](https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/) 
+for FRDC, a WebApp GUI for ecologists to adjust annotations.

From 1953480b286ad35cf83da653dbf593cb5f82ec44 Mon Sep 17 00:00:00 2001
From: Eve-ning <dev_evening@hotmail.com>
Date: Mon, 23 Oct 2023 13:25:15 +0800
Subject: [PATCH 03/28] Add detailed Getting Started guide in project docs

Created a comprehensive Getting Started guide under Writerside/topics, detailing the developer environment setup, Google Cloud setup, optional setups like pre-commit hooks, testing information, and overview of the repository structure.
---
 Writerside/topics/Getting-Started.md | 186 +++++++++++++++++++++++++++
 1 file changed, 186 insertions(+)
 create mode 100644 Writerside/topics/Getting-Started.md

diff --git a/Writerside/topics/Getting-Started.md b/Writerside/topics/Getting-Started.md
new file mode 100644
index 00000000..d246b5d8
--- /dev/null
+++ b/Writerside/topics/Getting-Started.md
@@ -0,0 +1,186 @@
+# Getting Started
+
+<procedure title="Installing the Dev. Environment" id="install">
+    <step>Ensure that you have the right version of Python.
+        The required Python version can be seen in <code>pyproject.toml</code>
+        <code-block lang="ini">
+            [tool.poetry.dependencies]
+            python = "..."
+        </code-block>
+    </step>
+    <step>Start by cloning our repository.
+        <code-block lang="shell">
+          git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
+        </code-block>
+    </step>
+    <step>Then, create a Python Virtual Env <code>pyvenv</code>
+        <tabs>
+        <tab title="Windows">
+          <code-block lang="shell">python -m venv venv/</code-block>
+        </tab>
+        <tab title="Linux">
+          <code-block lang="shell">python3 -m venv venv/</code-block>
+        </tab>
+        </tabs> 
+    </step>
+    <step>
+        <a href="https://python-poetry.org/docs/">Install Poetry</a>
+        Then check if it's installed with
+        <code-block lang="shell">poetry --version</code-block>
+        <warning>
+        If <code>poetry</code> is not found, it's likely not in the user PATH.
+        </warning>
+    </step>
+    <step>Activate the virtual environment
+        <tabs>
+        <tab title="Windows">
+        <code-block lang="shell">
+            cd venv/Scripts
+            activate
+            cd ../..
+        </code-block>
+        </tab>
+        <tab title="Linux">
+        <code-block lang="shell">
+            source venv/bin/activate
+        </code-block>
+        </tab>
+        </tabs> 
+    </step>
+    <step>Install the dependencies. You should be in the same directory as
+        <code>pyproject.toml</code>
+        <code-block lang="shell">
+            poetry install --with dev
+        </code-block>
+    </step>
+    <step>Install Pre-Commit Hooks
+        <code-block lang="shell">
+            pre-commit install
+        </code-block>
+    </step>
+</procedure>
+
+<procedure title="Setting Up Google Cloud" id="gcloud">
+    <step>
+        We use Google Cloud to store our datasets. To set up Google Cloud,
+        <a href="https://cloud.google.com/sdk/docs/install">
+          install the Google Cloud CLI
+        </a>
+    </step>
+    <step>
+        Then, you need to
+        <a href="https://cloud.google.com/sdk/docs/initializing">
+          authenticate your account
+        </a>.
+    </step>
+    <step>
+        To make sure everything is working, run the following command:
+        <code-block lang="shell">
+            gsutil ls
+        </code-block>
+    </step>
+</procedure>
+
+<procedure title="Pre-commit Hooks" collapsible="true">
+    <note>This is optional but recommended.
+    Pre-commit hooks are a way to ensure that your code is formatted correctly.
+    This is done by running a series of checks before you commit your code.
+    </note>
+    <step>
+        <code-block lang="shell">
+            pre-commit install
+        </code-block>
+    </step>
+</procedure>
+
+<procedure title="Running the Tests" collapsible="true">
+    <step>
+        Run the tests to make sure everything is working
+        <code-block lang="shell">
+            pytest
+        </code-block>
+    </step>
+<step>
+    In case of errors:
+    <deflist>
+        <def title="google.auth.exceptions.DefaultCredentialsError">
+            If you get this error, it means that you haven't authenticated your
+            Google Cloud account.
+            See <a anchor="gcloud">Setting Up Google Cloud</a>
+        </def>
+        <def title="ModuleNotFoundError" collapsible="true">
+            If you get this error, it means that you haven't installed the
+            dependencies.
+            See <a anchor="install">Installing the Dev. Environment</a>
+        </def>
+    </deflist>
+</step>
+</procedure>
+
+
+## Our Repository Structure
+
+Before starting development, take a look at our repository structure. This will
+help you understand where to put your code.
+
+```mermaid
+graph LR
+    FRDC -- " Core Dependencies " --> src/frdc/
+    FRDC -- " Resources " --> rsc/
+    FRDC -- " Pipeline " --> pipeline/
+    FRDC -- " Tests " --> tests/
+    FRDC -- " Repo Dependencies " --> pyproject.toml,poetry.lock
+    src/frdc/ -- " Dataset Loaders " --> ./load/
+    src/frdc/ -- " Preprocessing Fn. " --> ./preprocess/
+    src/frdc/ -- " Train Deps " --> ./train/
+    src/frdc/ -- " Model Architectures " --> ./models/
+    rsc/ -- " Datasets ... " --> ./dataset_name/
+    pipeline/ -- " Model Training Pipeline " --> ./model_tests/
+```
+
+src/frdc/
+: Source Code for our package. These are the unit components of our pipeline.
+
+rsc/
+: Resources. These are usually cached datasets
+
+pipeline/
+: Pipeline code. These are the full ML tests of our pipeline.
+
+tests/
+: PyTest tests. These are unit tests & integration tests.
+
+### Unit, Integration, and Pipeline Tests
+
+We have 3 types of tests:
+
+- Unit Tests are usually small, single function tests.
+- Integration Tests are larger tests that tests a mock pipeline.
+- Pipeline Tests are the true production pipeline tests that will generate a
+  model.
+
+### Where Should I contribute?
+
+<deflist>
+<def title="Changing a small component">
+If you're changing a small component, such as a argument for preprocessing,
+a new model architecture, or a new configuration for a dataset, take a look
+at the <code>src/frdc/</code> directory.
+</def>
+<def title="Adding a test">
+By adding a new component, you'll need to add a new test. Take a look at the
+<code>tests/</code> directory.
+</def>
+<def title="Changing the pipeline">
+If you're a ML Researcher, you'll probably be changing the pipeline. Take a
+look at the <code>pipeline/</code> directory.
+</def>
+<def title="Adding a dependency">
+If you're adding a new dependency, use <code>poetry add PACKAGE</code> and
+commit the changes to <code>pyproject.toml</code> and <code>poetry.lock</code>.
+<note>
+    E.g. Adding <code>numpy</code> is the same as 
+    <code>poetry add numpy</code>
+</note>
+</def>
+</deflist>
\ No newline at end of file

From 579c01e82df07d1ba324d92891dcb65cdcb7268a Mon Sep 17 00:00:00 2001
From: Eve-ning <dev_evening@hotmail.com>
Date: Mon, 23 Oct 2023 13:28:41 +0800
Subject: [PATCH 04/28] Remove unused .gitignore files

---
 .gitignore | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/.gitignore b/.gitignore
index 8e26edb0..ca71a60e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -164,9 +164,4 @@ cython_debug/
 # Ignores the raw .tif files
 rsc/**/*.tif
 
-# Ignore any secrets files
-.secrets/
-# REMOVE ONLY IF THE SECRET FILES ARE IN .secrets
-*.json
-
 **/*/lightning_logs
\ No newline at end of file

From 22f4b76dc9ec3e834c21cd6d84203264495b8ce5 Mon Sep 17 00:00:00 2001
From: Eve-ning <dev_evening@hotmail.com>
Date: Mon, 23 Oct 2023 13:29:10 +0800
Subject: [PATCH 05/28] Add Writerside rendered documentation

---
 docs/HelpTOC.json         |  1 +
 docs/Map.jhm              |  1 +
 docs/config.json          |  1 +
 docs/current.help.version |  1 +
 docs/getting-started.html | 22 +++++++++++
 docs/index.html           |  9 +++++
 docs/mermaid.css          | 81 +++++++++++++++++++++++++++++++++++++++
 docs/overview.html        |  1 +
 8 files changed, 117 insertions(+)
 create mode 100644 docs/HelpTOC.json
 create mode 100644 docs/Map.jhm
 create mode 100644 docs/config.json
 create mode 100644 docs/current.help.version
 create mode 100644 docs/getting-started.html
 create mode 100644 docs/index.html
 create mode 100644 docs/mermaid.css
 create mode 100644 docs/overview.html

diff --git a/docs/HelpTOC.json b/docs/HelpTOC.json
new file mode 100644
index 00000000..cf714597
--- /dev/null
+++ b/docs/HelpTOC.json
@@ -0,0 +1 @@
+{"entities":{"pages":{"Overview":{"id":"Overview","title":"Overview","url":"overview.html","level":0,"tabIndex":0},"Getting-Started":{"id":"Getting-Started","title":"Getting Started","url":"getting-started.html","level":0,"tabIndex":1}}},"topLevelIds":["Overview","Getting-Started"]}
\ No newline at end of file
diff --git a/docs/Map.jhm b/docs/Map.jhm
new file mode 100644
index 00000000..a0b29f95
--- /dev/null
+++ b/docs/Map.jhm
@@ -0,0 +1 @@
+<?xml version='1.0' encoding='UTF-8'?><map version="2.0"><mapID target="Overview.md" url="overview.html" default="yes"/><mapID target="Overview" url="overview.html" default="yes"/><mapID target="Getting-Started.md" url="getting-started.html" default="no"/><mapID target="Getting-Started" url="getting-started.html" default="no"/></map>
\ No newline at end of file
diff --git a/docs/config.json b/docs/config.json
new file mode 100644
index 00000000..88bdd5dc
--- /dev/null
+++ b/docs/config.json
@@ -0,0 +1 @@
+{"productVersion":"0.0.4","productId":"d","stage":"release","downloadTitle":"Get Documentation","keymaps":{},"searchMaxHits":75,"productName":"Documentation"}
\ No newline at end of file
diff --git a/docs/current.help.version b/docs/current.help.version
new file mode 100644
index 00000000..05b19b1f
--- /dev/null
+++ b/docs/current.help.version
@@ -0,0 +1 @@
+0.0.4
\ No newline at end of file
diff --git a/docs/getting-started.html b/docs/getting-started.html
new file mode 100644
index 00000000..98e067b9
--- /dev/null
+++ b/docs/getting-started.html
@@ -0,0 +1,22 @@
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T13:26:38.1660893"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="4b254e0d_28422"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="4b254e0d_28423">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
+            [tool.poetry.dependencies]
+            python = &quot;...&quot;
+        </div></li><li class="list__item" id="4b254e0d_28425"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
+          git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
+        </div></li><li class="list__item" id="4b254e0d_28427"><p>Then, create a Python Virtual Env <code class="code" id="4b254e0d_28428">pyvenv</code></p><div class="tabs" id="4b254e0d_28429"><div class="tabs__content" data-gtm="tab" id="4b254e0d_28430"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="4b254e0d_28432"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="4b254e0d_28434"><p><a href="https://python-poetry.org/docs/" id="4b254e0d_28435"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="4b254e0d_28437"><p>If <code class="code" id="4b254e0d_28438">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="4b254e0d_28439"><p>Activate the virtual environment </p><div class="tabs" id="4b254e0d_28440"><div class="tabs__content" data-gtm="tab" id="4b254e0d_28441"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+            cd venv/Scripts
+            activate
+            cd ../..
+        </div></div><div class="tabs__content" data-gtm="tab" id="4b254e0d_28443"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+            source venv/bin/activate
+        </div></div></div></li><li class="list__item" id="4b254e0d_28445"><p>Install the dependencies. You should be in the same directory as <code class="code" id="4b254e0d_28446">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
+            poetry install --with dev
+        </div></li><li class="list__item" id="4b254e0d_28448"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
+            pre-commit install
+        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="4b254e0d_28450"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="4b254e0d_28451"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="4b254e0d_28452"><p>Then, you need to <a href="https://cloud.google.com/sdk/docs/initializing" id="4b254e0d_28453"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>.</p></li><li class="list__item" id="4b254e0d_28454"><p>To make sure everything is working, run the following command: </p><div class="code-block" data-lang="bash"         >
+            gsutil ls
+        </div></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="4b254e0d_28456" data-toc="4b254e0d_28456">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="4b254e0d_28457"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="4b254e0d_28458"><div class="code-block" data-lang="bash"         >
+            pre-commit install
+        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="4b254e0d_28460" data-toc="4b254e0d_28460">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="4b254e0d_28461"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
+            pytest
+        </div></li><li class="list__item" id="4b254e0d_28463"><p>In case of errors: </p><dl id="4b254e0d_28464" data-style="title-top"><dt id="4b254e0d_28465" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="4b254e0d_28466" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="4b254e0d_28467" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="4b254e0d_28468" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="4b254e0d_28469">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="4b254e0d_28475" data-expandable="false">rsc/</dt><dd><p id="4b254e0d_28477">Resources. These are usually cached datasets</p></dd><dt id="4b254e0d_28478" data-expandable="false">pipeline/</dt><dd><p id="4b254e0d_28480">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="4b254e0d_28481" data-expandable="false">tests/</dt><dd><p id="4b254e0d_28483">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="4b254e0d_28484">We have 3 types of tests:</p><ul class="list _ul" id="4b254e0d_28485"    ><li class="list__item" id="4b254e0d_28486"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="4b254e0d_28487"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="4b254e0d_28488"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="4b254e0d_28489" data-style="title-top"><dt id="4b254e0d_28490" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="4b254e0d_28491">src/frdc/</code> directory.</p></dd><dt id="4b254e0d_28492" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="4b254e0d_28493">tests/</code> directory.</p></dd><dt id="4b254e0d_28494" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="4b254e0d_28495">pipeline/</code> directory.</p></dd><dt id="4b254e0d_28496" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="4b254e0d_28497">poetry add PACKAGE</code> and commit the changes to <code class="code" id="4b254e0d_28498">pyproject.toml</code> and <code class="code" id="4b254e0d_28499">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="4b254e0d_28500"><p>E.g. Adding <code class="code" id="4b254e0d_28501">numpy</code> is the same as <code class="code" id="4b254e0d_28502">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+#mermaid .label, #mermaid {font-family:"trebuchet ms",arial,sans-serif; font-size:16px;fill:#333;}#mermaid .error-icon{fill:#552222;}#mermaid .error-text{fill:#552222;stroke:#552222;}#mermaid .edge-thickness-normal{stroke-width:2px;}#mermaid .edge-thickness-thick{stroke-width:3.5px;}#mermaid .edge-pattern-solid{stroke-dasharray:0;}#mermaid .edge-pattern-dashed{stroke-dasharray:3;}#mermaid .edge-pattern-dotted{stroke-dasharray:2;}#mermaid .marker{fill:#333333;stroke:#333333;}#mermaid .marker.cross{stroke:#333333;} #mermaid {max-width: 100%;}
+/** Flowchart */
+#mermaid .cluster-label text{fill:#333;}#mermaid .cluster-label span{color:#333;}#mermaid .label text,#mermaid span{fill:#333;color:#333;}#mermaid .node rect,#mermaid .node circle,#mermaid .node ellipse,#mermaid .node polygon,#mermaid .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid .node .label{text-align:center;}#mermaid .node.clickable{cursor:pointer;}#mermaid .arrowheadPath{fill:#333333;}#mermaid .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid .flowchart-link{stroke:#333333;fill:none;}#mermaid .edgeLabel{background-color:#e8e8e8;text-align:center;}#mermaid .edgeLabel rect{opacity:0.5;background-color:#e8e8e8;fill:#e8e8e8;}#mermaid .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid .cluster text{fill:#333;}#mermaid .cluster span{color:#333;}#mermaid div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}
+/** Sequence diagram */
+#mermaid .actor{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid text.actor>tspan{fill:black;stroke:none;}#mermaid .actor-line{stroke:grey;}#mermaid .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:#333;}#mermaid .messageLine1{stroke-width:1.5;stroke-dasharray:2,2;stroke:#333;}#mermaid #arrowhead path{fill:#333;stroke:#333;}#mermaid .sequenceNumber{fill:white;}#mermaid #sequencenumber{fill:#333;}#mermaid #crosshead path{fill:#333;stroke:#333;}#mermaid .messageText{fill:#333;stroke:none;}#mermaid .labelBox{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid .labelText,#mermaid .labelText>tspan{fill:black;stroke:none;}#mermaid .loopText,#mermaid .loopText>tspan{fill:black;stroke:none;}#mermaid .loopLine{stroke-width:2px;stroke-dasharray:2,2;stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);}#mermaid .note{stroke:#aaaa33;fill:#fff5ad;}#mermaid .noteText,#mermaid .noteText>tspan{fill:black;stroke:none;}#mermaid .activation0{fill:#f4f4f4;stroke:#666;}#mermaid .activation1{fill:#f4f4f4;stroke:#666;}#mermaid .activation2{fill:#f4f4f4;stroke:#666;}#mermaid .actorPopupMenu{position:absolute;}#mermaid .actorPopupMenuPanel{position:absolute;fill:#ECECFF;box-shadow:0px 8px 16px 0px rgba(0,0,0,0.2);filter:drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));}#mermaid .actor-man line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid .actor-man circle,#mermaid line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;stroke-width:2px;}
+/** Gantt */
+#mermaid .exclude-range{fill:#eeeeee;}#mermaid .section{stroke:none;opacity:0.2;}#mermaid .section0{fill:rgba(102, 102, 255, 0.49);}#mermaid .section2{fill:#fff400;}#mermaid .section1,#mermaid .section3{fill:white;opacity:0.2;}#mermaid .sectionTitle0{fill:#333;}#mermaid .sectionTitle1{fill:#333;}#mermaid .sectionTitle2{fill:#333;}#mermaid .sectionTitle3{fill:#333;}#mermaid .sectionTitle{text-anchor:start;}#mermaid .grid .tick{stroke:lightgrey;opacity:0.8;shape-rendering:crispEdges;}#mermaid .grid .tick text{fill:#333;}#mermaid .grid path{stroke-width:0;}#mermaid .today{fill:none;stroke:red;stroke-width:2px;}#mermaid .task{stroke-width:2;}#mermaid .taskText{text-anchor:middle;}#mermaid .taskTextOutsideRight{fill:black;text-anchor:start;}#mermaid .taskTextOutsideLeft{fill:black;text-anchor:end;}#mermaid .task.clickable{cursor:pointer;}#mermaid .taskText.clickable{cursor:pointer;fill:#003163!important;font-weight:bold;}#mermaid .taskTextOutsideLeft.clickable{cursor:pointer;fill:#003163!important;font-weight:bold;}#mermaid .taskTextOutsideRight.clickable{cursor:pointer;fill:#003163!important;font-weight:bold;}#mermaid .taskText0,#mermaid .taskText1,#mermaid .taskText2,#mermaid .taskText3{fill:white;}#mermaid .task0,#mermaid .task1,#mermaid .task2,#mermaid .task3{fill:#8a90dd;stroke:#534fbc;}#mermaid .taskTextOutside0,#mermaid .taskTextOutside2{fill:black;}#mermaid .taskTextOutside1,#mermaid .taskTextOutside3{fill:black;}#mermaid .active0,#mermaid .active1,#mermaid .active2,#mermaid .active3{fill:#bfc7ff;stroke:#534fbc;}#mermaid .activeText0,#mermaid .activeText1,#mermaid .activeText2,#mermaid .activeText3{fill:black!important;}#mermaid .done0,#mermaid .done1,#mermaid .done2,#mermaid .done3{stroke:grey;fill:lightgrey;stroke-width:2;}#mermaid .doneText0,#mermaid .doneText1,#mermaid .doneText2,#mermaid .doneText3{fill:black!important;}#mermaid .crit0,#mermaid .crit1,#mermaid .crit2,#mermaid .crit3{stroke:#ff8888;fill:red;stroke-width:2;}#mermaid .activeCrit0,#mermaid .activeCrit1,#mermaid .activeCrit2,#mermaid .activeCrit3{stroke:#ff8888;fill:#bfc7ff;stroke-width:2;}#mermaid .doneCrit0,#mermaid .doneCrit1,#mermaid .doneCrit2,#mermaid .doneCrit3{stroke:#ff8888;fill:lightgrey;stroke-width:2;cursor:pointer;shape-rendering:crispEdges;}#mermaid .milestone{transform:rotate(45deg) scale(0.8,0.8);}#mermaid .milestoneText{font-style:italic;}#mermaid .doneCritText0,#mermaid .doneCritText1,#mermaid .doneCritText2,#mermaid .doneCritText3{fill:black!important;}#mermaid .activeCritText0,#mermaid .activeCritText1,#mermaid .activeCritText2,#mermaid .activeCritText3{fill:black!important;}#mermaid .titleText{text-anchor:middle;font-size:18px;fill:#333;}
+/** Class diagram */
+#mermaid g.classGroup text{fill:#9370DB;fill:#131300;stroke:none;font-size:10px;}#mermaid g.classGroup text .title{font-weight:bolder;}#mermaid .nodeLabel,#mermaid .edgeLabel{color:#131300;}#mermaid .edgeLabel .label rect{fill:#ECECFF;}#mermaid .label text{fill:#131300;}#mermaid .edgeLabel .label span{background:#ECECFF;}#mermaid .classTitle{font-weight:bolder;}#mermaid .node rect,#mermaid .node circle,#mermaid .node ellipse,#mermaid .node polygon,#mermaid .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid .divider{stroke:#9370DB;stroke:1;}#mermaid g.clickable{cursor:pointer;}#mermaid g.classGroup rect{fill:#ECECFF;stroke:#9370DB;}#mermaid g.classGroup line{stroke:#9370DB;stroke-width:1;}#mermaid .classLabel .box{stroke:none;stroke-width:0;fill:#ECECFF;opacity:0.5;}#mermaid .classLabel .label{fill:#9370DB;font-size:10px;}#mermaid .relation{stroke:#333333;stroke-width:1;fill:none;}#mermaid .dashed-line{stroke-dasharray:3;}#mermaid #compositionStart,#mermaid .composition{fill:#333333!important;stroke:#333333!important;stroke-width:1;}#mermaid #compositionEnd,#mermaid .composition{fill:#333333!important;stroke:#333333!important;stroke-width:1;}#mermaid #dependencyStart,#mermaid .dependency{fill:#333333!important;stroke:#333333!important;stroke-width:1;}#mermaid #dependencyStart,#mermaid .dependency{fill:#333333!important;stroke:#333333!important;stroke-width:1;}#mermaid #extensionStart,#mermaid .extension{fill:#333333!important;stroke:#333333!important;stroke-width:1;}#mermaid #extensionEnd,#mermaid .extension{fill:#333333!important;stroke:#333333!important;stroke-width:1;}#mermaid #aggregationStart,#mermaid .aggregation{fill:#ECECFF!important;stroke:#333333!important;stroke-width:1;}#mermaid #aggregationEnd,#mermaid .aggregation{fill:#ECECFF!important;stroke:#333333!important;stroke-width:1;}#mermaid #lollipopStart,#mermaid .lollipop{fill:#ECECFF!important;stroke:#333333!important;stroke-width:1;}#mermaid #lollipopEnd,#mermaid .lollipop{fill:#ECECFF!important;stroke:#333333!important;stroke-width:1;}#mermaid .edgeTerminals{font-size:11px;}
+/** Git graph */
+#mermaid .commit-id,#mermaid .commit-msg{fill:lightgrey;}#mermaid .commit0{stroke:hsl(240, 100%, 46.2745098039%);fill:hsl(240, 100%, 46.2745098039%);}#mermaid .commit-highlight0{stroke:hsl(60, 100%, 3.7254901961%);fill:hsl(60, 100%, 3.7254901961%);}#mermaid .label0{fill:hsl(240, 100%, 46.2745098039%);}#mermaid .arrow0{stroke:hsl(240, 100%, 46.2745098039%);}#mermaid .branch-label1{fill:black;}#mermaid .commit1{stroke:hsl(60, 100%, 43.5294117647%);fill:hsl(60, 100%, 43.5294117647%);}#mermaid .commit-highlight1{stroke:rgb(0, 0, 160.5);fill:rgb(0, 0, 160.5);}#mermaid .label1{fill:hsl(60, 100%, 43.5294117647%);}#mermaid .arrow1{stroke:hsl(60, 100%, 43.5294117647%);}#mermaid .branch-label2{fill:black;}#mermaid .commit2{stroke:hsl(80, 100%, 46.2745098039%);fill:hsl(80, 100%, 46.2745098039%);}#mermaid .commit-highlight2{stroke:rgb(48.8333333334, 0, 146.5000000001);fill:rgb(48.8333333334, 0, 146.5000000001);}#mermaid .label2{fill:hsl(80, 100%, 46.2745098039%);}#mermaid .arrow2{stroke:hsl(80, 100%, 46.2745098039%);}#mermaid .branch-label3{fill:#ffffff;}#mermaid .commit3{stroke:hsl(210, 100%, 46.2745098039%);fill:hsl(210, 100%, 46.2745098039%);}#mermaid .commit-highlight3{stroke:rgb(146.5000000001, 73.2500000001, 0);fill:rgb(146.5000000001, 73.2500000001, 0);}#mermaid .label3{fill:hsl(210, 100%, 46.2745098039%);}#mermaid .arrow3{stroke:hsl(210, 100%, 46.2745098039%);}#mermaid .branch-label4{fill:black;}#mermaid .commit4{stroke:hsl(180, 100%, 46.2745098039%);fill:hsl(180, 100%, 46.2745098039%);}#mermaid .commit-highlight4{stroke:rgb(146.5000000001, 0, 0);fill:rgb(146.5000000001, 0, 0);}#mermaid .label4{fill:hsl(180, 100%, 46.2745098039%);}#mermaid .arrow4{stroke:hsl(180, 100%, 46.2745098039%);}#mermaid .branch-label5{fill:black;}#mermaid .commit5{stroke:hsl(150, 100%, 46.2745098039%);fill:hsl(150, 100%, 46.2745098039%);}#mermaid .commit-highlight5{stroke:rgb(146.5000000001, 0, 73.2500000001);fill:rgb(146.5000000001, 0, 73.2500000001);}#mermaid .label5{fill:hsl(150, 100%, 46.2745098039%);}#mermaid .arrow5{stroke:hsl(150, 100%, 46.2745098039%);}#mermaid .branch-label6{fill:black;}#mermaid .commit6{stroke:hsl(300, 100%, 46.2745098039%);fill:hsl(300, 100%, 46.2745098039%);}#mermaid .commit-highlight6{stroke:rgb(0, 146.5000000001, 0);fill:rgb(0, 146.5000000001, 0);}#mermaid .label6{fill:hsl(300, 100%, 46.2745098039%);}#mermaid .arrow6{stroke:hsl(300, 100%, 46.2745098039%);}#mermaid .branch-label7{fill:black;}#mermaid .commit7{stroke:hsl(0, 100%, 46.2745098039%);fill:hsl(0, 100%, 46.2745098039%);}#mermaid .commit-highlight7{stroke:rgb(0, 146.5000000001, 146.5000000001);fill:rgb(0, 146.5000000001, 146.5000000001);}#mermaid .label7{fill:hsl(0, 100%, 46.2745098039%);}#mermaid .arrow7{stroke:hsl(0, 100%, 46.2745098039%);}#mermaid .branch{stroke-width:1;stroke:#333333;stroke-dasharray:2;}#mermaid .commit-label{font-size:10px;fill:#000021;}#mermaid .commit-label-bkg{font-size:10px;fill:#ffffde;opacity:0.5;}#mermaid .tag-label{font-size:10px;fill:#131300;}#mermaid .tag-label-bkg{fill:#ECECFF;stroke:hsl(240, 60%, 86.2745098039%);}#mermaid .tag-hole{fill:#333;}#mermaid .commit-merge{stroke:#ECECFF;fill:#ECECFF;}#mermaid .commit-reverse{stroke:#ECECFF;fill:#ECECFF;stroke-width:3;}#mermaid .commit-highlight-inner{stroke:#ECECFF;fill:#ECECFF;}#mermaid .arrow{stroke-width:8;stroke-linecap:round;fill:none;}
+#mermaid .branchLabel .label {font-size:16px;} #mermaid g.label.branch-label0 text, #mermaid g.label.branch-label3 text { fill: #eee; }
+/** Entity relationship */
+#mermaid .entityBox{fill:#ECECFF;stroke:#9370DB;}#mermaid .attributeBoxOdd{fill:#ffffff;stroke:#9370DB;}#mermaid .attributeBoxEven{fill:#f2f2f2;stroke:#9370DB;}#mermaid .relationshipLabelBox{fill:hsl(80, 100%, 96.2745098039%);opacity:0.7;background-color:hsl(80, 100%, 96.2745098039%);}#mermaid .relationshipLabelBox rect{opacity:0.5;}#mermaid .relationshipLine{stroke:#333333;}
+/** Journey */
+#mermaid .label{color:#333;}#mermaid .mouth{stroke:#666;}#mermaid line{stroke:#333;}#mermaid .legend{fill:#333;}#mermaid .label text{fill:#333;}#mermaid .label{color:#333;}#mermaid .face{fill:#FFF8DC;stroke:#999;}#mermaid .node rect,#mermaid .node circle,#mermaid .node ellipse,#mermaid .node polygon,#mermaid .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid .node .label{text-align:center;}#mermaid .node.clickable{cursor:pointer;}#mermaid .arrowheadPath{fill:#333333;}#mermaid .edgePath .path{stroke:#333333;stroke-width:1.5px;}#mermaid .flowchart-link{stroke:#333333;fill:none;}#mermaid .edgeLabel{background-color:#e8e8e8;text-align:center;}#mermaid .edgeLabel rect{opacity:0.5;}#mermaid .cluster text{fill:#333;}#mermaid div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid .task-type-0,#mermaid .section-type-0{fill:#ECECFF;}#mermaid .task-type-1,#mermaid .section-type-1{fill:#ffffde;}#mermaid .task-type-2,#mermaid .section-type-2{fill:hsl(304, 100%, 96.2745098039%);}#mermaid .task-type-3,#mermaid .section-type-3{fill:hsl(124, 100%, 93.5294117647%);}#mermaid .task-type-4,#mermaid .section-type-4{fill:hsl(176, 100%, 96.2745098039%);}#mermaid .task-type-5,#mermaid .section-type-5{fill:hsl(-4, 100%, 93.5294117647%);}#mermaid .task-type-6,#mermaid .section-type-6{fill:hsl(8, 100%, 96.2745098039%);}#mermaid .task-type-7,#mermaid .section-type-7{fill:hsl(188, 100%, 93.5294117647%);}
+/** State diagram */
+#mermaid defs #statediagram-barbEnd{fill:#333333;stroke:#333333;}#mermaid g.stateGroup text{fill:#9370DB;stroke:none;font-size:10px;}#mermaid g.stateGroup text{fill:#333;stroke:none;font-size:10px;}#mermaid g.stateGroup .state-title{font-weight:bolder;fill:#131300;}#mermaid g.stateGroup rect{fill:#ECECFF;stroke:#9370DB;}#mermaid g.stateGroup line{stroke:#333333;stroke-width:1;}#mermaid .transition{stroke:#333333;stroke-width:1;fill:none;}#mermaid .stateGroup .composit{fill:white;border-bottom:1px;}#mermaid .stateGroup .alt-composit{fill:#e0e0e0;border-bottom:1px;}#mermaid .state-note{stroke:#aaaa33;fill:#fff5ad;}#mermaid .state-note text{fill:black;stroke:none;font-size:10px;}#mermaid .stateLabel .box{stroke:none;stroke-width:0;fill:#ECECFF;opacity:0.5;}#mermaid .edgeLabel .label rect{fill:#ECECFF;opacity:0.5;}#mermaid .edgeLabel .label text{fill:#333;}#mermaid .label div .edgeLabel{color:#333;}#mermaid .stateLabel text{fill:#131300;font-size:10px;font-weight:bold;}#mermaid .node circle.state-start{fill:#333333;stroke:#333333;}#mermaid .node .fork-join{fill:#333333;stroke:#333333;}#mermaid .node circle.state-end{fill:#9370DB;stroke:white;stroke-width:1.5;}#mermaid .end-state-inner{fill:white;stroke-width:1.5;}#mermaid .node rect{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid .node polygon{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid #statediagram-barbEnd{fill:#333333;}#mermaid .statediagram-cluster rect{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid .cluster-label,#mermaid .nodeLabel{color:#131300;}#mermaid .statediagram-cluster rect.outer{rx:5px;ry:5px;}#mermaid .statediagram-state .divider{stroke:#9370DB;}#mermaid .statediagram-state .title-state{rx:5px;ry:5px;}#mermaid .statediagram-cluster.statediagram-cluster .inner{fill:white;}#mermaid .statediagram-cluster.statediagram-cluster-alt .inner{fill:#f0f0f0;}#mermaid .statediagram-cluster .inner{rx:0;ry:0;}#mermaid .statediagram-state rect.basic{rx:5px;ry:5px;}#mermaid .statediagram-state rect.divider{stroke-dasharray:10,10;fill:#f0f0f0;}#mermaid .note-edge{stroke-dasharray:5;}#mermaid .statediagram-note rect{fill:#fff5ad;stroke:#aaaa33;stroke-width:1px;rx:0;ry:0;}#mermaid .statediagram-note rect{fill:#fff5ad;stroke:#aaaa33;stroke-width:1px;rx:0;ry:0;}#mermaid .statediagram-note text{fill:black;}#mermaid .statediagram-note .nodeLabel{color:black;}#mermaid .statediagram .edgeLabel{color:red;}#mermaid #dependencyStart,#mermaid #dependencyEnd{fill:#333333;stroke:#333333;stroke-width:1;}
+/** Pie chart */
+#mermaid .pieCircle{stroke:#222;stroke-width:1px;}#mermaid .pieTitleText{text-anchor:middle;font-size:25px;fill:black;}#mermaid .slice{fill:#333;font-size:17px;}#mermaid .legend text{fill:black;font-size:17px;}
+/** Requirements */
+#mermaid marker{fill:#333333;stroke:#333333;}#mermaid marker.cross{stroke:#333333;}#mermaid svg{font-size:16px;}#mermaid .reqBox{fill:#ECECFF;fill-opacity:100%;stroke:hsl(240, 60%, 86.2745098039%);stroke-width:hsl(240, 60%, 86.2745098039%);}#mermaid .reqTitle,#mermaid .reqLabel{fill:#131300;}#mermaid .reqLabelBox{fill:#e8e8e8;fill-opacity:100%;}#mermaid .req-title-line{stroke:hsl(240, 60%, 86.2745098039%);stroke-width:hsl(240, 60%, 86.2745098039%);}#mermaid .relationshipLine{stroke:#333333;stroke-width:1;}#mermaid .relationshipLabel{fill:black;}
+/** C4 system */
+#mermaid .person{stroke:calculated;fill:calculated;}
+/** Mind map */
+#mermaid .section-root.mindmap-node { fill: hsl(240, 100%, 50%); } #mermaid .section-root.mindmap-node text { fill: white; }
+#mermaid .mindmap-edges .edge-depth-0 { stroke-width: 14; }
+#mermaid .mindmap-edges .edge-depth-1 { stroke-width: 11; }
+#mermaid .mindmap-edges .edge-depth-2 { stroke-width: 8; }
+#mermaid .mindmap-edges .edge-depth-3 { stroke-width: 5; }
+#mermaid .mindmap-edges .edge-depth-4 { stroke-width: 3; }
+#mermaid .mindmap-edges .edge-depth-5 { stroke-width: 2; }
+#mermaid .mindmap-edges .edge-depth-6 { stroke-width: 1; }
+#mermaid .mindmap-edges .section-edge-0,  #mermaid .mindmap-node.section-0  path { fill: hsl(60, 100%, 70%); stroke: hsl(60, 100%, 70%); } #mermaid .mindmap-node.section-0  text { fill: black; }
+#mermaid .mindmap-edges .section-edge-1,  #mermaid .mindmap-node.section-1  path { fill: hsl(170, 90%, 70%); stroke: hsl(170, 90%, 70%); } #mermaid .mindmap-node.section-1  text { fill: black; }
+#mermaid .mindmap-edges .section-edge-2,  #mermaid .mindmap-node.section-2  path { fill: hsl(280, 90%, 70%); stroke: hsl(280, 90%, 70%); } #mermaid .mindmap-node.section-2  text { fill: white; }
+#mermaid .mindmap-edges .section-edge-3,  #mermaid .mindmap-node.section-3  path { fill: hsl(30, 90%, 70%);  stroke: hsl(30, 90%, 70%);  } #mermaid .mindmap-node.section-3  text { fill: black; }
+#mermaid .mindmap-edges .section-edge-4,  #mermaid .mindmap-node.section-4  path { fill: hsl(140, 90%, 70%); stroke: hsl(140, 90%, 70%); } #mermaid .mindmap-node.section-4  text { fill: black; }
+#mermaid .mindmap-edges .section-edge-5,  #mermaid .mindmap-node.section-5  path { fill: hsl(250, 90%, 70%); stroke: hsl(250, 90%, 70%); } #mermaid .mindmap-node.section-5  text { fill: white; }
+#mermaid .mindmap-edges .section-edge-6,  #mermaid .mindmap-node.section-6  path { fill: hsl(0, 90%, 70%);   stroke: hsl(0, 90%, 70%);   } #mermaid .mindmap-node.section-6  text { fill: black; }
+#mermaid .mindmap-edges .section-edge-7,  #mermaid .mindmap-node.section-7  path { fill: hsl(110, 90%, 70%); stroke: hsl(110, 90%, 70%); } #mermaid .mindmap-node.section-7  text { fill: black; }
+#mermaid .mindmap-edges .section-edge-8,  #mermaid .mindmap-node.section-8  path { fill: hsl(220, 90%, 70%); stroke: hsl(220, 90%, 70%); } #mermaid .mindmap-node.section-8  text { fill: white; }
+#mermaid .mindmap-edges .section-edge-9,  #mermaid .mindmap-node.section-9  path { fill: hsl(330, 90%, 70%); stroke: hsl(330, 90%, 70%); } #mermaid .mindmap-node.section-9  text { fill: black; }
+#mermaid .mindmap-edges .section-edge-10, #mermaid .mindmap-node.section-10 path { fill: hsl(80, 90%, 70%);  stroke: hsl(80, 90%, 70%);  } #mermaid .mindmap-node.section-10 text { fill: black; }
+#mermaid .mindmap-edges .section-edge-11, #mermaid .mindmap-node.section-11 path { fill: hsl(190, 90%, 70%); stroke: hsl(190, 90%, 70%); } #mermaid .mindmap-node.section-11 text { fill: black; }
+#mermaid .mindmap-edges .section-edge-12, #mermaid .mindmap-node.section-12 path { fill: hsl(300, 90%, 70%); stroke: hsl(300, 90%, 70%); } #mermaid .mindmap-node.section-12 text { fill: black; }
+/** Timeline */
+#mermaid .timeline text tspan { fill: #222; } #mermaid .timeline-node path { fill: #ccc; }
+
+/* Dark theme */
+/** Common */
+html.theme-dark #mermaid {fill:#ccc;}html.theme-dark #mermaid .error-icon{fill:#a44141;}html.theme-dark #mermaid .error-text{fill:#ddd;stroke:#ddd;}html.theme-dark #mermaid .edge-pattern-solid{stroke-dasharray:0;}html.theme-dark #mermaid .edge-pattern-dashed{stroke-dasharray:3;}html.theme-dark #mermaid .edge-pattern-dotted{stroke-dasharray:2;}html.theme-dark #mermaid .marker{fill:lightgrey;stroke:lightgrey;}html.theme-dark #mermaid .marker.cross{stroke:lightgrey;}
+/** Flowchart */
+html.theme-dark #mermaid .label{color:#ccc;}html.theme-dark #mermaid .cluster-label text{fill:#F9FFFE;}html.theme-dark #mermaid .cluster-label span{color:#F9FFFE;}html.theme-dark #mermaid .label text,html.theme-dark #mermaid span{fill:#ccc;color:#ccc;}html.theme-dark #mermaid .node rect,html.theme-dark #mermaid .node circle,html.theme-dark #mermaid .node ellipse,html.theme-dark #mermaid .node polygon,html.theme-dark #mermaid .node path{fill:#1f2020;stroke:#81B1DB;stroke-width:1px;}html.theme-dark #mermaid .node .label{text-align:center;}html.theme-dark #mermaid .node.clickable{cursor:pointer;}html.theme-dark #mermaid .arrowheadPath{fill:lightgrey;}html.theme-dark #mermaid .edgePath .path{stroke:lightgrey;stroke-width:2.0px;}html.theme-dark #mermaid .flowchart-link{stroke:lightgrey;fill:none;}html.theme-dark #mermaid .edgeLabel{background-color:hsl(0, 0%, 34.4117647059%);text-align:center;}html.theme-dark #mermaid .edgeLabel rect{opacity:0.5;background-color:hsl(0, 0%, 34.4117647059%);fill:hsl(0, 0%, 34.4117647059%);}html.theme-dark #mermaid .cluster rect{fill:hsl(180, 1.5873015873%, 28.3529411765%);stroke:rgba(255, 255, 255, 0.25);stroke-width:1px;}html.theme-dark #mermaid .cluster text{fill:#F9FFFE;}html.theme-dark #mermaid .cluster span{color:#F9FFFE;}html.theme-dark #mermaid div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;background:hsl(20, 1.5873015873%, 12.3529411765%);border:1px solid rgba(255, 255, 255, 0.25);border-radius:2px;pointer-events:none;z-index:100;}html.theme-dark #mermaid :root{--mermaid-}
+/** Sequence diagram */
+html.theme-dark #mermaid .actor{stroke:#81B1DB;fill:#1f2020;}html.theme-dark #mermaid text.actor>tspan{fill:lightgrey;stroke:none;}html.theme-dark #mermaid .actor-line{stroke:lightgrey;}html.theme-dark #mermaid .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:lightgrey;}html.theme-dark #mermaid .messageLine1{stroke-width:1.5;stroke-dasharray:2,2;stroke:lightgrey;}html.theme-dark #mermaid #arrowhead path{fill:lightgrey;stroke:lightgrey;}html.theme-dark #mermaid .sequenceNumber{fill:black;}html.theme-dark #mermaid #sequencenumber{fill:lightgrey;}html.theme-dark #mermaid #crosshead path{fill:lightgrey;stroke:lightgrey;}html.theme-dark #mermaid .messageText{fill:lightgrey;stroke:none;}html.theme-dark #mermaid .labelBox{stroke:#81B1DB;fill:#1f2020;}html.theme-dark #mermaid .labelText,html.theme-dark #mermaid .labelText>tspan{fill:lightgrey;stroke:none;}html.theme-dark #mermaid .loopText,html.theme-dark #mermaid .loopText>tspan{fill:lightgrey;stroke:none;}html.theme-dark #mermaid .loopLine{stroke-width:2px;stroke-dasharray:2,2;stroke:#81B1DB;fill:#81B1DB;}html.theme-dark #mermaid .note{stroke:hsl(180, 0%, 18.3529411765%);fill:hsl(180, 1.5873015873%, 28.3529411765%);}html.theme-dark #mermaid .noteText,html.theme-dark #mermaid .noteText>tspan{fill:rgb(183.8476190475, 181.5523809523, 181.5523809523);stroke:none;}html.theme-dark #mermaid .activation0{fill:hsl(180, 1.5873015873%, 28.3529411765%);stroke:#81B1DB;}html.theme-dark #mermaid .activation1{fill:hsl(180, 1.5873015873%, 28.3529411765%);stroke:#81B1DB;}html.theme-dark #mermaid .activation2{fill:hsl(180, 1.5873015873%, 28.3529411765%);stroke:#81B1DB;}html.theme-dark #mermaid .actorPopupMenu{position:absolute;}html.theme-dark #mermaid .actorPopupMenuPanel{position:absolute;fill:#1f2020;box-shadow:0px 8px 16px 0px rgba(0,0,0,0.2);filter:drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));}html.theme-dark #mermaid .actor-man line{stroke:#81B1DB;fill:#1f2020;}html.theme-dark #mermaid .actor-man circle,html.theme-dark #mermaid line{stroke:#81B1DB;fill:#1f2020;stroke-width:2px;}html.theme-dark #mermaid :root{--mermaid-}
+/** Gantt */
+html.theme-dark #mermaid .exclude-range{fill:#444;}html.theme-dark #mermaid .section{stroke:none;opacity:0.2;}html.theme-dark #mermaid .section0{fill:hsl(52.9411764706, 28.813559322%, 58.431372549%);}html.theme-dark #mermaid .section2{fill:#EAE8D9;}html.theme-dark #mermaid .section1,html.theme-dark #mermaid .section3{fill:#333;opacity:0.2;}html.theme-dark #mermaid .sectionTitle0{fill:#F9FFFE;}html.theme-dark #mermaid .sectionTitle1{fill:#F9FFFE;}html.theme-dark #mermaid .sectionTitle2{fill:#F9FFFE;}html.theme-dark #mermaid .sectionTitle3{fill:#F9FFFE;}html.theme-dark #mermaid .sectionTitle{text-anchor:start;}html.theme-dark #mermaid .grid .tick{stroke:lightgrey;opacity:0.8;shape-rendering:crispEdges;}html.theme-dark #mermaid .grid .tick text{fill:#ccc;}html.theme-dark #mermaid .grid path{stroke-width:0;}html.theme-dark #mermaid .today{fill:none;stroke:#DB5757;stroke-width:2px;}html.theme-dark #mermaid .task{stroke-width:2;}html.theme-dark #mermaid .taskText{text-anchor:middle;}html.theme-dark #mermaid .taskTextOutsideRight{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%);text-anchor:start;}html.theme-dark #mermaid .taskTextOutsideLeft{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%);text-anchor:end;}html.theme-dark #mermaid .task.clickable{cursor:pointer;}html.theme-dark #mermaid .taskText.clickable{cursor:pointer;fill:#003163!important;font-weight:bold;}html.theme-dark #mermaid .taskTextOutsideLeft.clickable{cursor:pointer;fill:#003163!important;font-weight:bold;}html.theme-dark #mermaid .taskTextOutsideRight.clickable{cursor:pointer;fill:#003163!important;font-weight:bold;}html.theme-dark #mermaid .taskText0,html.theme-dark #mermaid .taskText1,html.theme-dark #mermaid .taskText2,html.theme-dark #mermaid .taskText3{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%);}html.theme-dark #mermaid .task0,html.theme-dark #mermaid .task1,html.theme-dark #mermaid .task2,html.theme-dark #mermaid .task3{fill:hsl(180, 1.5873015873%, 35.3529411765%);stroke:#ffffff;}html.theme-dark #mermaid .taskTextOutside0,html.theme-dark #mermaid .taskTextOutside2{fill:lightgrey;}html.theme-dark #mermaid .taskTextOutside1,html.theme-dark #mermaid .taskTextOutside3{fill:lightgrey;}html.theme-dark #mermaid .active0,html.theme-dark #mermaid .active1,html.theme-dark #mermaid .active2,html.theme-dark #mermaid .active3{fill:#81B1DB;stroke:#ffffff;}html.theme-dark #mermaid .activeText0,html.theme-dark #mermaid .activeText1,html.theme-dark #mermaid .activeText2,html.theme-dark #mermaid .activeText3{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%)!important;}html.theme-dark #mermaid .done0,html.theme-dark #mermaid .done1,html.theme-dark #mermaid .done2,html.theme-dark #mermaid .done3{stroke:grey;fill:lightgrey;stroke-width:2;}html.theme-dark #mermaid .doneText0,html.theme-dark #mermaid .doneText1,html.theme-dark #mermaid .doneText2,html.theme-dark #mermaid .doneText3{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%)!important;}html.theme-dark #mermaid .crit0,html.theme-dark #mermaid .crit1,html.theme-dark #mermaid .crit2,html.theme-dark #mermaid .crit3{stroke:#E83737;fill:#E83737;stroke-width:2;}html.theme-dark #mermaid .activeCrit0,html.theme-dark #mermaid .activeCrit1,html.theme-dark #mermaid .activeCrit2,html.theme-dark #mermaid .activeCrit3{stroke:#E83737;fill:#81B1DB;stroke-width:2;}html.theme-dark #mermaid .doneCrit0,html.theme-dark #mermaid .doneCrit1,html.theme-dark #mermaid .doneCrit2,html.theme-dark #mermaid .doneCrit3{stroke:#E83737;fill:lightgrey;stroke-width:2;cursor:pointer;shape-rendering:crispEdges;}html.theme-dark #mermaid .milestone{transform:rotate(45deg) scale(0.8,0.8);}html.theme-dark #mermaid .milestoneText{font-style:italic;}html.theme-dark #mermaid .doneCritText0,html.theme-dark #mermaid .doneCritText1,html.theme-dark #mermaid .doneCritText2,html.theme-dark #mermaid .doneCritText3{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%)!important;}html.theme-dark #mermaid .activeCritText0,html.theme-dark #mermaid .activeCritText1,html.theme-dark #mermaid .activeCritText2,html.theme-dark #mermaid .activeCritText3{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%)!important;}html.theme-dark #mermaid .titleText{text-anchor:middle;fill:#ccc;}html.theme-dark #mermaid :root{--mermaid-}
+/** Class diagram */
+html.theme-dark #mermaid g.classGroup text{fill:#e0dfdf;stroke:none;}html.theme-dark #mermaid g.classGroup text .title{font-weight:bolder;}html.theme-dark #mermaid .nodeLabel,html.theme-dark #mermaid .edgeLabel{color:#e0dfdf;}html.theme-dark #mermaid .edgeLabel .label rect{fill:#1f2020;}html.theme-dark #mermaid .label text{fill:#e0dfdf;}html.theme-dark #mermaid .edgeLabel .label span{background:#1f2020;}html.theme-dark #mermaid .classTitle{font-weight:bolder;}html.theme-dark #mermaid .node rect,html.theme-dark #mermaid .node circle,html.theme-dark #mermaid .node ellipse,html.theme-dark #mermaid .node polygon,html.theme-dark #mermaid .node path{fill:#1f2020;stroke:#81B1DB;stroke-width:1px;}html.theme-dark #mermaid .divider{stroke:#81B1DB;stroke:1;}html.theme-dark #mermaid g.clickable{cursor:pointer;}html.theme-dark #mermaid g.classGroup rect{fill:#1f2020;stroke:#81B1DB;}html.theme-dark #mermaid g.classGroup line{stroke:#81B1DB;stroke-width:1;}html.theme-dark #mermaid .classLabel .box{stroke:none;stroke-width:0;fill:#1f2020;opacity:0.5;}html.theme-dark #mermaid .classLabel .label{fill:#81B1DB;}html.theme-dark #mermaid .relation{stroke:lightgrey;stroke-width:1;fill:none;}html.theme-dark #mermaid .dashed-line{stroke-dasharray:3;}html.theme-dark #mermaid #compositionStart,html.theme-dark #mermaid .composition{fill:lightgrey!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #compositionEnd,html.theme-dark #mermaid .composition{fill:lightgrey!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #dependencyStart,html.theme-dark #mermaid .dependency{fill:lightgrey!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #dependencyStart,html.theme-dark #mermaid .dependency{fill:lightgrey!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #extensionStart,html.theme-dark #mermaid .extension{fill:lightgrey!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #extensionEnd,html.theme-dark #mermaid .extension{fill:lightgrey!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #aggregationStart,html.theme-dark #mermaid .aggregation{fill:#1f2020!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #aggregationEnd,html.theme-dark #mermaid .aggregation{fill:#1f2020!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #lollipopStart,html.theme-dark #mermaid .lollipop{fill:#1f2020!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid #lollipopEnd,html.theme-dark #mermaid .lollipop{fill:#1f2020!important;stroke:lightgrey!important;stroke-width:1;}html.theme-dark #mermaid .edgeTerminals{}html.theme-dark #mermaid :root{--mermaid-}
+/** Git graph */
+html.theme-dark #mermaid .commit-id,html.theme-dark #mermaid .commit-msg,html.theme-dark #mermaid .branch-label{fill:lightgrey;color:lightgrey;}html.theme-dark #mermaid .branch-label0{fill:undefined;}html.theme-dark #mermaid .commit0{stroke:hsl(180, 1.5873015873%, 48.3529411765%);fill:hsl(180, 1.5873015873%, 48.3529411765%);}html.theme-dark #mermaid .commit-highlight0{stroke:rgb(133.6571428571, 129.7428571428, 129.7428571428);fill:rgb(133.6571428571, 129.7428571428, 129.7428571428);}html.theme-dark #mermaid .label0{fill:hsl(180, 1.5873015873%, 48.3529411765%);}html.theme-dark #mermaid .arrow0{stroke:hsl(180, 1.5873015873%, 48.3529411765%);}html.theme-dark #mermaid .branch-label1{fill:undefined;}html.theme-dark #mermaid .commit1{stroke:hsl(321.6393442623, 65.5913978495%, 38.2352941176%);fill:hsl(321.6393442623, 65.5913978495%, 38.2352941176%);}html.theme-dark #mermaid .commit-highlight1{stroke:rgb(93.5483870969, 221.4516129033, 139.677419355);fill:rgb(93.5483870969, 221.4516129033, 139.677419355);}html.theme-dark #mermaid .label1{fill:hsl(321.6393442623, 65.5913978495%, 38.2352941176%);}html.theme-dark #mermaid .arrow1{stroke:hsl(321.6393442623, 65.5913978495%, 38.2352941176%);}html.theme-dark #mermaid .branch-label2{fill:undefined;}html.theme-dark #mermaid .commit2{stroke:hsl(194.4, 16.5562913907%, 49.6078431373%);fill:hsl(194.4, 16.5562913907%, 49.6078431373%);}html.theme-dark #mermaid .commit-highlight2{stroke:rgb(149.4437086091, 117.6092715231, 107.5562913906);fill:rgb(149.4437086091, 117.6092715231, 107.5562913906);}html.theme-dark #mermaid .label2{fill:hsl(194.4, 16.5562913907%, 49.6078431373%);}html.theme-dark #mermaid .arrow2{stroke:hsl(194.4, 16.5562913907%, 49.6078431373%);}html.theme-dark #mermaid .branch-label3{fill:undefined;}html.theme-dark #mermaid .commit3{stroke:hsl(23.0769230769, 49.0566037736%, 40.7843137255%);fill:hsl(23.0769230769, 49.0566037736%, 40.7843137255%);}html.theme-dark #mermaid .commit-highlight3{stroke:rgb(99.9811320754, 162.7735849057, 202.0188679245);fill:rgb(99.9811320754, 162.7735849057, 202.0188679245);}html.theme-dark #mermaid .label3{fill:hsl(23.0769230769, 49.0566037736%, 40.7843137255%);}html.theme-dark #mermaid .arrow3{stroke:hsl(23.0769230769, 49.0566037736%, 40.7843137255%);}html.theme-dark #mermaid .branch-label4{fill:undefined;}html.theme-dark #mermaid .commit4{stroke:hsl(0, 83.3333333333%, 43.5294117647%);fill:hsl(0, 83.3333333333%, 43.5294117647%);}html.theme-dark #mermaid .commit-highlight4{stroke:rgb(51.5000000001, 236.5, 236.5);fill:rgb(51.5000000001, 236.5, 236.5);}html.theme-dark #mermaid .label4{fill:hsl(0, 83.3333333333%, 43.5294117647%);}html.theme-dark #mermaid .arrow4{stroke:hsl(0, 83.3333333333%, 43.5294117647%);}html.theme-dark #mermaid .branch-label5{fill:undefined;}html.theme-dark #mermaid .commit5{stroke:hsl(289.1666666667, 100%, 24.1176470588%);fill:hsl(289.1666666667, 100%, 24.1176470588%);}html.theme-dark #mermaid .commit-highlight5{stroke:rgb(154.2083333334, 255, 132.0000000001);fill:rgb(154.2083333334, 255, 132.0000000001);}html.theme-dark #mermaid .label5{fill:hsl(289.1666666667, 100%, 24.1176470588%);}html.theme-dark #mermaid .arrow5{stroke:hsl(289.1666666667, 100%, 24.1176470588%);}html.theme-dark #mermaid .branch-label6{fill:undefined;}html.theme-dark #mermaid .commit6{stroke:hsl(35.1315789474, 98.7012987013%, 40.1960784314%);fill:hsl(35.1315789474, 98.7012987013%, 40.1960784314%);}html.theme-dark #mermaid .commit-highlight6{stroke:rgb(51.331168831, 135.1948051946, 253.6688311688);fill:rgb(51.331168831, 135.1948051946, 253.6688311688);}html.theme-dark #mermaid .label6{fill:hsl(35.1315789474, 98.7012987013%, 40.1960784314%);}html.theme-dark #mermaid .arrow6{stroke:hsl(35.1315789474, 98.7012987013%, 40.1960784314%);}html.theme-dark #mermaid .branch-label7{fill:undefined;}html.theme-dark #mermaid .commit7{stroke:hsl(106.1538461538, 84.4155844156%, 35.0980392157%);fill:hsl(106.1538461538, 84.4155844156%, 35.0980392157%);}html.theme-dark #mermaid .commit-highlight7{stroke:rgb(206.1818181817, 89.948051948, 241.051948052);fill:rgb(206.1818181817, 89.948051948, 241.051948052);}html.theme-dark #mermaid .label7{fill:hsl(106.1538461538, 84.4155844156%, 35.0980392157%);}html.theme-dark #mermaid .arrow7{stroke:hsl(106.1538461538, 84.4155844156%, 35.0980392157%);}html.theme-dark #mermaid .branch{stroke-width:1;stroke:lightgrey;stroke-dasharray:2;}html.theme-dark #mermaid .commit-label{fill:rgb(183.8476190475, 181.5523809523, 181.5523809523);}html.theme-dark #mermaid .commit-label-bkg{fill:hsl(180, 1.5873015873%, 28.3529411765%);opacity:0.5;}html.theme-dark #mermaid .tag-label{fill:#e0dfdf;}html.theme-dark #mermaid .tag-label-bkg{fill:#1f2020;stroke:#cccccc;}html.theme-dark #mermaid .tag-hole{fill:#ccc;}html.theme-dark #mermaid .commit-merge{stroke:#1f2020;fill:#1f2020;}html.theme-dark #mermaid .commit-reverse{stroke:#1f2020;fill:#1f2020;stroke-width:3;}html.theme-dark #mermaid .commit-highlight-inner{stroke:#1f2020;fill:#1f2020;}html.theme-dark #mermaid .arrow{stroke-width:8;stroke-linecap:round;fill:none;}:root{--mermaid-}
+/** Entity relationship */
+html.theme-dark #mermaid .entityBox{fill:#1f2020;stroke:#81B1DB;}html.theme-dark #mermaid .attributeBoxOdd{fill:hsl(0, 0%, 32%);stroke:#81B1DB;}html.theme-dark #mermaid .attributeBoxEven{fill:hsl(0, 0%, 22%);stroke:#81B1DB;}html.theme-dark #mermaid .relationshipLabelBox{fill:hsl(20, 1.5873015873%, 12.3529411765%);opacity:0.7;background-color:hsl(20, 1.5873015873%, 12.3529411765%);}html.theme-dark #mermaid .relationshipLabelBox rect{opacity:0.5;}html.theme-dark #mermaid .relationshipLine{stroke:lightgrey;}html.theme-dark #mermaid :root{--mermaid-}
+/** Journey */
+html.theme-dark #mermaid .label{color:#ccc;}html.theme-dark #mermaid .mouth{stroke:#666;}html.theme-dark #mermaid line{stroke:#ccc;}html.theme-dark #mermaid .legend{fill:#ccc;}html.theme-dark #mermaid .label text{fill:#333;}html.theme-dark #mermaid .label{color:#ccc;}html.theme-dark #mermaid .face{fill:#FFF8DC;stroke:#999;}html.theme-dark #mermaid .node rect,html.theme-dark #mermaid .node circle,html.theme-dark #mermaid .node ellipse,html.theme-dark #mermaid .node polygon,html.theme-dark #mermaid .node path{fill:#1f2020;stroke:#81B1DB;stroke-width:1px;}html.theme-dark #mermaid .node .label{text-align:center;}html.theme-dark #mermaid .node.clickable{cursor:pointer;}html.theme-dark #mermaid .arrowheadPath{fill:lightgrey;}html.theme-dark #mermaid .edgePath .path{stroke:lightgrey;stroke-width:1.5px;}html.theme-dark #mermaid .flowchart-link{stroke:lightgrey;fill:none;}html.theme-dark #mermaid .edgeLabel{background-color:hsl(0, 0%, 34.4117647059%);text-align:center;}html.theme-dark #mermaid .edgeLabel rect{opacity:0.5;}html.theme-dark #mermaid .cluster text{fill:#F9FFFE;}html.theme-dark #mermaid div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;background:hsl(20, 1.5873015873%, 12.3529411765%);border:1px solid rgba(255, 255, 255, 0.25);border-radius:2px;pointer-events:none;z-index:100;}html.theme-dark #mermaid .task-type-0,html.theme-dark #mermaid .section-type-0{fill:#1f2020;}html.theme-dark #mermaid .task-type-1,html.theme-dark #mermaid .section-type-1{fill:hsl(180, 1.5873015873%, 28.3529411765%);}html.theme-dark #mermaid .task-type-2,html.theme-dark #mermaid .section-type-2{fill:hsl(244, 1.5873015873%, 12.3529411765%);}html.theme-dark #mermaid .task-type-3,html.theme-dark #mermaid .section-type-3{fill:hsl(244, 1.5873015873%, 28.3529411765%);}html.theme-dark #mermaid .task-type-4,html.theme-dark #mermaid .section-type-4{fill:hsl(116, 1.5873015873%, 12.3529411765%);}html.theme-dark #mermaid .task-type-5,html.theme-dark #mermaid .section-type-5{fill:hsl(116, 1.5873015873%, 28.3529411765%);}html.theme-dark #mermaid .task-type-6,html.theme-dark #mermaid .section-type-6{fill:hsl(308, 1.5873015873%, 12.3529411765%);}html.theme-dark #mermaid .task-type-7,html.theme-dark #mermaid .section-type-7{fill:hsl(308, 1.5873015873%, 28.3529411765%);}html.theme-dark #mermaid :root{--mermaid-}
+/** State diagram */
+html.theme-dark #mermaid defs #statediagram-barbEnd{fill:lightgrey;stroke:lightgrey;}html.theme-dark #mermaid g.stateGroup text{fill:#81B1DB;stroke:none;}html.theme-dark #mermaid g.stateGroup text{fill:#ccc;stroke:none;}html.theme-dark #mermaid g.stateGroup .state-title{font-weight:bolder;fill:#e0dfdf;}html.theme-dark #mermaid g.stateGroup rect{fill:#1f2020;stroke:#81B1DB;}html.theme-dark #mermaid g.stateGroup line{stroke:lightgrey;stroke-width:1;}html.theme-dark #mermaid .transition{stroke:lightgrey;stroke-width:1;fill:none;}html.theme-dark #mermaid .stateGroup .composit{fill:#333;border-bottom:1px;}html.theme-dark #mermaid .stateGroup .alt-composit{fill:#e0e0e0;border-bottom:1px;}html.theme-dark #mermaid .state-note{stroke:hsl(180, 0%, 18.3529411765%);fill:hsl(180, 1.5873015873%, 28.3529411765%);}html.theme-dark #mermaid .state-note text{fill:rgb(183.8476190475, 181.5523809523, 181.5523809523);stroke:none;}html.theme-dark #mermaid .stateLabel .box{stroke:none;stroke-width:0;fill:#1f2020;opacity:0.5;}html.theme-dark #mermaid .edgeLabel .label rect{fill:#1f2020;opacity:0.5;}html.theme-dark #mermaid .edgeLabel .label text{fill:#ccc;}html.theme-dark #mermaid .label div .edgeLabel{color:#ccc;}html.theme-dark #mermaid .stateLabel text{fill:#e0dfdf;font-weight:bold;}html.theme-dark #mermaid .node circle.state-start{fill:#f4f4f4;stroke:#f4f4f4;}html.theme-dark #mermaid .node .fork-join{fill:#f4f4f4;stroke:#f4f4f4;}html.theme-dark #mermaid .node circle.state-end{fill:#cccccc;stroke:#333;stroke-width:1.5;}html.theme-dark #mermaid .end-state-inner{fill:#333;stroke-width:1.5;}html.theme-dark #mermaid .node rect{fill:#1f2020;stroke:#81B1DB;stroke-width:1px;}html.theme-dark #mermaid .node polygon{fill:#1f2020;stroke:#81B1DB;stroke-width:1px;}html.theme-dark #mermaid #statediagram-barbEnd{fill:lightgrey;}html.theme-dark #mermaid .statediagram-cluster rect{fill:#1f2020;stroke:#81B1DB;stroke-width:1px;}html.theme-dark #mermaid .cluster-label,html.theme-dark #mermaid .nodeLabel{color:#e0dfdf;}html.theme-dark #mermaid .statediagram-cluster rect.outer{rx:5px;ry:5px;}html.theme-dark #mermaid .statediagram-state .divider{stroke:#81B1DB;}html.theme-dark #mermaid .statediagram-state .title-state{rx:5px;ry:5px;}html.theme-dark #mermaid .statediagram-cluster.statediagram-cluster .inner{fill:#333;}html.theme-dark #mermaid .statediagram-cluster.statediagram-cluster-alt .inner{fill:#555;}html.theme-dark #mermaid .statediagram-cluster .inner{rx:0;ry:0;}html.theme-dark #mermaid .statediagram-state rect.basic{rx:5px;ry:5px;}html.theme-dark #mermaid .statediagram-state rect.divider{stroke-dasharray:10,10;fill:#555;}html.theme-dark #mermaid .note-edge{stroke-dasharray:5;}html.theme-dark #mermaid .statediagram-note rect{fill:hsl(180, 1.5873015873%, 28.3529411765%);stroke:hsl(180, 0%, 18.3529411765%);stroke-width:1px;rx:0;ry:0;}html.theme-dark #mermaid .statediagram-note rect{fill:hsl(180, 1.5873015873%, 28.3529411765%);stroke:hsl(180, 0%, 18.3529411765%);stroke-width:1px;rx:0;ry:0;}html.theme-dark #mermaid .statediagram-note text{fill:rgb(183.8476190475, 181.5523809523, 181.5523809523);}html.theme-dark #mermaid .statediagram-note .nodeLabel{color:rgb(183.8476190475, 181.5523809523, 181.5523809523);}html.theme-dark #mermaid .statediagram .edgeLabel{color:red;}html.theme-dark #mermaid #dependencyStart,html.theme-dark #mermaid #dependencyEnd{fill:lightgrey;stroke:lightgrey;stroke-width:1;}html.theme-dark #mermaid :root{--mermaid-}
+/** Pie chart */
+html.theme-dark #mermaid .pieCircle{filter: brightness(0.7);} html.theme-dark #mermaid[aria-roledescription=pie] .legend rect { filter: brightness(0.7); } html.theme-dark #mermaid .pieTitleText{text-anchor:middle;fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%);}html.theme-dark #mermaid .slice{fill:#222;}html.theme-dark #mermaid .legend text{fill:hsl(28.5714285714, 17.3553719008%, 86.2745098039%);}html.theme-dark #mermaid :root{--mermaid-}
+/** Requirements */
+html.theme-dark #mermaid marker{fill:lightgrey;stroke:lightgrey;}html.theme-dark #mermaid marker.cross{stroke:lightgrey;}html.theme-dark #mermaid svg{}html.theme-dark #mermaid .reqBox{fill:#1f2020;fill-opacity:100%;stroke:#cccccc;stroke-width:#cccccc;}html.theme-dark #mermaid .reqTitle,html.theme-dark #mermaid .reqLabel{fill:#e0dfdf;}html.theme-dark #mermaid .reqLabelBox{fill:hsl(180, 1.5873015873%, 28.3529411765%);fill-opacity:100%;}html.theme-dark #mermaid .req-title-line{stroke:#cccccc;stroke-width:#cccccc;}html.theme-dark #mermaid .relationshipLine{stroke:lightgrey;stroke-width:1;}html.theme-dark #mermaid .relationshipLabel{fill:lightgrey;}html.theme-dark #mermaid :root{--mermaid-}
+/** C4 system */
+html.theme-dark #mermaid .person{stroke:calculated;fill:calculated;} html.theme-dark #mermaid[aria-roledescription=c4] rect {stroke: #ddd;} html.theme-dark #mermaid[aria-roledescription=c4] text {fill:#eee;}
+/** Mind map */
+html.theme-dark #mermaid .section-root.mindmap-node { fill: #ccc; } html.theme-dark #mermaid .section-root.mindmap-node text { fill: black; }
+html.theme-dark #mermaid .mindmap-edges path, html.theme-dark #mermaid .mindmap-node { filter: hue-rotate(180deg) invert(0.9); }
+/** Timeline */
+html.theme-dark #mermaid .timeline text tspan { fill: #eee; } html.theme-dark #mermaid .timeline-node path { fill: #222; }
diff --git a/docs/overview.html b/docs/overview.html
new file mode 100644
index 00000000..959ec732
--- /dev/null
+++ b/docs/overview.html
@@ -0,0 +1 @@
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T13:26:37.957627"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="e86100a7_1184">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="e86100a7_1185">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="e86100a7_1186"><a href="getting-started.html" id="e86100a7_1187" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="e86100a7_1188">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="e86100a7_1189" data-style="title-top"><dt id="e86100a7_1190" data-expandable="false">FRDC-UI</dt><dd><p id="e86100a7_1192"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="e86100a7_1193"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file

From e15858340cd7b5b98009c44b77e88b3820b62fd3 Mon Sep 17 00:00:00 2001
From: Eve-ning <dev_evening@hotmail.com>
Date: Mon, 23 Oct 2023 14:23:42 +0800
Subject: [PATCH 06/28] Update instructions for Google Cloud setup

Added instructions for Google Cloud authentication and setting up Application Default Credentials (ADC) in the 'Getting-Started' documentation. Replaced 'gsutil ls' command with a step to run tests to verify the setup. This change provides more comprehensive and clear instructions for users on how to setup Google Cloud.
---
 Writerside/topics/Getting-Started.md | 17 +++++++++++------
 docs/getting-started.html            | 20 +++++++++-----------
 docs/overview.html                   |  2 +-
 3 files changed, 21 insertions(+), 18 deletions(-)

diff --git a/Writerside/topics/Getting-Started.md b/Writerside/topics/Getting-Started.md
index d246b5d8..7615174d 100644
--- a/Writerside/topics/Getting-Started.md
+++ b/Writerside/topics/Getting-Started.md
@@ -68,16 +68,21 @@
         </a>
     </step>
     <step>
-        Then, you need to
+        Then,
         <a href="https://cloud.google.com/sdk/docs/initializing">
           authenticate your account
         </a>.
+        <code-block lang="shell">gcloud auth login</code-block>
     </step>
     <step>
-        To make sure everything is working, run the following command:
-        <code-block lang="shell">
-            gsutil ls
-        </code-block>
+        Finally, 
+        <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc">
+          set up Application Default Credentials (ADC)
+        </a>.
+        <code-block lang="shell">gcloud auth application-default login</code-block>
+    </step>
+    <step>
+        To make sure everything is working, <a anchor="tests">run the tests</a>.
     </step>
 </procedure>
 
@@ -93,7 +98,7 @@
     </step>
 </procedure>
 
-<procedure title="Running the Tests" collapsible="true">
+<procedure title="Running the Tests" collapsible="true" id="tests">
     <step>
         Run the tests to make sure everything is working
         <code-block lang="shell">
diff --git a/docs/getting-started.html b/docs/getting-started.html
index 98e067b9..2d8d1e29 100644
--- a/docs/getting-started.html
+++ b/docs/getting-started.html
@@ -1,22 +1,20 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T13:26:38.1660893"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="4b254e0d_28422"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="4b254e0d_28423">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T14:21:53.4408946"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="a901f86f_6922"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="a901f86f_6923">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
             [tool.poetry.dependencies]
             python = &quot;...&quot;
-        </div></li><li class="list__item" id="4b254e0d_28425"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="a901f86f_6925"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
           git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
-        </div></li><li class="list__item" id="4b254e0d_28427"><p>Then, create a Python Virtual Env <code class="code" id="4b254e0d_28428">pyvenv</code></p><div class="tabs" id="4b254e0d_28429"><div class="tabs__content" data-gtm="tab" id="4b254e0d_28430"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="4b254e0d_28432"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="4b254e0d_28434"><p><a href="https://python-poetry.org/docs/" id="4b254e0d_28435"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="4b254e0d_28437"><p>If <code class="code" id="4b254e0d_28438">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="4b254e0d_28439"><p>Activate the virtual environment </p><div class="tabs" id="4b254e0d_28440"><div class="tabs__content" data-gtm="tab" id="4b254e0d_28441"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></li><li class="list__item" id="a901f86f_6927"><p>Then, create a Python Virtual Env <code class="code" id="a901f86f_6928">pyvenv</code></p><div class="tabs" id="a901f86f_6929"><div class="tabs__content" data-gtm="tab" id="a901f86f_6930"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="a901f86f_6932"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="a901f86f_6934"><p><a href="https://python-poetry.org/docs/" id="a901f86f_6935"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="a901f86f_6937"><p>If <code class="code" id="a901f86f_6938">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="a901f86f_6939"><p>Activate the virtual environment </p><div class="tabs" id="a901f86f_6940"><div class="tabs__content" data-gtm="tab" id="a901f86f_6941"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             cd venv/Scripts
             activate
             cd ../..
-        </div></div><div class="tabs__content" data-gtm="tab" id="4b254e0d_28443"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></div><div class="tabs__content" data-gtm="tab" id="a901f86f_6943"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             source venv/bin/activate
-        </div></div></div></li><li class="list__item" id="4b254e0d_28445"><p>Install the dependencies. You should be in the same directory as <code class="code" id="4b254e0d_28446">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
+        </div></div></div></li><li class="list__item" id="a901f86f_6945"><p>Install the dependencies. You should be in the same directory as <code class="code" id="a901f86f_6946">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
             poetry install --with dev
-        </div></li><li class="list__item" id="4b254e0d_28448"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="a901f86f_6948"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="4b254e0d_28450"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="4b254e0d_28451"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="4b254e0d_28452"><p>Then, you need to <a href="https://cloud.google.com/sdk/docs/initializing" id="4b254e0d_28453"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>.</p></li><li class="list__item" id="4b254e0d_28454"><p>To make sure everything is working, run the following command: </p><div class="code-block" data-lang="bash"         >
-            gsutil ls
-        </div></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="4b254e0d_28456" data-toc="4b254e0d_28456">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="4b254e0d_28457"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="4b254e0d_28458"><div class="code-block" data-lang="bash"         >
+        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="a901f86f_6950"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="a901f86f_6951"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="a901f86f_6952"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="a901f86f_6953"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="a901f86f_6955"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="a901f86f_6956"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="a901f86f_6958"><p>To make sure everything is working, <a href="#tests" id="a901f86f_6959" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="a901f86f_6960" data-toc="a901f86f_6960">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="a901f86f_6961"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="a901f86f_6962"><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="4b254e0d_28460" data-toc="4b254e0d_28460">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="4b254e0d_28461"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
+        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="a901f86f_6964"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
             pytest
-        </div></li><li class="list__item" id="4b254e0d_28463"><p>In case of errors: </p><dl id="4b254e0d_28464" data-style="title-top"><dt id="4b254e0d_28465" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="4b254e0d_28466" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="4b254e0d_28467" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="4b254e0d_28468" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="4b254e0d_28469">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="4b254e0d_28475" data-expandable="false">rsc/</dt><dd><p id="4b254e0d_28477">Resources. These are usually cached datasets</p></dd><dt id="4b254e0d_28478" data-expandable="false">pipeline/</dt><dd><p id="4b254e0d_28480">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="4b254e0d_28481" data-expandable="false">tests/</dt><dd><p id="4b254e0d_28483">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="4b254e0d_28484">We have 3 types of tests:</p><ul class="list _ul" id="4b254e0d_28485"    ><li class="list__item" id="4b254e0d_28486"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="4b254e0d_28487"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="4b254e0d_28488"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="4b254e0d_28489" data-style="title-top"><dt id="4b254e0d_28490" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="4b254e0d_28491">src/frdc/</code> directory.</p></dd><dt id="4b254e0d_28492" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="4b254e0d_28493">tests/</code> directory.</p></dd><dt id="4b254e0d_28494" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="4b254e0d_28495">pipeline/</code> directory.</p></dd><dt id="4b254e0d_28496" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="4b254e0d_28497">poetry add PACKAGE</code> and commit the changes to <code class="code" id="4b254e0d_28498">pyproject.toml</code> and <code class="code" id="4b254e0d_28499">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="4b254e0d_28500"><p>E.g. Adding <code class="code" id="4b254e0d_28501">numpy</code> is the same as <code class="code" id="4b254e0d_28502">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+        </div></li><li class="list__item" id="a901f86f_6966"><p>In case of errors: </p><dl id="a901f86f_6967" data-style="title-top"><dt id="a901f86f_6968" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="a901f86f_6969" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="a901f86f_6970" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="a901f86f_6971" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="a901f86f_6972">Before starting development, take a look at our repository structure. 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transform="translate(734.7578125, 185)" id="flowchart-./train/-1181" class="node default default flowchart-label"><rect height="34" width="71.9375" y="-17" x="-35.96875" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-28.46875, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="56.9375"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">./train/</span></div></foreignObject></g></g><g transform="translate(734.7578125, 269)" id="flowchart-./models/-1183" class="node default default flowchart-label"><rect height="34" width="88.359375" y="-17" x="-44.1796875" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-36.6796875, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="73.359375"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">./models/</span></div></foreignObject></g></g><g transform="translate(734.7578125, 353)" id="flowchart-./dataset_name/-1185" class="node default default flowchart-label"><rect height="34" width="138.828125" y="-17" x="-69.4140625" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-61.9140625, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="123.828125"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">./dataset_name/</span></div></foreignObject></g></g><g transform="translate(734.7578125, 437)" id="flowchart-./model_tests/-1187" class="node default default flowchart-label"><rect height="34" width="124.640625" y="-17" x="-62.3203125" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-54.8203125, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="109.640625"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">./model_tests/</span></div></foreignObject></g></g></g></g></g></svg><dl id="a901f86f_6974" data-style="title-top"><dt id="a901f86f_6975" data-expandable="false">src/frdc/</dt><dd><p id="a901f86f_6977">Source Code for our package. These are the unit components of our pipeline.</p></dd><dt id="a901f86f_6978" data-expandable="false">rsc/</dt><dd><p id="a901f86f_6980">Resources. These are usually cached datasets</p></dd><dt id="a901f86f_6981" data-expandable="false">pipeline/</dt><dd><p id="a901f86f_6983">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="a901f86f_6984" data-expandable="false">tests/</dt><dd><p id="a901f86f_6986">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="a901f86f_6987">We have 3 types of tests:</p><ul class="list _ul" id="a901f86f_6988"    ><li class="list__item" id="a901f86f_6989"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="a901f86f_6990"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="a901f86f_6991"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="a901f86f_6992" data-style="title-top"><dt id="a901f86f_6993" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="a901f86f_6994">src/frdc/</code> directory.</p></dd><dt id="a901f86f_6995" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="a901f86f_6996">tests/</code> directory.</p></dd><dt id="a901f86f_6997" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="a901f86f_6998">pipeline/</code> directory.</p></dd><dt id="a901f86f_6999" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="a901f86f_7000">poetry add PACKAGE</code> and commit the changes to <code class="code" id="a901f86f_7001">pyproject.toml</code> and <code class="code" id="a901f86f_7002">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="a901f86f_7003"><p>E.g. Adding <code class="code" id="a901f86f_7004">numpy</code> is the same as <code class="code" id="a901f86f_7005">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
diff --git a/docs/overview.html b/docs/overview.html
index 959ec732..aa465425 100644
--- a/docs/overview.html
+++ b/docs/overview.html
@@ -1 +1 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T13:26:37.957627"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="e86100a7_1184">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="e86100a7_1185">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="e86100a7_1186"><a href="getting-started.html" id="e86100a7_1187" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="e86100a7_1188">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="e86100a7_1189" data-style="title-top"><dt id="e86100a7_1190" data-expandable="false">FRDC-UI</dt><dd><p id="e86100a7_1192"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="e86100a7_1193"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T14:21:53.436548"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="bbfad7ae_138">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="bbfad7ae_139">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="bbfad7ae_140"><a href="getting-started.html" id="bbfad7ae_141" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="bbfad7ae_142">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="bbfad7ae_143" data-style="title-top"><dt id="bbfad7ae_144" data-expandable="false">FRDC-UI</dt><dd><p id="bbfad7ae_146"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="bbfad7ae_147"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file

From df9c46cbc5f8f8b77e3901b2d6e7a02cbe6cd34e Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 16:11:41 +0800
Subject: [PATCH 07/28] Add Datasets Doc

---
 Writerside/d.tree            |   1 +
 Writerside/topics/Dataset.md | 154 +++++++++++++++++++++++++++++++++++
 2 files changed, 155 insertions(+)
 create mode 100644 Writerside/topics/Dataset.md

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 72afc82a..83c05651 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -8,4 +8,5 @@
 
     <toc-element topic="Overview.md"/>
     <toc-element topic="Getting-Started.md"/>
+    <toc-element topic="Dataset.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/Writerside/topics/Dataset.md b/Writerside/topics/Dataset.md
new file mode 100644
index 00000000..8a3623e6
--- /dev/null
+++ b/Writerside/topics/Dataset.md
@@ -0,0 +1,154 @@
+# Dataset
+
+> You need to Set-Up [Google Cloud](Getting-Started.md#gcloud) with the
+> appropriate permissions to use this library.
+> {style='warning'}
+
+<tldr>
+Load datasets from our GCS bucket.
+</tldr>
+
+## Classes
+
+<deflist>
+<def title="FRDCDownloader">
+This facilitates authentication and downloading from GCS.
+</def>
+<def title="FRDCDataset">
+This uses the Downloader to download and load the dataset.
+It also implements useful helper functions to load FRDC-specific datasets,
+such as loading our images and labels.
+</def>
+</deflist>
+
+## Usage
+
+An example loading our Chestnut Nature Park dataset. We retrieve the
+
+- hyperspectral bands
+- order of the bands
+- bounding boxes
+- labels
+
+```python
+from frdc.load import FRDCDataset
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+```
+
+### Custom Authentication & Downloads {collapsible="true"}
+
+If you need granular control over
+
+- where the files are downloaded
+- the credentials used
+- the project used
+- the bucket used
+
+Then pass in a `FRDCDownloader` object to `FRDCDataset`.
+
+```python
+from frdc.load import FRDCDownloader, FRDCDataset
+
+dl = FRDCDownloader(credentials=...,
+                    local_dataset_root_dir=...,
+                    project_id=...,
+                    bucket_name=...)
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None,
+                 dl=dl)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+```
+
+If you have a file not easily downloadable by `FRDCDataset`, you can use
+`FRDCDownloader` to download it.
+
+```python
+from frdc.load import FRDCDownloader
+
+dl = FRDCDownloader(credentials=...,
+                    local_dataset_root_dir=...,
+                    project_id=...,
+                    bucket_name=...)
+
+dl.download_file(path_glob="path/to/gcs/file")
+```
+
+<tip>This will automatically save the file to the local dataset root dir.</tip>
+
+## API
+
+### FRDCDataset
+
+<deflist>
+<def title="FRDCDataset(site, date, version, dl)">
+<b>Initializes the dataset downloader.</b><br/>
+This doesn't immediately download the dataset, but only when you call the
+<code>get_*</code> functions.<br/>
+The site, date, version must match the dataset path on GCS. For example
+if the dataset is at
+<code>gs://frdc-scan/my-site/date/90deg/map</code>,
+<list>
+<li>site: <code>'my-site'</code></li>
+<li>date: <code>'20201218'</code></li>
+<li>version: <code>'90deg/map'</code></li>
+</list>
+<note>
+If you don't want to search up GCS, you can use FRDCDownloader to list all
+datasets, and their versions with 
+<code>FRDCDownloader().list_gcs_datasets()</code>
+</note>
+<tip>
+If <code>dl</code> is None, it will create a new FRDCDownloader. Usually,
+you don't need to pass this in unless you have a custom credential, or project.
+</tip>
+</def>
+<def title="get_ar_bands()">
+<b>Gets the NDArray bands (H x W x C) and channel order as 
+<code>tuple[np.ndarray, list[str]]</code>.</b><br/>
+This downloads (if missing) and retrieves the stacked NDArray bands.
+This wraps around <code>get_ar_bands_as_dict()</code>, thus if you want more
+control over how the bands are loaded, use that instead. 
+</def>
+<def title="get_ar_bands_as_dict()">
+<b>Gets the NDArray bands (H x W) as a <code>dict[str, np.ndarray]</code>.</b><br/>
+This downloads (if missing) and retrieves the individual NDArray bands as a
+dictionary. The keys are the band names, and the values are the NDArray bands.
+</def>
+<def title="get_bounds_and_labels()">
+<b>Gets the bounding boxes and labels as 
+<code>tuple[list[Rect], list[str]]</code>.</b><br/>
+This downloads (if missing) and retrieves the bounding boxes and labels as a
+tuple. The first element is a list of bounding boxes, and the second element
+is a list of labels.<br/>   
+<tip>The bounding boxes are in the format of <code>Rect</code>, a 
+<code>namedtuple</code> of x0, y0, x1, y1.</tip>
+</def>
+</deflist>
+
+### FRDCDownloader
+
+<deflist>
+<def title="list_gcs_datasets(anchor)">
+<b>Lists all GCS datasets in the bucket as a DataFrame.</b><br/>
+This works by checking which folders have a specific file, which we call the
+<code>anchor</code>.
+</def>
+<def title="download_file(path_glob, local_exists_ok)">
+<b>Downloads a file from GCS.</b><br/>
+This takes in a path glob, a string containing wildcards, and downloads exactly
+1 file. If it matches 0 or more than 1 file, it will raise an error.<br/>
+If <code>local_exists_ok</code> is True, it will not download the file if it
+already exists locally. However, if it's False, it will download the file
+only if the hashes don't match.
+
+<note>Use this if you have a file on GCS that can't be downloaded via
+FRDCDataset.</note>
+</def>
+</deflist>
\ No newline at end of file

From 0a832e0b06e80b8cb602471f18c0413a1d04bbbf Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 16:26:47 +0800
Subject: [PATCH 08/28] Update dataset topic naming

---
 Writerside/topics/Dataset.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Writerside/topics/Dataset.md b/Writerside/topics/Dataset.md
index 8a3623e6..e4019c64 100644
--- a/Writerside/topics/Dataset.md
+++ b/Writerside/topics/Dataset.md
@@ -1,4 +1,4 @@
-# Dataset
+# load.dataset
 
 > You need to Set-Up [Google Cloud](Getting-Started.md#gcloud) with the
 > appropriate permissions to use this library.

From 36c497fa1694e6a30da8da79100b53363e46ae09 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 16:26:58 +0800
Subject: [PATCH 09/28] Update load.dataset.md

---
 Writerside/topics/{Dataset.md => load.dataset.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Writerside/topics/{Dataset.md => load.dataset.md} (100%)

diff --git a/Writerside/topics/Dataset.md b/Writerside/topics/load.dataset.md
similarity index 100%
rename from Writerside/topics/Dataset.md
rename to Writerside/topics/load.dataset.md

From 35290a6f745737cddcc9f784419c45fb4fc5af4e Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 16:27:02 +0800
Subject: [PATCH 10/28] Add scale doc

---
 Writerside/d.tree                        | 10 ++--
 Writerside/topics/preprocessing.scale.md | 70 ++++++++++++++++++++++++
 2 files changed, 76 insertions(+), 4 deletions(-)
 create mode 100644 Writerside/topics/preprocessing.scale.md

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 83c05651..0f485d96 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -1,12 +1,14 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <!DOCTYPE instance-profile
-        SYSTEM "https://resources.jetbrains.com/writerside/1.0/product-profile.dtd">
+        SYSTEM
+        "https://resources.jetbrains.com/writerside/1.0/product-profile.dtd">
 
 <instance-profile id="d"
-                 name="Documentation"
-                 start-page="Overview.md">
+                  name="Documentation"
+                  start-page="Overview.md">
 
     <toc-element topic="Overview.md"/>
     <toc-element topic="Getting-Started.md"/>
-    <toc-element topic="Dataset.md"/>
+    <toc-element topic="load.dataset.md"/>
+    <toc-element topic="preprocessing.scale.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/Writerside/topics/preprocessing.scale.md b/Writerside/topics/preprocessing.scale.md
new file mode 100644
index 00000000..02222bcf
--- /dev/null
+++ b/Writerside/topics/preprocessing.scale.md
@@ -0,0 +1,70 @@
+# preprocessing.scale
+
+<tldr>
+Scales the NDArray bands.
+</tldr>
+
+## Functions
+
+<warning>
+Scale assumes H x W x C, where C is the number of bands.
+</warning>
+
+<deflist type="medium">
+<def title="scale_0_1_per_band">
+<b>Scales the NDArray bands to [0, 1] per band.</b><br/>
+</def>
+<def title="scale_normal_per_band">
+<b>Scales the NDArray bands to zero mean unit variance per band.</b><br/>
+</def>
+<def title="scale_static_per_band">
+<b>Scales the NDArray bands by a predefined configuration.</b><br/>
+</def>
+</deflist>
+
+## Usage
+
+```python
+from frdc.load import FRDCDataset
+from frdc.preprocess.scale import (
+    scale_0_1_per_band, scale_normal_per_band, scale_static_per_band
+)
+from frdc.conf import BAND_MAX_CONFIG
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+ar_01 = scale_0_1_per_band(ar)
+ar_norm = scale_normal_per_band(ar)
+ar_static = scale_static_per_band(ar, order, BAND_MAX_CONFIG)
+```
+
+> The static scaling has a default config infers the max bit depth from the
+> capturing device.
+
+## API
+
+<deflist>
+<def title="scale_0_1_per_band(ar)">
+<b>Scales the NDArray bands to [0, 1] per band.</b><br/>
+<code-block lang="tex">
+(x - \min(x)) / (\max(x) - \min(x))
+</code-block>
+</def>
+<def title="scale_normal_per_band(ar)">
+<b>Scales the NDArray bands to zero mean unit variance per band.</b><br/>
+<code-block lang="tex">
+(x - \mu) / \sigma
+</code-block>
+</def>
+<def title="scale_static_per_band(ar, order, config)">
+<b>Scales the NDArray bands by a predefined configuration.</b><br/>
+The <code>config</code> is of <code>dict[str, tuple[int, int]]</code> where
+the key is the band name, and the value is a tuple of <code>(min, max)</code>.
+Take a look at <code>frdc.conf.BAND_MAX_CONFIG</code> for an example.
+<code-block lang="tex">
+(x - c_0) / (c_1 - c_0)
+</code-block>
+</def>
+</deflist>
\ No newline at end of file

From e90cc3e81c959cda48419975ebfc9e5b92efd584 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 17:55:58 +0800
Subject: [PATCH 11/28] Format dataframe to code

---
 Writerside/topics/load.dataset.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Writerside/topics/load.dataset.md b/Writerside/topics/load.dataset.md
index e4019c64..b319848c 100644
--- a/Writerside/topics/load.dataset.md
+++ b/Writerside/topics/load.dataset.md
@@ -136,7 +136,7 @@ is a list of labels.<br/>
 
 <deflist>
 <def title="list_gcs_datasets(anchor)">
-<b>Lists all GCS datasets in the bucket as a DataFrame.</b><br/>
+<b>Lists all GCS datasets in the bucket as <code>DataFrame</code></b><br/>
 This works by checking which folders have a specific file, which we call the
 <code>anchor</code>.
 </def>

From a383a9d3116f9b22650ebe65a2e1b8f5c9452d5b Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 17:56:08 +0800
Subject: [PATCH 12/28] Remove unnecessary brs

---
 Writerside/topics/preprocessing.scale.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Writerside/topics/preprocessing.scale.md b/Writerside/topics/preprocessing.scale.md
index 02222bcf..1b543942 100644
--- a/Writerside/topics/preprocessing.scale.md
+++ b/Writerside/topics/preprocessing.scale.md
@@ -12,13 +12,13 @@ Scale assumes H x W x C, where C is the number of bands.
 
 <deflist type="medium">
 <def title="scale_0_1_per_band">
-<b>Scales the NDArray bands to [0, 1] per band.</b><br/>
+<b>Scales the NDArray bands to [0, 1] per band.</b>
 </def>
 <def title="scale_normal_per_band">
-<b>Scales the NDArray bands to zero mean unit variance per band.</b><br/>
+<b>Scales the NDArray bands to zero mean unit variance per band.</b>
 </def>
 <def title="scale_static_per_band">
-<b>Scales the NDArray bands by a predefined configuration.</b><br/>
+<b>Scales the NDArray bands by a predefined configuration.</b>
 </def>
 </deflist>
 

From 27a11120927f4114c27614a9fbf9e7856118e868 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 17:56:20 +0800
Subject: [PATCH 13/28] Add morphology topic

---
 Writerside/topics/preprocessing.morphology.md | 66 +++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 Writerside/topics/preprocessing.morphology.md

diff --git a/Writerside/topics/preprocessing.morphology.md b/Writerside/topics/preprocessing.morphology.md
new file mode 100644
index 00000000..bc97456b
--- /dev/null
+++ b/Writerside/topics/preprocessing.morphology.md
@@ -0,0 +1,66 @@
+# preprocessing.morphology
+
+<tldr>
+Performs morphological operations on the NDArray bands.
+</tldr>
+
+> This is currently only used for auto-segmentation. If you want to use
+> predefined segmentation see
+> [preprocessing.extract_segments](preprocessing.extract_segments.md).
+
+## Functions
+
+<warning>
+Scale assumes H x W x C, where C is the number of bands.
+</warning>
+
+<deflist type="medium">
+<def title="threshold_binary_mask">
+<b>Thresholds a selected NDArray bands to yield a binary mask.</b>
+</def>
+<def title="binary_watershed">
+<b>Performs watershed on a binary mask to yield a mapped label
+classification</b>
+</def>
+</deflist>
+
+## Usage
+
+Perform auto-segmentation on a dataset to yield a label classification.
+
+```python
+from frdc.load import FRDCDataset
+from frdc.preprocess.morphology import (
+    threshold_binary_mask, binary_watershed
+)
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+mask = threshold_binary_mask(ar, order.index('NIR'), 90 / 256)
+ar_label = binary_watershed(mask)
+```
+
+## API
+
+<deflist>
+<def title="threshold_binary_mask(ar, band_idx, threshold_value)">
+<b>Thresholds a selected NDArray bands to yield a binary mask as 
+<code>np.ndarray</code></b><br/>
+This is equivalent to 
+<code-block lang="python">
+ar[..., band_idx] > threshold_value
+</code-block>
+</def>
+<def title="binary_watershed(ar_mask, peaks_footprint, watershed_compactness)">
+<b>Performs watershed on a binary mask to yield a mapped label
+classification as a <code>np.ndarray</code></b><br/>
+<list>
+<li> <code>peaks_footprint</code> is the footprint of 
+<code>skimage.feature.peak_local_max</code> </li>
+<li> <code>watershed_compactness</code> is the compactness of
+<code>skimage.morphology.watershed</code> </li>
+</list>
+</def>
+</deflist>
\ No newline at end of file

From 04befb0e1520c963cd79d56b306d2845ddc75bde Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 17:56:26 +0800
Subject: [PATCH 14/28] Add extract segments topic

---
 Writerside/d.tree                             |   2 +
 .../topics/preprocessing.extract_segments.md  | 203 ++++++++++++++++++
 2 files changed, 205 insertions(+)
 create mode 100644 Writerside/topics/preprocessing.extract_segments.md

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 0f485d96..3b1d2149 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -11,4 +11,6 @@
     <toc-element topic="Getting-Started.md"/>
     <toc-element topic="load.dataset.md"/>
     <toc-element topic="preprocessing.scale.md"/>
+    <toc-element topic="preprocessing.morphology.md"/>
+    <toc-element topic="preprocessing.extract_segments.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/Writerside/topics/preprocessing.extract_segments.md b/Writerside/topics/preprocessing.extract_segments.md
new file mode 100644
index 00000000..fee4d656
--- /dev/null
+++ b/Writerside/topics/preprocessing.extract_segments.md
@@ -0,0 +1,203 @@
+# preprocessing.extract_segments
+
+<tldr>
+Extracts segments from a label classification or bounds and labels.
+</tldr>
+
+## Functions
+
+<deflist type="medium">
+<def title="extract_segments_from_labels">
+<b>Extracts segments from a label classification.</b>
+</def>
+<def title="extract_segments_from_bounds">
+<b>Extracts segments from <code>Rect</code> bounds.</b>
+</def>
+<def title="remove_small_segments_from_labels">
+<b>Removes small segments from a label classification.</b>
+</def>
+</deflist>
+
+### Extract with Boundaries
+
+A boundary is a `Rect` object that represents the minimum bounding box of a
+segment, with x0, y0, x1, y1 coordinates.
+
+It simply slices the original image to the bounding box. The origin is
+the top left corner of the image.
+
+<tabs>
+<tab title="Cropped = True">
+<code-block>
++-----------------+                +-----------+      
+| Original        |                | Segmented |      
+| Image           |                | Image     |      
++-----+-----+-----+                +-----+-----+      
+|  1  |  2  |  3  |                |  2  |  3  |
++-----+-----+-----+                +-----+-----+
+|  4  |  5  |  6  |  -----------&gt;  |  5  |  6  |      
++-----+-----+-----+   1, 2, 0, 2   +-----+-----+
+|  7  |  8  |  9  |   x0 y0 x1 y1  |  8  |  9  |
++-----+-----+-----+                +-----+-----+
+</code-block>
+</tab>
+<tab title="Cropped = False">
+<code-block>
++-----------------+                +-----------------+      
+| Original        |                | Segmented       |      
+| Image           |                | Image           |      
++-----+-----+-----+                +-----+-----+-----+      
+|  1  |  2  |  3  |                |  0  |  2  |  3  |      
++-----+-----+-----+                +-----+-----+-----+      
+|  4  |  5  |  6  |  -----------&gt;  |  0  |  5  |  6  |             
++-----+-----+-----+   1, 2, 0, 2   +-----+-----+-----+      
+|  7  |  8  |  9  |   x0 y0 x1 y1  |  0  |  8  |  9  |      
++-----+-----+-----+                +-----+-----+-----+      
+</code-block>
+</tab>
+</tabs>
+
+<warning>
+The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing
+with the bounds, make sure that you're slicing the correct axis.
+
+The correct syntax should be <code>ar[y0:y1,x0:x1]</code>.
+</warning>
+
+### Extract with Labels {collapsible="true"}
+
+A label classification is a `np.ndarray` where each pixel is mapped to a
+segment. The segments are mapped to a unique integer.
+In our project, the 0th label is the background.
+
+For example, a label classification of 3 segments will look like this:
+
+```
++-----------------+  +-----------------+
+| Label           |  | Original        |
+| Classification  |  | Image           |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  2  |  0  |  |  1  |  2  |  3  |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  2  |  2  |  |  4  |  5  |  6  |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  1  |  0  |  |  7  |  8  |  9  |
++-----+-----+-----+  +-----+-----+-----+
+```
+
+The extraction will take the **minimum bounding box** of each segment and
+return a list of segments.
+
+For example, the label 1 and 2 extracted images will be
+
+<tabs>
+<tab title="Cropped = True">
+<code-block>
++-----------+  +-----------+
+| Extracted |  | Extracted |
+| Segment 1 |  | Segment 2 |
++-----+-----+  +-----+-----+
+|  1  |  0  |  |  2  |  0  |
++-----+-----+  +-----+-----+
+|  4  |  0  |  |  5  |  6  |
++-----+-----+  +-----+-----+
+|  7  |  8  |  
++-----+-----+  
+</code-block>
+</tab>
+<tab title="Cropped = False">
+<code-block>
++-----------------+  +-----------------+
+| Extracted       |  | Extracted       |
+| Segment 1       |  | Segment 2       |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  0  |  0  |  |  0  |  2  |  0  |
++-----+-----+-----+  +-----+-----+-----+
+|  4  |  0  |  0  |  |  0  |  5  |  6  |
++-----+-----+-----+  +-----+-----+-----+
+|  7  |  8  |  0  |  |  0  |  0  |  0  |
++-----+-----+-----+  +-----+-----+-----+
+</code-block>
+</tab>
+</tabs>
+
+## Usage
+
+- If **cropped is False**, the segments are padded with 0s to the
+  original image size. While this can ensure shape consistency, it can consume
+  more memory for large images.
+- If **cropped is True**, the segments are cropped to the minimum bounding box.
+  This can save memory, but the shape of the segments will be inconsistent.
+
+### Extract from Bounds and Labels
+
+Extract segments from bounds and labels.
+
+```python
+import numpy as np
+from frdc.load import FRDCDataset
+from frdc.preprocess.extract_segments import extract_segments_from_bounds
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+
+segments: list[np.ndarray] = extract_segments_from_bounds(ar, bounds)
+```
+
+### Extract from Auto-Segmentation {collapsible="true"}
+
+Extract segments from a label classification.
+
+```python
+from skimage.morphology import remove_small_objects, remove_small_holes
+import numpy as np
+
+from frdc.load import FRDCDataset
+from frdc.preprocess.morphology import (
+    threshold_binary_mask, binary_watershed
+)
+from frdc.preprocess.scale import scale_0_1_per_band
+from frdc.preprocess.extract_segments import (
+    extract_segments_from_labels, remove_small_segments_from_labels
+)
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+ar = scale_0_1_per_band(ar)
+ar_mask = threshold_binary_mask(ar, -1, 90 / 256)
+ar_mask = remove_small_objects(ar_mask, min_size=100, connectivity=2)
+ar_mask = remove_small_holes(ar_mask, area_threshold=100, connectivity=2)
+ar_labels = binary_watershed(ar_mask)
+ar_labels = remove_small_segments_from_labels(ar_labels,
+                                              min_height=10, min_width=10)
+
+segments: list[np.ndarray] = extract_segments_from_labels(ar, ar_labels)
+```
+
+> The `remove_small_objects` and `remove_small_holes` are used to remove
+> small noise from the binary mask. This is recommended and used in the
+> original paper.
+> {style='note'}
+
+## API
+
+<deflist>
+<def title="extract_segments_from_labels(ar, ar_labels, cropped)">
+<b>Extracts segments from a label classification.</b><br/>
+<code>ar_labels</code> is a label classification as a <code>np.ndarray</code>
+</def>
+<def title="extract_segments_from_bounds(ar, bounds, cropped)">
+<b>Extracts segments from <code>Rect</code> bounds.</b><br/>
+<code>bounds</code> is a list of <code>Rect</code> bounds.
+</def>
+<def title="remove_small_segments_from_labels(ar_labels, min_height, min_width)">
+<b>Removes small segments from a label classification.</b><br/>
+
+</def>
+
+</deflist>
\ No newline at end of file

From 5c1be99552f9a664d21db7a0d4d4e3f6bb09ab25 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 17:57:49 +0800
Subject: [PATCH 15/28] Bump docs

---
 Writerside/d.tree                        |   5 +-
 docs/HelpTOC.json                        |   2 +-
 docs/Map.jhm                             |   2 +-
 docs/getting-started.html                |  18 ++---
 docs/load-dataset.html                   |  31 ++++++++
 docs/overview.html                       |   2 +-
 docs/preprocessing-extract-segments.html |  94 +++++++++++++++++++++++
 docs/preprocessing-morphology.html       |  15 ++++
 docs/preprocessing-scale.html            |  15 ++++
 webHelpD2-all.zip                        | Bin 0 -> 33679 bytes
 10 files changed, 170 insertions(+), 14 deletions(-)
 create mode 100644 docs/load-dataset.html
 create mode 100644 docs/preprocessing-extract-segments.html
 create mode 100644 docs/preprocessing-morphology.html
 create mode 100644 docs/preprocessing-scale.html
 create mode 100644 webHelpD2-all.zip

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 3b1d2149..711500b2 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -10,7 +10,8 @@
     <toc-element topic="Overview.md"/>
     <toc-element topic="Getting-Started.md"/>
     <toc-element topic="load.dataset.md"/>
-    <toc-element topic="preprocessing.scale.md"/>
-    <toc-element topic="preprocessing.morphology.md"/>
+    <toc-element topic="preprocessing.scale.md">
+    </toc-element>
     <toc-element topic="preprocessing.extract_segments.md"/>
+    <toc-element topic="preprocessing.morphology.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/docs/HelpTOC.json b/docs/HelpTOC.json
index cf714597..5564d1c0 100644
--- a/docs/HelpTOC.json
+++ b/docs/HelpTOC.json
@@ -1 +1 @@
-{"entities":{"pages":{"Overview":{"id":"Overview","title":"Overview","url":"overview.html","level":0,"tabIndex":0},"Getting-Started":{"id":"Getting-Started","title":"Getting Started","url":"getting-started.html","level":0,"tabIndex":1}}},"topLevelIds":["Overview","Getting-Started"]}
\ No newline at end of file
+{"entities":{"pages":{"Overview":{"id":"Overview","title":"Overview","url":"overview.html","level":0,"tabIndex":0},"Getting-Started":{"id":"Getting-Started","title":"Getting Started","url":"getting-started.html","level":0,"tabIndex":1},"load.dataset":{"id":"load.dataset","title":"load.dataset","url":"load-dataset.html","level":0,"tabIndex":2},"preprocessing.scale":{"id":"preprocessing.scale","title":"preprocessing.scale","url":"preprocessing-scale.html","level":0,"tabIndex":3},"preprocessing.extract_segments":{"id":"preprocessing.extract_segments","title":"preprocessing.extract_segments","url":"preprocessing-extract-segments.html","level":0,"tabIndex":4},"preprocessing.morphology":{"id":"preprocessing.morphology","title":"preprocessing.morphology","url":"preprocessing-morphology.html","level":0,"tabIndex":5}}},"topLevelIds":["Overview","Getting-Started","load.dataset","preprocessing.scale","preprocessing.extract_segments","preprocessing.morphology"]}
\ No newline at end of file
diff --git a/docs/Map.jhm b/docs/Map.jhm
index a0b29f95..d5ac1aa1 100644
--- a/docs/Map.jhm
+++ b/docs/Map.jhm
@@ -1 +1 @@
-<?xml version='1.0' encoding='UTF-8'?><map version="2.0"><mapID target="Overview.md" url="overview.html" default="yes"/><mapID target="Overview" url="overview.html" default="yes"/><mapID target="Getting-Started.md" url="getting-started.html" default="no"/><mapID target="Getting-Started" url="getting-started.html" default="no"/></map>
\ No newline at end of file
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diff --git a/docs/getting-started.html b/docs/getting-started.html
index 2d8d1e29..f1aaf4b0 100644
--- a/docs/getting-started.html
+++ b/docs/getting-started.html
@@ -1,20 +1,20 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T14:21:53.4408946"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="a901f86f_6922"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="a901f86f_6923">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_91681"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="8604a237_91682">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
             [tool.poetry.dependencies]
             python = &quot;...&quot;
-        </div></li><li class="list__item" id="a901f86f_6925"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="8604a237_91684"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
           git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
-        </div></li><li class="list__item" id="a901f86f_6927"><p>Then, create a Python Virtual Env <code class="code" id="a901f86f_6928">pyvenv</code></p><div class="tabs" id="a901f86f_6929"><div class="tabs__content" data-gtm="tab" id="a901f86f_6930"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="a901f86f_6932"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="a901f86f_6934"><p><a href="https://python-poetry.org/docs/" id="a901f86f_6935"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="a901f86f_6937"><p>If <code class="code" id="a901f86f_6938">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="a901f86f_6939"><p>Activate the virtual environment </p><div class="tabs" id="a901f86f_6940"><div class="tabs__content" data-gtm="tab" id="a901f86f_6941"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></li><li class="list__item" id="8604a237_91686"><p>Then, create a Python Virtual Env <code class="code" id="8604a237_91687">pyvenv</code></p><div class="tabs" id="8604a237_91688"><div class="tabs__content" data-gtm="tab" id="8604a237_91689"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="8604a237_91691"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="8604a237_91693"><p><a href="https://python-poetry.org/docs/" id="8604a237_91694"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="8604a237_91696"><p>If <code class="code" id="8604a237_91697">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="8604a237_91698"><p>Activate the virtual environment </p><div class="tabs" id="8604a237_91699"><div class="tabs__content" data-gtm="tab" id="8604a237_91700"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             cd venv/Scripts
             activate
             cd ../..
-        </div></div><div class="tabs__content" data-gtm="tab" id="a901f86f_6943"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></div><div class="tabs__content" data-gtm="tab" id="8604a237_91702"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             source venv/bin/activate
-        </div></div></div></li><li class="list__item" id="a901f86f_6945"><p>Install the dependencies. You should be in the same directory as <code class="code" id="a901f86f_6946">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
+        </div></div></div></li><li class="list__item" id="8604a237_91704"><p>Install the dependencies. You should be in the same directory as <code class="code" id="8604a237_91705">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
             poetry install --with dev
-        </div></li><li class="list__item" id="a901f86f_6948"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="8604a237_91707"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="a901f86f_6950"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="a901f86f_6951"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="a901f86f_6952"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="a901f86f_6953"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="a901f86f_6955"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="a901f86f_6956"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="a901f86f_6958"><p>To make sure everything is working, <a href="#tests" id="a901f86f_6959" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="a901f86f_6960" data-toc="a901f86f_6960">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="a901f86f_6961"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="a901f86f_6962"><div class="code-block" data-lang="bash"         >
+        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_91709"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="8604a237_91710"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="8604a237_91711"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="8604a237_91712"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="8604a237_91714"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="8604a237_91715"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="8604a237_91717"><p>To make sure everything is working, <a href="#tests" id="8604a237_91718" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="8604a237_91719" data-toc="8604a237_91719">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="8604a237_91720"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="8604a237_91721"><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="a901f86f_6964"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
+        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="8604a237_91723"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
             pytest
-        </div></li><li class="list__item" id="a901f86f_6966"><p>In case of errors: </p><dl id="a901f86f_6967" data-style="title-top"><dt id="a901f86f_6968" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="a901f86f_6969" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="a901f86f_6970" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="a901f86f_6971" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="a901f86f_6972">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="a901f86f_6978" data-expandable="false">rsc/</dt><dd><p id="a901f86f_6980">Resources. These are usually cached datasets</p></dd><dt id="a901f86f_6981" data-expandable="false">pipeline/</dt><dd><p id="a901f86f_6983">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="a901f86f_6984" data-expandable="false">tests/</dt><dd><p id="a901f86f_6986">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="a901f86f_6987">We have 3 types of tests:</p><ul class="list _ul" id="a901f86f_6988"    ><li class="list__item" id="a901f86f_6989"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="a901f86f_6990"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="a901f86f_6991"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="a901f86f_6992" data-style="title-top"><dt id="a901f86f_6993" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="a901f86f_6994">src/frdc/</code> directory.</p></dd><dt id="a901f86f_6995" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="a901f86f_6996">tests/</code> directory.</p></dd><dt id="a901f86f_6997" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="a901f86f_6998">pipeline/</code> directory.</p></dd><dt id="a901f86f_6999" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="a901f86f_7000">poetry add PACKAGE</code> and commit the changes to <code class="code" id="a901f86f_7001">pyproject.toml</code> and <code class="code" id="a901f86f_7002">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="a901f86f_7003"><p>E.g. Adding <code class="code" id="a901f86f_7004">numpy</code> is the same as <code class="code" id="a901f86f_7005">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+        </div></li><li class="list__item" id="8604a237_91725"><p>In case of errors: </p><dl id="8604a237_91726" data-style="title-top"><dt id="8604a237_91727" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="8604a237_91728" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="8604a237_91729" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="8604a237_91730" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="8604a237_91731">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="8604a237_91737" data-expandable="false">rsc/</dt><dd><p id="8604a237_91739">Resources. These are usually cached datasets</p></dd><dt id="8604a237_91740" data-expandable="false">pipeline/</dt><dd><p id="8604a237_91742">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="8604a237_91743" data-expandable="false">tests/</dt><dd><p id="8604a237_91745">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="8604a237_91746">We have 3 types of tests:</p><ul class="list _ul" id="8604a237_91747"    ><li class="list__item" id="8604a237_91748"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="8604a237_91749"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="8604a237_91750"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="8604a237_91751" data-style="title-top"><dt id="8604a237_91752" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="8604a237_91753">src/frdc/</code> directory.</p></dd><dt id="8604a237_91754" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="8604a237_91755">tests/</code> directory.</p></dd><dt id="8604a237_91756" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="8604a237_91757">pipeline/</code> directory.</p></dd><dt id="8604a237_91758" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="8604a237_91759">poetry add PACKAGE</code> and commit the changes to <code class="code" id="8604a237_91760">pyproject.toml</code> and <code class="code" id="8604a237_91761">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="8604a237_91762"><p>E.g. Adding <code class="code" id="8604a237_91763">numpy</code> is the same as <code class="code" id="8604a237_91764">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/load-dataset.html#webpage", "url": "/d/0.0.4/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"  id="load.dataset.md"  >load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;feb21bd9_2797\&quot;\u003eLoad datasets from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="feb21bd9_2721"><p id="feb21bd9_2722">You need to Set-Up <a href="getting-started.html#gcloud" id="feb21bd9_2723" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><dl id="feb21bd9_2725" data-style="title-top"><dt id="feb21bd9_2726" data-expandable="false">FRDCDownloader</dt><dd><p>This facilitates authentication and downloading from GCS.</p></dd><dt id="feb21bd9_2727" data-expandable="false">FRDCDataset</dt><dd><p>This uses the Downloader to download and load the dataset. It also implements useful helper functions to load FRDC-specific datasets, such as loading our images and labels.</p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="feb21bd9_2728">An example loading our Chestnut Nature Park dataset. We retrieve the</p><ul class="list _ul" id="feb21bd9_2729"    ><li class="list__item" id="feb21bd9_2730"><p>hyperspectral bands</p></li><li class="list__item" id="feb21bd9_2731"><p>order of the bands</p></li><li class="list__item" id="feb21bd9_2732"><p>bounding boxes</p></li><li class="list__item" id="feb21bd9_2733"><p>labels</p></li></ul><div class="code-block" data-lang="python"         >
+from frdc.load import FRDCDataset
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="custom-authentication-downloads" data-toc="custom-authentication-downloads">Custom Authentication &amp; Downloads</h3></div><div class="collapse__content"><p id="feb21bd9_2735">If you need granular control over</p><ul class="list _ul" id="feb21bd9_2736"    ><li class="list__item" id="feb21bd9_2737"><p>where the files are downloaded</p></li><li class="list__item" id="feb21bd9_2738"><p>the credentials used</p></li><li class="list__item" id="feb21bd9_2739"><p>the project used</p></li><li class="list__item" id="feb21bd9_2740"><p>the bucket used</p></li></ul><p id="feb21bd9_2741">Then pass in a <code class="code" id="feb21bd9_2742">FRDCDownloader</code> object to <code class="code" id="feb21bd9_2743">FRDCDataset</code>.</p><div class="code-block" data-lang="python"         >
+from frdc.load import FRDCDownloader, FRDCDataset
+
+dl = FRDCDownloader(credentials=...,
+                    local_dataset_root_dir=...,
+                    project_id=...,
+                    bucket_name=...)
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None,
+                 dl=dl)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+</div><p id="feb21bd9_2745">If you have a file not easily downloadable by <code class="code" id="feb21bd9_2746">FRDCDataset</code>, you can use <code class="code" id="feb21bd9_2747">FRDCDownloader</code> to download it.</p><div class="code-block" data-lang="python"         >
+from frdc.load import FRDCDownloader
+
+dl = FRDCDownloader(credentials=...,
+                    local_dataset_root_dir=...,
+                    project_id=...,
+                    bucket_name=...)
+
+dl.download_file(path_glob=&quot;path/to/gcs/file&quot;)
+</div><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_2749"><p>This will automatically save the file to the local dataset root dir.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><section class="chapter"><h3 id="frdcdataset" data-toc="frdcdataset">FRDCDataset</h3><dl id="feb21bd9_2750" data-style="title-top"><dt id="feb21bd9_2751" data-expandable="false">FRDCDataset(site, date, version, dl)</dt><dd><p><b id="feb21bd9_2752">Initializes the dataset downloader.</b></p><br><p> This doesn't immediately download the dataset, but only when you call the <code class="code" id="feb21bd9_2754">get_*</code> functions. </p><br><p> The site, date, version must match the dataset path on GCS. For example if the dataset is at <code class="code" id="feb21bd9_2756">gs://frdc-scan/my-site/date/90deg/map</code>, </p><ul class="list _ul" id="feb21bd9_2757"    ><li class="list__item" id="feb21bd9_2758"><p>site: <code class="code" id="feb21bd9_2759">'my-site'</code></p></li><li class="list__item" id="feb21bd9_2760"><p>date: <code class="code" id="feb21bd9_2761">'20201218'</code></p></li><li class="list__item" id="feb21bd9_2762"><p>version: <code class="code" id="feb21bd9_2763">'90deg/map'</code></p></li></ul><aside class="prompt" data-type="note" data-title="" id="feb21bd9_2764"><p>If you don't want to search up GCS, you can use FRDCDownloader to list all datasets, and their versions with <code class="code" id="feb21bd9_2765">FRDCDownloader().list_gcs_datasets()</code></p></aside><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_2766"><p>If <code class="code" id="feb21bd9_2767">dl</code> is None, it will create a new FRDCDownloader. Usually, you don't need to pass this in unless you have a custom credential, or project.</p></aside></dd><dt id="feb21bd9_2768" data-expandable="false">get_ar_bands()</dt><dd><p><b id="feb21bd9_2769">Gets the NDArray bands (H x W x C) and channel order as <code class="code" id="feb21bd9_2770">tuple[np.ndarray, list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the stacked NDArray bands. This wraps around <code class="code" id="feb21bd9_2772">get_ar_bands_as_dict()</code>, thus if you want more control over how the bands are loaded, use that instead.</p></dd><dt id="feb21bd9_2773" data-expandable="false">get_ar_bands_as_dict()</dt><dd><p><b id="feb21bd9_2774">Gets the NDArray bands (H x W) as a <code class="code" id="feb21bd9_2775">dict[str, np.ndarray]</code>.</b></p><br><p> This downloads (if missing) and retrieves the individual NDArray bands as a dictionary. The keys are the band names, and the values are the NDArray bands.</p></dd><dt id="feb21bd9_2777" data-expandable="false">get_bounds_and_labels()</dt><dd><p><b id="feb21bd9_2778">Gets the bounding boxes and labels as <code class="code" id="feb21bd9_2779">tuple[list[Rect], list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the bounding boxes and labels as a tuple. The first element is a list of bounding boxes, and the second element is a list of labels. </p><br><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_2782"><p>The bounding boxes are in the format of <code class="code" id="feb21bd9_2783">Rect</code>, a <code class="code" id="feb21bd9_2784">namedtuple</code> of x0, y0, x1, y1.</p></aside></dd></dl></section><section class="chapter"><h3 id="frdcdownloader" data-toc="frdcdownloader">FRDCDownloader</h3><dl id="feb21bd9_2785" data-style="title-top"><dt id="feb21bd9_2786" data-expandable="false">list_gcs_datasets(anchor)</dt><dd><p><b id="feb21bd9_2787">Lists all GCS datasets in the bucket as <code class="code" id="feb21bd9_2788">DataFrame</code></b></p><br><p> This works by checking which folders have a specific file, which we call the <code class="code" id="feb21bd9_2790">anchor</code>.</p></dd><dt id="feb21bd9_2791" data-expandable="false">download_file(path_glob, local_exists_ok)</dt><dd><p><b id="feb21bd9_2792">Downloads a file from GCS.</b></p><br><p> This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1 file, it will raise an error. </p><br><p> If <code class="code" id="feb21bd9_2795">local_exists_ok</code> is True, it will not download the file if it already exists locally. However, if it's False, it will download the file only if the hashes don't match. </p><aside class="prompt" data-type="note" data-title="" id="feb21bd9_2796"><p>Use this if you have a file on GCS that can't be downloaded via FRDCDataset.</p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/overview.html b/docs/overview.html
index aa465425..e5c0bf72 100644
--- a/docs/overview.html
+++ b/docs/overview.html
@@ -1 +1 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-23T14:21:53.436548"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="bbfad7ae_138">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="bbfad7ae_139">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="bbfad7ae_140"><a href="getting-started.html" id="bbfad7ae_141" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="bbfad7ae_142">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="bbfad7ae_143" data-style="title-top"><dt id="bbfad7ae_144" data-expandable="false">FRDC-UI</dt><dd><p id="bbfad7ae_146"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="bbfad7ae_147"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 23 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="8aa9237a_5370">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="8aa9237a_5371">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="8aa9237a_5372"><a href="getting-started.html" id="8aa9237a_5373" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="8aa9237a_5374">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="8aa9237a_5375" data-style="title-top"><dt id="8aa9237a_5376" data-expandable="false">FRDC-UI</dt><dd><p id="8aa9237a_5378"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="8aa9237a_5379"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/preprocessing-extract-segments.html b/docs/preprocessing-extract-segments.html
new file mode 100644
index 00000000..24a7d73a
--- /dev/null
+++ b/docs/preprocessing-extract-segments.html
@@ -0,0 +1,94 @@
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.extract_segments | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-extract-segments.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.extract_segments | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-extract-segments.html#webpage", "url": "/d/0.0.4/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"  id="preprocessing.extract_segments.md"  >preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;30711d30_11612\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="30711d30_11553" data-style="title-left" data-title-width="medium"><dt id="30711d30_11554" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="30711d30_11555">Extracts segments from a label classification.</b></p></dd><dt id="30711d30_11556" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="30711d30_11557">Extracts segments from <code class="code" id="30711d30_11558">Rect</code> bounds.</b></p></dd><dt id="30711d30_11559" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="30711d30_11560">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="30711d30_11561">A boundary is a <code class="code" id="30711d30_11562">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="30711d30_11563">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="30711d30_11564"><div class="tabs__content" data-gtm="tab" id="30711d30_11565"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
++-----------------+                +-----------+      
+| Original        |                | Segmented |      
+| Image           |                | Image     |      
++-----+-----+-----+                +-----+-----+      
+|  1  |  2  |  3  |                |  2  |  3  |
++-----+-----+-----+                +-----+-----+
+|  4  |  5  |  6  |  -----------&gt;  |  5  |  6  |      
++-----+-----+-----+   1, 2, 0, 2   +-----+-----+
+|  7  |  8  |  9  |   x0 y0 x1 y1  |  8  |  9  |
++-----+-----+-----+                +-----+-----+
+</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11567"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
++-----------------+                +-----------------+      
+| Original        |                | Segmented       |      
+| Image           |                | Image           |      
++-----+-----+-----+                +-----+-----+-----+      
+|  1  |  2  |  3  |                |  0  |  2  |  3  |      
++-----+-----+-----+                +-----+-----+-----+      
+|  4  |  5  |  6  |  -----------&gt;  |  0  |  5  |  6  |             
++-----+-----+-----+   1, 2, 0, 2   +-----+-----+-----+      
+|  7  |  8  |  9  |   x0 y0 x1 y1  |  0  |  8  |  9  |      
++-----+-----+-----+                +-----+-----+-----+      
+</div></div></div><aside class="prompt" data-type="warning" data-title="" id="30711d30_11569"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="30711d30_11570">The correct syntax should be <code class="code" id="30711d30_11571">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="30711d30_11572">A label classification is a <code class="code" id="30711d30_11573">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="30711d30_11574">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
++-----------------+  +-----------------+
+| Label           |  | Original        |
+| Classification  |  | Image           |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  2  |  0  |  |  1  |  2  |  3  |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  2  |  2  |  |  4  |  5  |  6  |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  1  |  0  |  |  7  |  8  |  9  |
++-----+-----+-----+  +-----+-----+-----+
+</div><p id="30711d30_11576">The extraction will take the <span class="control" id="30711d30_11577">minimum bounding box</span> of each segment and return a list of segments.</p><p id="30711d30_11578">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="30711d30_11579"><div class="tabs__content" data-gtm="tab" id="30711d30_11580"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
++-----------+  +-----------+
+| Extracted |  | Extracted |
+| Segment 1 |  | Segment 2 |
++-----+-----+  +-----+-----+
+|  1  |  0  |  |  2  |  0  |
++-----+-----+  +-----+-----+
+|  4  |  0  |  |  5  |  6  |
++-----+-----+  +-----+-----+
+|  7  |  8  |  
++-----+-----+  
+</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11582"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
++-----------------+  +-----------------+
+| Extracted       |  | Extracted       |
+| Segment 1       |  | Segment 2       |
++-----+-----+-----+  +-----+-----+-----+
+|  1  |  0  |  0  |  |  0  |  2  |  0  |
++-----+-----+-----+  +-----+-----+-----+
+|  4  |  0  |  0  |  |  0  |  5  |  6  |
++-----+-----+-----+  +-----+-----+-----+
+|  7  |  8  |  0  |  |  0  |  0  |  0  |
++-----+-----+-----+  +-----+-----+-----+
+</div></div></div></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><ul class="list _ul" id="30711d30_11584"    ><li class="list__item" id="30711d30_11585"><p>If <span class="control" id="30711d30_11586">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="30711d30_11587"><p>If <span class="control" id="30711d30_11588">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="30711d30_11589">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
+import numpy as np
+from frdc.load import FRDCDataset
+from frdc.preprocess.extract_segments import extract_segments_from_bounds
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+
+segments: list[np.ndarray] = extract_segments_from_bounds(ar, bounds)
+</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="30711d30_11591">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
+from skimage.morphology import remove_small_objects, remove_small_holes
+import numpy as np
+
+from frdc.load import FRDCDataset
+from frdc.preprocess.morphology import (
+    threshold_binary_mask, binary_watershed
+)
+from frdc.preprocess.scale import scale_0_1_per_band
+from frdc.preprocess.extract_segments import (
+    extract_segments_from_labels, remove_small_segments_from_labels
+)
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+ar = scale_0_1_per_band(ar)
+ar_mask = threshold_binary_mask(ar, -1, 90 / 256)
+ar_mask = remove_small_objects(ar_mask, min_size=100, connectivity=2)
+ar_mask = remove_small_holes(ar_mask, area_threshold=100, connectivity=2)
+ar_labels = binary_watershed(ar_mask)
+ar_labels = remove_small_segments_from_labels(ar_labels,
+                                              min_height=10, min_width=10)
+
+segments: list[np.ndarray] = extract_segments_from_labels(ar, ar_labels)
+</div><aside class="prompt" data-type="note" data-title="" id="30711d30_11593"><p id="30711d30_11594">The <code class="code" id="30711d30_11595">remove_small_objects</code> and <code class="code" id="30711d30_11596">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="30711d30_11597" data-style="title-top"><dt id="30711d30_11598" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="30711d30_11599">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="30711d30_11601">ar_labels</code> is a label classification as a <code class="code" id="30711d30_11602">np.ndarray</code></p></dd><dt id="30711d30_11603" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="30711d30_11604">Extracts segments from <code class="code" id="30711d30_11605">Rect</code> bounds.</b></p><br><p> <code class="code" id="30711d30_11607">bounds</code> is a list of <code class="code" id="30711d30_11608">Rect</code> bounds.</p></dd><dt id="30711d30_11609" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="30711d30_11610">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-morphology.html#webpage", "url": "/d/0.0.4/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"  id="preprocessing.morphology.md"  >preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;d851343f_4817\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="d851343f_4789"><p id="d851343f_4790">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="d851343f_4791" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="d851343f_4792"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="d851343f_4793" data-style="title-left" data-title-width="medium"><dt id="d851343f_4794" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="d851343f_4795">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="d851343f_4796" data-expandable="false">binary_watershed</dt><dd><p><b id="d851343f_4797">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="d851343f_4798">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
+from frdc.load import FRDCDataset
+from frdc.preprocess.morphology import (
+    threshold_binary_mask, binary_watershed
+)
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+mask = threshold_binary_mask(ar, order.index('NIR'), 90 / 256)
+ar_label = binary_watershed(mask)
+</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="d851343f_4800" data-style="title-top"><dt id="d851343f_4801" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="d851343f_4802">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="d851343f_4803">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
+ar[..., band_idx] &gt; threshold_value
+</div></dd><dt id="d851343f_4806" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="d851343f_4807">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="d851343f_4808">np.ndarray</code></b></p><br><ul class="list _ul" id="d851343f_4810"    ><li class="list__item" id="d851343f_4811"><p><code class="code" id="d851343f_4812">peaks_footprint</code> is the footprint of <code class="code" id="d851343f_4813">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="d851343f_4814"><p><code class="code" id="d851343f_4815">watershed_compactness</code> is the compactness of <code class="code" id="d851343f_4816">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.scale | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.scale | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-scale.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.scale | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-scale.html#webpage", "url": "/d/0.0.4/preprocessing-scale.html", "name": "preprocessing.scale | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"  id="preprocessing.scale.md"  >preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;8b28dc1_8438\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="8b28dc1_8410"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="8b28dc1_8411" data-style="title-left" data-title-width="medium"><dt id="8b28dc1_8412" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="8b28dc1_8413">Scales the NDArray bands to [0, 1] per band.</b></p></dd><dt id="8b28dc1_8414" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="8b28dc1_8415">Scales the NDArray bands to zero mean unit variance per band.</b></p></dd><dt id="8b28dc1_8416" data-expandable="false">scale_static_per_band</dt><dd><p><b id="8b28dc1_8417">Scales the NDArray bands by a predefined configuration.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python"         >
+from frdc.load import FRDCDataset
+from frdc.preprocess.scale import (
+    scale_0_1_per_band, scale_normal_per_band, scale_static_per_band
+)
+from frdc.conf import BAND_MAX_CONFIG
+
+ds = FRDCDataset(site='chestnut_nature_park',
+                 date='20201218',
+                 version=None, )
+ar, order = ds.get_ar_bands()
+ar_01 = scale_0_1_per_band(ar)
+ar_norm = scale_normal_per_band(ar)
+ar_static = scale_static_per_band(ar, order, BAND_MAX_CONFIG)
+</div><aside class="prompt" data-type="tip" data-title="" id="8b28dc1_8419"><p id="8b28dc1_8420">The static scaling has a default config infers the max bit depth from the capturing device.</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="8b28dc1_8421" data-style="title-top"><dt id="8b28dc1_8422" data-expandable="false">scale_0_1_per_band(ar)</dt><dd><p><b id="8b28dc1_8423">Scales the NDArray bands to [0, 1] per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-239-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path 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From 2019a29e99c21d2f6063ee4892e6df617770eae1 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 25 Oct 2023 18:04:33 +0800
Subject: [PATCH 16/28] Fix typos and update docs

---
 Writerside/topics/load.dataset.md             |  11 +++++++----
 .../topics/preprocessing.extract_segments.md  |   4 ++--
 Writerside/topics/preprocessing.scale.md      |   4 ++--
 docs/getting-started.html                     |  18 +++++++++---------
 docs/load-dataset.html                        |   8 ++++----
 docs/overview.html                            |   2 +-
 docs/preprocessing-extract-segments.html      |  16 ++++++++--------
 docs/preprocessing-morphology.html            |   6 +++---
 docs/preprocessing-scale.html                 |   4 ++--
 webHelpD2-all.zip                             | Bin 33679 -> 33722 bytes
 10 files changed, 38 insertions(+), 35 deletions(-)

diff --git a/Writerside/topics/load.dataset.md b/Writerside/topics/load.dataset.md
index b319848c..4462b3ca 100644
--- a/Writerside/topics/load.dataset.md
+++ b/Writerside/topics/load.dataset.md
@@ -93,12 +93,15 @@ This doesn't immediately download the dataset, but only when you call the
 <code>get_*</code> functions.<br/>
 The site, date, version must match the dataset path on GCS. For example
 if the dataset is at
-<code>gs://frdc-scan/my-site/date/90deg/map</code>,
+<code>gs://frdc-scan/my-site/20201218/90deg/map</code>,
 <list>
-<li>site: <code>'my-site'</code></li>
-<li>date: <code>'20201218'</code></li>
-<li>version: <code>'90deg/map'</code></li>
+<li><code>site='my-site'</code></li>
+<li><code>date='20201218'</code></li>
+<li><code>version='90deg/map'</code></li>
 </list>
+If the dataset doesn't have a "version", for example:
+<code>gs://frdc-scan/my-site/20201218</code>,
+then you can pass in <code>version=None</code>.<br/>
 <note>
 If you don't want to search up GCS, you can use FRDCDownloader to list all
 datasets, and their versions with 
diff --git a/Writerside/topics/preprocessing.extract_segments.md b/Writerside/topics/preprocessing.extract_segments.md
index fee4d656..ed257e8d 100644
--- a/Writerside/topics/preprocessing.extract_segments.md
+++ b/Writerside/topics/preprocessing.extract_segments.md
@@ -121,14 +121,14 @@ For example, the label 1 and 2 extracted images will be
 </tab>
 </tabs>
 
-## Usage
-
 - If **cropped is False**, the segments are padded with 0s to the
   original image size. While this can ensure shape consistency, it can consume
   more memory for large images.
 - If **cropped is True**, the segments are cropped to the minimum bounding box.
   This can save memory, but the shape of the segments will be inconsistent.
 
+## Usage
+
 ### Extract from Bounds and Labels
 
 Extract segments from bounds and labels.
diff --git a/Writerside/topics/preprocessing.scale.md b/Writerside/topics/preprocessing.scale.md
index 1b543942..17bf8e02 100644
--- a/Writerside/topics/preprocessing.scale.md
+++ b/Writerside/topics/preprocessing.scale.md
@@ -40,8 +40,8 @@ ar_norm = scale_normal_per_band(ar)
 ar_static = scale_static_per_band(ar, order, BAND_MAX_CONFIG)
 ```
 
-> The static scaling has a default config infers the max bit depth from the
-> capturing device.
+> The static scaling has a default config, which was inferred by our capturing
+> device.
 
 ## API
 
diff --git a/docs/getting-started.html b/docs/getting-started.html
index f1aaf4b0..d305d68c 100644
--- a/docs/getting-started.html
+++ b/docs/getting-started.html
@@ -1,20 +1,20 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_91681"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="8604a237_91682">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_96004"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="8604a237_96005">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
             [tool.poetry.dependencies]
             python = &quot;...&quot;
-        </div></li><li class="list__item" id="8604a237_91684"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="8604a237_96007"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
           git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
-        </div></li><li class="list__item" id="8604a237_91686"><p>Then, create a Python Virtual Env <code class="code" id="8604a237_91687">pyvenv</code></p><div class="tabs" id="8604a237_91688"><div class="tabs__content" data-gtm="tab" id="8604a237_91689"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="8604a237_91691"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="8604a237_91693"><p><a href="https://python-poetry.org/docs/" id="8604a237_91694"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="8604a237_91696"><p>If <code class="code" id="8604a237_91697">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="8604a237_91698"><p>Activate the virtual environment </p><div class="tabs" id="8604a237_91699"><div class="tabs__content" data-gtm="tab" id="8604a237_91700"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></li><li class="list__item" id="8604a237_96009"><p>Then, create a Python Virtual Env <code class="code" id="8604a237_96010">pyvenv</code></p><div class="tabs" id="8604a237_96011"><div class="tabs__content" data-gtm="tab" id="8604a237_96012"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="8604a237_96014"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="8604a237_96016"><p><a href="https://python-poetry.org/docs/" id="8604a237_96017"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="8604a237_96019"><p>If <code class="code" id="8604a237_96020">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="8604a237_96021"><p>Activate the virtual environment </p><div class="tabs" id="8604a237_96022"><div class="tabs__content" data-gtm="tab" id="8604a237_96023"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             cd venv/Scripts
             activate
             cd ../..
-        </div></div><div class="tabs__content" data-gtm="tab" id="8604a237_91702"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></div><div class="tabs__content" data-gtm="tab" id="8604a237_96025"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             source venv/bin/activate
-        </div></div></div></li><li class="list__item" id="8604a237_91704"><p>Install the dependencies. You should be in the same directory as <code class="code" id="8604a237_91705">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
+        </div></div></div></li><li class="list__item" id="8604a237_96027"><p>Install the dependencies. You should be in the same directory as <code class="code" id="8604a237_96028">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
             poetry install --with dev
-        </div></li><li class="list__item" id="8604a237_91707"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="8604a237_96030"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_91709"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="8604a237_91710"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="8604a237_91711"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="8604a237_91712"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="8604a237_91714"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="8604a237_91715"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="8604a237_91717"><p>To make sure everything is working, <a href="#tests" id="8604a237_91718" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="8604a237_91719" data-toc="8604a237_91719">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="8604a237_91720"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="8604a237_91721"><div class="code-block" data-lang="bash"         >
+        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_96032"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="8604a237_96033"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="8604a237_96034"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="8604a237_96035"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="8604a237_96037"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="8604a237_96038"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="8604a237_96040"><p>To make sure everything is working, <a href="#tests" id="8604a237_96041" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="8604a237_96042" data-toc="8604a237_96042">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="8604a237_96043"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="8604a237_96044"><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="8604a237_91723"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
+        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="8604a237_96046"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
             pytest
-        </div></li><li class="list__item" id="8604a237_91725"><p>In case of errors: </p><dl id="8604a237_91726" data-style="title-top"><dt id="8604a237_91727" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="8604a237_91728" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="8604a237_91729" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="8604a237_91730" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="8604a237_91731">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="8604a237_91737" data-expandable="false">rsc/</dt><dd><p id="8604a237_91739">Resources. These are usually cached datasets</p></dd><dt id="8604a237_91740" data-expandable="false">pipeline/</dt><dd><p id="8604a237_91742">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="8604a237_91743" data-expandable="false">tests/</dt><dd><p id="8604a237_91745">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="8604a237_91746">We have 3 types of tests:</p><ul class="list _ul" id="8604a237_91747"    ><li class="list__item" id="8604a237_91748"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="8604a237_91749"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="8604a237_91750"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="8604a237_91751" data-style="title-top"><dt id="8604a237_91752" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="8604a237_91753">src/frdc/</code> directory.</p></dd><dt id="8604a237_91754" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="8604a237_91755">tests/</code> directory.</p></dd><dt id="8604a237_91756" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="8604a237_91757">pipeline/</code> directory.</p></dd><dt id="8604a237_91758" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="8604a237_91759">poetry add PACKAGE</code> and commit the changes to <code class="code" id="8604a237_91760">pyproject.toml</code> and <code class="code" id="8604a237_91761">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="8604a237_91762"><p>E.g. Adding <code class="code" id="8604a237_91763">numpy</code> is the same as <code class="code" id="8604a237_91764">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+        </div></li><li class="list__item" id="8604a237_96048"><p>In case of errors: </p><dl id="8604a237_96049" data-style="title-top"><dt id="8604a237_96050" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="8604a237_96051" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="8604a237_96052" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="8604a237_96053" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="8604a237_96054">Before starting development, take a look at our repository structure. 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inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">./model_tests/</span></div></foreignObject></g></g></g></g></g></svg><dl id="8604a237_96056" data-style="title-top"><dt id="8604a237_96057" data-expandable="false">src/frdc/</dt><dd><p id="8604a237_96059">Source Code for our package. These are the unit components of our pipeline.</p></dd><dt id="8604a237_96060" data-expandable="false">rsc/</dt><dd><p id="8604a237_96062">Resources. These are usually cached datasets</p></dd><dt id="8604a237_96063" data-expandable="false">pipeline/</dt><dd><p id="8604a237_96065">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="8604a237_96066" data-expandable="false">tests/</dt><dd><p id="8604a237_96068">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="8604a237_96069">We have 3 types of tests:</p><ul class="list _ul" id="8604a237_96070"    ><li class="list__item" id="8604a237_96071"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="8604a237_96072"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="8604a237_96073"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="8604a237_96074" data-style="title-top"><dt id="8604a237_96075" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="8604a237_96076">src/frdc/</code> directory.</p></dd><dt id="8604a237_96077" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="8604a237_96078">tests/</code> directory.</p></dd><dt id="8604a237_96079" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="8604a237_96080">pipeline/</code> directory.</p></dd><dt id="8604a237_96081" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="8604a237_96082">poetry add PACKAGE</code> and commit the changes to <code class="code" id="8604a237_96083">pyproject.toml</code> and <code class="code" id="8604a237_96084">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="8604a237_96085"><p>E.g. Adding <code class="code" id="8604a237_96086">numpy</code> is the same as <code class="code" id="8604a237_96087">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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index bf3dc731..80e3d822 100644
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@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/load-dataset.html#webpage", "url": "/d/0.0.4/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"  id="load.dataset.md"  >load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;feb21bd9_2797\&quot;\u003eLoad datasets from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="feb21bd9_2721"><p id="feb21bd9_2722">You need to Set-Up <a href="getting-started.html#gcloud" id="feb21bd9_2723" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><dl id="feb21bd9_2725" data-style="title-top"><dt id="feb21bd9_2726" data-expandable="false">FRDCDownloader</dt><dd><p>This facilitates authentication and downloading from GCS.</p></dd><dt id="feb21bd9_2727" data-expandable="false">FRDCDataset</dt><dd><p>This uses the Downloader to download and load the dataset. It also implements useful helper functions to load FRDC-specific datasets, such as loading our images and labels.</p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="feb21bd9_2728">An example loading our Chestnut Nature Park dataset. We retrieve the</p><ul class="list _ul" id="feb21bd9_2729"    ><li class="list__item" id="feb21bd9_2730"><p>hyperspectral bands</p></li><li class="list__item" id="feb21bd9_2731"><p>order of the bands</p></li><li class="list__item" id="feb21bd9_2732"><p>bounding boxes</p></li><li class="list__item" id="feb21bd9_2733"><p>labels</p></li></ul><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/load-dataset.html#webpage", "url": "/d/0.0.4/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"  id="load.dataset.md"  >load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;feb21bd9_3936\&quot;\u003eLoad datasets from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="feb21bd9_3857"><p id="feb21bd9_3858">You need to Set-Up <a href="getting-started.html#gcloud" id="feb21bd9_3859" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><dl id="feb21bd9_3861" data-style="title-top"><dt id="feb21bd9_3862" data-expandable="false">FRDCDownloader</dt><dd><p>This facilitates authentication and downloading from GCS.</p></dd><dt id="feb21bd9_3863" data-expandable="false">FRDCDataset</dt><dd><p>This uses the Downloader to download and load the dataset. It also implements useful helper functions to load FRDC-specific datasets, such as loading our images and labels.</p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="feb21bd9_3864">An example loading our Chestnut Nature Park dataset. We retrieve the</p><ul class="list _ul" id="feb21bd9_3865"    ><li class="list__item" id="feb21bd9_3866"><p>hyperspectral bands</p></li><li class="list__item" id="feb21bd9_3867"><p>order of the bands</p></li><li class="list__item" id="feb21bd9_3868"><p>bounding boxes</p></li><li class="list__item" id="feb21bd9_3869"><p>labels</p></li></ul><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 
 ds = FRDCDataset(site='chestnut_nature_park',
@@ -6,7 +6,7 @@
                  version=None, )
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
-</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="custom-authentication-downloads" data-toc="custom-authentication-downloads">Custom Authentication &amp; Downloads</h3></div><div class="collapse__content"><p id="feb21bd9_2735">If you need granular control over</p><ul class="list _ul" id="feb21bd9_2736"    ><li class="list__item" id="feb21bd9_2737"><p>where the files are downloaded</p></li><li class="list__item" id="feb21bd9_2738"><p>the credentials used</p></li><li class="list__item" id="feb21bd9_2739"><p>the project used</p></li><li class="list__item" id="feb21bd9_2740"><p>the bucket used</p></li></ul><p id="feb21bd9_2741">Then pass in a <code class="code" id="feb21bd9_2742">FRDCDownloader</code> object to <code class="code" id="feb21bd9_2743">FRDCDataset</code>.</p><div class="code-block" data-lang="python"         >
+</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="custom-authentication-downloads" data-toc="custom-authentication-downloads">Custom Authentication &amp; Downloads</h3></div><div class="collapse__content"><p id="feb21bd9_3871">If you need granular control over</p><ul class="list _ul" id="feb21bd9_3872"    ><li class="list__item" id="feb21bd9_3873"><p>where the files are downloaded</p></li><li class="list__item" id="feb21bd9_3874"><p>the credentials used</p></li><li class="list__item" id="feb21bd9_3875"><p>the project used</p></li><li class="list__item" id="feb21bd9_3876"><p>the bucket used</p></li></ul><p id="feb21bd9_3877">Then pass in a <code class="code" id="feb21bd9_3878">FRDCDownloader</code> object to <code class="code" id="feb21bd9_3879">FRDCDataset</code>.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDownloader, FRDCDataset
 
 dl = FRDCDownloader(credentials=...,
@@ -19,7 +19,7 @@
                  dl=dl)
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
-</div><p id="feb21bd9_2745">If you have a file not easily downloadable by <code class="code" id="feb21bd9_2746">FRDCDataset</code>, you can use <code class="code" id="feb21bd9_2747">FRDCDownloader</code> to download it.</p><div class="code-block" data-lang="python"         >
+</div><p id="feb21bd9_3881">If you have a file not easily downloadable by <code class="code" id="feb21bd9_3882">FRDCDataset</code>, you can use <code class="code" id="feb21bd9_3883">FRDCDownloader</code> to download it.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDownloader
 
 dl = FRDCDownloader(credentials=...,
@@ -28,4 +28,4 @@
                     bucket_name=...)
 
 dl.download_file(path_glob=&quot;path/to/gcs/file&quot;)
-</div><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_2749"><p>This will automatically save the file to the local dataset root dir.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><section class="chapter"><h3 id="frdcdataset" data-toc="frdcdataset">FRDCDataset</h3><dl id="feb21bd9_2750" data-style="title-top"><dt id="feb21bd9_2751" data-expandable="false">FRDCDataset(site, date, version, dl)</dt><dd><p><b id="feb21bd9_2752">Initializes the dataset downloader.</b></p><br><p> This doesn't immediately download the dataset, but only when you call the <code class="code" id="feb21bd9_2754">get_*</code> functions. </p><br><p> The site, date, version must match the dataset path on GCS. For example if the dataset is at <code class="code" id="feb21bd9_2756">gs://frdc-scan/my-site/date/90deg/map</code>, </p><ul class="list _ul" id="feb21bd9_2757"    ><li class="list__item" id="feb21bd9_2758"><p>site: <code class="code" id="feb21bd9_2759">'my-site'</code></p></li><li class="list__item" id="feb21bd9_2760"><p>date: <code class="code" id="feb21bd9_2761">'20201218'</code></p></li><li class="list__item" id="feb21bd9_2762"><p>version: <code class="code" id="feb21bd9_2763">'90deg/map'</code></p></li></ul><aside class="prompt" data-type="note" data-title="" id="feb21bd9_2764"><p>If you don't want to search up GCS, you can use FRDCDownloader to list all datasets, and their versions with <code class="code" id="feb21bd9_2765">FRDCDownloader().list_gcs_datasets()</code></p></aside><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_2766"><p>If <code class="code" id="feb21bd9_2767">dl</code> is None, it will create a new FRDCDownloader. Usually, you don't need to pass this in unless you have a custom credential, or project.</p></aside></dd><dt id="feb21bd9_2768" data-expandable="false">get_ar_bands()</dt><dd><p><b id="feb21bd9_2769">Gets the NDArray bands (H x W x C) and channel order as <code class="code" id="feb21bd9_2770">tuple[np.ndarray, list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the stacked NDArray bands. This wraps around <code class="code" id="feb21bd9_2772">get_ar_bands_as_dict()</code>, thus if you want more control over how the bands are loaded, use that instead.</p></dd><dt id="feb21bd9_2773" data-expandable="false">get_ar_bands_as_dict()</dt><dd><p><b id="feb21bd9_2774">Gets the NDArray bands (H x W) as a <code class="code" id="feb21bd9_2775">dict[str, np.ndarray]</code>.</b></p><br><p> This downloads (if missing) and retrieves the individual NDArray bands as a dictionary. The keys are the band names, and the values are the NDArray bands.</p></dd><dt id="feb21bd9_2777" data-expandable="false">get_bounds_and_labels()</dt><dd><p><b id="feb21bd9_2778">Gets the bounding boxes and labels as <code class="code" id="feb21bd9_2779">tuple[list[Rect], list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the bounding boxes and labels as a tuple. The first element is a list of bounding boxes, and the second element is a list of labels. </p><br><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_2782"><p>The bounding boxes are in the format of <code class="code" id="feb21bd9_2783">Rect</code>, a <code class="code" id="feb21bd9_2784">namedtuple</code> of x0, y0, x1, y1.</p></aside></dd></dl></section><section class="chapter"><h3 id="frdcdownloader" data-toc="frdcdownloader">FRDCDownloader</h3><dl id="feb21bd9_2785" data-style="title-top"><dt id="feb21bd9_2786" data-expandable="false">list_gcs_datasets(anchor)</dt><dd><p><b id="feb21bd9_2787">Lists all GCS datasets in the bucket as <code class="code" id="feb21bd9_2788">DataFrame</code></b></p><br><p> This works by checking which folders have a specific file, which we call the <code class="code" id="feb21bd9_2790">anchor</code>.</p></dd><dt id="feb21bd9_2791" data-expandable="false">download_file(path_glob, local_exists_ok)</dt><dd><p><b id="feb21bd9_2792">Downloads a file from GCS.</b></p><br><p> This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1 file, it will raise an error. </p><br><p> If <code class="code" id="feb21bd9_2795">local_exists_ok</code> is True, it will not download the file if it already exists locally. However, if it's False, it will download the file only if the hashes don't match. </p><aside class="prompt" data-type="note" data-title="" id="feb21bd9_2796"><p>Use this if you have a file on GCS that can't be downloaded via FRDCDataset.</p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+</div><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_3885"><p>This will automatically save the file to the local dataset root dir.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><section class="chapter"><h3 id="frdcdataset" data-toc="frdcdataset">FRDCDataset</h3><dl id="feb21bd9_3886" data-style="title-top"><dt id="feb21bd9_3887" data-expandable="false">FRDCDataset(site, date, version, dl)</dt><dd><p><b id="feb21bd9_3888">Initializes the dataset downloader.</b></p><br><p> This doesn't immediately download the dataset, but only when you call the <code class="code" id="feb21bd9_3890">get_*</code> functions. </p><br><p> The site, date, version must match the dataset path on GCS. For example if the dataset is at <code class="code" id="feb21bd9_3892">gs://frdc-scan/my-site/20201218/90deg/map</code>, </p><ul class="list _ul" id="feb21bd9_3893"    ><li class="list__item" id="feb21bd9_3894"><p><code class="code" id="feb21bd9_3895">site='my-site'</code></p></li><li class="list__item" id="feb21bd9_3896"><p><code class="code" id="feb21bd9_3897">date='20201218'</code></p></li><li class="list__item" id="feb21bd9_3898"><p><code class="code" id="feb21bd9_3899">version='90deg/map'</code></p></li></ul><p> If the dataset doesn't have a &quot;version&quot;, for example: <code class="code" id="feb21bd9_3900">gs://frdc-scan/my-site/20201218</code>, then you can pass in <code class="code" id="feb21bd9_3901">version=None</code>. </p><br><aside class="prompt" data-type="note" data-title="" id="feb21bd9_3903"><p>If you don't want to search up GCS, you can use FRDCDownloader to list all datasets, and their versions with <code class="code" id="feb21bd9_3904">FRDCDownloader().list_gcs_datasets()</code></p></aside><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_3905"><p>If <code class="code" id="feb21bd9_3906">dl</code> is None, it will create a new FRDCDownloader. Usually, you don't need to pass this in unless you have a custom credential, or project.</p></aside></dd><dt id="feb21bd9_3907" data-expandable="false">get_ar_bands()</dt><dd><p><b id="feb21bd9_3908">Gets the NDArray bands (H x W x C) and channel order as <code class="code" id="feb21bd9_3909">tuple[np.ndarray, list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the stacked NDArray bands. This wraps around <code class="code" id="feb21bd9_3911">get_ar_bands_as_dict()</code>, thus if you want more control over how the bands are loaded, use that instead.</p></dd><dt id="feb21bd9_3912" data-expandable="false">get_ar_bands_as_dict()</dt><dd><p><b id="feb21bd9_3913">Gets the NDArray bands (H x W) as a <code class="code" id="feb21bd9_3914">dict[str, np.ndarray]</code>.</b></p><br><p> This downloads (if missing) and retrieves the individual NDArray bands as a dictionary. The keys are the band names, and the values are the NDArray bands.</p></dd><dt id="feb21bd9_3916" data-expandable="false">get_bounds_and_labels()</dt><dd><p><b id="feb21bd9_3917">Gets the bounding boxes and labels as <code class="code" id="feb21bd9_3918">tuple[list[Rect], list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the bounding boxes and labels as a tuple. The first element is a list of bounding boxes, and the second element is a list of labels. </p><br><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_3921"><p>The bounding boxes are in the format of <code class="code" id="feb21bd9_3922">Rect</code>, a <code class="code" id="feb21bd9_3923">namedtuple</code> of x0, y0, x1, y1.</p></aside></dd></dl></section><section class="chapter"><h3 id="frdcdownloader" data-toc="frdcdownloader">FRDCDownloader</h3><dl id="feb21bd9_3924" data-style="title-top"><dt id="feb21bd9_3925" data-expandable="false">list_gcs_datasets(anchor)</dt><dd><p><b id="feb21bd9_3926">Lists all GCS datasets in the bucket as <code class="code" id="feb21bd9_3927">DataFrame</code></b></p><br><p> This works by checking which folders have a specific file, which we call the <code class="code" id="feb21bd9_3929">anchor</code>.</p></dd><dt id="feb21bd9_3930" data-expandable="false">download_file(path_glob, local_exists_ok)</dt><dd><p><b id="feb21bd9_3931">Downloads a file from GCS.</b></p><br><p> This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1 file, it will raise an error. </p><br><p> If <code class="code" id="feb21bd9_3934">local_exists_ok</code> is True, it will not download the file if it already exists locally. However, if it's False, it will download the file only if the hashes don't match. </p><aside class="prompt" data-type="note" data-title="" id="feb21bd9_3935"><p>Use this if you have a file on GCS that can't be downloaded via FRDCDataset.</p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="8aa9237a_5370">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="8aa9237a_5371">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="8aa9237a_5372"><a href="getting-started.html" id="8aa9237a_5373" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="8aa9237a_5374">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="8aa9237a_5375" data-style="title-top"><dt id="8aa9237a_5376" data-expandable="false">FRDC-UI</dt><dd><p id="8aa9237a_5378"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="8aa9237a_5379"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="8aa9237a_5506">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="8aa9237a_5507">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="8aa9237a_5508"><a href="getting-started.html" id="8aa9237a_5509" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="8aa9237a_5510">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="8aa9237a_5511" data-style="title-top"><dt id="8aa9237a_5512" data-expandable="false">FRDC-UI</dt><dd><p id="8aa9237a_5514"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="8aa9237a_5515"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/preprocessing-extract-segments.html b/docs/preprocessing-extract-segments.html
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@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.extract_segments | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-extract-segments.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.extract_segments | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-extract-segments.html#webpage", "url": "/d/0.0.4/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"  id="preprocessing.extract_segments.md"  >preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;30711d30_11612\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="30711d30_11553" data-style="title-left" data-title-width="medium"><dt id="30711d30_11554" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="30711d30_11555">Extracts segments from a label classification.</b></p></dd><dt id="30711d30_11556" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="30711d30_11557">Extracts segments from <code class="code" id="30711d30_11558">Rect</code> bounds.</b></p></dd><dt id="30711d30_11559" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="30711d30_11560">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="30711d30_11561">A boundary is a <code class="code" id="30711d30_11562">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="30711d30_11563">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="30711d30_11564"><div class="tabs__content" data-gtm="tab" id="30711d30_11565"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" 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"/d/0.0.4/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"  id="preprocessing.extract_segments.md"  >preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;30711d30_11917\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="30711d30_11858" data-style="title-left" data-title-width="medium"><dt id="30711d30_11859" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="30711d30_11860">Extracts segments from a label classification.</b></p></dd><dt id="30711d30_11861" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="30711d30_11862">Extracts segments from <code class="code" id="30711d30_11863">Rect</code> bounds.</b></p></dd><dt id="30711d30_11864" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="30711d30_11865">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="30711d30_11866">A boundary is a <code class="code" id="30711d30_11867">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="30711d30_11868">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="30711d30_11869"><div class="tabs__content" data-gtm="tab" id="30711d30_11870"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+                +-----------+      
 | Original        |                | Segmented |      
 | Image           |                | Image     |      
@@ -9,7 +9,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  8  |  9  |
 +-----+-----+-----+                +-----+-----+
-</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11567"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11872"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+                +-----------------+      
 | Original        |                | Segmented       |      
 | Image           |                | Image           |      
@@ -20,7 +20,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+-----+      
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  0  |  8  |  9  |      
 +-----+-----+-----+                +-----+-----+-----+      
-</div></div></div><aside class="prompt" data-type="warning" data-title="" id="30711d30_11569"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="30711d30_11570">The correct syntax should be <code class="code" id="30711d30_11571">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="30711d30_11572">A label classification is a <code class="code" id="30711d30_11573">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="30711d30_11574">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
+</div></div></div><aside class="prompt" data-type="warning" data-title="" id="30711d30_11874"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="30711d30_11875">The correct syntax should be <code class="code" id="30711d30_11876">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="30711d30_11877">A label classification is a <code class="code" id="30711d30_11878">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="30711d30_11879">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
 +-----------------+  +-----------------+
 | Label           |  | Original        |
 | Classification  |  | Image           |
@@ -31,7 +31,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  1  |  1  |  0  |  |  7  |  8  |  9  |
 +-----+-----+-----+  +-----+-----+-----+
-</div><p id="30711d30_11576">The extraction will take the <span class="control" id="30711d30_11577">minimum bounding box</span> of each segment and return a list of segments.</p><p id="30711d30_11578">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="30711d30_11579"><div class="tabs__content" data-gtm="tab" id="30711d30_11580"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div><p id="30711d30_11881">The extraction will take the <span class="control" id="30711d30_11882">minimum bounding box</span> of each segment and return a list of segments.</p><p id="30711d30_11883">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="30711d30_11884"><div class="tabs__content" data-gtm="tab" id="30711d30_11885"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------+  +-----------+
 | Extracted |  | Extracted |
 | Segment 1 |  | Segment 2 |
@@ -42,7 +42,7 @@
 +-----+-----+  +-----+-----+
 |  7  |  8  |  
 +-----+-----+  
-</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11582"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11887"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+  +-----------------+
 | Extracted       |  | Extracted       |
 | Segment 1       |  | Segment 2       |
@@ -53,7 +53,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  7  |  8  |  0  |  |  0  |  0  |  0  |
 +-----+-----+-----+  +-----+-----+-----+
-</div></div></div></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><ul class="list _ul" id="30711d30_11584"    ><li class="list__item" id="30711d30_11585"><p>If <span class="control" id="30711d30_11586">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="30711d30_11587"><p>If <span class="control" id="30711d30_11588">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="30711d30_11589">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
+</div></div></div><ul class="list _ul" id="30711d30_11889"    ><li class="list__item" id="30711d30_11890"><p>If <span class="control" id="30711d30_11891">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="30711d30_11892"><p>If <span class="control" id="30711d30_11893">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="30711d30_11894">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
 import numpy as np
 from frdc.load import FRDCDataset
 from frdc.preprocess.extract_segments import extract_segments_from_bounds
@@ -65,7 +65,7 @@
 bounds, labels = ds.get_bounds_and_labels()
 
 segments: list[np.ndarray] = extract_segments_from_bounds(ar, bounds)
-</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="30711d30_11591">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="30711d30_11896">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
 from skimage.morphology import remove_small_objects, remove_small_holes
 import numpy as np
 
@@ -91,4 +91,4 @@
                                               min_height=10, min_width=10)
 
 segments: list[np.ndarray] = extract_segments_from_labels(ar, ar_labels)
-</div><aside class="prompt" data-type="note" data-title="" id="30711d30_11593"><p id="30711d30_11594">The <code class="code" id="30711d30_11595">remove_small_objects</code> and <code class="code" id="30711d30_11596">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="30711d30_11597" data-style="title-top"><dt id="30711d30_11598" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="30711d30_11599">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="30711d30_11601">ar_labels</code> is a label classification as a <code class="code" id="30711d30_11602">np.ndarray</code></p></dd><dt id="30711d30_11603" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="30711d30_11604">Extracts segments from <code class="code" id="30711d30_11605">Rect</code> bounds.</b></p><br><p> <code class="code" id="30711d30_11607">bounds</code> is a list of <code class="code" id="30711d30_11608">Rect</code> bounds.</p></dd><dt id="30711d30_11609" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="30711d30_11610">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+</div><aside class="prompt" data-type="note" data-title="" id="30711d30_11898"><p id="30711d30_11899">The <code class="code" id="30711d30_11900">remove_small_objects</code> and <code class="code" id="30711d30_11901">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="30711d30_11902" data-style="title-top"><dt id="30711d30_11903" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="30711d30_11904">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="30711d30_11906">ar_labels</code> is a label classification as a <code class="code" id="30711d30_11907">np.ndarray</code></p></dd><dt id="30711d30_11908" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="30711d30_11909">Extracts segments from <code class="code" id="30711d30_11910">Rect</code> bounds.</b></p><br><p> <code class="code" id="30711d30_11912">bounds</code> is a list of <code class="code" id="30711d30_11913">Rect</code> bounds.</p></dd><dt id="30711d30_11914" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="30711d30_11915">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/preprocessing-morphology.html b/docs/preprocessing-morphology.html
index b3da6f18..d09ff367 100644
--- a/docs/preprocessing-morphology.html
+++ b/docs/preprocessing-morphology.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T17:57:20.5857123"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-morphology.html#webpage", "url": "/d/0.0.4/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"  id="preprocessing.morphology.md"  >preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;d851343f_4817\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="d851343f_4789"><p id="d851343f_4790">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="d851343f_4791" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="d851343f_4792"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="d851343f_4793" data-style="title-left" data-title-width="medium"><dt id="d851343f_4794" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="d851343f_4795">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="d851343f_4796" data-expandable="false">binary_watershed</dt><dd><p><b id="d851343f_4797">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="d851343f_4798">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-morphology.html#webpage", "url": "/d/0.0.4/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"  id="preprocessing.morphology.md"  >preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;d851343f_4997\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="d851343f_4969"><p id="d851343f_4970">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="d851343f_4971" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="d851343f_4972"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="d851343f_4973" data-style="title-left" data-title-width="medium"><dt id="d851343f_4974" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="d851343f_4975">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="d851343f_4976" data-expandable="false">binary_watershed</dt><dd><p><b id="d851343f_4977">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="d851343f_4978">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 from frdc.preprocess.morphology import (
     threshold_binary_mask, binary_watershed
@@ -10,6 +10,6 @@
 ar, order = ds.get_ar_bands()
 mask = threshold_binary_mask(ar, order.index('NIR'), 90 / 256)
 ar_label = binary_watershed(mask)
-</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="d851343f_4800" data-style="title-top"><dt id="d851343f_4801" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="d851343f_4802">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="d851343f_4803">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="d851343f_4980" data-style="title-top"><dt id="d851343f_4981" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="d851343f_4982">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="d851343f_4983">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
 ar[..., band_idx] &gt; threshold_value
-</div></dd><dt id="d851343f_4806" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="d851343f_4807">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="d851343f_4808">np.ndarray</code></b></p><br><ul class="list _ul" id="d851343f_4810"    ><li class="list__item" id="d851343f_4811"><p><code class="code" id="d851343f_4812">peaks_footprint</code> is the footprint of <code class="code" id="d851343f_4813">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="d851343f_4814"><p><code class="code" id="d851343f_4815">watershed_compactness</code> is the compactness of <code class="code" id="d851343f_4816">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+</div></dd><dt id="d851343f_4986" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="d851343f_4987">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="d851343f_4988">np.ndarray</code></b></p><br><ul class="list _ul" id="d851343f_4990"    ><li class="list__item" id="d851343f_4991"><p><code class="code" id="d851343f_4992">peaks_footprint</code> is the footprint of <code class="code" id="d851343f_4993">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="d851343f_4994"><p><code class="code" id="d851343f_4995">watershed_compactness</code> is the compactness of <code class="code" id="d851343f_4996">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
diff --git a/docs/preprocessing-scale.html b/docs/preprocessing-scale.html
index 2b23cf38..4fcaa6c5 100644
--- a/docs/preprocessing-scale.html
+++ b/docs/preprocessing-scale.html
@@ -1,4 +1,4 @@
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"preprocessing.scale | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"  id="preprocessing.scale.md"  >preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;8b28dc1_8438\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="8b28dc1_8410"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="8b28dc1_8411" data-style="title-left" data-title-width="medium"><dt id="8b28dc1_8412" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="8b28dc1_8413">Scales the NDArray bands to [0, 1] per band.</b></p></dd><dt id="8b28dc1_8414" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="8b28dc1_8415">Scales the NDArray bands to zero mean unit variance per band.</b></p></dd><dt id="8b28dc1_8416" data-expandable="false">scale_static_per_band</dt><dd><p><b id="8b28dc1_8417">Scales the NDArray bands by a predefined configuration.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.scale | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" 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"preprocessing.scale | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"  id="preprocessing.scale.md"  >preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;8b28dc1_8713\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="8b28dc1_8685"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="8b28dc1_8686" data-style="title-left" data-title-width="medium"><dt id="8b28dc1_8687" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="8b28dc1_8688">Scales the NDArray bands to [0, 1] per band.</b></p></dd><dt id="8b28dc1_8689" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="8b28dc1_8690">Scales the NDArray bands to zero mean unit variance per band.</b></p></dd><dt id="8b28dc1_8691" data-expandable="false">scale_static_per_band</dt><dd><p><b id="8b28dc1_8692">Scales the NDArray bands by a predefined configuration.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 from frdc.preprocess.scale import (
     scale_0_1_per_band, scale_normal_per_band, scale_static_per_band
@@ -12,4 +12,4 @@
 ar_01 = scale_0_1_per_band(ar)
 ar_norm = scale_normal_per_band(ar)
 ar_static = scale_static_per_band(ar, order, BAND_MAX_CONFIG)
-</div><aside class="prompt" data-type="tip" data-title="" id="8b28dc1_8419"><p id="8b28dc1_8420">The static scaling has a default config infers the max bit depth from the capturing device.</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="8b28dc1_8421" data-style="title-top"><dt id="8b28dc1_8422" data-expandable="false">scale_0_1_per_band(ar)</dt><dd><p><b id="8b28dc1_8423">Scales the NDArray bands to [0, 1] per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-239-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path 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transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-239-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-239-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-239-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-239-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-239-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-239-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-239-TEX-N-29"></use></g></g></g></svg></dd><dt id="8b28dc1_8426" data-expandable="false">scale_normal_per_band(ar)</dt><dd><p><b id="8b28dc1_8427">Scales the NDArray bands to zero mean unit variance per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" 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<code class="code" id="8b28dc1_8433">config</code> is of <code class="code" id="8b28dc1_8434">dict[str, tuple[int, int]]</code> where the key is the band name, and the value is a tuple of <code class="code" id="8b28dc1_8435">(min, max)</code>. 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+</div><aside class="prompt" data-type="tip" data-title="" id="8b28dc1_8694"><p id="8b28dc1_8695">The static scaling has a default config, which was inferred by our capturing device.</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="8b28dc1_8696" data-style="title-top"><dt id="8b28dc1_8697" data-expandable="false">scale_0_1_per_band(ar)</dt><dd><p><b id="8b28dc1_8698">Scales the NDArray bands to [0, 1] per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-254-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path 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transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-254-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-254-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-254-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-254-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-254-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-254-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-254-TEX-N-29"></use></g></g></g></svg></dd><dt id="8b28dc1_8701" data-expandable="false">scale_normal_per_band(ar)</dt><dd><p><b id="8b28dc1_8702">Scales the NDArray bands to zero mean unit variance per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" 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<code class="code" id="8b28dc1_8708">config</code> is of <code class="code" id="8b28dc1_8709">dict[str, tuple[int, int]]</code> where the key is the band name, and the value is a tuple of <code class="code" id="8b28dc1_8710">(min, max)</code>. 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From 221a5907da6cf150277c23a8f8b308fca80518b8 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 10:30:57 +0800
Subject: [PATCH 17/28] Reword for consistent warning

---
 Writerside/topics/preprocessing.morphology.md | 2 +-
 Writerside/topics/preprocessing.scale.md      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/Writerside/topics/preprocessing.morphology.md b/Writerside/topics/preprocessing.morphology.md
index bc97456b..50b5d7b2 100644
--- a/Writerside/topics/preprocessing.morphology.md
+++ b/Writerside/topics/preprocessing.morphology.md
@@ -11,7 +11,7 @@ Performs morphological operations on the NDArray bands.
 ## Functions
 
 <warning>
-Scale assumes H x W x C, where C is the number of bands.
+Assumes shape H x W x C, where C is the number of bands.
 </warning>
 
 <deflist type="medium">
diff --git a/Writerside/topics/preprocessing.scale.md b/Writerside/topics/preprocessing.scale.md
index 17bf8e02..e210493c 100644
--- a/Writerside/topics/preprocessing.scale.md
+++ b/Writerside/topics/preprocessing.scale.md
@@ -7,7 +7,7 @@ Scales the NDArray bands.
 ## Functions
 
 <warning>
-Scale assumes H x W x C, where C is the number of bands.
+Assumes shape H x W x C, where C is the number of bands.
 </warning>
 
 <deflist type="medium">

From a7cb8ce0724ecf089f7ce85b50d4222d556de487 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 10:31:06 +0800
Subject: [PATCH 18/28] Add GLCM padded doc

---
 Writerside/d.tree                             |   1 +
 .../topics/preprocessing.glcm_padded.md       | 129 ++++++++++++++++++
 2 files changed, 130 insertions(+)
 create mode 100644 Writerside/topics/preprocessing.glcm_padded.md

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 711500b2..bdbc963c 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -14,4 +14,5 @@
     </toc-element>
     <toc-element topic="preprocessing.extract_segments.md"/>
     <toc-element topic="preprocessing.morphology.md"/>
+    <toc-element topic="preprocessing.glcm_padded.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/Writerside/topics/preprocessing.glcm_padded.md b/Writerside/topics/preprocessing.glcm_padded.md
new file mode 100644
index 00000000..c9b9d175
--- /dev/null
+++ b/Writerside/topics/preprocessing.glcm_padded.md
@@ -0,0 +1,129 @@
+# preprocessing.glcm_padded
+
+<tldr>
+Computes the GLCM of the NDArray bands with padding.
+</tldr>
+
+> This is largely a handy wrapper around
+> my [glcm-cupy](https://github.com/Eve-ning/glcm-cupy) package.
+> This auto-computes the necessary padding so that the GLCM is the same size
+> as the original image.
+
+> The GLCM computation is rather slow, so it is recommended to use it
+> only if necessary.
+> {style='warning'}
+
+## Functions
+
+<warning>
+Assumes shape H x W x C, where C is the number of bands.
+</warning>
+
+<deflist type="medium">
+<def title="glcm_padded">
+<b>Computes the GLCM of the NDArray bands with padding.</b>
+</def>
+<def title="glcm_padded_cached">
+<b>Computes the GLCM of the NDArray bands with padding, and caches it.</b>
+</def>
+<def title="append_glcm_padded_cached">
+<b>Computes the GLCM of the NDArray bands with padding, and caches it and
+also appends it onto the original array.</b>
+</def>
+</deflist>
+
+## Usage
+
+We show a few examples of how to use the GLCM functions.
+
+```python
+import numpy as np
+from glcm_cupy import Features
+
+from frdc.preprocess.glcm_padded import (
+    append_glcm_padded_cached, glcm_padded, glcm_padded_cached
+)
+
+ar = np.random.rand(50, 25, 4)
+
+# Returns a shape of H x W x C x GLCM Features
+ar_glcm = glcm_padded(ar, bin_from=1, bin_to=4, radius=3, )
+
+# Returns a shape of H x W x C x 2
+ar_glcm_2_features = glcm_padded(ar, bin_from=1, bin_to=4, radius=3,
+                                 features=[Features.CONTRAST,
+                                           Features.CORRELATION])
+
+# Returns a shape of H x W x C x GLCM Features
+ar_glcm_cached = glcm_padded_cached(ar, bin_from=1, bin_to=4, radius=3)
+
+# Returns a shape of H x W x (C x GLCM Features + C)
+ar_glcm_cached_appended = append_glcm_padded_cached(ar, bin_from=1, bin_to=4,
+                                                    radius=3)
+
+```
+
+- `ar_glcm` is the GLCM of the original array, with the last dimension being
+  the GLCM features. The number of features is determined by the `features`
+  parameter, which defaults to all features.
+- `ar_glcm_2_features` selects only 2 features, with the last dimension being
+  the 2 GLCM features specified.
+- `ar_glcm_cached` caches the GLCM so that if you call it again,
+  it will return the cached version. It stores its data at the project root
+  dir, under `.cache/`.
+- `ar_glcm_cached_appended` is a wrapper around `ar_glcm_cached`, it
+  appends the GLCM features onto the original array. It's equivalent to calling
+  `ar_glcm_cached` and then `np.concatenate` on the final axes.
+
+### Caching
+
+GLCM is an expensive operation, thus we recommend to cache it if the input
+parameters will be the same. This is especially useful if you're
+experimenting with the same dataset with constant parameters.
+
+> This cache is automatically invalidated if the parameters change. Thus, if
+> you perform augmentation, the cache will not be used and will be recomputed.
+> This can be wasteful, so it is recommended to perform augmentation after
+> the GLCM computation if possible.
+> {style='warning'}
+
+> The cache is stored at the project root dir, under `.cache/`. It is safe to
+> delete this folder if you want to clear the cache.
+> {style='note'}
+
+## API
+
+<deflist>
+<def title="glcm_padded(ar, bin_from, bin_to, radius, step_size, features)">
+<b>Computes the GLCM of the NDArray bands with padding.</b><br/>
+<list>
+<li><code>ar</code> is the input array</li>
+<li><code>bin_from</code> is the upper bound of the input</li>
+<li><code>bin_to</code> is the upper bound of the GLCM input, i.e. the 
+resolution that GLCM operates on</li>
+<li><code>radius</code> is the radius of the GLCM</li>
+<li><code>step_size</code> is the step size of the GLCM</li>
+<li><code>features</code> is the list of GLCM features to compute</li>
+</list>
+The return shape is
+<code-block lang="tex">
+H \times W \times C \times \text{GLCM Features}
+</code-block>
+See <code>glcm_cupy</code> for the GLCM Features.
+</def>
+<def title="glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)">
+<b>Computes the GLCM of the NDArray bands with padding, and caches it.</b><br/>
+See <code>glcm_padded</code> for the parameters and output shape
+</def>
+<def title="append_glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)">
+<b>Computes the GLCM of the NDArray bands with padding, and caches it and
+also appends it onto the original array.</b><br/>
+See <code>glcm_padded</code> for the parameters<br/>
+The return shape is:
+<code-block lang="tex">
+H \times W \times (C \times \text{GLCM Features} + C)
+</code-block>
+The function automatically flattens the last 2 dimensions of the GLCM
+features, and appends it onto the original array.
+</def>
+</deflist>

From dee5baa11174cbdb5c157033d7c81a8ca334ffaf Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 10:31:14 +0800
Subject: [PATCH 19/28] Minor Linting

---
 src/frdc/evaluate/evaluate.py | 2 +-
 src/frdc/models/__init__.py   | 2 +-
 src/frdc/train/train.py       | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/frdc/evaluate/evaluate.py b/src/frdc/evaluate/evaluate.py
index 7f3477a4..a0ccfbed 100644
--- a/src/frdc/evaluate/evaluate.py
+++ b/src/frdc/evaluate/evaluate.py
@@ -9,7 +9,7 @@ def dummy_evaluate(
     feature_extraction: Callable[[np.ndarray], np.ndarray],
     classifier: ClassifierMixin,
     X_test: np.ndarray,
-    y_test: np.ndarray
+    y_test: np.ndarray,
 ) -> float:
     """Dummy Evaluation function.
 
diff --git a/src/frdc/models/__init__.py b/src/frdc/models/__init__.py
index 07ba2a3f..5420b607 100644
--- a/src/frdc/models/__init__.py
+++ b/src/frdc/models/__init__.py
@@ -1,4 +1,4 @@
-from .facenet import *
+from .facenet import FaceNet
 
 __all__ = [
     "FaceNet",
diff --git a/src/frdc/train/train.py b/src/frdc/train/train.py
index 3c7b193a..c3d49049 100644
--- a/src/frdc/train/train.py
+++ b/src/frdc/train/train.py
@@ -11,7 +11,7 @@ def dummy_train(
     X_train: np.ndarray,
     y_train: np.ndarray,
     X_val: np.ndarray,
-    y_val: np.ndarray
+    y_val: np.ndarray,
 ) -> tuple[Callable[[np.ndarray], np.ndarray], ClassifierMixin, float]:
     """Dummy Training function.
 

From 374987841f264eaefa3c15260aa5ae87374b6d83 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 10:31:55 +0800
Subject: [PATCH 20/28] Update poetry.lock

---
 poetry.lock | 440 ++++++++++++++++++++++++++--------------------------
 1 file changed, 220 insertions(+), 220 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index abcb8082..c863d7fe 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -153,29 +153,29 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte
 
 [[package]]
 name = "black"
-version = "23.10.0"
+version = "23.10.1"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -195,13 +195,13 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "cachetools"
-version = "5.3.1"
+version = "5.3.2"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.1-py3-none-any.whl", hash = "sha256:95ef631eeaea14ba2e36f06437f36463aac3a096799e876ee55e5cdccb102590"},
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+    {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
+    {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
 ]
 
 [[package]]
@@ -228,101 +228,101 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.0"
+version = "3.3.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 name = "nodeenv"
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 [package.extras]
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 [package.dependencies]
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+    {file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"},
+    {file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"},
 ]
 
 [package.extras]
@@ -2550,13 +2550,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.24.5"
+version = "20.24.6"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.24.5-py3-none-any.whl", hash = "sha256:b80039f280f4919c77b30f1c23294ae357c4c8701042086e3fc005963e4e537b"},
-    {file = "virtualenv-20.24.5.tar.gz", hash = "sha256:e8361967f6da6fbdf1426483bfe9fca8287c242ac0bc30429905721cefbff752"},
+    {file = "virtualenv-20.24.6-py3-none-any.whl", hash = "sha256:520d056652454c5098a00c0f073611ccbea4c79089331f60bf9d7ba247bb7381"},
+    {file = "virtualenv-20.24.6.tar.gz", hash = "sha256:02ece4f56fbf939dbbc33c0715159951d6bf14aaf5457b092e4548e1382455af"},
 ]
 
 [package.dependencies]

From 2f859d08bc4dd8bdf34850e9d62ec3935b069cb6 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 11:50:56 +0800
Subject: [PATCH 21/28] Add lightning class docs

---
 Writerside/d.tree                         |  1 +
 Writerside/topics/train.frdc_lightning.md | 70 +++++++++++++++++++++++
 2 files changed, 71 insertions(+)
 create mode 100644 Writerside/topics/train.frdc_lightning.md

diff --git a/Writerside/d.tree b/Writerside/d.tree
index bdbc963c..2f645f6f 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -15,4 +15,5 @@
     <toc-element topic="preprocessing.extract_segments.md"/>
     <toc-element topic="preprocessing.morphology.md"/>
     <toc-element topic="preprocessing.glcm_padded.md"/>
+    <toc-element topic="train.frdc_datamodule.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/Writerside/topics/train.frdc_lightning.md b/Writerside/topics/train.frdc_lightning.md
new file mode 100644
index 00000000..e5a358d2
--- /dev/null
+++ b/Writerside/topics/train.frdc_lightning.md
@@ -0,0 +1,70 @@
+# train.frdc_datamodule & frdc_module
+
+<tldr>
+The FRDC PyTorch LightningDataModule and LightningModule.
+</tldr>
+
+These are FRDC specific LightningDataModule and LightningModule,
+a core component in the PyTorch Lightning ecosystem to provide a simple
+interface to train and evaluate models.
+
+## Classes
+
+> It's optional to use these classes, you can use your own training loop
+> if you want. We'll use these for our training pipeline.
+> {style='note'}
+
+<deflist type="medium">
+<def title="FRDCDataModule">
+<b>The FRDC PyTorch Lightning DataModule.</b>
+</def>
+<def title="FRDCModule">
+<b>The FRDC PyTorch Lightning Module.</b>
+</def>
+</deflist>
+
+## Usage
+
+> See our training pipeline for a full example
+
+## API
+
+<deflist>
+<def title="FRDCDataModule(segments, labels, preprocess, augmentation, train_val_test_split, batch_size)">
+<b>Initializes the FRDC PyTorch Lightning DataModule.</b><br/>
+<list>
+<li><code>segments</code>, <code>labels</code> are retrieved from
+<list>
+<li><a href="load.dataset.md" anchor="frdcdataset">FRDCDataset</a></li>
+<li><a href="preprocessing.extract_segments.md">Segmentation</a></li>
+</list>
+</li>
+<li><code>preprocess</code> is a function that takes in a segment and returns a preprocessed
+segment. In particular, it should accept a list of NumPy NDArrays and return
+a single stacked PyToch Tensor.</li>
+<li><code>augmentation</code> is a function that takes in a segment and returns an augmented
+segment. In particular, it takes in a PyTorch Tensor and returns another.</li>
+<li><code>train_val_test_split</code> is a function that takes a TensorDataset and returns
+a list of 3 TensorDatasets, for train, val and test respectively.</li>
+<li><code>batch_size</code> is the batch size.</li>
+</list>
+<note>For now, the <code>augmentation</code> is only applied to training
+data</note>
+</def>
+<def title="FRDCModule(model_cls, model_kwargs, optim_cls, optim_kwargs)">
+<b>Initializes the FRDC PyTorch Lightning Module.</b><br/>
+<list>
+<li><code>model_cls</code> is the Class of the model.</li>
+<li><code>model_kwargs</code> is the kwargs to pass to the model.</li>
+<li><code>optim_cls</code> is the Class of the optimizer.</li>
+<li><code>optim_kwargs</code> is the kwargs to pass to the optimizer.</li>
+</list>
+Internally, the module will initialize the model and optimizer as follows:
+<code-block lang="python">
+model = model_cls(**model_kwargs)
+optim = optim_cls(model.parameters(), **optim_kwargs)
+</code-block>
+<note>We do not accept the instances of the Model and Optimizer so
+that we can pickle them.</note>
+</def>
+</deflist>

From 93f64ae5a4e7dbd72d760d4302845c6d61cbbb81 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 13:20:13 +0800
Subject: [PATCH 22/28] Add model methodology

---
 Writerside/d.tree                             |   3 +-
 Writerside/images/cm-chestnut-maydec.png      | Bin 0 -> 100972 bytes
 Writerside/images/graph-chestnut-maydec.png   | Bin 0 -> 125907 bytes
 .../topics/Model-Test-Chestnut-May-Dec.md     | 113 ++++++++++++++++++
 4 files changed, 115 insertions(+), 1 deletion(-)
 create mode 100644 Writerside/images/cm-chestnut-maydec.png
 create mode 100644 Writerside/images/graph-chestnut-maydec.png
 create mode 100644 Writerside/topics/Model-Test-Chestnut-May-Dec.md

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 2f645f6f..692d0784 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -9,11 +9,12 @@
 
     <toc-element topic="Overview.md"/>
     <toc-element topic="Getting-Started.md"/>
+    <toc-element topic="Model-Test-Chestnut-May-Dec.md"/>
     <toc-element topic="load.dataset.md"/>
     <toc-element topic="preprocessing.scale.md">
     </toc-element>
     <toc-element topic="preprocessing.extract_segments.md"/>
     <toc-element topic="preprocessing.morphology.md"/>
     <toc-element topic="preprocessing.glcm_padded.md"/>
-    <toc-element topic="train.frdc_datamodule.md"/>
+    <toc-element topic="train.frdc_lightning.md"/>
 </instance-profile>
\ No newline at end of file
diff --git a/Writerside/images/cm-chestnut-maydec.png b/Writerside/images/cm-chestnut-maydec.png
new file mode 100644
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diff --git a/Writerside/topics/Model-Test-Chestnut-May-Dec.md b/Writerside/topics/Model-Test-Chestnut-May-Dec.md
new file mode 100644
index 00000000..f1ce790d
--- /dev/null
+++ b/Writerside/topics/Model-Test-Chestnut-May-Dec.md
@@ -0,0 +1,113 @@
+# Model Test Chestnut May-Dec
+
+This test is used to evaluate the model performance on the Chestnut Nature Park
+May & December dataset.
+
+See this script in <code>pipeline/model_tests/chestnut_dec_may/main.py</code>.
+
+## Motivation
+
+The usage of this model will be to classify trees in unseen datasets under
+different conditions. In this test, we'll evaluate it under a different season.
+
+A caveat is that it'll be evaluated on the same set of trees, so it's not a
+representative of a field-test. However, given difficulties of yielding
+datasets, this still gives us a good preliminary idea of how the model will
+perform in different conditions.
+
+## Methodology
+
+We simply train on the December dataset, and test on the May dataset.
+
+```mermaid
+graph LR
+    Model -- Train --> DecDataset
+    Model -- Test --> MayDataset
+```
+
+> The inverse of this test is also plausible.
+
+> Ideally, we should have a Validation set to tune the hyperparameters, but
+> given the limitations of the dataset, we'll skip this step.
+> {style='warning'}
+
+## Model
+
+The current Model used is a simple InceptionV3 Transfer Learning model, with
+the last layer replaced with a fully connected layer(s).
+
+```mermaid
+graph LR
+    Input --> InceptionV3
+    InceptionV3[InceptionV3 Frozen] --> FC["FC Layer(s)"]
+    FC --> Softmax
+    Softmax --> Output
+    Input -- Cross Entropy Loss --> Output
+```
+
+> We didn't find significant evidence of improvements of using a more complex
+> FC layer, so multiple or single FC layer are feasible.
+
+## Preprocessing
+
+We perform the following steps:
+
+```mermaid
+graph v
+    Segment --> Scale[Scale Values to 0-1]
+    Scale --> GLCM[GLCM Step 7, Rad 3, Bin 128, Mean Feature]
+    GLCM --> ScaleNorm[Scale Values to 0 Mean 1 Var]
+    ScaleNorm --> Resize[Resize to 299x299]
+```
+
+> We need to scale to 0-1 before GLCM, so that GLCM can bin the values
+> correctly.
+
+### Augmentation
+
+The following augmentations are used:
+
+```mermaid
+graph >
+    Segment --> HFLip[Horizontal Flip 50%]
+    HFLip --> VFLip[Vertical Flip 50%]
+```
+
+> This only operates on training data.
+
+## Hyperparameters
+
+The following hyperparameters are used:
+
+- Optimizer: Adam
+- Learning Rate: 1e-3
+- Batch Size: 5
+- Epochs: 100
+- Early Stopping: 4
+
+## Results
+
+We yield around 40% accuracy on the test set, compared to around 65% for the
+training set. Raising the training accuracy with a more complex model may
+improve the test accuracy, however, due to instability of our test
+results, we can't be sure of this.
+
+### Result Images {collapsible="true"}
+
+<tabs>
+<tab title="Training Graph">
+<img src="graph-chestnut-maydec.png" alt="graph-chestnut-maydec.png" />
+</tab>
+<tab title="Confusion Matrix">
+<img src="cm-chestnut-maydec.png" alt="cm-chestnut-maydec.png" />
+</tab>
+</tabs>
+
+### Caveats
+
+- The test set is very small, so the results are not very representative.
+- The test set is the same set of trees, so it's not a true test of the model
+  performance in different conditions.
+- There are many classes with 1 sample, so the model may not be able to learn
+  the features of these classes well.
+ 
\ No newline at end of file

From eb77fb5a5dbefa922883039c5f82f8dd499be809 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 13:20:38 +0800
Subject: [PATCH 23/28] Update docs

---
 docs/HelpTOC.json                        |   2 +-
 docs/Map.jhm                             |   2 +-
 docs/getting-started.html                |  18 ++++++++--------
 docs/images/cm-chestnut-maydec.png       | Bin 0 -> 100972 bytes
 docs/images/graph-chestnut-maydec.png    | Bin 0 -> 125907 bytes
 docs/load-dataset.html                   |   8 +++----
 docs/model-test-chestnut-may-dec.html    |   1 +
 docs/overview.html                       |   2 +-
 docs/preprocessing-extract-segments.html |  16 +++++++-------
 docs/preprocessing-glcm-padded.html      |  26 +++++++++++++++++++++++
 docs/preprocessing-morphology.html       |   6 +++---
 docs/preprocessing-scale.html            |   4 ++--
 docs/train-frdc-lightning.html           |   4 ++++
 webHelpD2-all.zip                        | Bin 33722 -> 242632 bytes
 14 files changed, 60 insertions(+), 29 deletions(-)
 create mode 100644 docs/images/cm-chestnut-maydec.png
 create mode 100644 docs/images/graph-chestnut-maydec.png
 create mode 100644 docs/model-test-chestnut-may-dec.html
 create mode 100644 docs/preprocessing-glcm-padded.html
 create mode 100644 docs/train-frdc-lightning.html

diff --git a/docs/HelpTOC.json b/docs/HelpTOC.json
index 5564d1c0..9596ec5b 100644
--- a/docs/HelpTOC.json
+++ b/docs/HelpTOC.json
@@ -1 +1 @@
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\ No newline at end of file
+{"entities":{"pages":{"Overview":{"id":"Overview","title":"Overview","url":"overview.html","level":0,"tabIndex":0},"Getting-Started":{"id":"Getting-Started","title":"Getting Started","url":"getting-started.html","level":0,"tabIndex":1},"Model-Test-Chestnut-May-Dec":{"id":"Model-Test-Chestnut-May-Dec","title":"Model Test Chestnut May-Dec","url":"model-test-chestnut-may-dec.html","level":0,"tabIndex":2},"load.dataset":{"id":"load.dataset","title":"load.dataset","url":"load-dataset.html","level":0,"tabIndex":3},"preprocessing.scale":{"id":"preprocessing.scale","title":"preprocessing.scale","url":"preprocessing-scale.html","level":0,"tabIndex":4},"preprocessing.extract_segments":{"id":"preprocessing.extract_segments","title":"preprocessing.extract_segments","url":"preprocessing-extract-segments.html","level":0,"tabIndex":5},"preprocessing.morphology":{"id":"preprocessing.morphology","title":"preprocessing.morphology","url":"preprocessing-morphology.html","level":0,"tabIndex":6},"preprocessing.glcm_padded":{"id":"preprocessing.glcm_padded","title":"preprocessing.glcm_padded","url":"preprocessing-glcm-padded.html","level":0,"tabIndex":7},"train.frdc_lightning":{"id":"train.frdc_lightning","title":"train.frdc_datamodule \u0026 frdc_module","url":"train-frdc-lightning.html","level":0,"tabIndex":8}}},"topLevelIds":["Overview","Getting-Started","Model-Test-Chestnut-May-Dec","load.dataset","preprocessing.scale","preprocessing.extract_segments","preprocessing.morphology","preprocessing.glcm_padded","train.frdc_lightning"]}
\ No newline at end of file
diff --git a/docs/Map.jhm b/docs/Map.jhm
index d5ac1aa1..3785e5e0 100644
--- a/docs/Map.jhm
+++ b/docs/Map.jhm
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/docs/getting-started.html b/docs/getting-started.html
index d305d68c..64c56e7f 100644
--- a/docs/getting-started.html
+++ b/docs/getting-started.html
@@ -1,20 +1,20 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_96004"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="8604a237_96005">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7999471"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="2b098176_4885"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="2b098176_4886">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
             [tool.poetry.dependencies]
             python = &quot;...&quot;
-        </div></li><li class="list__item" id="8604a237_96007"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="2b098176_4888"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
           git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
-        </div></li><li class="list__item" id="8604a237_96009"><p>Then, create a Python Virtual Env <code class="code" id="8604a237_96010">pyvenv</code></p><div class="tabs" id="8604a237_96011"><div class="tabs__content" data-gtm="tab" id="8604a237_96012"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="8604a237_96014"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="8604a237_96016"><p><a href="https://python-poetry.org/docs/" id="8604a237_96017"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="8604a237_96019"><p>If <code class="code" id="8604a237_96020">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="8604a237_96021"><p>Activate the virtual environment </p><div class="tabs" id="8604a237_96022"><div class="tabs__content" data-gtm="tab" id="8604a237_96023"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></li><li class="list__item" id="2b098176_4890"><p>Then, create a Python Virtual Env <code class="code" id="2b098176_4891">pyvenv</code></p><div class="tabs" id="2b098176_4892"><div class="tabs__content" data-gtm="tab" id="2b098176_4893"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="2b098176_4895"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="2b098176_4897"><p><a href="https://python-poetry.org/docs/" id="2b098176_4898"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="2b098176_4900"><p>If <code class="code" id="2b098176_4901">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="2b098176_4902"><p>Activate the virtual environment </p><div class="tabs" id="2b098176_4903"><div class="tabs__content" data-gtm="tab" id="2b098176_4904"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             cd venv/Scripts
             activate
             cd ../..
-        </div></div><div class="tabs__content" data-gtm="tab" id="8604a237_96025"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></div><div class="tabs__content" data-gtm="tab" id="2b098176_4906"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             source venv/bin/activate
-        </div></div></div></li><li class="list__item" id="8604a237_96027"><p>Install the dependencies. You should be in the same directory as <code class="code" id="8604a237_96028">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
+        </div></div></div></li><li class="list__item" id="2b098176_4908"><p>Install the dependencies. You should be in the same directory as <code class="code" id="2b098176_4909">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
             poetry install --with dev
-        </div></li><li class="list__item" id="8604a237_96030"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="2b098176_4911"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="8604a237_96032"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="8604a237_96033"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="8604a237_96034"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="8604a237_96035"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="8604a237_96037"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="8604a237_96038"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="8604a237_96040"><p>To make sure everything is working, <a href="#tests" id="8604a237_96041" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="8604a237_96042" data-toc="8604a237_96042">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="8604a237_96043"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="8604a237_96044"><div class="code-block" data-lang="bash"         >
+        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="2b098176_4913"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="2b098176_4914"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="2b098176_4915"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="2b098176_4916"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="2b098176_4918"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="2b098176_4919"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="2b098176_4921"><p>To make sure everything is working, <a href="#tests" id="2b098176_4922" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="2b098176_4923" data-toc="2b098176_4923">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="2b098176_4924"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="2b098176_4925"><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="8604a237_96046"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
+        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="2b098176_4927"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
             pytest
-        </div></li><li class="list__item" id="8604a237_96048"><p>In case of errors: </p><dl id="8604a237_96049" data-style="title-top"><dt id="8604a237_96050" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="8604a237_96051" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="8604a237_96052" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="8604a237_96053" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="8604a237_96054">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="8604a237_96060" data-expandable="false">rsc/</dt><dd><p id="8604a237_96062">Resources. These are usually cached datasets</p></dd><dt id="8604a237_96063" data-expandable="false">pipeline/</dt><dd><p id="8604a237_96065">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="8604a237_96066" data-expandable="false">tests/</dt><dd><p id="8604a237_96068">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="8604a237_96069">We have 3 types of tests:</p><ul class="list _ul" id="8604a237_96070"    ><li class="list__item" id="8604a237_96071"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="8604a237_96072"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="8604a237_96073"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="8604a237_96074" data-style="title-top"><dt id="8604a237_96075" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="8604a237_96076">src/frdc/</code> directory.</p></dd><dt id="8604a237_96077" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="8604a237_96078">tests/</code> directory.</p></dd><dt id="8604a237_96079" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="8604a237_96080">pipeline/</code> directory.</p></dd><dt id="8604a237_96081" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="8604a237_96082">poetry add PACKAGE</code> and commit the changes to <code class="code" id="8604a237_96083">pyproject.toml</code> and <code class="code" id="8604a237_96084">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="8604a237_96085"><p>E.g. Adding <code class="code" id="8604a237_96086">numpy</code> is the same as <code class="code" id="8604a237_96087">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+        </div></li><li class="list__item" id="2b098176_4929"><p>In case of errors: </p><dl id="2b098176_4930" data-style="title-top"><dt id="2b098176_4931" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="2b098176_4932" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="2b098176_4933" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="2b098176_4934" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="2b098176_4935">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="2b098176_4941" data-expandable="false">rsc/</dt><dd><p id="2b098176_4943">Resources. These are usually cached datasets</p></dd><dt id="2b098176_4944" data-expandable="false">pipeline/</dt><dd><p id="2b098176_4946">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="2b098176_4947" data-expandable="false">tests/</dt><dd><p id="2b098176_4949">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="2b098176_4950">We have 3 types of tests:</p><ul class="list _ul" id="2b098176_4951"    ><li class="list__item" id="2b098176_4952"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="2b098176_4953"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="2b098176_4954"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="2b098176_4955" data-style="title-top"><dt id="2b098176_4956" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="2b098176_4957">src/frdc/</code> directory.</p></dd><dt id="2b098176_4958" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="2b098176_4959">tests/</code> directory.</p></dd><dt id="2b098176_4960" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="2b098176_4961">pipeline/</code> directory.</p></dd><dt id="2b098176_4962" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="2b098176_4963">poetry add PACKAGE</code> and commit the changes to <code class="code" id="2b098176_4964">pyproject.toml</code> and <code class="code" id="2b098176_4965">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="2b098176_4966"><p>E.g. Adding <code class="code" id="2b098176_4967">numpy</code> is the same as <code class="code" id="2b098176_4968">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+++ b/docs/load-dataset.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/load-dataset.html#webpage", "url": "/d/0.0.4/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"  id="load.dataset.md"  >load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;feb21bd9_3936\&quot;\u003eLoad datasets from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="feb21bd9_3857"><p id="feb21bd9_3858">You need to Set-Up <a href="getting-started.html#gcloud" id="feb21bd9_3859" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><dl id="feb21bd9_3861" data-style="title-top"><dt id="feb21bd9_3862" data-expandable="false">FRDCDownloader</dt><dd><p>This facilitates authentication and downloading from GCS.</p></dd><dt id="feb21bd9_3863" data-expandable="false">FRDCDataset</dt><dd><p>This uses the Downloader to download and load the dataset. It also implements useful helper functions to load FRDC-specific datasets, such as loading our images and labels.</p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="feb21bd9_3864">An example loading our Chestnut Nature Park dataset. We retrieve the</p><ul class="list _ul" id="feb21bd9_3865"    ><li class="list__item" id="feb21bd9_3866"><p>hyperspectral bands</p></li><li class="list__item" id="feb21bd9_3867"><p>order of the bands</p></li><li class="list__item" id="feb21bd9_3868"><p>bounding boxes</p></li><li class="list__item" id="feb21bd9_3869"><p>labels</p></li></ul><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/load-dataset.html#webpage", "url": "/d/0.0.4/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"  id="load.dataset.md"  >load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;5098d101_33914\&quot;\u003eLoad datasets from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="5098d101_33835"><p id="5098d101_33836">You need to Set-Up <a href="getting-started.html#gcloud" id="5098d101_33837" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><dl id="5098d101_33839" data-style="title-top"><dt id="5098d101_33840" data-expandable="false">FRDCDownloader</dt><dd><p>This facilitates authentication and downloading from GCS.</p></dd><dt id="5098d101_33841" data-expandable="false">FRDCDataset</dt><dd><p>This uses the Downloader to download and load the dataset. It also implements useful helper functions to load FRDC-specific datasets, such as loading our images and labels.</p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="5098d101_33842">An example loading our Chestnut Nature Park dataset. We retrieve the</p><ul class="list _ul" id="5098d101_33843"    ><li class="list__item" id="5098d101_33844"><p>hyperspectral bands</p></li><li class="list__item" id="5098d101_33845"><p>order of the bands</p></li><li class="list__item" id="5098d101_33846"><p>bounding boxes</p></li><li class="list__item" id="5098d101_33847"><p>labels</p></li></ul><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 
 ds = FRDCDataset(site='chestnut_nature_park',
@@ -6,7 +6,7 @@
                  version=None, )
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
-</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="custom-authentication-downloads" data-toc="custom-authentication-downloads">Custom Authentication &amp; Downloads</h3></div><div class="collapse__content"><p id="feb21bd9_3871">If you need granular control over</p><ul class="list _ul" id="feb21bd9_3872"    ><li class="list__item" id="feb21bd9_3873"><p>where the files are downloaded</p></li><li class="list__item" id="feb21bd9_3874"><p>the credentials used</p></li><li class="list__item" id="feb21bd9_3875"><p>the project used</p></li><li class="list__item" id="feb21bd9_3876"><p>the bucket used</p></li></ul><p id="feb21bd9_3877">Then pass in a <code class="code" id="feb21bd9_3878">FRDCDownloader</code> object to <code class="code" id="feb21bd9_3879">FRDCDataset</code>.</p><div class="code-block" data-lang="python"         >
+</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="custom-authentication-downloads" data-toc="custom-authentication-downloads">Custom Authentication &amp; Downloads</h3></div><div class="collapse__content"><p id="5098d101_33849">If you need granular control over</p><ul class="list _ul" id="5098d101_33850"    ><li class="list__item" id="5098d101_33851"><p>where the files are downloaded</p></li><li class="list__item" id="5098d101_33852"><p>the credentials used</p></li><li class="list__item" id="5098d101_33853"><p>the project used</p></li><li class="list__item" id="5098d101_33854"><p>the bucket used</p></li></ul><p id="5098d101_33855">Then pass in a <code class="code" id="5098d101_33856">FRDCDownloader</code> object to <code class="code" id="5098d101_33857">FRDCDataset</code>.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDownloader, FRDCDataset
 
 dl = FRDCDownloader(credentials=...,
@@ -19,7 +19,7 @@
                  dl=dl)
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
-</div><p id="feb21bd9_3881">If you have a file not easily downloadable by <code class="code" id="feb21bd9_3882">FRDCDataset</code>, you can use <code class="code" id="feb21bd9_3883">FRDCDownloader</code> to download it.</p><div class="code-block" data-lang="python"         >
+</div><p id="5098d101_33859">If you have a file not easily downloadable by <code class="code" id="5098d101_33860">FRDCDataset</code>, you can use <code class="code" id="5098d101_33861">FRDCDownloader</code> to download it.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDownloader
 
 dl = FRDCDownloader(credentials=...,
@@ -28,4 +28,4 @@
                     bucket_name=...)
 
 dl.download_file(path_glob=&quot;path/to/gcs/file&quot;)
-</div><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_3885"><p>This will automatically save the file to the local dataset root dir.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><section class="chapter"><h3 id="frdcdataset" data-toc="frdcdataset">FRDCDataset</h3><dl id="feb21bd9_3886" data-style="title-top"><dt id="feb21bd9_3887" data-expandable="false">FRDCDataset(site, date, version, dl)</dt><dd><p><b id="feb21bd9_3888">Initializes the dataset downloader.</b></p><br><p> This doesn't immediately download the dataset, but only when you call the <code class="code" id="feb21bd9_3890">get_*</code> functions. </p><br><p> The site, date, version must match the dataset path on GCS. For example if the dataset is at <code class="code" id="feb21bd9_3892">gs://frdc-scan/my-site/20201218/90deg/map</code>, </p><ul class="list _ul" id="feb21bd9_3893"    ><li class="list__item" id="feb21bd9_3894"><p><code class="code" id="feb21bd9_3895">site='my-site'</code></p></li><li class="list__item" id="feb21bd9_3896"><p><code class="code" id="feb21bd9_3897">date='20201218'</code></p></li><li class="list__item" id="feb21bd9_3898"><p><code class="code" id="feb21bd9_3899">version='90deg/map'</code></p></li></ul><p> If the dataset doesn't have a &quot;version&quot;, for example: <code class="code" id="feb21bd9_3900">gs://frdc-scan/my-site/20201218</code>, then you can pass in <code class="code" id="feb21bd9_3901">version=None</code>. </p><br><aside class="prompt" data-type="note" data-title="" id="feb21bd9_3903"><p>If you don't want to search up GCS, you can use FRDCDownloader to list all datasets, and their versions with <code class="code" id="feb21bd9_3904">FRDCDownloader().list_gcs_datasets()</code></p></aside><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_3905"><p>If <code class="code" id="feb21bd9_3906">dl</code> is None, it will create a new FRDCDownloader. Usually, you don't need to pass this in unless you have a custom credential, or project.</p></aside></dd><dt id="feb21bd9_3907" data-expandable="false">get_ar_bands()</dt><dd><p><b id="feb21bd9_3908">Gets the NDArray bands (H x W x C) and channel order as <code class="code" id="feb21bd9_3909">tuple[np.ndarray, list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the stacked NDArray bands. This wraps around <code class="code" id="feb21bd9_3911">get_ar_bands_as_dict()</code>, thus if you want more control over how the bands are loaded, use that instead.</p></dd><dt id="feb21bd9_3912" data-expandable="false">get_ar_bands_as_dict()</dt><dd><p><b id="feb21bd9_3913">Gets the NDArray bands (H x W) as a <code class="code" id="feb21bd9_3914">dict[str, np.ndarray]</code>.</b></p><br><p> This downloads (if missing) and retrieves the individual NDArray bands as a dictionary. The keys are the band names, and the values are the NDArray bands.</p></dd><dt id="feb21bd9_3916" data-expandable="false">get_bounds_and_labels()</dt><dd><p><b id="feb21bd9_3917">Gets the bounding boxes and labels as <code class="code" id="feb21bd9_3918">tuple[list[Rect], list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the bounding boxes and labels as a tuple. The first element is a list of bounding boxes, and the second element is a list of labels. </p><br><aside class="prompt" data-type="tip" data-title="" id="feb21bd9_3921"><p>The bounding boxes are in the format of <code class="code" id="feb21bd9_3922">Rect</code>, a <code class="code" id="feb21bd9_3923">namedtuple</code> of x0, y0, x1, y1.</p></aside></dd></dl></section><section class="chapter"><h3 id="frdcdownloader" data-toc="frdcdownloader">FRDCDownloader</h3><dl id="feb21bd9_3924" data-style="title-top"><dt id="feb21bd9_3925" data-expandable="false">list_gcs_datasets(anchor)</dt><dd><p><b id="feb21bd9_3926">Lists all GCS datasets in the bucket as <code class="code" id="feb21bd9_3927">DataFrame</code></b></p><br><p> This works by checking which folders have a specific file, which we call the <code class="code" id="feb21bd9_3929">anchor</code>.</p></dd><dt id="feb21bd9_3930" data-expandable="false">download_file(path_glob, local_exists_ok)</dt><dd><p><b id="feb21bd9_3931">Downloads a file from GCS.</b></p><br><p> This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1 file, it will raise an error. </p><br><p> If <code class="code" id="feb21bd9_3934">local_exists_ok</code> is True, it will not download the file if it already exists locally. However, if it's False, it will download the file only if the hashes don't match. </p><aside class="prompt" data-type="note" data-title="" id="feb21bd9_3935"><p>Use this if you have a file on GCS that can't be downloaded via FRDCDataset.</p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+</div><aside class="prompt" data-type="tip" data-title="" id="5098d101_33863"><p>This will automatically save the file to the local dataset root dir.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><section class="chapter"><h3 id="frdcdataset" data-toc="frdcdataset">FRDCDataset</h3><dl id="5098d101_33864" data-style="title-top"><dt id="5098d101_33865" data-expandable="false">FRDCDataset(site, date, version, dl)</dt><dd><p><b id="5098d101_33866">Initializes the dataset downloader.</b></p><br><p> This doesn't immediately download the dataset, but only when you call the <code class="code" id="5098d101_33868">get_*</code> functions. </p><br><p> The site, date, version must match the dataset path on GCS. For example if the dataset is at <code class="code" id="5098d101_33870">gs://frdc-scan/my-site/20201218/90deg/map</code>, </p><ul class="list _ul" id="5098d101_33871"    ><li class="list__item" id="5098d101_33872"><p><code class="code" id="5098d101_33873">site='my-site'</code></p></li><li class="list__item" id="5098d101_33874"><p><code class="code" id="5098d101_33875">date='20201218'</code></p></li><li class="list__item" id="5098d101_33876"><p><code class="code" id="5098d101_33877">version='90deg/map'</code></p></li></ul><p> If the dataset doesn't have a &quot;version&quot;, for example: <code class="code" id="5098d101_33878">gs://frdc-scan/my-site/20201218</code>, then you can pass in <code class="code" id="5098d101_33879">version=None</code>. </p><br><aside class="prompt" data-type="note" data-title="" id="5098d101_33881"><p>If you don't want to search up GCS, you can use FRDCDownloader to list all datasets, and their versions with <code class="code" id="5098d101_33882">FRDCDownloader().list_gcs_datasets()</code></p></aside><aside class="prompt" data-type="tip" data-title="" id="5098d101_33883"><p>If <code class="code" id="5098d101_33884">dl</code> is None, it will create a new FRDCDownloader. Usually, you don't need to pass this in unless you have a custom credential, or project.</p></aside></dd><dt id="5098d101_33885" data-expandable="false">get_ar_bands()</dt><dd><p><b id="5098d101_33886">Gets the NDArray bands (H x W x C) and channel order as <code class="code" id="5098d101_33887">tuple[np.ndarray, list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the stacked NDArray bands. This wraps around <code class="code" id="5098d101_33889">get_ar_bands_as_dict()</code>, thus if you want more control over how the bands are loaded, use that instead.</p></dd><dt id="5098d101_33890" data-expandable="false">get_ar_bands_as_dict()</dt><dd><p><b id="5098d101_33891">Gets the NDArray bands (H x W) as a <code class="code" id="5098d101_33892">dict[str, np.ndarray]</code>.</b></p><br><p> This downloads (if missing) and retrieves the individual NDArray bands as a dictionary. The keys are the band names, and the values are the NDArray bands.</p></dd><dt id="5098d101_33894" data-expandable="false">get_bounds_and_labels()</dt><dd><p><b id="5098d101_33895">Gets the bounding boxes and labels as <code class="code" id="5098d101_33896">tuple[list[Rect], list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the bounding boxes and labels as a tuple. The first element is a list of bounding boxes, and the second element is a list of labels. </p><br><aside class="prompt" data-type="tip" data-title="" id="5098d101_33899"><p>The bounding boxes are in the format of <code class="code" id="5098d101_33900">Rect</code>, a <code class="code" id="5098d101_33901">namedtuple</code> of x0, y0, x1, y1.</p></aside></dd></dl></section><section class="chapter"><h3 id="frdcdownloader" data-toc="frdcdownloader">FRDCDownloader</h3><dl id="5098d101_33902" data-style="title-top"><dt id="5098d101_33903" data-expandable="false">list_gcs_datasets(anchor)</dt><dd><p><b id="5098d101_33904">Lists all GCS datasets in the bucket as <code class="code" id="5098d101_33905">DataFrame</code></b></p><br><p> This works by checking which folders have a specific file, which we call the <code class="code" id="5098d101_33907">anchor</code>.</p></dd><dt id="5098d101_33908" data-expandable="false">download_file(path_glob, local_exists_ok)</dt><dd><p><b id="5098d101_33909">Downloads a file from GCS.</b></p><br><p> This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1 file, it will raise an error. </p><br><p> If <code class="code" id="5098d101_33912">local_exists_ok</code> is True, it will not download the file if it already exists locally. However, if it's False, it will download the file only if the hashes don't match. </p><aside class="prompt" data-type="note" data-title="" id="5098d101_33913"><p>Use this if you have a file on GCS that can't be downloaded via FRDCDataset.</p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7999471"><meta name="build-number" content="${buildNumber}">       <title>Model Test Chestnut May-Dec | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"motivation","level":0,"title":"Motivation","anchor":"#motivation"},{"id":"methodology","level":0,"title":"Methodology","anchor":"#methodology"},{"id":"model","level":0,"title":"Model","anchor":"#model"},{"id":"preprocessing","level":0,"title":"Preprocessing","anchor":"#preprocessing"},{"id":"hyperparameters","level":0,"title":"Hyperparameters","anchor":"#hyperparameters"},{"id":"results","level":0,"title":"Results","anchor":"#results"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Model Test Chestnut May-Dec | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/model-test-chestnut-may-dec.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Model Test Chestnut May-Dec | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/model-test-chestnut-may-dec.html#webpage", "url": "/d/0.0.4/model-test-chestnut-may-dec.html", "name": "Model Test Chestnut May-Dec | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Model-Test-Chestnut-May-Dec" data-main-title="Model Test Chestnut May-Dec" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Model-Test-Chestnut-May-Dec"  id="Model-Test-Chestnut-May-Dec.md"  >Model Test Chestnut May-Dec</h1>  <p id="4763b031_4613">This test is used to evaluate the model performance on the Chestnut Nature Park May &amp; December dataset.</p><p id="4763b031_4614">See this script in <code class="code" id="4763b031_4615">pipeline/model_tests/chestnut_dec_may/main.py</code>.</p><section class="chapter"><h2 id="motivation" data-toc="motivation">Motivation</h2><p id="4763b031_4616">The usage of this model will be to classify trees in unseen datasets under different conditions. In this test, we'll evaluate it under a different season.</p><p id="4763b031_4617">A caveat is that it'll be evaluated on the same set of trees, so it's not a representative of a field-test. However, given difficulties of yielding datasets, this still gives us a good preliminary idea of how the model will perform in different conditions.</p></section><section class="chapter"><h2 id="methodology" data-toc="methodology">Methodology</h2><p id="4763b031_4618">We simply train on the December dataset, and test on the May dataset.</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 255.9375 134"  xmlns="http://www.w3.org/2000/svg" width="255.9375" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; stroke-dasharray: 1, 0;" class="arrowMarkerPath" d="M 0 0 L 10 5 L 0 10 z"></path></marker><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="0" viewBox="0 0 10 10" class="marker flowchart" id="flowchart-pointStart"><path style="stroke-width: 1; 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Raising the training accuracy with a more complex model may improve the test accuracy, however, due to instability of our test results, we can't be sure of this.</p><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="result-images" data-toc="result-images">Result Images</h3></div><div class="collapse__content"><div class="tabs" id="4763b031_4644"><div class="tabs__content" data-gtm="tab" id="4763b031_4645"   data-title="Training Graph" ><figure  id="4763b031_4646"><img alt="graph-chestnut-maydec.png" title="graph-chestnut-maydec.png" src="images/graph-chestnut-maydec.png"  class="" width="1068" height="1280"/></figure></div><div class="tabs__content" data-gtm="tab" id="4763b031_4647"   data-title="Confusion Matrix" ><figure  id="4763b031_4648"><img alt="cm-chestnut-maydec.png" title="cm-chestnut-maydec.png" src="images/cm-chestnut-maydec.png"  class="" width="1000" height="1000"/></figure></div></div></div></div></section><section class="chapter"><h3 id="caveats" data-toc="caveats">Caveats</h3><ul class="list _ul" id="4763b031_4649"    ><li class="list__item" id="4763b031_4650"><p>The test set is very small, so the results are not very representative.</p></li><li class="list__item" id="4763b031_4651"><p>The test set is the same set of trees, so it's not a true test of the model performance in different conditions.</p></li><li class="list__item" id="4763b031_4652"><p>There are many classes with 1 sample, so the model may not be able to learn the features of these classes well.</p></li></ul></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="8aa9237a_5506">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="8aa9237a_5507">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="8aa9237a_5508"><a href="getting-started.html" id="8aa9237a_5509" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="8aa9237a_5510">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="8aa9237a_5511" data-style="title-top"><dt id="8aa9237a_5512" data-expandable="false">FRDC-UI</dt><dd><p id="8aa9237a_5514"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="8aa9237a_5515"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="d05d8f2e_4960">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="d05d8f2e_4961">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="d05d8f2e_4962"><a href="getting-started.html" id="d05d8f2e_4963" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="d05d8f2e_4964">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="d05d8f2e_4965" data-style="title-top"><dt id="d05d8f2e_4966" data-expandable="false">FRDC-UI</dt><dd><p id="d05d8f2e_4968"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="d05d8f2e_4969"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/preprocessing-extract-segments.html b/docs/preprocessing-extract-segments.html
index a2465dfc..163678d2 100644
--- a/docs/preprocessing-extract-segments.html
+++ b/docs/preprocessing-extract-segments.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" 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"/d/0.0.4/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"  id="preprocessing.extract_segments.md"  >preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;30711d30_11917\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="30711d30_11858" data-style="title-left" data-title-width="medium"><dt id="30711d30_11859" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="30711d30_11860">Extracts segments from a label classification.</b></p></dd><dt id="30711d30_11861" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="30711d30_11862">Extracts segments from <code class="code" id="30711d30_11863">Rect</code> bounds.</b></p></dd><dt id="30711d30_11864" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="30711d30_11865">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="30711d30_11866">A boundary is a <code class="code" id="30711d30_11867">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="30711d30_11868">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="30711d30_11869"><div class="tabs__content" data-gtm="tab" id="30711d30_11870"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" 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content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.extract_segments | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-extract-segments.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.extract_segments | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-extract-segments.html#webpage", "url": "/d/0.0.4/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"  id="preprocessing.extract_segments.md"  >preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;c4e72d8d_5049\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="c4e72d8d_4990" data-style="title-left" data-title-width="medium"><dt id="c4e72d8d_4991" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="c4e72d8d_4992">Extracts segments from a label classification.</b></p></dd><dt id="c4e72d8d_4993" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="c4e72d8d_4994">Extracts segments from <code class="code" id="c4e72d8d_4995">Rect</code> bounds.</b></p></dd><dt id="c4e72d8d_4996" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="c4e72d8d_4997">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="c4e72d8d_4998">A boundary is a <code class="code" id="c4e72d8d_4999">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="c4e72d8d_5000">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="c4e72d8d_5001"><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5002"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+                +-----------+      
 | Original        |                | Segmented |      
 | Image           |                | Image     |      
@@ -9,7 +9,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  8  |  9  |
 +-----+-----+-----+                +-----+-----+
-</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11872"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5004"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+                +-----------------+      
 | Original        |                | Segmented       |      
 | Image           |                | Image           |      
@@ -20,7 +20,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+-----+      
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  0  |  8  |  9  |      
 +-----+-----+-----+                +-----+-----+-----+      
-</div></div></div><aside class="prompt" data-type="warning" data-title="" id="30711d30_11874"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="30711d30_11875">The correct syntax should be <code class="code" id="30711d30_11876">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="30711d30_11877">A label classification is a <code class="code" id="30711d30_11878">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="30711d30_11879">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
+</div></div></div><aside class="prompt" data-type="warning" data-title="" id="c4e72d8d_5006"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="c4e72d8d_5007">The correct syntax should be <code class="code" id="c4e72d8d_5008">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="c4e72d8d_5009">A label classification is a <code class="code" id="c4e72d8d_5010">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="c4e72d8d_5011">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
 +-----------------+  +-----------------+
 | Label           |  | Original        |
 | Classification  |  | Image           |
@@ -31,7 +31,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  1  |  1  |  0  |  |  7  |  8  |  9  |
 +-----+-----+-----+  +-----+-----+-----+
-</div><p id="30711d30_11881">The extraction will take the <span class="control" id="30711d30_11882">minimum bounding box</span> of each segment and return a list of segments.</p><p id="30711d30_11883">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="30711d30_11884"><div class="tabs__content" data-gtm="tab" id="30711d30_11885"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div><p id="c4e72d8d_5013">The extraction will take the <span class="control" id="c4e72d8d_5014">minimum bounding box</span> of each segment and return a list of segments.</p><p id="c4e72d8d_5015">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="c4e72d8d_5016"><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5017"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------+  +-----------+
 | Extracted |  | Extracted |
 | Segment 1 |  | Segment 2 |
@@ -42,7 +42,7 @@
 +-----+-----+  +-----+-----+
 |  7  |  8  |  
 +-----+-----+  
-</div></div><div class="tabs__content" data-gtm="tab" id="30711d30_11887"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5019"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+  +-----------------+
 | Extracted       |  | Extracted       |
 | Segment 1       |  | Segment 2       |
@@ -53,7 +53,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  7  |  8  |  0  |  |  0  |  0  |  0  |
 +-----+-----+-----+  +-----+-----+-----+
-</div></div></div><ul class="list _ul" id="30711d30_11889"    ><li class="list__item" id="30711d30_11890"><p>If <span class="control" id="30711d30_11891">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="30711d30_11892"><p>If <span class="control" id="30711d30_11893">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="30711d30_11894">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
+</div></div></div><ul class="list _ul" id="c4e72d8d_5021"    ><li class="list__item" id="c4e72d8d_5022"><p>If <span class="control" id="c4e72d8d_5023">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="c4e72d8d_5024"><p>If <span class="control" id="c4e72d8d_5025">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="c4e72d8d_5026">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
 import numpy as np
 from frdc.load import FRDCDataset
 from frdc.preprocess.extract_segments import extract_segments_from_bounds
@@ -65,7 +65,7 @@
 bounds, labels = ds.get_bounds_and_labels()
 
 segments: list[np.ndarray] = extract_segments_from_bounds(ar, bounds)
-</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="30711d30_11896">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="c4e72d8d_5028">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
 from skimage.morphology import remove_small_objects, remove_small_holes
 import numpy as np
 
@@ -91,4 +91,4 @@
                                               min_height=10, min_width=10)
 
 segments: list[np.ndarray] = extract_segments_from_labels(ar, ar_labels)
-</div><aside class="prompt" data-type="note" data-title="" id="30711d30_11898"><p id="30711d30_11899">The <code class="code" id="30711d30_11900">remove_small_objects</code> and <code class="code" id="30711d30_11901">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="30711d30_11902" data-style="title-top"><dt id="30711d30_11903" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="30711d30_11904">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="30711d30_11906">ar_labels</code> is a label classification as a <code class="code" id="30711d30_11907">np.ndarray</code></p></dd><dt id="30711d30_11908" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="30711d30_11909">Extracts segments from <code class="code" id="30711d30_11910">Rect</code> bounds.</b></p><br><p> <code class="code" id="30711d30_11912">bounds</code> is a list of <code class="code" id="30711d30_11913">Rect</code> bounds.</p></dd><dt id="30711d30_11914" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="30711d30_11915">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+</div><aside class="prompt" data-type="note" data-title="" id="c4e72d8d_5030"><p id="c4e72d8d_5031">The <code class="code" id="c4e72d8d_5032">remove_small_objects</code> and <code class="code" id="c4e72d8d_5033">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="c4e72d8d_5034" data-style="title-top"><dt id="c4e72d8d_5035" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="c4e72d8d_5036">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="c4e72d8d_5038">ar_labels</code> is a label classification as a <code class="code" id="c4e72d8d_5039">np.ndarray</code></p></dd><dt id="c4e72d8d_5040" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="c4e72d8d_5041">Extracts segments from <code class="code" id="c4e72d8d_5042">Rect</code> bounds.</b></p><br><p> <code class="code" id="c4e72d8d_5044">bounds</code> is a list of <code class="code" id="c4e72d8d_5045">Rect</code> bounds.</p></dd><dt id="c4e72d8d_5046" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="c4e72d8d_5047">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/preprocessing-glcm-padded.html b/docs/preprocessing-glcm-padded.html
new file mode 100644
index 00000000..32feea67
--- /dev/null
+++ b/docs/preprocessing-glcm-padded.html
@@ -0,0 +1,26 @@
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.glcm_padded | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.glcm_padded | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-glcm-padded.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.glcm_padded | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-glcm-padded.html#webpage", "url": "/d/0.0.4/preprocessing-glcm-padded.html", "name": "preprocessing.glcm_padded | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.glcm_padded" data-main-title="preprocessing.glcm_padded" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.glcm_padded"  id="preprocessing.glcm_padded.md"  >preprocessing.glcm_padded</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;9890df50_11924\&quot;\u003eComputes the GLCM of the NDArray bands with padding.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="9890df50_11860"><p id="9890df50_11861">This is largely a handy wrapper around my <a href="https://github.com/Eve-ning/glcm-cupy" id="9890df50_11862"   data-external="true" rel="noopener noreferrer" >glcm-cupy</a> package. This auto-computes the necessary padding so that the GLCM is the same size as the original image.</p></aside><aside class="prompt" data-type="warning" data-title="" id="9890df50_11863"><p id="9890df50_11864">The GLCM computation is rather slow, so it is recommended to use it only if necessary.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="9890df50_11865"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="9890df50_11866" data-style="title-left" data-title-width="medium"><dt id="9890df50_11867" data-expandable="false">glcm_padded</dt><dd><p><b id="9890df50_11868">Computes the GLCM of the NDArray bands with padding.</b></p></dd><dt id="9890df50_11869" data-expandable="false">glcm_padded_cached</dt><dd><p><b id="9890df50_11870">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p></dd><dt id="9890df50_11871" data-expandable="false">append_glcm_padded_cached</dt><dd><p><b id="9890df50_11872">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="9890df50_11873">We show a few examples of how to use the GLCM functions.</p><div class="code-block" data-lang="python"         >
+import numpy as np
+from glcm_cupy import Features
+
+from frdc.preprocess.glcm_padded import (
+    append_glcm_padded_cached, glcm_padded, glcm_padded_cached
+)
+
+ar = np.random.rand(50, 25, 4)
+
+# Returns a shape of H x W x C x GLCM Features
+ar_glcm = glcm_padded(ar, bin_from=1, bin_to=4, radius=3, )
+
+# Returns a shape of H x W x C x 2
+ar_glcm_2_features = glcm_padded(ar, bin_from=1, bin_to=4, radius=3,
+                                 features=[Features.CONTRAST,
+                                           Features.CORRELATION])
+
+# Returns a shape of H x W x C x GLCM Features
+ar_glcm_cached = glcm_padded_cached(ar, bin_from=1, bin_to=4, radius=3)
+
+# Returns a shape of H x W x (C x GLCM Features + C)
+ar_glcm_cached_appended = append_glcm_padded_cached(ar, bin_from=1, bin_to=4,
+                                                    radius=3)
+
+</div><ul class="list _ul" id="9890df50_11875"    ><li class="list__item" id="9890df50_11876"><p><code class="code" id="9890df50_11877">ar_glcm</code> is the GLCM of the original array, with the last dimension being the GLCM features. The number of features is determined by the <code class="code" id="9890df50_11878">features</code> parameter, which defaults to all features.</p></li><li class="list__item" id="9890df50_11879"><p><code class="code" id="9890df50_11880">ar_glcm_2_features</code> selects only 2 features, with the last dimension being the 2 GLCM features specified.</p></li><li class="list__item" id="9890df50_11881"><p><code class="code" id="9890df50_11882">ar_glcm_cached</code> caches the GLCM so that if you call it again, it will return the cached version. It stores its data at the project root dir, under <code class="code" id="9890df50_11883">.cache/</code>.</p></li><li class="list__item" id="9890df50_11884"><p><code class="code" id="9890df50_11885">ar_glcm_cached_appended</code> is a wrapper around <code class="code" id="9890df50_11886">ar_glcm_cached</code>, it appends the GLCM features onto the original array. It's equivalent to calling <code class="code" id="9890df50_11887">ar_glcm_cached</code> and then <code class="code" id="9890df50_11888">np.concatenate</code> on the final axes.</p></li></ul><section class="chapter"><h3 id="caching" data-toc="caching">Caching</h3><p id="9890df50_11889">GLCM is an expensive operation, thus we recommend to cache it if the input parameters will be the same. This is especially useful if you're experimenting with the same dataset with constant parameters.</p><aside class="prompt" data-type="warning" data-title="" id="9890df50_11890"><p id="9890df50_11891">This cache is automatically invalidated if the parameters change. Thus, if you perform augmentation, the cache will not be used and will be recomputed. This can be wasteful, so it is recommended to perform augmentation after the GLCM computation if possible.</p></aside><aside class="prompt" data-type="note" data-title="" id="9890df50_11892"><p id="9890df50_11893">The cache is stored at the project root dir, under <code class="code" id="9890df50_11894">.cache/</code>. It is safe to delete this folder if you want to clear the cache.</p></aside></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="9890df50_11895" data-style="title-top"><dt id="9890df50_11896" data-expandable="false">glcm_padded(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_11897">Computes the GLCM of the NDArray bands with padding.</b></p><br><ul class="list _ul" id="9890df50_11899"    ><li class="list__item" id="9890df50_11900"><p><code class="code" id="9890df50_11901">ar</code> is the input array</p></li><li class="list__item" id="9890df50_11902"><p><code class="code" id="9890df50_11903">bin_from</code> is the upper bound of the input</p></li><li class="list__item" id="9890df50_11904"><p><code class="code" id="9890df50_11905">bin_to</code> is the upper bound of the GLCM input, i.e. the resolution that GLCM operates on</p></li><li class="list__item" id="9890df50_11906"><p><code class="code" id="9890df50_11907">radius</code> is the radius of the GLCM</p></li><li class="list__item" id="9890df50_11908"><p><code class="code" id="9890df50_11909">step_size</code> is the step size of the GLCM</p></li><li class="list__item" id="9890df50_11910"><p><code class="code" id="9890df50_11911">features</code> is the list of GLCM features to compute</p></li></ul><p> The return shape is </p><svg xmlns="http://www.w3.org/2000/svg" width="30.394ex" height="1.645ex" role="img" focusable="false" viewBox="0 -705 13434.3 727" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.05ex;"><defs><path id="MJX-178-TEX-I-1D43B" d="M228 637Q194 637 192 641Q191 643 191 649Q191 673 202 682Q204 683 219 683Q260 681 355 681Q389 681 418 681T463 682T483 682Q499 682 499 672Q499 670 497 658Q492 641 487 638H485Q483 638 480 638T473 638T464 637T455 637Q416 636 405 634T387 623Q384 619 355 500Q348 474 340 442T328 395L324 380Q324 378 469 378H614L615 381Q615 384 646 504Q674 619 674 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transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-178-TEX-N-73" transform="translate(6677,0)"></use></g></g></g></svg><p> See <code class="code" id="9890df50_11913">glcm_cupy</code> for the GLCM Features.</p></dd><dt id="9890df50_11914" data-expandable="false">glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_11915">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p><br><p> See <code class="code" id="9890df50_11917">glcm_padded</code> for the parameters and output shape</p></dd><dt id="9890df50_11918" data-expandable="false">append_glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_11919">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p><br><p> See <code class="code" id="9890df50_11921">glcm_padded</code> for the parameters</p><br><p> The return shape is: </p><svg 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xlink:href="#MJX-180-TEX-I-1D44A"></use></g><g data-mml-node="mo" transform="translate(3380.7,0)"><use data-c="D7" xlink:href="#MJX-180-TEX-N-D7"></use></g><g data-mml-node="mo" transform="translate(4380.9,0)"><use data-c="28" xlink:href="#MJX-180-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(4769.9,0)"><use data-c="1D436" xlink:href="#MJX-180-TEX-I-1D436"></use></g><g data-mml-node="mo" transform="translate(5752.1,0)"><use data-c="D7" xlink:href="#MJX-180-TEX-N-D7"></use></g><g data-mml-node="mtext" transform="translate(6752.3,0)"><use data-c="47" xlink:href="#MJX-180-TEX-N-47"></use><use data-c="4C" xlink:href="#MJX-180-TEX-N-4C" transform="translate(785,0)"></use><use data-c="43" xlink:href="#MJX-180-TEX-N-43" transform="translate(1410,0)"></use><use data-c="4D" xlink:href="#MJX-180-TEX-N-4D" transform="translate(2132,0)"></use><use data-c="20" xlink:href="#MJX-180-TEX-N-20" transform="translate(3049,0)"></use><use data-c="46" xlink:href="#MJX-180-TEX-N-46" transform="translate(3299,0)"></use><use data-c="65" xlink:href="#MJX-180-TEX-N-65" transform="translate(3952,0)"></use><use data-c="61" xlink:href="#MJX-180-TEX-N-61" transform="translate(4396,0)"></use><use data-c="74" xlink:href="#MJX-180-TEX-N-74" transform="translate(4896,0)"></use><use data-c="75" xlink:href="#MJX-180-TEX-N-75" transform="translate(5285,0)"></use><use data-c="72" xlink:href="#MJX-180-TEX-N-72" transform="translate(5841,0)"></use><use data-c="65" xlink:href="#MJX-180-TEX-N-65" transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-180-TEX-N-73" transform="translate(6677,0)"></use></g><g data-mml-node="mo" transform="translate(14045.6,0)"><use data-c="2B" xlink:href="#MJX-180-TEX-N-2B"></use></g><g data-mml-node="mi" transform="translate(15045.8,0)"><use data-c="1D436" xlink:href="#MJX-180-TEX-I-1D436"></use></g><g data-mml-node="mo" transform="translate(15805.8,0)"><use data-c="29" xlink:href="#MJX-180-TEX-N-29"></use></g></g></g></svg><p> The function automatically flattens the last 2 dimensions of the GLCM features, and appends it onto the original array.</p></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-morphology.html">preprocessing.morphology</a>   <a class="navigation-links__next" href="train-frdc-lightning.html">train.frdc_datamodule &amp; frdc_module</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/preprocessing-morphology.html b/docs/preprocessing-morphology.html
index d09ff367..39df4e66 100644
--- a/docs/preprocessing-morphology.html
+++ b/docs/preprocessing-morphology.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-morphology.html#webpage", "url": "/d/0.0.4/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"  id="preprocessing.morphology.md"  >preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;d851343f_4997\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="d851343f_4969"><p id="d851343f_4970">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="d851343f_4971" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="d851343f_4972"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="d851343f_4973" data-style="title-left" data-title-width="medium"><dt id="d851343f_4974" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="d851343f_4975">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="d851343f_4976" data-expandable="false">binary_watershed</dt><dd><p><b id="d851343f_4977">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="d851343f_4978">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-morphology.html#webpage", "url": "/d/0.0.4/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"  id="preprocessing.morphology.md"  >preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;30ae7463_4950\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="30ae7463_4922"><p id="30ae7463_4923">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="30ae7463_4924" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="30ae7463_4925"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="30ae7463_4926" data-style="title-left" data-title-width="medium"><dt id="30ae7463_4927" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="30ae7463_4928">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="30ae7463_4929" data-expandable="false">binary_watershed</dt><dd><p><b id="30ae7463_4930">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="30ae7463_4931">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 from frdc.preprocess.morphology import (
     threshold_binary_mask, binary_watershed
@@ -10,6 +10,6 @@
 ar, order = ds.get_ar_bands()
 mask = threshold_binary_mask(ar, order.index('NIR'), 90 / 256)
 ar_label = binary_watershed(mask)
-</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="d851343f_4980" data-style="title-top"><dt id="d851343f_4981" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="d851343f_4982">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="d851343f_4983">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="30ae7463_4933" data-style="title-top"><dt id="30ae7463_4934" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="30ae7463_4935">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="30ae7463_4936">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
 ar[..., band_idx] &gt; threshold_value
-</div></dd><dt id="d851343f_4986" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="d851343f_4987">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="d851343f_4988">np.ndarray</code></b></p><br><ul class="list _ul" id="d851343f_4990"    ><li class="list__item" id="d851343f_4991"><p><code class="code" id="d851343f_4992">peaks_footprint</code> is the footprint of <code class="code" id="d851343f_4993">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="d851343f_4994"><p><code class="code" id="d851343f_4995">watershed_compactness</code> is the compactness of <code class="code" id="d851343f_4996">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 25 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+</div></dd><dt id="30ae7463_4939" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="30ae7463_4940">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="30ae7463_4941">np.ndarray</code></b></p><br><ul class="list _ul" id="30ae7463_4943"    ><li class="list__item" id="30ae7463_4944"><p><code class="code" id="30ae7463_4945">peaks_footprint</code> is the footprint of <code class="code" id="30ae7463_4946">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="30ae7463_4947"><p><code class="code" id="30ae7463_4948">watershed_compactness</code> is the compactness of <code class="code" id="30ae7463_4949">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   <a class="navigation-links__next" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
diff --git a/docs/preprocessing-scale.html b/docs/preprocessing-scale.html
index 4fcaa6c5..41504a1d 100644
--- a/docs/preprocessing-scale.html
+++ b/docs/preprocessing-scale.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-25T18:04:11.701805"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.scale | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.scale | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-scale.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.scale | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-scale.html#webpage", "url": "/d/0.0.4/preprocessing-scale.html", "name": "preprocessing.scale | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"  id="preprocessing.scale.md"  >preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;8b28dc1_8713\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="8b28dc1_8685"><p>Scale assumes H x W x C, where C is the number of bands.</p></aside><dl id="8b28dc1_8686" data-style="title-left" data-title-width="medium"><dt id="8b28dc1_8687" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="8b28dc1_8688">Scales the NDArray bands to [0, 1] per band.</b></p></dd><dt id="8b28dc1_8689" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="8b28dc1_8690">Scales the NDArray bands to zero mean unit variance per band.</b></p></dd><dt id="8b28dc1_8691" data-expandable="false">scale_static_per_band</dt><dd><p><b id="8b28dc1_8692">Scales the NDArray bands by a predefined configuration.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.scale | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" 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"preprocessing.scale | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"  id="preprocessing.scale.md"  >preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;bd8b6dca_4909\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="bd8b6dca_4881"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="bd8b6dca_4882" data-style="title-left" data-title-width="medium"><dt id="bd8b6dca_4883" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="bd8b6dca_4884">Scales the NDArray bands to [0, 1] per band.</b></p></dd><dt id="bd8b6dca_4885" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="bd8b6dca_4886">Scales the NDArray bands to zero mean unit variance per band.</b></p></dd><dt id="bd8b6dca_4887" data-expandable="false">scale_static_per_band</dt><dd><p><b id="bd8b6dca_4888">Scales the NDArray bands by a predefined configuration.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 from frdc.preprocess.scale import (
     scale_0_1_per_band, scale_normal_per_band, scale_static_per_band
@@ -12,4 +12,4 @@
 ar_01 = scale_0_1_per_band(ar)
 ar_norm = scale_normal_per_band(ar)
 ar_static = scale_static_per_band(ar, order, BAND_MAX_CONFIG)
-</div><aside class="prompt" data-type="tip" data-title="" id="8b28dc1_8694"><p id="8b28dc1_8695">The static scaling has a default config, which was inferred by our capturing device.</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="8b28dc1_8696" data-style="title-top"><dt id="8b28dc1_8697" data-expandable="false">scale_0_1_per_band(ar)</dt><dd><p><b id="8b28dc1_8698">Scales the NDArray bands to [0, 1] per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-254-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path 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transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-254-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-254-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-254-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-254-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-254-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-254-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-254-TEX-N-29"></use></g></g></g></svg></dd><dt id="8b28dc1_8701" data-expandable="false">scale_normal_per_band(ar)</dt><dd><p><b id="8b28dc1_8702">Scales the NDArray bands to zero mean unit variance per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" 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<code class="code" id="8b28dc1_8708">config</code> is of <code class="code" id="8b28dc1_8709">dict[str, tuple[int, int]]</code> where the key is the band name, and the value is a tuple of <code class="code" id="8b28dc1_8710">(min, max)</code>. 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+</div><aside class="prompt" data-type="tip" data-title="" id="bd8b6dca_4890"><p id="bd8b6dca_4891">The static scaling has a default config, which was inferred by our capturing device.</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="bd8b6dca_4892" data-style="title-top"><dt id="bd8b6dca_4893" data-expandable="false">scale_0_1_per_band(ar)</dt><dd><p><b id="bd8b6dca_4894">Scales the NDArray bands to [0, 1] per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-177-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path 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transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-177-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-177-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-177-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-177-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-177-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-177-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-177-TEX-N-29"></use></g></g></g></svg></dd><dt id="bd8b6dca_4897" data-expandable="false">scale_normal_per_band(ar)</dt><dd><p><b id="bd8b6dca_4898">Scales the NDArray bands to zero mean unit variance per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" 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The <code class="code" id="bd8b6dca_4904">config</code> is of <code class="code" id="bd8b6dca_4905">dict[str, tuple[int, int]]</code> where the key is the band name, and the value is a tuple of <code class="code" id="bd8b6dca_4906">(min, max)</code>. 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<a class="navigation-links__prev" href="load-dataset.html">load.dataset</a>   <a class="navigation-links__next" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>train.frdc_datamodule &amp; frdc_module | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/train-frdc-lightning.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/train-frdc-lightning.html#webpage", "url": "/d/0.0.4/train-frdc-lightning.html", "name": "train.frdc_datamodule &amp; frdc_module | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="train.frdc_lightning" data-main-title="train.frdc_datamodule &amp; frdc_module" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="train.frdc_lightning"  id="train.frdc_lightning.md"  >train.frdc_datamodule &amp; frdc_module</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;c6b8a6a0_1147\&quot;\u003eThe FRDC PyTorch LightningDataModule and LightningModule.\u003c/p\u003e&quot;]}"></div> <p id="c6b8a6a0_1100">These are FRDC specific LightningDataModule and LightningModule, a core component in the PyTorch Lightning ecosystem to provide a simple interface to train and evaluate models.</p><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_1101"><p id="c6b8a6a0_1102">It's optional to use these classes, you can use your own training loop if you want. We'll use these for our training pipeline.</p></aside><dl id="c6b8a6a0_1103" data-style="title-left" data-title-width="medium"><dt id="c6b8a6a0_1104" data-expandable="false">FRDCDataModule</dt><dd><p><b id="c6b8a6a0_1105">The FRDC PyTorch Lightning DataModule.</b></p></dd><dt id="c6b8a6a0_1106" data-expandable="false">FRDCModule</dt><dd><p><b id="c6b8a6a0_1107">The FRDC PyTorch Lightning Module.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><aside class="prompt" data-type="tip" data-title="" id="c6b8a6a0_1108"><p id="c6b8a6a0_1109">See our training pipeline for a full example</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="c6b8a6a0_1110" data-style="title-top"><dt id="c6b8a6a0_1111" data-expandable="false">FRDCDataModule(segments, labels, preprocess, augmentation, train_val_test_split, batch_size)</dt><dd><p><b id="c6b8a6a0_1112">Initializes the FRDC PyTorch Lightning DataModule.</b></p><br><ul class="list _ul" id="c6b8a6a0_1114"    ><li class="list__item" id="c6b8a6a0_1115"><p><code class="code" id="c6b8a6a0_1116">segments</code>, <code class="code" id="c6b8a6a0_1117">labels</code> are retrieved from </p><ul class="list _ul" id="c6b8a6a0_1118"    ><li class="list__item" id="c6b8a6a0_1119"><p><a href="load-dataset.html#frdcdataset" id="c6b8a6a0_1120" data-tooltip="Initializes the dataset downloader. This doesn't immediately download the dataset, but only when you call the get_* functions. The site, date, version must match the dataset path on GCS. For example if the dataset is at gs://frdc-scan/my-site/20201218/90deg/map, site='my-site'&hellip;"  >FRDCDataset</a></p></li><li class="list__item" id="c6b8a6a0_1121"><p><a href="preprocessing-extract-segments.html" id="c6b8a6a0_1122" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >Segmentation</a></p></li></ul></li><li class="list__item" id="c6b8a6a0_1123"><p><code class="code" id="c6b8a6a0_1124">preprocess</code> is a function that takes in a segment and returns a preprocessed segment. In particular, it should accept a list of NumPy NDArrays and return a single stacked PyToch Tensor.</p></li><li class="list__item" id="c6b8a6a0_1125"><p><code class="code" id="c6b8a6a0_1126">augmentation</code> is a function that takes in a segment and returns an augmented segment. In particular, it takes in a PyTorch Tensor and returns another.</p></li><li class="list__item" id="c6b8a6a0_1127"><p><code class="code" id="c6b8a6a0_1128">train_val_test_split</code> is a function that takes a TensorDataset and returns a list of 3 TensorDatasets, for train, val and test respectively.</p></li><li class="list__item" id="c6b8a6a0_1129"><p><code class="code" id="c6b8a6a0_1130">batch_size</code> is the batch size.</p></li></ul><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_1131"><p>For now, the <code class="code" id="c6b8a6a0_1132">augmentation</code> is only applied to training data</p></aside></dd><dt id="c6b8a6a0_1133" data-expandable="false">FRDCModule(model_cls, model_kwargs, optim_cls, optim_kwargs)</dt><dd><p><b id="c6b8a6a0_1134">Initializes the FRDC PyTorch Lightning Module.</b></p><br><ul class="list _ul" id="c6b8a6a0_1136"    ><li class="list__item" id="c6b8a6a0_1137"><p><code class="code" id="c6b8a6a0_1138">model_cls</code> is the Class of the model.</p></li><li class="list__item" id="c6b8a6a0_1139"><p><code class="code" id="c6b8a6a0_1140">model_kwargs</code> is the kwargs to pass to the model.</p></li><li class="list__item" id="c6b8a6a0_1141"><p><code class="code" id="c6b8a6a0_1142">optim_cls</code> is the Class of the optimizer.</p></li><li class="list__item" id="c6b8a6a0_1143"><p><code class="code" id="c6b8a6a0_1144">optim_kwargs</code> is the kwargs to pass to the optimizer.</p></li></ul><p> Internally, the module will initialize the model and optimizer as follows: </p><div class="code-block" data-lang="python"         >
+model = model_cls(**model_kwargs)
+optim = optim_cls(model.parameters(), **optim_kwargs)
+</div><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_1146"><p>We do not accept the instances of the Model and Optimizer so that we can pickle them.</p></aside></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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From 884051b2943eb1e9600a0c887e07b3587f5342ee Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 13:20:53 +0800
Subject: [PATCH 24/28] Remove zip

---
 webHelpD2-all.zip | Bin 242632 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 webHelpD2-all.zip

diff --git a/webHelpD2-all.zip b/webHelpD2-all.zip
deleted file mode 100644
index 30564821f9753b932360e036f92fc8549596a0c2..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 242632
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From c0e87776becb5aaed44e1bcf1757e529904da804 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 13:21:05 +0800
Subject: [PATCH 25/28] Ignore zips

---
 .gitignore | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index ca71a60e..2c949384 100644
--- a/.gitignore
+++ b/.gitignore
@@ -164,4 +164,5 @@ cython_debug/
 # Ignores the raw .tif files
 rsc/**/*.tif
 
-**/*/lightning_logs
\ No newline at end of file
+**/*/lightning_logs
+*.zip
\ No newline at end of file

From 0e57ace9a6af2224ae5943d47137946dddffb447 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 15:06:51 +0800
Subject: [PATCH 26/28] Reorder the tree

---
 Writerside/d.tree | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/Writerside/d.tree b/Writerside/d.tree
index 692d0784..faf0dd16 100644
--- a/Writerside/d.tree
+++ b/Writerside/d.tree
@@ -9,12 +9,18 @@
 
     <toc-element topic="Overview.md"/>
     <toc-element topic="Getting-Started.md"/>
-    <toc-element topic="Model-Test-Chestnut-May-Dec.md"/>
-    <toc-element topic="load.dataset.md"/>
-    <toc-element topic="preprocessing.scale.md">
+    <toc-element toc-title="Tutorials">
+        <toc-element topic="Retrieve-our-Datasets.md"/>
+    </toc-element>
+    <toc-element toc-title="Model Tests">
+        <toc-element topic="Model-Test-Chestnut-May-Dec.md"/>
+    </toc-element>
+    <toc-element toc-title="API">
+        <toc-element topic="load.dataset.md"/>
+        <toc-element topic="preprocessing.scale.md"/>
+        <toc-element topic="preprocessing.extract_segments.md"/>
+        <toc-element topic="preprocessing.morphology.md"/>
+        <toc-element topic="preprocessing.glcm_padded.md"/>
+        <toc-element topic="train.frdc_lightning.md"/>
     </toc-element>
-    <toc-element topic="preprocessing.extract_segments.md"/>
-    <toc-element topic="preprocessing.morphology.md"/>
-    <toc-element topic="preprocessing.glcm_padded.md"/>
-    <toc-element topic="train.frdc_lightning.md"/>
 </instance-profile>
\ No newline at end of file

From 329b60a9b5f8557c83b399818310509b21f9877a Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 15:07:07 +0800
Subject: [PATCH 27/28] Create dataset retrieval tutorial

---
 Writerside/topics/Retrieve-our-Datasets.md | 137 +++++++++++++++++++++
 1 file changed, 137 insertions(+)
 create mode 100644 Writerside/topics/Retrieve-our-Datasets.md

diff --git a/Writerside/topics/Retrieve-our-Datasets.md b/Writerside/topics/Retrieve-our-Datasets.md
new file mode 100644
index 00000000..41ef9272
--- /dev/null
+++ b/Writerside/topics/Retrieve-our-Datasets.md
@@ -0,0 +1,137 @@
+# Retrieve our Datasets
+
+<tldr>A tutorial to retrieve our datasets</tldr>
+
+In this tutorial, we'll learn how to :
+
+- Retrieve FRDC's Hyperspectral Image Data as `np.ndarray`
+- Retrieve FRDC's Ground Truth bounds and labels
+- Slice/segment the image data by the bounds
+
+## Prerequisites
+
+- New here? [Get Started](Getting-Started.md).
+- Setup the Google Cloud Authorization to download the data.
+
+## Retrieve the Data
+
+To retrieve the data, use [FRDCDataset](load.dataset.md#frdcdataset)
+
+Here, we'll download and load our
+
+- `ar`: Hyperspectral Image Data
+- `order`: The order of the bands
+- `bounds`: The bounds of the trees (segments)
+- `labels`: The labels of the trees (segments)
+
+```python
+from frdc.load.dataset import FRDCDataset
+
+ds = FRDCDataset(site="chestnut_nature_park", date="20201218", version=None)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+```
+
+### What Datasets are there? {collapsible="true"}
+
+> To know what datasets are available, you can run
+> [FRDCDownloader](load.dataset.md#frdcdownloader)'s `list_gcs_datasets()`
+> method
+
+> Note that some datasets do not have `bounds` and `labels` available as they
+> have not been annotated yet.
+> {style='warning'}
+
+```console
+>>> from frdc.load.dataset import FRDCDownloader
+>>> df = FRDCDownloader().list_gcs_datasets()
+>>> print(df)
+# 0  DEBUG/0
+# 1  casuarina/20220418/183deg
+# 2  casuarina/20220418/93deg
+# 3  chestnut_nature_park/20201218
+# ...
+```
+
+- The first part of the path is the `site`, and the second part is the `date`.
+- The `version` is the rest of the path, if there isn't any, use `None`.
+
+<tabs>
+<tab title="ds/date/ver/">
+<list>
+<li><code>site=&quot;ds&quot;</code></li>
+<li><code>date=&quot;date&quot;</code></li>
+<li><code>version=&quot;ver&quot;</code></li>
+</list>
+</tab>
+<tab title="ds/date/ver/01/data/">
+<list>
+<li><code>site=&quot;ds&quot;</code></li>
+<li><code>date=&quot;date&quot;</code></li>
+<li><code>version=&quot;ver/01/data&quot;</code></li>
+</list>
+</tab>
+<tab title="ds/date/">
+<list>
+<li><code>site=&quot;ds&quot;</code></li>
+<li><code>date=&quot;date&quot;</code></li>
+<li><code>version=None</code></li>
+</list>
+</tab>
+</tabs>
+
+## Segment the Data
+
+To segment the data, use [Extract Segments](preprocessing.extract_segments.md).
+
+Here, we'll segment the data by the bounds.
+
+```python
+from frdc.load.dataset import FRDCDataset
+from frdc.preprocess.extract_segments import extract_segments_from_bounds
+
+ds = FRDCDataset(site="chestnut_nature_park", date="20201218", version=None)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+segments = extract_segments_from_bounds(ar, bounds)
+```
+
+`segments` is a list of `np.ndarray` of shape H, W, C, representing a tree.
+The order of `segments` is the same as `labels`, so you can use `labels` to
+identify the tree.
+
+> While we have not used `order` in our example, it's useful to determine the
+> order of the bands in `ar` in other applications.
+
+## Plot the Data (Optional) {collapsible="true"}
+
+We can then use these data to plot out the first tree segment.
+
+```python
+import matplotlib.pyplot as plt
+
+from frdc.load.dataset import FRDCDataset
+from frdc.preprocess.extract_segments import extract_segments_from_bounds
+from frdc.preprocess.scale import scale_0_1_per_band
+
+ds = FRDCDataset(site="chestnut_nature_park", date="20201218", version=None)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+segments = extract_segments_from_bounds(ar, bounds)
+segment_0_bgr = segments[0]
+segment_0_rgb = segment_0_bgr[..., [2, 1, 0]]
+segment_0_rgb_scaled = scale_0_1_per_band(segment_0_rgb)
+
+plt.imshow(segment_0_rgb_scaled)
+plt.title(f"Tree {labels[0]}")
+plt.show()
+```
+See also: [preprocessing.scale.scale_0_1_per_band](preprocessing.scale.md)
+
+MatPlotLib cannot show the data correctly as-is, so we need to
+- Convert the data from BGR to RGB
+- Scale the data to 0-1 per band
+
+> Remember that the library returns the band `order`? This is useful in 
+> debugging the data. If we had shown it in BGR, it'll look off!
+{style='note'}

From 8eb93b6c28dfa5b7c0539a14d29947870f99e1e3 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Thu, 26 Oct 2023 15:07:44 +0800
Subject: [PATCH 28/28] Update docs

---
 docs/HelpTOC.json                        |  2 +-
 docs/Map.jhm                             |  2 +-
 docs/getting-started.html                | 18 +++++-----
 docs/load-dataset.html                   |  8 ++---
 docs/model-test-chestnut-may-dec.html    |  2 +-
 docs/overview.html                       |  2 +-
 docs/preprocessing-extract-segments.html | 16 ++++-----
 docs/preprocessing-glcm-padded.html      |  4 +--
 docs/preprocessing-morphology.html       |  6 ++--
 docs/preprocessing-scale.html            |  4 +--
 docs/retrieve-our-datasets.html          | 42 ++++++++++++++++++++++++
 docs/train-frdc-lightning.html           |  4 +--
 12 files changed, 76 insertions(+), 34 deletions(-)
 create mode 100644 docs/retrieve-our-datasets.html

diff --git a/docs/HelpTOC.json b/docs/HelpTOC.json
index 9596ec5b..322920fe 100644
--- a/docs/HelpTOC.json
+++ b/docs/HelpTOC.json
@@ -1 +1 @@
-{"entities":{"pages":{"Overview":{"id":"Overview","title":"Overview","url":"overview.html","level":0,"tabIndex":0},"Getting-Started":{"id":"Getting-Started","title":"Getting Started","url":"getting-started.html","level":0,"tabIndex":1},"Model-Test-Chestnut-May-Dec":{"id":"Model-Test-Chestnut-May-Dec","title":"Model Test Chestnut May-Dec","url":"model-test-chestnut-may-dec.html","level":0,"tabIndex":2},"load.dataset":{"id":"load.dataset","title":"load.dataset","url":"load-dataset.html","level":0,"tabIndex":3},"preprocessing.scale":{"id":"preprocessing.scale","title":"preprocessing.scale","url":"preprocessing-scale.html","level":0,"tabIndex":4},"preprocessing.extract_segments":{"id":"preprocessing.extract_segments","title":"preprocessing.extract_segments","url":"preprocessing-extract-segments.html","level":0,"tabIndex":5},"preprocessing.morphology":{"id":"preprocessing.morphology","title":"preprocessing.morphology","url":"preprocessing-morphology.html","level":0,"tabIndex":6},"preprocessing.glcm_padded":{"id":"preprocessing.glcm_padded","title":"preprocessing.glcm_padded","url":"preprocessing-glcm-padded.html","level":0,"tabIndex":7},"train.frdc_lightning":{"id":"train.frdc_lightning","title":"train.frdc_datamodule \u0026 frdc_module","url":"train-frdc-lightning.html","level":0,"tabIndex":8}}},"topLevelIds":["Overview","Getting-Started","Model-Test-Chestnut-May-Dec","load.dataset","preprocessing.scale","preprocessing.extract_segments","preprocessing.morphology","preprocessing.glcm_padded","train.frdc_lightning"]}
\ No newline at end of file
+{"entities":{"pages":{"Overview":{"id":"Overview","title":"Overview","url":"overview.html","level":0,"tabIndex":0},"Getting-Started":{"id":"Getting-Started","title":"Getting Started","url":"getting-started.html","level":0,"tabIndex":1},"87c6272d_78682":{"id":"87c6272d_78682","title":"Tutorials","level":0,"pages":["Retrieve-our-Datasets"],"tabIndex":2},"Retrieve-our-Datasets":{"id":"Retrieve-our-Datasets","title":"Retrieve our Datasets","url":"retrieve-our-datasets.html","level":1,"parentId":"87c6272d_78682","tabIndex":0},"87c6272d_78684":{"id":"87c6272d_78684","title":"Model Tests","level":0,"pages":["Model-Test-Chestnut-May-Dec"],"tabIndex":3},"Model-Test-Chestnut-May-Dec":{"id":"Model-Test-Chestnut-May-Dec","title":"Model Test Chestnut May-Dec","url":"model-test-chestnut-may-dec.html","level":1,"parentId":"87c6272d_78684","tabIndex":0},"87c6272d_78686":{"id":"87c6272d_78686","title":"API","level":0,"pages":["load.dataset","preprocessing.scale","preprocessing.extract_segments","preprocessing.morphology","preprocessing.glcm_padded","train.frdc_lightning"],"tabIndex":4},"load.dataset":{"id":"load.dataset","title":"load.dataset","url":"load-dataset.html","level":1,"parentId":"87c6272d_78686","tabIndex":0},"preprocessing.scale":{"id":"preprocessing.scale","title":"preprocessing.scale","url":"preprocessing-scale.html","level":1,"parentId":"87c6272d_78686","tabIndex":1},"preprocessing.extract_segments":{"id":"preprocessing.extract_segments","title":"preprocessing.extract_segments","url":"preprocessing-extract-segments.html","level":1,"parentId":"87c6272d_78686","tabIndex":2},"preprocessing.morphology":{"id":"preprocessing.morphology","title":"preprocessing.morphology","url":"preprocessing-morphology.html","level":1,"parentId":"87c6272d_78686","tabIndex":3},"preprocessing.glcm_padded":{"id":"preprocessing.glcm_padded","title":"preprocessing.glcm_padded","url":"preprocessing-glcm-padded.html","level":1,"parentId":"87c6272d_78686","tabIndex":4},"train.frdc_lightning":{"id":"train.frdc_lightning","title":"train.frdc_datamodule \u0026 frdc_module","url":"train-frdc-lightning.html","level":1,"parentId":"87c6272d_78686","tabIndex":5}}},"topLevelIds":["Overview","Getting-Started","87c6272d_78682","87c6272d_78684","87c6272d_78686"]}
\ No newline at end of file
diff --git a/docs/Map.jhm b/docs/Map.jhm
index 3785e5e0..2de2b668 100644
--- a/docs/Map.jhm
+++ b/docs/Map.jhm
@@ -1 +1 @@
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diff --git a/docs/getting-started.html b/docs/getting-started.html
index 64c56e7f..ddf5b5e8 100644
--- a/docs/getting-started.html
+++ b/docs/getting-started.html
@@ -1,20 +1,20 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7999471"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="2b098176_4885"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="2b098176_4886">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4601076"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/getting-started.html#webpage", "url": "/d/0.0.4/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"  id="Getting-Started.md"  >Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="2b098176_7300"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="2b098176_7301">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
             [tool.poetry.dependencies]
             python = &quot;...&quot;
-        </div></li><li class="list__item" id="2b098176_4888"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="2b098176_7303"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
           git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
-        </div></li><li class="list__item" id="2b098176_4890"><p>Then, create a Python Virtual Env <code class="code" id="2b098176_4891">pyvenv</code></p><div class="tabs" id="2b098176_4892"><div class="tabs__content" data-gtm="tab" id="2b098176_4893"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="2b098176_4895"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="2b098176_4897"><p><a href="https://python-poetry.org/docs/" id="2b098176_4898"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="2b098176_4900"><p>If <code class="code" id="2b098176_4901">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="2b098176_4902"><p>Activate the virtual environment </p><div class="tabs" id="2b098176_4903"><div class="tabs__content" data-gtm="tab" id="2b098176_4904"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></li><li class="list__item" id="2b098176_7305"><p>Then, create a Python Virtual Env <code class="code" id="2b098176_7306">pyvenv</code></p><div class="tabs" id="2b098176_7307"><div class="tabs__content" data-gtm="tab" id="2b098176_7308"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="2b098176_7310"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="2b098176_7312"><p><a href="https://python-poetry.org/docs/" id="2b098176_7313"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="2b098176_7315"><p>If <code class="code" id="2b098176_7316">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="2b098176_7317"><p>Activate the virtual environment </p><div class="tabs" id="2b098176_7318"><div class="tabs__content" data-gtm="tab" id="2b098176_7319"   data-title="Windows" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             cd venv/Scripts
             activate
             cd ../..
-        </div></div><div class="tabs__content" data-gtm="tab" id="2b098176_4906"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></div><div class="tabs__content" data-gtm="tab" id="2b098176_7321"   data-title="Linux" ><div class="code-block" data-lang="bash"    data-title="Shell"     >
             source venv/bin/activate
-        </div></div></div></li><li class="list__item" id="2b098176_4908"><p>Install the dependencies. You should be in the same directory as <code class="code" id="2b098176_4909">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
+        </div></div></div></li><li class="list__item" id="2b098176_7323"><p>Install the dependencies. You should be in the same directory as <code class="code" id="2b098176_7324">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
             poetry install --with dev
-        </div></li><li class="list__item" id="2b098176_4911"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="2b098176_7326"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="2b098176_4913"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="2b098176_4914"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="2b098176_4915"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="2b098176_4916"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="2b098176_4918"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="2b098176_4919"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="2b098176_4921"><p>To make sure everything is working, <a href="#tests" id="2b098176_4922" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="2b098176_4923" data-toc="2b098176_4923">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="2b098176_4924"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="2b098176_4925"><div class="code-block" data-lang="bash"         >
+        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="2b098176_7328"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="2b098176_7329"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="2b098176_7330"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="2b098176_7331"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="2b098176_7333"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="2b098176_7334"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="2b098176_7336"><p>To make sure everything is working, <a href="#tests" id="2b098176_7337" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="2b098176_7338" data-toc="2b098176_7338">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="2b098176_7339"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="2b098176_7340"><div class="code-block" data-lang="bash"         >
             pre-commit install
-        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="2b098176_4927"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
+        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="2b098176_7342"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
             pytest
-        </div></li><li class="list__item" id="2b098176_4929"><p>In case of errors: </p><dl id="2b098176_4930" data-style="title-top"><dt id="2b098176_4931" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="2b098176_4932" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="2b098176_4933" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="2b098176_4934" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="2b098176_4935">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="2b098176_4941" data-expandable="false">rsc/</dt><dd><p id="2b098176_4943">Resources. These are usually cached datasets</p></dd><dt id="2b098176_4944" data-expandable="false">pipeline/</dt><dd><p id="2b098176_4946">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="2b098176_4947" data-expandable="false">tests/</dt><dd><p id="2b098176_4949">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="2b098176_4950">We have 3 types of tests:</p><ul class="list _ul" id="2b098176_4951"    ><li class="list__item" id="2b098176_4952"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="2b098176_4953"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="2b098176_4954"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="2b098176_4955" data-style="title-top"><dt id="2b098176_4956" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="2b098176_4957">src/frdc/</code> directory.</p></dd><dt id="2b098176_4958" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="2b098176_4959">tests/</code> directory.</p></dd><dt id="2b098176_4960" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="2b098176_4961">pipeline/</code> directory.</p></dd><dt id="2b098176_4962" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="2b098176_4963">poetry add PACKAGE</code> and commit the changes to <code class="code" id="2b098176_4964">pyproject.toml</code> and <code class="code" id="2b098176_4965">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="2b098176_4966"><p>E.g. Adding <code class="code" id="2b098176_4967">numpy</code> is the same as <code class="code" id="2b098176_4968">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+        </div></li><li class="list__item" id="2b098176_7344"><p>In case of errors: </p><dl id="2b098176_7345" data-style="title-top"><dt id="2b098176_7346" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="2b098176_7347" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="2b098176_7348" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="2b098176_7349" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="2b098176_7350">Before starting development, take a look at our repository structure. 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inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">./model_tests/</span></div></foreignObject></g></g></g></g></g></svg><dl id="2b098176_7352" data-style="title-top"><dt id="2b098176_7353" data-expandable="false">src/frdc/</dt><dd><p id="2b098176_7355">Source Code for our package. These are the unit components of our pipeline.</p></dd><dt id="2b098176_7356" data-expandable="false">rsc/</dt><dd><p id="2b098176_7358">Resources. These are usually cached datasets</p></dd><dt id="2b098176_7359" data-expandable="false">pipeline/</dt><dd><p id="2b098176_7361">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="2b098176_7362" data-expandable="false">tests/</dt><dd><p id="2b098176_7364">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="2b098176_7365">We have 3 types of tests:</p><ul class="list _ul" id="2b098176_7366"    ><li class="list__item" id="2b098176_7367"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="2b098176_7368"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="2b098176_7369"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="2b098176_7370" data-style="title-top"><dt id="2b098176_7371" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="2b098176_7372">src/frdc/</code> directory.</p></dd><dt id="2b098176_7373" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="2b098176_7374">tests/</code> directory.</p></dd><dt id="2b098176_7375" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="2b098176_7376">pipeline/</code> directory.</p></dd><dt id="2b098176_7377" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="2b098176_7378">poetry add PACKAGE</code> and commit the changes to <code class="code" id="2b098176_7379">pyproject.toml</code> and <code class="code" id="2b098176_7380">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="2b098176_7381"><p>E.g. Adding <code class="code" id="2b098176_7382">numpy</code> is the same as <code class="code" id="2b098176_7383">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="retrieve-our-datasets.html">Retrieve our Datasets</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/load-dataset.html#webpage", "url": "/d/0.0.4/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"  id="load.dataset.md"  >load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;5098d101_33914\&quot;\u003eLoad datasets from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="5098d101_33835"><p id="5098d101_33836">You need to Set-Up <a href="getting-started.html#gcloud" id="5098d101_33837" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><dl id="5098d101_33839" data-style="title-top"><dt id="5098d101_33840" data-expandable="false">FRDCDownloader</dt><dd><p>This facilitates authentication and downloading from GCS.</p></dd><dt id="5098d101_33841" data-expandable="false">FRDCDataset</dt><dd><p>This uses the Downloader to download and load the dataset. It also implements useful helper functions to load FRDC-specific datasets, such as loading our images and labels.</p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="5098d101_33842">An example loading our Chestnut Nature Park dataset. We retrieve the</p><ul class="list _ul" id="5098d101_33843"    ><li class="list__item" id="5098d101_33844"><p>hyperspectral bands</p></li><li class="list__item" id="5098d101_33845"><p>order of the bands</p></li><li class="list__item" id="5098d101_33846"><p>bounding boxes</p></li><li class="list__item" id="5098d101_33847"><p>labels</p></li></ul><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4581118"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/load-dataset.html#webpage", "url": "/d/0.0.4/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"  id="load.dataset.md"  >load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;5098d101_81126\&quot;\u003eLoad datasets from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="5098d101_81047"><p id="5098d101_81048">You need to Set-Up <a href="getting-started.html#gcloud" id="5098d101_81049" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><dl id="5098d101_81051" data-style="title-top"><dt id="5098d101_81052" data-expandable="false">FRDCDownloader</dt><dd><p>This facilitates authentication and downloading from GCS.</p></dd><dt id="5098d101_81053" data-expandable="false">FRDCDataset</dt><dd><p>This uses the Downloader to download and load the dataset. It also implements useful helper functions to load FRDC-specific datasets, such as loading our images and labels.</p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="5098d101_81054">An example loading our Chestnut Nature Park dataset. We retrieve the</p><ul class="list _ul" id="5098d101_81055"    ><li class="list__item" id="5098d101_81056"><p>hyperspectral bands</p></li><li class="list__item" id="5098d101_81057"><p>order of the bands</p></li><li class="list__item" id="5098d101_81058"><p>bounding boxes</p></li><li class="list__item" id="5098d101_81059"><p>labels</p></li></ul><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 
 ds = FRDCDataset(site='chestnut_nature_park',
@@ -6,7 +6,7 @@
                  version=None, )
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
-</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="custom-authentication-downloads" data-toc="custom-authentication-downloads">Custom Authentication &amp; Downloads</h3></div><div class="collapse__content"><p id="5098d101_33849">If you need granular control over</p><ul class="list _ul" id="5098d101_33850"    ><li class="list__item" id="5098d101_33851"><p>where the files are downloaded</p></li><li class="list__item" id="5098d101_33852"><p>the credentials used</p></li><li class="list__item" id="5098d101_33853"><p>the project used</p></li><li class="list__item" id="5098d101_33854"><p>the bucket used</p></li></ul><p id="5098d101_33855">Then pass in a <code class="code" id="5098d101_33856">FRDCDownloader</code> object to <code class="code" id="5098d101_33857">FRDCDataset</code>.</p><div class="code-block" data-lang="python"         >
+</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="custom-authentication-downloads" data-toc="custom-authentication-downloads">Custom Authentication &amp; Downloads</h3></div><div class="collapse__content"><p id="5098d101_81061">If you need granular control over</p><ul class="list _ul" id="5098d101_81062"    ><li class="list__item" id="5098d101_81063"><p>where the files are downloaded</p></li><li class="list__item" id="5098d101_81064"><p>the credentials used</p></li><li class="list__item" id="5098d101_81065"><p>the project used</p></li><li class="list__item" id="5098d101_81066"><p>the bucket used</p></li></ul><p id="5098d101_81067">Then pass in a <code class="code" id="5098d101_81068">FRDCDownloader</code> object to <code class="code" id="5098d101_81069">FRDCDataset</code>.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDownloader, FRDCDataset
 
 dl = FRDCDownloader(credentials=...,
@@ -19,7 +19,7 @@
                  dl=dl)
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
-</div><p id="5098d101_33859">If you have a file not easily downloadable by <code class="code" id="5098d101_33860">FRDCDataset</code>, you can use <code class="code" id="5098d101_33861">FRDCDownloader</code> to download it.</p><div class="code-block" data-lang="python"         >
+</div><p id="5098d101_81071">If you have a file not easily downloadable by <code class="code" id="5098d101_81072">FRDCDataset</code>, you can use <code class="code" id="5098d101_81073">FRDCDownloader</code> to download it.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDownloader
 
 dl = FRDCDownloader(credentials=...,
@@ -28,4 +28,4 @@
                     bucket_name=...)
 
 dl.download_file(path_glob=&quot;path/to/gcs/file&quot;)
-</div><aside class="prompt" data-type="tip" data-title="" id="5098d101_33863"><p>This will automatically save the file to the local dataset root dir.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><section class="chapter"><h3 id="frdcdataset" data-toc="frdcdataset">FRDCDataset</h3><dl id="5098d101_33864" data-style="title-top"><dt id="5098d101_33865" data-expandable="false">FRDCDataset(site, date, version, dl)</dt><dd><p><b id="5098d101_33866">Initializes the dataset downloader.</b></p><br><p> This doesn't immediately download the dataset, but only when you call the <code class="code" id="5098d101_33868">get_*</code> functions. </p><br><p> The site, date, version must match the dataset path on GCS. For example if the dataset is at <code class="code" id="5098d101_33870">gs://frdc-scan/my-site/20201218/90deg/map</code>, </p><ul class="list _ul" id="5098d101_33871"    ><li class="list__item" id="5098d101_33872"><p><code class="code" id="5098d101_33873">site='my-site'</code></p></li><li class="list__item" id="5098d101_33874"><p><code class="code" id="5098d101_33875">date='20201218'</code></p></li><li class="list__item" id="5098d101_33876"><p><code class="code" id="5098d101_33877">version='90deg/map'</code></p></li></ul><p> If the dataset doesn't have a &quot;version&quot;, for example: <code class="code" id="5098d101_33878">gs://frdc-scan/my-site/20201218</code>, then you can pass in <code class="code" id="5098d101_33879">version=None</code>. </p><br><aside class="prompt" data-type="note" data-title="" id="5098d101_33881"><p>If you don't want to search up GCS, you can use FRDCDownloader to list all datasets, and their versions with <code class="code" id="5098d101_33882">FRDCDownloader().list_gcs_datasets()</code></p></aside><aside class="prompt" data-type="tip" data-title="" id="5098d101_33883"><p>If <code class="code" id="5098d101_33884">dl</code> is None, it will create a new FRDCDownloader. Usually, you don't need to pass this in unless you have a custom credential, or project.</p></aside></dd><dt id="5098d101_33885" data-expandable="false">get_ar_bands()</dt><dd><p><b id="5098d101_33886">Gets the NDArray bands (H x W x C) and channel order as <code class="code" id="5098d101_33887">tuple[np.ndarray, list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the stacked NDArray bands. This wraps around <code class="code" id="5098d101_33889">get_ar_bands_as_dict()</code>, thus if you want more control over how the bands are loaded, use that instead.</p></dd><dt id="5098d101_33890" data-expandable="false">get_ar_bands_as_dict()</dt><dd><p><b id="5098d101_33891">Gets the NDArray bands (H x W) as a <code class="code" id="5098d101_33892">dict[str, np.ndarray]</code>.</b></p><br><p> This downloads (if missing) and retrieves the individual NDArray bands as a dictionary. The keys are the band names, and the values are the NDArray bands.</p></dd><dt id="5098d101_33894" data-expandable="false">get_bounds_and_labels()</dt><dd><p><b id="5098d101_33895">Gets the bounding boxes and labels as <code class="code" id="5098d101_33896">tuple[list[Rect], list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the bounding boxes and labels as a tuple. The first element is a list of bounding boxes, and the second element is a list of labels. </p><br><aside class="prompt" data-type="tip" data-title="" id="5098d101_33899"><p>The bounding boxes are in the format of <code class="code" id="5098d101_33900">Rect</code>, a <code class="code" id="5098d101_33901">namedtuple</code> of x0, y0, x1, y1.</p></aside></dd></dl></section><section class="chapter"><h3 id="frdcdownloader" data-toc="frdcdownloader">FRDCDownloader</h3><dl id="5098d101_33902" data-style="title-top"><dt id="5098d101_33903" data-expandable="false">list_gcs_datasets(anchor)</dt><dd><p><b id="5098d101_33904">Lists all GCS datasets in the bucket as <code class="code" id="5098d101_33905">DataFrame</code></b></p><br><p> This works by checking which folders have a specific file, which we call the <code class="code" id="5098d101_33907">anchor</code>.</p></dd><dt id="5098d101_33908" data-expandable="false">download_file(path_glob, local_exists_ok)</dt><dd><p><b id="5098d101_33909">Downloads a file from GCS.</b></p><br><p> This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1 file, it will raise an error. </p><br><p> If <code class="code" id="5098d101_33912">local_exists_ok</code> is True, it will not download the file if it already exists locally. However, if it's False, it will download the file only if the hashes don't match. </p><aside class="prompt" data-type="note" data-title="" id="5098d101_33913"><p>Use this if you have a file on GCS that can't be downloaded via FRDCDataset.</p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+</div><aside class="prompt" data-type="tip" data-title="" id="5098d101_81075"><p>This will automatically save the file to the local dataset root dir.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><section class="chapter"><h3 id="frdcdataset" data-toc="frdcdataset">FRDCDataset</h3><dl id="5098d101_81076" data-style="title-top"><dt id="5098d101_81077" data-expandable="false">FRDCDataset(site, date, version, dl)</dt><dd><p><b id="5098d101_81078">Initializes the dataset downloader.</b></p><br><p> This doesn't immediately download the dataset, but only when you call the <code class="code" id="5098d101_81080">get_*</code> functions. </p><br><p> The site, date, version must match the dataset path on GCS. For example if the dataset is at <code class="code" id="5098d101_81082">gs://frdc-scan/my-site/20201218/90deg/map</code>, </p><ul class="list _ul" id="5098d101_81083"    ><li class="list__item" id="5098d101_81084"><p><code class="code" id="5098d101_81085">site='my-site'</code></p></li><li class="list__item" id="5098d101_81086"><p><code class="code" id="5098d101_81087">date='20201218'</code></p></li><li class="list__item" id="5098d101_81088"><p><code class="code" id="5098d101_81089">version='90deg/map'</code></p></li></ul><p> If the dataset doesn't have a &quot;version&quot;, for example: <code class="code" id="5098d101_81090">gs://frdc-scan/my-site/20201218</code>, then you can pass in <code class="code" id="5098d101_81091">version=None</code>. </p><br><aside class="prompt" data-type="note" data-title="" id="5098d101_81093"><p>If you don't want to search up GCS, you can use FRDCDownloader to list all datasets, and their versions with <code class="code" id="5098d101_81094">FRDCDownloader().list_gcs_datasets()</code></p></aside><aside class="prompt" data-type="tip" data-title="" id="5098d101_81095"><p>If <code class="code" id="5098d101_81096">dl</code> is None, it will create a new FRDCDownloader. Usually, you don't need to pass this in unless you have a custom credential, or project.</p></aside></dd><dt id="5098d101_81097" data-expandable="false">get_ar_bands()</dt><dd><p><b id="5098d101_81098">Gets the NDArray bands (H x W x C) and channel order as <code class="code" id="5098d101_81099">tuple[np.ndarray, list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the stacked NDArray bands. This wraps around <code class="code" id="5098d101_81101">get_ar_bands_as_dict()</code>, thus if you want more control over how the bands are loaded, use that instead.</p></dd><dt id="5098d101_81102" data-expandable="false">get_ar_bands_as_dict()</dt><dd><p><b id="5098d101_81103">Gets the NDArray bands (H x W) as a <code class="code" id="5098d101_81104">dict[str, np.ndarray]</code>.</b></p><br><p> This downloads (if missing) and retrieves the individual NDArray bands as a dictionary. The keys are the band names, and the values are the NDArray bands.</p></dd><dt id="5098d101_81106" data-expandable="false">get_bounds_and_labels()</dt><dd><p><b id="5098d101_81107">Gets the bounding boxes and labels as <code class="code" id="5098d101_81108">tuple[list[Rect], list[str]]</code>.</b></p><br><p> This downloads (if missing) and retrieves the bounding boxes and labels as a tuple. The first element is a list of bounding boxes, and the second element is a list of labels. </p><br><aside class="prompt" data-type="tip" data-title="" id="5098d101_81111"><p>The bounding boxes are in the format of <code class="code" id="5098d101_81112">Rect</code>, a <code class="code" id="5098d101_81113">namedtuple</code> of x0, y0, x1, y1.</p></aside></dd></dl></section><section class="chapter"><h3 id="frdcdownloader" data-toc="frdcdownloader">FRDCDownloader</h3><dl id="5098d101_81114" data-style="title-top"><dt id="5098d101_81115" data-expandable="false">list_gcs_datasets(anchor)</dt><dd><p><b id="5098d101_81116">Lists all GCS datasets in the bucket as <code class="code" id="5098d101_81117">DataFrame</code></b></p><br><p> This works by checking which folders have a specific file, which we call the <code class="code" id="5098d101_81119">anchor</code>.</p></dd><dt id="5098d101_81120" data-expandable="false">download_file(path_glob, local_exists_ok)</dt><dd><p><b id="5098d101_81121">Downloads a file from GCS.</b></p><br><p> This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1 file, it will raise an error. </p><br><p> If <code class="code" id="5098d101_81124">local_exists_ok</code> is True, it will not download the file if it already exists locally. However, if it's False, it will download the file only if the hashes don't match. </p><aside class="prompt" data-type="note" data-title="" id="5098d101_81125"><p>Use this if you have a file on GCS that can't be downloaded via FRDCDataset.</p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7999471"><meta name="build-number" content="${buildNumber}">       <title>Model Test Chestnut May-Dec | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"motivation","level":0,"title":"Motivation","anchor":"#motivation"},{"id":"methodology","level":0,"title":"Methodology","anchor":"#methodology"},{"id":"model","level":0,"title":"Model","anchor":"#model"},{"id":"preprocessing","level":0,"title":"Preprocessing","anchor":"#preprocessing"},{"id":"hyperparameters","level":0,"title":"Hyperparameters","anchor":"#hyperparameters"},{"id":"results","level":0,"title":"Results","anchor":"#results"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Model Test Chestnut May-Dec | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/model-test-chestnut-may-dec.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Model Test Chestnut May-Dec | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/model-test-chestnut-may-dec.html#webpage", "url": "/d/0.0.4/model-test-chestnut-may-dec.html", "name": "Model Test Chestnut May-Dec | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Model-Test-Chestnut-May-Dec" data-main-title="Model Test Chestnut May-Dec" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Model-Test-Chestnut-May-Dec"  id="Model-Test-Chestnut-May-Dec.md"  >Model Test Chestnut May-Dec</h1>  <p id="4763b031_4613">This test is used to evaluate the model performance on the Chestnut Nature Park May &amp; December dataset.</p><p id="4763b031_4614">See this script in <code class="code" id="4763b031_4615">pipeline/model_tests/chestnut_dec_may/main.py</code>.</p><section class="chapter"><h2 id="motivation" data-toc="motivation">Motivation</h2><p id="4763b031_4616">The usage of this model will be to classify trees in unseen datasets under different conditions. In this test, we'll evaluate it under a different season.</p><p id="4763b031_4617">A caveat is that it'll be evaluated on the same set of trees, so it's not a representative of a field-test. However, given difficulties of yielding datasets, this still gives us a good preliminary idea of how the model will perform in different conditions.</p></section><section class="chapter"><h2 id="methodology" data-toc="methodology">Methodology</h2><p id="4763b031_4618">We simply train on the December dataset, and test on the May dataset.</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 255.9375 134"  xmlns="http://www.w3.org/2000/svg" width="255.9375" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; stroke-dasharray: 1, 0;" class="arrowMarkerPath" d="M 0 0 L 10 5 L 0 10 z"></path></marker><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="0" viewBox="0 0 10 10" class="marker flowchart" id="flowchart-pointStart"><path style="stroke-width: 1; 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Raising the training accuracy with a more complex model may improve the test accuracy, however, due to instability of our test results, we can't be sure of this.</p><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="result-images" data-toc="result-images">Result Images</h3></div><div class="collapse__content"><div class="tabs" id="4763b031_4644"><div class="tabs__content" data-gtm="tab" id="4763b031_4645"   data-title="Training Graph" ><figure  id="4763b031_4646"><img alt="graph-chestnut-maydec.png" title="graph-chestnut-maydec.png" src="images/graph-chestnut-maydec.png"  class="" width="1068" height="1280"/></figure></div><div class="tabs__content" data-gtm="tab" id="4763b031_4647"   data-title="Confusion Matrix" ><figure  id="4763b031_4648"><img alt="cm-chestnut-maydec.png" title="cm-chestnut-maydec.png" src="images/cm-chestnut-maydec.png"  class="" width="1000" height="1000"/></figure></div></div></div></div></section><section class="chapter"><h3 id="caveats" data-toc="caveats">Caveats</h3><ul class="list _ul" id="4763b031_4649"    ><li class="list__item" id="4763b031_4650"><p>The test set is very small, so the results are not very representative.</p></li><li class="list__item" id="4763b031_4651"><p>The test set is the same set of trees, so it's not a true test of the model performance in different conditions.</p></li><li class="list__item" id="4763b031_4652"><p>There are many classes with 1 sample, so the model may not be able to learn the features of these classes well.</p></li></ul></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4591089"><meta name="build-number" content="${buildNumber}">       <title>Model Test Chestnut May-Dec | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"motivation","level":0,"title":"Motivation","anchor":"#motivation"},{"id":"methodology","level":0,"title":"Methodology","anchor":"#methodology"},{"id":"model","level":0,"title":"Model","anchor":"#model"},{"id":"preprocessing","level":0,"title":"Preprocessing","anchor":"#preprocessing"},{"id":"hyperparameters","level":0,"title":"Hyperparameters","anchor":"#hyperparameters"},{"id":"results","level":0,"title":"Results","anchor":"#results"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Model Test Chestnut May-Dec | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/model-test-chestnut-may-dec.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Model Test Chestnut May-Dec | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/model-test-chestnut-may-dec.html#webpage", "url": "/d/0.0.4/model-test-chestnut-may-dec.html", "name": "Model Test Chestnut May-Dec | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Model-Test-Chestnut-May-Dec" data-main-title="Model Test Chestnut May-Dec" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="Model Tests"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Model-Test-Chestnut-May-Dec"  id="Model-Test-Chestnut-May-Dec.md"  >Model Test Chestnut May-Dec</h1>  <p id="4763b031_6986">This test is used to evaluate the model performance on the Chestnut Nature Park May &amp; December dataset.</p><p id="4763b031_6987">See this script in <code class="code" id="4763b031_6988">pipeline/model_tests/chestnut_dec_may/main.py</code>.</p><section class="chapter"><h2 id="motivation" data-toc="motivation">Motivation</h2><p id="4763b031_6989">The usage of this model will be to classify trees in unseen datasets under different conditions. In this test, we'll evaluate it under a different season.</p><p id="4763b031_6990">A caveat is that it'll be evaluated on the same set of trees, so it's not a representative of a field-test. However, given difficulties of yielding datasets, this still gives us a good preliminary idea of how the model will perform in different conditions.</p></section><section class="chapter"><h2 id="methodology" data-toc="methodology">Methodology</h2><p id="4763b031_6991">We simply train on the December dataset, and test on the May dataset.</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 255.9375 134"  xmlns="http://www.w3.org/2000/svg" width="255.9375" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; stroke-dasharray: 1, 0;" class="arrowMarkerPath" d="M 0 0 L 10 5 L 0 10 z"></path></marker><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="0" viewBox="0 0 10 10" class="marker flowchart" id="flowchart-pointStart"><path style="stroke-width: 1; 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white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Horizontal Flip 50%</span></div></foreignObject></g></g><g transform="translate(394.8359375, 17)" id="flowchart-VFLip-7868" class="node default default flowchart-label"><rect height="34" width="132.890625" y="-17" x="-66.4453125" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-58.9453125, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="117.890625"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Vertical Flip 50%</span></div></foreignObject></g></g></g></g></g></svg><aside class="prompt" data-type="tip" data-title="" id="4763b031_7007"><p id="4763b031_7008">This only operates on training data.</p></aside></section></section><section class="chapter"><h2 id="hyperparameters" data-toc="hyperparameters">Hyperparameters</h2><p id="4763b031_7009">The following hyperparameters are used:</p><ul class="list _ul" id="4763b031_7010"    ><li class="list__item" id="4763b031_7011"><p>Optimizer: Adam</p></li><li class="list__item" id="4763b031_7012"><p>Learning Rate: 1e-3</p></li><li class="list__item" id="4763b031_7013"><p>Batch Size: 5</p></li><li class="list__item" id="4763b031_7014"><p>Epochs: 100</p></li><li class="list__item" id="4763b031_7015"><p>Early Stopping: 4</p></li></ul></section><section class="chapter"><h2 id="results" data-toc="results">Results</h2><p id="4763b031_7016">We yield around 40% accuracy on the test set, compared to around 65% for the training set. Raising the training accuracy with a more complex model may improve the test accuracy, however, due to instability of our test results, we can't be sure of this.</p><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="result-images" data-toc="result-images">Result Images</h3></div><div class="collapse__content"><div class="tabs" id="4763b031_7017"><div class="tabs__content" data-gtm="tab" id="4763b031_7018"   data-title="Training Graph" ><figure  id="4763b031_7019"><img alt="graph-chestnut-maydec.png" title="graph-chestnut-maydec.png" src="images/graph-chestnut-maydec.png"  class="" width="1068" height="1280"/></figure></div><div class="tabs__content" data-gtm="tab" id="4763b031_7020"   data-title="Confusion Matrix" ><figure  id="4763b031_7021"><img alt="cm-chestnut-maydec.png" title="cm-chestnut-maydec.png" src="images/cm-chestnut-maydec.png"  class="" width="1000" height="1000"/></figure></div></div></div></div></section><section class="chapter"><h3 id="caveats" data-toc="caveats">Caveats</h3><ul class="list _ul" id="4763b031_7022"    ><li class="list__item" id="4763b031_7023"><p>The test set is very small, so the results are not very representative.</p></li><li class="list__item" id="4763b031_7024"><p>The test set is the same set of trees, so it's not a true test of the model performance in different conditions.</p></li><li class="list__item" id="4763b031_7025"><p>There are many classes with 1 sample, so the model may not be able to learn the features of these classes well.</p></li></ul></section></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="retrieve-our-datasets.html">Retrieve our Datasets</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/overview.html#webpage", "url": "/d/0.0.4/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"  id="Overview.md"  >Overview</h1>  <p id="d05d8f2e_4960">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="d05d8f2e_4961">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="d05d8f2e_4962"><a href="getting-started.html" id="d05d8f2e_4963" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="d05d8f2e_4964">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="d05d8f2e_4965" data-style="title-top"><dt id="d05d8f2e_4966" data-expandable="false">FRDC-UI</dt><dd><p id="d05d8f2e_4968"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="d05d8f2e_4969"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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This will help you understand where to put your code."  ><span class="control" id="d05d8f2e_7242">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="d05d8f2e_7243" data-style="title-top"><dt id="d05d8f2e_7244" data-expandable="false">FRDC-UI</dt><dd><p id="d05d8f2e_7246"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="d05d8f2e_7247"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" 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"/d/0.0.4/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"  id="preprocessing.extract_segments.md"  >preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;c4e72d8d_5049\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="c4e72d8d_4990" data-style="title-left" data-title-width="medium"><dt id="c4e72d8d_4991" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="c4e72d8d_4992">Extracts segments from a label classification.</b></p></dd><dt id="c4e72d8d_4993" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="c4e72d8d_4994">Extracts segments from <code class="code" id="c4e72d8d_4995">Rect</code> bounds.</b></p></dd><dt id="c4e72d8d_4996" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="c4e72d8d_4997">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="c4e72d8d_4998">A boundary is a <code class="code" id="c4e72d8d_4999">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="c4e72d8d_5000">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="c4e72d8d_5001"><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5002"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4701433"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" 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content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.extract_segments | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-extract-segments.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.extract_segments | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-extract-segments.html#webpage", "url": "/d/0.0.4/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"  id="preprocessing.extract_segments.md"  >preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;c4e72d8d_7285\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="c4e72d8d_7226" data-style="title-left" data-title-width="medium"><dt id="c4e72d8d_7227" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="c4e72d8d_7228">Extracts segments from a label classification.</b></p></dd><dt id="c4e72d8d_7229" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="c4e72d8d_7230">Extracts segments from <code class="code" id="c4e72d8d_7231">Rect</code> bounds.</b></p></dd><dt id="c4e72d8d_7232" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="c4e72d8d_7233">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="c4e72d8d_7234">A boundary is a <code class="code" id="c4e72d8d_7235">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="c4e72d8d_7236">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="c4e72d8d_7237"><div class="tabs__content" data-gtm="tab" id="c4e72d8d_7238"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+                +-----------+      
 | Original        |                | Segmented |      
 | Image           |                | Image     |      
@@ -9,7 +9,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  8  |  9  |
 +-----+-----+-----+                +-----+-----+
-</div></div><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5004"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="c4e72d8d_7240"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+                +-----------------+      
 | Original        |                | Segmented       |      
 | Image           |                | Image           |      
@@ -20,7 +20,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+-----+      
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  0  |  8  |  9  |      
 +-----+-----+-----+                +-----+-----+-----+      
-</div></div></div><aside class="prompt" data-type="warning" data-title="" id="c4e72d8d_5006"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="c4e72d8d_5007">The correct syntax should be <code class="code" id="c4e72d8d_5008">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="c4e72d8d_5009">A label classification is a <code class="code" id="c4e72d8d_5010">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="c4e72d8d_5011">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
+</div></div></div><aside class="prompt" data-type="warning" data-title="" id="c4e72d8d_7242"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="c4e72d8d_7243">The correct syntax should be <code class="code" id="c4e72d8d_7244">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="c4e72d8d_7245">A label classification is a <code class="code" id="c4e72d8d_7246">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="c4e72d8d_7247">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
 +-----------------+  +-----------------+
 | Label           |  | Original        |
 | Classification  |  | Image           |
@@ -31,7 +31,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  1  |  1  |  0  |  |  7  |  8  |  9  |
 +-----+-----+-----+  +-----+-----+-----+
-</div><p id="c4e72d8d_5013">The extraction will take the <span class="control" id="c4e72d8d_5014">minimum bounding box</span> of each segment and return a list of segments.</p><p id="c4e72d8d_5015">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="c4e72d8d_5016"><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5017"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div><p id="c4e72d8d_7249">The extraction will take the <span class="control" id="c4e72d8d_7250">minimum bounding box</span> of each segment and return a list of segments.</p><p id="c4e72d8d_7251">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="c4e72d8d_7252"><div class="tabs__content" data-gtm="tab" id="c4e72d8d_7253"   data-title="Cropped = True" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------+  +-----------+
 | Extracted |  | Extracted |
 | Segment 1 |  | Segment 2 |
@@ -42,7 +42,7 @@
 +-----+-----+  +-----+-----+
 |  7  |  8  |  
 +-----+-----+  
-</div></div><div class="tabs__content" data-gtm="tab" id="c4e72d8d_5019"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="c4e72d8d_7255"   data-title="Cropped = False" ><div class="code-block" data-lang="none"    data-title="None"     >
 +-----------------+  +-----------------+
 | Extracted       |  | Extracted       |
 | Segment 1       |  | Segment 2       |
@@ -53,7 +53,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  7  |  8  |  0  |  |  0  |  0  |  0  |
 +-----+-----+-----+  +-----+-----+-----+
-</div></div></div><ul class="list _ul" id="c4e72d8d_5021"    ><li class="list__item" id="c4e72d8d_5022"><p>If <span class="control" id="c4e72d8d_5023">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="c4e72d8d_5024"><p>If <span class="control" id="c4e72d8d_5025">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="c4e72d8d_5026">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
+</div></div></div><ul class="list _ul" id="c4e72d8d_7257"    ><li class="list__item" id="c4e72d8d_7258"><p>If <span class="control" id="c4e72d8d_7259">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="c4e72d8d_7260"><p>If <span class="control" id="c4e72d8d_7261">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="c4e72d8d_7262">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
 import numpy as np
 from frdc.load import FRDCDataset
 from frdc.preprocess.extract_segments import extract_segments_from_bounds
@@ -65,7 +65,7 @@
 bounds, labels = ds.get_bounds_and_labels()
 
 segments: list[np.ndarray] = extract_segments_from_bounds(ar, bounds)
-</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="c4e72d8d_5028">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="c4e72d8d_7264">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
 from skimage.morphology import remove_small_objects, remove_small_holes
 import numpy as np
 
@@ -91,4 +91,4 @@
                                               min_height=10, min_width=10)
 
 segments: list[np.ndarray] = extract_segments_from_labels(ar, ar_labels)
-</div><aside class="prompt" data-type="note" data-title="" id="c4e72d8d_5030"><p id="c4e72d8d_5031">The <code class="code" id="c4e72d8d_5032">remove_small_objects</code> and <code class="code" id="c4e72d8d_5033">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="c4e72d8d_5034" data-style="title-top"><dt id="c4e72d8d_5035" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="c4e72d8d_5036">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="c4e72d8d_5038">ar_labels</code> is a label classification as a <code class="code" id="c4e72d8d_5039">np.ndarray</code></p></dd><dt id="c4e72d8d_5040" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="c4e72d8d_5041">Extracts segments from <code class="code" id="c4e72d8d_5042">Rect</code> bounds.</b></p><br><p> <code class="code" id="c4e72d8d_5044">bounds</code> is a list of <code class="code" id="c4e72d8d_5045">Rect</code> bounds.</p></dd><dt id="c4e72d8d_5046" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="c4e72d8d_5047">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+</div><aside class="prompt" data-type="note" data-title="" id="c4e72d8d_7266"><p id="c4e72d8d_7267">The <code class="code" id="c4e72d8d_7268">remove_small_objects</code> and <code class="code" id="c4e72d8d_7269">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="c4e72d8d_7270" data-style="title-top"><dt id="c4e72d8d_7271" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="c4e72d8d_7272">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="c4e72d8d_7274">ar_labels</code> is a label classification as a <code class="code" id="c4e72d8d_7275">np.ndarray</code></p></dd><dt id="c4e72d8d_7276" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="c4e72d8d_7277">Extracts segments from <code class="code" id="c4e72d8d_7278">Rect</code> bounds.</b></p><br><p> <code class="code" id="c4e72d8d_7280">bounds</code> is a list of <code class="code" id="c4e72d8d_7281">Rect</code> bounds.</p></dd><dt id="c4e72d8d_7282" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="c4e72d8d_7283">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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diff --git a/docs/preprocessing-glcm-padded.html b/docs/preprocessing-glcm-padded.html
index 32feea67..11159608 100644
--- a/docs/preprocessing-glcm-padded.html
+++ b/docs/preprocessing-glcm-padded.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.glcm_padded | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.glcm_padded | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-glcm-padded.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.glcm_padded | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-glcm-padded.html#webpage", "url": "/d/0.0.4/preprocessing-glcm-padded.html", "name": "preprocessing.glcm_padded | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.glcm_padded" data-main-title="preprocessing.glcm_padded" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.glcm_padded"  id="preprocessing.glcm_padded.md"  >preprocessing.glcm_padded</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;9890df50_11924\&quot;\u003eComputes the GLCM of the NDArray bands with padding.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="9890df50_11860"><p id="9890df50_11861">This is largely a handy wrapper around my <a href="https://github.com/Eve-ning/glcm-cupy" id="9890df50_11862"   data-external="true" rel="noopener noreferrer" >glcm-cupy</a> package. This auto-computes the necessary padding so that the GLCM is the same size as the original image.</p></aside><aside class="prompt" data-type="warning" data-title="" id="9890df50_11863"><p id="9890df50_11864">The GLCM computation is rather slow, so it is recommended to use it only if necessary.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="9890df50_11865"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="9890df50_11866" data-style="title-left" data-title-width="medium"><dt id="9890df50_11867" data-expandable="false">glcm_padded</dt><dd><p><b id="9890df50_11868">Computes the GLCM of the NDArray bands with padding.</b></p></dd><dt id="9890df50_11869" data-expandable="false">glcm_padded_cached</dt><dd><p><b id="9890df50_11870">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p></dd><dt id="9890df50_11871" data-expandable="false">append_glcm_padded_cached</dt><dd><p><b id="9890df50_11872">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="9890df50_11873">We show a few examples of how to use the GLCM functions.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4761534"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.glcm_padded | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.glcm_padded | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-glcm-padded.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.glcm_padded | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-glcm-padded.html#webpage", "url": "/d/0.0.4/preprocessing-glcm-padded.html", "name": "preprocessing.glcm_padded | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.glcm_padded" data-main-title="preprocessing.glcm_padded" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.glcm_padded"  id="preprocessing.glcm_padded.md"  >preprocessing.glcm_padded</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;9890df50_14113\&quot;\u003eComputes the GLCM of the NDArray bands with padding.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="9890df50_14049"><p id="9890df50_14050">This is largely a handy wrapper around my <a href="https://github.com/Eve-ning/glcm-cupy" id="9890df50_14051"   data-external="true" rel="noopener noreferrer" >glcm-cupy</a> package. This auto-computes the necessary padding so that the GLCM is the same size as the original image.</p></aside><aside class="prompt" data-type="warning" data-title="" id="9890df50_14052"><p id="9890df50_14053">The GLCM computation is rather slow, so it is recommended to use it only if necessary.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="9890df50_14054"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="9890df50_14055" data-style="title-left" data-title-width="medium"><dt id="9890df50_14056" data-expandable="false">glcm_padded</dt><dd><p><b id="9890df50_14057">Computes the GLCM of the NDArray bands with padding.</b></p></dd><dt id="9890df50_14058" data-expandable="false">glcm_padded_cached</dt><dd><p><b id="9890df50_14059">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p></dd><dt id="9890df50_14060" data-expandable="false">append_glcm_padded_cached</dt><dd><p><b id="9890df50_14061">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="9890df50_14062">We show a few examples of how to use the GLCM functions.</p><div class="code-block" data-lang="python"         >
 import numpy as np
 from glcm_cupy import Features
 
@@ -23,4 +23,4 @@
 ar_glcm_cached_appended = append_glcm_padded_cached(ar, bin_from=1, bin_to=4,
                                                     radius=3)
 
-</div><ul class="list _ul" id="9890df50_11875"    ><li class="list__item" id="9890df50_11876"><p><code class="code" id="9890df50_11877">ar_glcm</code> is the GLCM of the original array, with the last dimension being the GLCM features. The number of features is determined by the <code class="code" id="9890df50_11878">features</code> parameter, which defaults to all features.</p></li><li class="list__item" id="9890df50_11879"><p><code class="code" id="9890df50_11880">ar_glcm_2_features</code> selects only 2 features, with the last dimension being the 2 GLCM features specified.</p></li><li class="list__item" id="9890df50_11881"><p><code class="code" id="9890df50_11882">ar_glcm_cached</code> caches the GLCM so that if you call it again, it will return the cached version. It stores its data at the project root dir, under <code class="code" id="9890df50_11883">.cache/</code>.</p></li><li class="list__item" id="9890df50_11884"><p><code class="code" id="9890df50_11885">ar_glcm_cached_appended</code> is a wrapper around <code class="code" id="9890df50_11886">ar_glcm_cached</code>, it appends the GLCM features onto the original array. It's equivalent to calling <code class="code" id="9890df50_11887">ar_glcm_cached</code> and then <code class="code" id="9890df50_11888">np.concatenate</code> on the final axes.</p></li></ul><section class="chapter"><h3 id="caching" data-toc="caching">Caching</h3><p id="9890df50_11889">GLCM is an expensive operation, thus we recommend to cache it if the input parameters will be the same. This is especially useful if you're experimenting with the same dataset with constant parameters.</p><aside class="prompt" data-type="warning" data-title="" id="9890df50_11890"><p id="9890df50_11891">This cache is automatically invalidated if the parameters change. Thus, if you perform augmentation, the cache will not be used and will be recomputed. This can be wasteful, so it is recommended to perform augmentation after the GLCM computation if possible.</p></aside><aside class="prompt" data-type="note" data-title="" id="9890df50_11892"><p id="9890df50_11893">The cache is stored at the project root dir, under <code class="code" id="9890df50_11894">.cache/</code>. It is safe to delete this folder if you want to clear the cache.</p></aside></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="9890df50_11895" data-style="title-top"><dt id="9890df50_11896" data-expandable="false">glcm_padded(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_11897">Computes the GLCM of the NDArray bands with padding.</b></p><br><ul class="list _ul" id="9890df50_11899"    ><li class="list__item" id="9890df50_11900"><p><code class="code" id="9890df50_11901">ar</code> is the input array</p></li><li class="list__item" id="9890df50_11902"><p><code class="code" id="9890df50_11903">bin_from</code> is the upper bound of the input</p></li><li class="list__item" id="9890df50_11904"><p><code class="code" id="9890df50_11905">bin_to</code> is the upper bound of the GLCM input, i.e. the resolution that GLCM operates on</p></li><li class="list__item" id="9890df50_11906"><p><code class="code" id="9890df50_11907">radius</code> is the radius of the GLCM</p></li><li class="list__item" id="9890df50_11908"><p><code class="code" id="9890df50_11909">step_size</code> is the step size of the GLCM</p></li><li class="list__item" id="9890df50_11910"><p><code class="code" id="9890df50_11911">features</code> is the list of GLCM features to compute</p></li></ul><p> The return shape is </p><svg xmlns="http://www.w3.org/2000/svg" width="30.394ex" height="1.645ex" role="img" focusable="false" viewBox="0 -705 13434.3 727" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.05ex;"><defs><path id="MJX-178-TEX-I-1D43B" d="M228 637Q194 637 192 641Q191 643 191 649Q191 673 202 682Q204 683 219 683Q260 681 355 681Q389 681 418 681T463 682T483 682Q499 682 499 672Q499 670 497 658Q492 641 487 638H485Q483 638 480 638T473 638T464 637T455 637Q416 636 405 634T387 623Q384 619 355 500Q348 474 340 442T328 395L324 380Q324 378 469 378H614L615 381Q615 384 646 504Q674 619 674 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transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-178-TEX-N-73" transform="translate(6677,0)"></use></g></g></g></svg><p> See <code class="code" id="9890df50_11913">glcm_cupy</code> for the GLCM Features.</p></dd><dt id="9890df50_11914" data-expandable="false">glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_11915">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p><br><p> See <code class="code" id="9890df50_11917">glcm_padded</code> for the parameters and output shape</p></dd><dt id="9890df50_11918" data-expandable="false">append_glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_11919">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p><br><p> See <code class="code" id="9890df50_11921">glcm_padded</code> for the parameters</p><br><p> The return shape is: </p><svg 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function automatically flattens the last 2 dimensions of the GLCM features, and appends it onto the original array.</p></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-morphology.html">preprocessing.morphology</a>   <a class="navigation-links__next" href="train-frdc-lightning.html">train.frdc_datamodule &amp; frdc_module</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+</div><ul class="list _ul" id="9890df50_14064"    ><li class="list__item" id="9890df50_14065"><p><code class="code" id="9890df50_14066">ar_glcm</code> is the GLCM of the original array, with the last dimension being the GLCM features. The number of features is determined by the <code class="code" id="9890df50_14067">features</code> parameter, which defaults to all features.</p></li><li class="list__item" id="9890df50_14068"><p><code class="code" id="9890df50_14069">ar_glcm_2_features</code> selects only 2 features, with the last dimension being the 2 GLCM features specified.</p></li><li class="list__item" id="9890df50_14070"><p><code class="code" id="9890df50_14071">ar_glcm_cached</code> caches the GLCM so that if you call it again, it will return the cached version. It stores its data at the project root dir, under <code class="code" id="9890df50_14072">.cache/</code>.</p></li><li class="list__item" id="9890df50_14073"><p><code class="code" id="9890df50_14074">ar_glcm_cached_appended</code> is a wrapper around <code class="code" id="9890df50_14075">ar_glcm_cached</code>, it appends the GLCM features onto the original array. It's equivalent to calling <code class="code" id="9890df50_14076">ar_glcm_cached</code> and then <code class="code" id="9890df50_14077">np.concatenate</code> on the final axes.</p></li></ul><section class="chapter"><h3 id="caching" data-toc="caching">Caching</h3><p id="9890df50_14078">GLCM is an expensive operation, thus we recommend to cache it if the input parameters will be the same. This is especially useful if you're experimenting with the same dataset with constant parameters.</p><aside class="prompt" data-type="warning" data-title="" id="9890df50_14079"><p id="9890df50_14080">This cache is automatically invalidated if the parameters change. Thus, if you perform augmentation, the cache will not be used and will be recomputed. This can be wasteful, so it is recommended to perform augmentation after the GLCM computation if possible.</p></aside><aside class="prompt" data-type="note" data-title="" id="9890df50_14081"><p id="9890df50_14082">The cache is stored at the project root dir, under <code class="code" id="9890df50_14083">.cache/</code>. It is safe to delete this folder if you want to clear the cache.</p></aside></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="9890df50_14084" data-style="title-top"><dt id="9890df50_14085" data-expandable="false">glcm_padded(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_14086">Computes the GLCM of the NDArray bands with padding.</b></p><br><ul class="list _ul" id="9890df50_14088"    ><li class="list__item" id="9890df50_14089"><p><code class="code" id="9890df50_14090">ar</code> is the input array</p></li><li class="list__item" id="9890df50_14091"><p><code class="code" id="9890df50_14092">bin_from</code> is the upper bound of the input</p></li><li class="list__item" id="9890df50_14093"><p><code class="code" id="9890df50_14094">bin_to</code> is the upper bound of the GLCM input, i.e. the resolution that GLCM operates on</p></li><li class="list__item" id="9890df50_14095"><p><code class="code" id="9890df50_14096">radius</code> is the radius of the GLCM</p></li><li class="list__item" id="9890df50_14097"><p><code class="code" id="9890df50_14098">step_size</code> is the step size of the GLCM</p></li><li class="list__item" id="9890df50_14099"><p><code class="code" id="9890df50_14100">features</code> is the list of GLCM features to compute</p></li></ul><p> The return shape is </p><svg xmlns="http://www.w3.org/2000/svg" width="30.394ex" height="1.645ex" role="img" focusable="false" viewBox="0 -705 13434.3 727" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.05ex;"><defs><path id="MJX-183-TEX-I-1D43B" d="M228 637Q194 637 192 641Q191 643 191 649Q191 673 202 682Q204 683 219 683Q260 681 355 681Q389 681 418 681T463 682T483 682Q499 682 499 672Q499 670 497 658Q492 641 487 638H485Q483 638 480 638T473 638T464 637T455 637Q416 636 405 634T387 623Q384 619 355 500Q348 474 340 442T328 395L324 380Q324 378 469 378H614L615 381Q615 384 646 504Q674 619 674 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data-mml-node="mtext" transform="translate(6363.3,0)"><use data-c="47" xlink:href="#MJX-183-TEX-N-47"></use><use data-c="4C" xlink:href="#MJX-183-TEX-N-4C" transform="translate(785,0)"></use><use data-c="43" xlink:href="#MJX-183-TEX-N-43" transform="translate(1410,0)"></use><use data-c="4D" xlink:href="#MJX-183-TEX-N-4D" transform="translate(2132,0)"></use><use data-c="20" xlink:href="#MJX-183-TEX-N-20" transform="translate(3049,0)"></use><use data-c="46" xlink:href="#MJX-183-TEX-N-46" transform="translate(3299,0)"></use><use data-c="65" xlink:href="#MJX-183-TEX-N-65" transform="translate(3952,0)"></use><use data-c="61" xlink:href="#MJX-183-TEX-N-61" transform="translate(4396,0)"></use><use data-c="74" xlink:href="#MJX-183-TEX-N-74" transform="translate(4896,0)"></use><use data-c="75" xlink:href="#MJX-183-TEX-N-75" transform="translate(5285,0)"></use><use data-c="72" xlink:href="#MJX-183-TEX-N-72" transform="translate(5841,0)"></use><use data-c="65" xlink:href="#MJX-183-TEX-N-65" transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-183-TEX-N-73" transform="translate(6677,0)"></use></g></g></g></svg><p> See <code class="code" id="9890df50_14102">glcm_cupy</code> for the GLCM Features.</p></dd><dt id="9890df50_14103" data-expandable="false">glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_14104">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p><br><p> See <code class="code" id="9890df50_14106">glcm_padded</code> for the parameters and output shape</p></dd><dt id="9890df50_14107" data-expandable="false">append_glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="9890df50_14108">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p><br><p> See <code class="code" id="9890df50_14110">glcm_padded</code> for the parameters</p><br><p> The return shape is: </p><svg 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function automatically flattens the last 2 dimensions of the GLCM features, and appends it onto the original array.</p></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-morphology.html">preprocessing.morphology</a>   <a class="navigation-links__next" href="train-frdc-lightning.html">train.frdc_datamodule &amp; frdc_module</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-morphology.html#webpage", "url": "/d/0.0.4/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"  id="preprocessing.morphology.md"  >preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;30ae7463_4950\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="30ae7463_4922"><p id="30ae7463_4923">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="30ae7463_4924" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="30ae7463_4925"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="30ae7463_4926" data-style="title-left" data-title-width="medium"><dt id="30ae7463_4927" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="30ae7463_4928">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="30ae7463_4929" data-expandable="false">binary_watershed</dt><dd><p><b id="30ae7463_4930">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="30ae7463_4931">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4731553"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/preprocessing-morphology.html#webpage", "url": "/d/0.0.4/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"  id="preprocessing.morphology.md"  >preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;30ae7463_7119\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="30ae7463_7091"><p id="30ae7463_7092">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="30ae7463_7093" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="30ae7463_7094"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="30ae7463_7095" data-style="title-left" data-title-width="medium"><dt id="30ae7463_7096" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="30ae7463_7097">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="30ae7463_7098" data-expandable="false">binary_watershed</dt><dd><p><b id="30ae7463_7099">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="30ae7463_7100">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 from frdc.preprocess.morphology import (
     threshold_binary_mask, binary_watershed
@@ -10,6 +10,6 @@
 ar, order = ds.get_ar_bands()
 mask = threshold_binary_mask(ar, order.index('NIR'), 90 / 256)
 ar_label = binary_watershed(mask)
-</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="30ae7463_4933" data-style="title-top"><dt id="30ae7463_4934" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="30ae7463_4935">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="30ae7463_4936">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="30ae7463_7102" data-style="title-top"><dt id="30ae7463_7103" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="30ae7463_7104">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="30ae7463_7105">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
 ar[..., band_idx] &gt; threshold_value
-</div></dd><dt id="30ae7463_4939" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="30ae7463_4940">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="30ae7463_4941">np.ndarray</code></b></p><br><ul class="list _ul" id="30ae7463_4943"    ><li class="list__item" id="30ae7463_4944"><p><code class="code" id="30ae7463_4945">peaks_footprint</code> is the footprint of <code class="code" id="30ae7463_4946">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="30ae7463_4947"><p><code class="code" id="30ae7463_4948">watershed_compactness</code> is the compactness of <code class="code" id="30ae7463_4949">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   <a class="navigation-links__next" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
+</div></dd><dt id="30ae7463_7108" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="30ae7463_7109">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="30ae7463_7110">np.ndarray</code></b></p><br><ul class="list _ul" id="30ae7463_7112"    ><li class="list__item" id="30ae7463_7113"><p><code class="code" id="30ae7463_7114">peaks_footprint</code> is the footprint of <code class="code" id="30ae7463_7115">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="30ae7463_7116"><p><code class="code" id="30ae7463_7117">watershed_compactness</code> is the compactness of <code class="code" id="30ae7463_7118">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   <a class="navigation-links__next" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
\ No newline at end of file
diff --git a/docs/preprocessing-scale.html b/docs/preprocessing-scale.html
index 41504a1d..28868ce7 100644
--- a/docs/preprocessing-scale.html
+++ b/docs/preprocessing-scale.html
@@ -1,4 +1,4 @@
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"preprocessing.scale | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"  id="preprocessing.scale.md"  >preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;bd8b6dca_4909\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="bd8b6dca_4881"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="bd8b6dca_4882" data-style="title-left" data-title-width="medium"><dt id="bd8b6dca_4883" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="bd8b6dca_4884">Scales the NDArray bands to [0, 1] per band.</b></p></dd><dt id="bd8b6dca_4885" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="bd8b6dca_4886">Scales the NDArray bands to zero mean unit variance per band.</b></p></dd><dt id="bd8b6dca_4887" data-expandable="false">scale_static_per_band</dt><dd><p><b id="bd8b6dca_4888">Scales the NDArray bands by a predefined configuration.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4686308"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.scale | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" 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"preprocessing.scale | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"  id="preprocessing.scale.md"  >preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;bd8b6dca_7175\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="bd8b6dca_7147"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="bd8b6dca_7148" data-style="title-left" data-title-width="medium"><dt id="bd8b6dca_7149" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="bd8b6dca_7150">Scales the NDArray bands to [0, 1] per band.</b></p></dd><dt id="bd8b6dca_7151" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="bd8b6dca_7152">Scales the NDArray bands to zero mean unit variance per band.</b></p></dd><dt id="bd8b6dca_7153" data-expandable="false">scale_static_per_band</dt><dd><p><b id="bd8b6dca_7154">Scales the NDArray bands by a predefined configuration.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python"         >
 from frdc.load import FRDCDataset
 from frdc.preprocess.scale import (
     scale_0_1_per_band, scale_normal_per_band, scale_static_per_band
@@ -12,4 +12,4 @@
 ar_01 = scale_0_1_per_band(ar)
 ar_norm = scale_normal_per_band(ar)
 ar_static = scale_static_per_band(ar, order, BAND_MAX_CONFIG)
-</div><aside class="prompt" data-type="tip" data-title="" id="bd8b6dca_4890"><p id="bd8b6dca_4891">The static scaling has a default config, which was inferred by our capturing device.</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="bd8b6dca_4892" data-style="title-top"><dt id="bd8b6dca_4893" data-expandable="false">scale_0_1_per_band(ar)</dt><dd><p><b id="bd8b6dca_4894">Scales the NDArray bands to [0, 1] per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-177-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path 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transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-177-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-177-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-177-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-177-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-177-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-177-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-177-TEX-N-29"></use></g></g></g></svg></dd><dt id="bd8b6dca_4897" data-expandable="false">scale_normal_per_band(ar)</dt><dd><p><b id="bd8b6dca_4898">Scales the NDArray bands to zero mean unit variance per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" 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The <code class="code" id="bd8b6dca_4904">config</code> is of <code class="code" id="bd8b6dca_4905">dict[str, tuple[int, int]]</code> where the key is the band name, and the value is a tuple of <code class="code" id="bd8b6dca_4906">(min, max)</code>. 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+</div><aside class="prompt" data-type="tip" data-title="" id="bd8b6dca_7156"><p id="bd8b6dca_7157">The static scaling has a default config, which was inferred by our capturing device.</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="bd8b6dca_7158" data-style="title-top"><dt id="bd8b6dca_7159" data-expandable="false">scale_0_1_per_band(ar)</dt><dd><p><b id="bd8b6dca_7160">Scales the NDArray bands to [0, 1] per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-182-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path 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transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-182-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-182-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-182-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-182-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-182-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-182-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-182-TEX-N-29"></use></g></g></g></svg></dd><dt id="bd8b6dca_7163" data-expandable="false">scale_normal_per_band(ar)</dt><dd><p><b id="bd8b6dca_7164">Scales the NDArray bands to zero mean unit variance per band.</b></p><br><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" 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The <code class="code" id="bd8b6dca_7170">config</code> is of <code class="code" id="bd8b6dca_7171">dict[str, tuple[int, int]]</code> where the key is the band name, and the value is a tuple of <code class="code" id="bd8b6dca_7172">(min, max)</code>. 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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4581118"><meta name="build-number" content="${buildNumber}">       <title>Retrieve our Datasets | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"prerequisites","level":0,"title":"Prerequisites","anchor":"#prerequisites"},{"id":"retrieve-the-data","level":0,"title":"Retrieve the Data","anchor":"#retrieve-the-data"},{"id":"segment-the-data","level":0,"title":"Segment the Data","anchor":"#segment-the-data"},{"id":"plot-the-data-optional","level":0,"title":"Plot the Data (Optional)","anchor":"#plot-the-data-optional"}]</script><script id="topic-shortcuts" type="application/json"></script><link 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class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Retrieve-our-Datasets"  id="Retrieve-our-Datasets.md"  >Retrieve our Datasets</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;1bd94640_6572\&quot;\u003eA tutorial to retrieve our datasets\u003c/p\u003e&quot;]}"></div> <p id="1bd94640_6482">In this tutorial, we'll learn how to :</p><ul class="list _ul" id="1bd94640_6483"    ><li class="list__item" id="1bd94640_6484"><p>Retrieve FRDC's Hyperspectral Image Data as <code class="code" id="1bd94640_6485">np.ndarray</code></p></li><li class="list__item" id="1bd94640_6486"><p>Retrieve FRDC's Ground Truth bounds and labels</p></li><li class="list__item" id="1bd94640_6487"><p>Slice/segment the image data by the bounds</p></li></ul><section class="chapter"><h2 id="prerequisites" data-toc="prerequisites">Prerequisites</h2><ul class="list _ul" id="1bd94640_6488"    ><li class="list__item" id="1bd94640_6489"><p>New here? <a href="getting-started.html" id="1bd94640_6490" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Get Started</a>.</p></li><li class="list__item" id="1bd94640_6491"><p>Setup the Google Cloud Authorization to download the data.</p></li></ul></section><section class="chapter"><h2 id="retrieve-the-data" data-toc="retrieve-the-data">Retrieve the Data</h2><p id="1bd94640_6492">To retrieve the data, use <a href="load-dataset.html#frdcdataset" id="1bd94640_6493" data-tooltip="Initializes the dataset downloader. This doesn't immediately download the dataset, but only when you call the get_* functions. The site, date, version must match the dataset path on GCS. For example if the dataset is at gs://frdc-scan/my-site/20201218/90deg/map, site='my-site'&hellip;"  >FRDCDataset</a></p><p id="1bd94640_6494">Here, we'll download and load our</p><ul class="list _ul" id="1bd94640_6495"    ><li class="list__item" id="1bd94640_6496"><p><code class="code" id="1bd94640_6497">ar</code>: Hyperspectral Image Data</p></li><li class="list__item" id="1bd94640_6498"><p><code class="code" id="1bd94640_6499">order</code>: The order of the bands</p></li><li class="list__item" id="1bd94640_6500"><p><code class="code" id="1bd94640_6501">bounds</code>: The bounds of the trees (segments)</p></li><li class="list__item" id="1bd94640_6502"><p><code class="code" id="1bd94640_6503">labels</code>: The labels of the trees (segments)</p></li></ul><div class="code-block" data-lang="python"         >
+from frdc.load.dataset import FRDCDataset
+
+ds = FRDCDataset(site=&quot;chestnut_nature_park&quot;, date=&quot;20201218&quot;, version=None)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="what-datasets-are-there" data-toc="what-datasets-are-there">What Datasets are there?</h3></div><div class="collapse__content"><aside class="prompt" data-type="tip" data-title="" id="1bd94640_6505"><p id="1bd94640_6506">To know what datasets are available, you can run <a href="load-dataset.html#frdcdownloader" id="1bd94640_6507" data-tooltip="Lists all GCS datasets in the bucket as DataFrame This works by checking which folders have a specific file, which we call the anchor. Downloads a file from GCS. This takes in a path glob, a string containing wildcards, and downloads exactly 1 file. If it matches 0 or more than 1&hellip;"  >FRDCDownloader</a>'s <code class="code" id="1bd94640_6508">list_gcs_datasets()</code> method</p></aside><aside class="prompt" data-type="warning" data-title="" id="1bd94640_6509"><p id="1bd94640_6510">Note that some datasets do not have <code class="code" id="1bd94640_6511">bounds</code> and <code class="code" id="1bd94640_6512">labels</code> available as they have not been annotated yet.</p></aside><div class="code-block" data-lang="bash"         >
+&gt;&gt;&gt; from frdc.load.dataset import FRDCDownloader
+&gt;&gt;&gt; df = FRDCDownloader().list_gcs_datasets()
+&gt;&gt;&gt; print(df)
+# 0  DEBUG/0
+# 1  casuarina/20220418/183deg
+# 2  casuarina/20220418/93deg
+# 3  chestnut_nature_park/20201218
+# ...
+</div><ul class="list _ul" id="1bd94640_6514"    ><li class="list__item" id="1bd94640_6515"><p>The first part of the path is the <code class="code" id="1bd94640_6516">site</code>, and the second part is the <code class="code" id="1bd94640_6517">date</code>.</p></li><li class="list__item" id="1bd94640_6518"><p>The <code class="code" id="1bd94640_6519">version</code> is the rest of the path, if there isn't any, use <code class="code" id="1bd94640_6520">None</code>.</p></li></ul><div class="tabs" id="1bd94640_6521"><div class="tabs__content" data-gtm="tab" id="1bd94640_6522"   data-title="ds/date/ver/" ><ul class="list _ul" id="1bd94640_6523"    ><li class="list__item" id="1bd94640_6524"><p><code class="code" id="1bd94640_6525">site=&quot;ds&quot;</code></p></li><li class="list__item" id="1bd94640_6526"><p><code class="code" id="1bd94640_6527">date=&quot;date&quot;</code></p></li><li class="list__item" id="1bd94640_6528"><p><code class="code" id="1bd94640_6529">version=&quot;ver&quot;</code></p></li></ul></div><div class="tabs__content" data-gtm="tab" id="1bd94640_6530"   data-title="ds/date/ver/01/data/" ><ul class="list _ul" id="1bd94640_6531"    ><li class="list__item" id="1bd94640_6532"><p><code class="code" id="1bd94640_6533">site=&quot;ds&quot;</code></p></li><li class="list__item" id="1bd94640_6534"><p><code class="code" id="1bd94640_6535">date=&quot;date&quot;</code></p></li><li class="list__item" id="1bd94640_6536"><p><code class="code" id="1bd94640_6537">version=&quot;ver/01/data&quot;</code></p></li></ul></div><div class="tabs__content" data-gtm="tab" id="1bd94640_6538"   data-title="ds/date/" ><ul class="list _ul" id="1bd94640_6539"    ><li class="list__item" id="1bd94640_6540"><p><code class="code" id="1bd94640_6541">site=&quot;ds&quot;</code></p></li><li class="list__item" id="1bd94640_6542"><p><code class="code" id="1bd94640_6543">date=&quot;date&quot;</code></p></li><li class="list__item" id="1bd94640_6544"><p><code class="code" id="1bd94640_6545">version=None</code></p></li></ul></div></div></div></div></section></section><section class="chapter"><h2 id="segment-the-data" data-toc="segment-the-data">Segment the Data</h2><p id="1bd94640_6546">To segment the data, use <a href="preprocessing-extract-segments.html" id="1bd94640_6547" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >Extract Segments</a>.</p><p id="1bd94640_6548">Here, we'll segment the data by the bounds.</p><div class="code-block" data-lang="python"         >
+from frdc.load.dataset import FRDCDataset
+from frdc.preprocess.extract_segments import extract_segments_from_bounds
+
+ds = FRDCDataset(site=&quot;chestnut_nature_park&quot;, date=&quot;20201218&quot;, version=None)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+segments = extract_segments_from_bounds(ar, bounds)
+</div><p id="1bd94640_6551"><code class="code" id="1bd94640_6552">segments</code> is a list of <code class="code" id="1bd94640_6553">np.ndarray</code> of shape H, W, C, representing a tree. The order of <code class="code" id="1bd94640_6554">segments</code> is the same as <code class="code" id="1bd94640_6555">labels</code>, so you can use <code class="code" id="1bd94640_6556">labels</code> to identify the tree.</p><aside class="prompt" data-type="tip" data-title="" id="1bd94640_6557"><p id="1bd94640_6558">While we have not used <code class="code" id="1bd94640_6559">order</code> in our example, it's useful to determine the order of the bands in <code class="code" id="1bd94640_6560">ar</code> in other applications.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h2 id="plot-the-data-optional" data-toc="plot-the-data-optional">Plot the Data (Optional)</h2></div><div class="collapse__content"><p id="1bd94640_6561">We can then use these data to plot out the first tree segment.</p><div class="code-block" data-lang="python"         >
+import matplotlib.pyplot as plt
+
+from frdc.load.dataset import FRDCDataset
+from frdc.preprocess.extract_segments import extract_segments_from_bounds
+from frdc.preprocess.scale import scale_0_1_per_band
+
+ds = FRDCDataset(site=&quot;chestnut_nature_park&quot;, date=&quot;20201218&quot;, version=None)
+ar, order = ds.get_ar_bands()
+bounds, labels = ds.get_bounds_and_labels()
+segments = extract_segments_from_bounds(ar, bounds)
+segment_0_bgr = segments[0]
+segment_0_rgb = segment_0_bgr[..., [2, 1, 0]]
+segment_0_rgb_scaled = scale_0_1_per_band(segment_0_rgb)
+
+plt.imshow(segment_0_rgb_scaled)
+plt.title(f&quot;Tree {labels[0]}&quot;)
+plt.show()
+</div><p id="1bd94640_6563">See also: <a href="preprocessing-scale.html" id="1bd94640_6564" data-tooltip="The static scaling has a default config, which was inferred by our capturing device."  >preprocessing.scale.scale_0_1_per_band</a></p><p id="1bd94640_6565">MatPlotLib cannot show the data correctly as-is, so we need to</p><ul class="list _ul" id="1bd94640_6566"    ><li class="list__item" id="1bd94640_6567"><p>Convert the data from BGR to RGB</p></li><li class="list__item" id="1bd94640_6568"><p>Scale the data to 0-1 per band</p></li></ul><aside class="prompt" data-type="note" data-title="" id="1bd94640_6569"><p id="1bd94640_6570">Remember that the library returns the band <code class="code" id="1bd94640_6571">order</code>? This is useful in debugging the data. If we had shown it in BGR, it'll look off!</p></aside></div></div></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T13:20:17.7989394"><meta name="build-number" content="${buildNumber}">       <title>train.frdc_datamodule &amp; frdc_module | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/train-frdc-lightning.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/train-frdc-lightning.html#webpage", "url": "/d/0.0.4/train-frdc-lightning.html", "name": "train.frdc_datamodule &amp; frdc_module | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="train.frdc_lightning" data-main-title="train.frdc_datamodule &amp; frdc_module" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="train.frdc_lightning"  id="train.frdc_lightning.md"  >train.frdc_datamodule &amp; frdc_module</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;c6b8a6a0_1147\&quot;\u003eThe FRDC PyTorch LightningDataModule and LightningModule.\u003c/p\u003e&quot;]}"></div> <p id="c6b8a6a0_1100">These are FRDC specific LightningDataModule and LightningModule, a core component in the PyTorch Lightning ecosystem to provide a simple interface to train and evaluate models.</p><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_1101"><p id="c6b8a6a0_1102">It's optional to use these classes, you can use your own training loop if you want. We'll use these for our training pipeline.</p></aside><dl id="c6b8a6a0_1103" data-style="title-left" data-title-width="medium"><dt id="c6b8a6a0_1104" data-expandable="false">FRDCDataModule</dt><dd><p><b id="c6b8a6a0_1105">The FRDC PyTorch Lightning DataModule.</b></p></dd><dt id="c6b8a6a0_1106" data-expandable="false">FRDCModule</dt><dd><p><b id="c6b8a6a0_1107">The FRDC PyTorch Lightning Module.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><aside class="prompt" data-type="tip" data-title="" id="c6b8a6a0_1108"><p id="c6b8a6a0_1109">See our training pipeline for a full example</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="c6b8a6a0_1110" data-style="title-top"><dt id="c6b8a6a0_1111" data-expandable="false">FRDCDataModule(segments, labels, preprocess, augmentation, train_val_test_split, batch_size)</dt><dd><p><b id="c6b8a6a0_1112">Initializes the FRDC PyTorch Lightning DataModule.</b></p><br><ul class="list _ul" id="c6b8a6a0_1114"    ><li class="list__item" id="c6b8a6a0_1115"><p><code class="code" id="c6b8a6a0_1116">segments</code>, <code class="code" id="c6b8a6a0_1117">labels</code> are retrieved from </p><ul class="list _ul" id="c6b8a6a0_1118"    ><li class="list__item" id="c6b8a6a0_1119"><p><a href="load-dataset.html#frdcdataset" id="c6b8a6a0_1120" data-tooltip="Initializes the dataset downloader. This doesn't immediately download the dataset, but only when you call the get_* functions. The site, date, version must match the dataset path on GCS. For example if the dataset is at gs://frdc-scan/my-site/20201218/90deg/map, site='my-site'&hellip;"  >FRDCDataset</a></p></li><li class="list__item" id="c6b8a6a0_1121"><p><a href="preprocessing-extract-segments.html" id="c6b8a6a0_1122" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >Segmentation</a></p></li></ul></li><li class="list__item" id="c6b8a6a0_1123"><p><code class="code" id="c6b8a6a0_1124">preprocess</code> is a function that takes in a segment and returns a preprocessed segment. In particular, it should accept a list of NumPy NDArrays and return a single stacked PyToch Tensor.</p></li><li class="list__item" id="c6b8a6a0_1125"><p><code class="code" id="c6b8a6a0_1126">augmentation</code> is a function that takes in a segment and returns an augmented segment. In particular, it takes in a PyTorch Tensor and returns another.</p></li><li class="list__item" id="c6b8a6a0_1127"><p><code class="code" id="c6b8a6a0_1128">train_val_test_split</code> is a function that takes a TensorDataset and returns a list of 3 TensorDatasets, for train, val and test respectively.</p></li><li class="list__item" id="c6b8a6a0_1129"><p><code class="code" id="c6b8a6a0_1130">batch_size</code> is the batch size.</p></li></ul><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_1131"><p>For now, the <code class="code" id="c6b8a6a0_1132">augmentation</code> is only applied to training data</p></aside></dd><dt id="c6b8a6a0_1133" data-expandable="false">FRDCModule(model_cls, model_kwargs, optim_cls, optim_kwargs)</dt><dd><p><b id="c6b8a6a0_1134">Initializes the FRDC PyTorch Lightning Module.</b></p><br><ul class="list _ul" id="c6b8a6a0_1136"    ><li class="list__item" id="c6b8a6a0_1137"><p><code class="code" id="c6b8a6a0_1138">model_cls</code> is the Class of the model.</p></li><li class="list__item" id="c6b8a6a0_1139"><p><code class="code" id="c6b8a6a0_1140">model_kwargs</code> is the kwargs to pass to the model.</p></li><li class="list__item" id="c6b8a6a0_1141"><p><code class="code" id="c6b8a6a0_1142">optim_cls</code> is the Class of the optimizer.</p></li><li class="list__item" id="c6b8a6a0_1143"><p><code class="code" id="c6b8a6a0_1144">optim_kwargs</code> is the kwargs to pass to the optimizer.</p></li></ul><p> Internally, the module will initialize the model and optimizer as follows: </p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-10-26T15:07:24.4741539"><meta name="build-number" content="${buildNumber}">       <title>train.frdc_datamodule &amp; frdc_module | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.4/train-frdc-lightning.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.4/train-frdc-lightning.html#webpage", "url": "/d/0.0.4/train-frdc-lightning.html", "name": "train.frdc_datamodule &amp; frdc_module | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --> <!-- Mermaid light/dark themes -->  <link rel="stylesheet" type="text/css" href="mermaid.css">  </head>     <body data-id="train.frdc_lightning" data-main-title="train.frdc_datamodule &amp; frdc_module" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.4 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="train.frdc_lightning"  id="train.frdc_lightning.md"  >train.frdc_datamodule &amp; frdc_module</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;c6b8a6a0_3303\&quot;\u003eThe FRDC PyTorch LightningDataModule and LightningModule.\u003c/p\u003e&quot;]}"></div> <p id="c6b8a6a0_3256">These are FRDC specific LightningDataModule and LightningModule, a core component in the PyTorch Lightning ecosystem to provide a simple interface to train and evaluate models.</p><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_3257"><p id="c6b8a6a0_3258">It's optional to use these classes, you can use your own training loop if you want. We'll use these for our training pipeline.</p></aside><dl id="c6b8a6a0_3259" data-style="title-left" data-title-width="medium"><dt id="c6b8a6a0_3260" data-expandable="false">FRDCDataModule</dt><dd><p><b id="c6b8a6a0_3261">The FRDC PyTorch Lightning DataModule.</b></p></dd><dt id="c6b8a6a0_3262" data-expandable="false">FRDCModule</dt><dd><p><b id="c6b8a6a0_3263">The FRDC PyTorch Lightning Module.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><aside class="prompt" data-type="tip" data-title="" id="c6b8a6a0_3264"><p id="c6b8a6a0_3265">See our training pipeline for a full example</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="c6b8a6a0_3266" data-style="title-top"><dt id="c6b8a6a0_3267" data-expandable="false">FRDCDataModule(segments, labels, preprocess, augmentation, train_val_test_split, batch_size)</dt><dd><p><b id="c6b8a6a0_3268">Initializes the FRDC PyTorch Lightning DataModule.</b></p><br><ul class="list _ul" id="c6b8a6a0_3270"    ><li class="list__item" id="c6b8a6a0_3271"><p><code class="code" id="c6b8a6a0_3272">segments</code>, <code class="code" id="c6b8a6a0_3273">labels</code> are retrieved from </p><ul class="list _ul" id="c6b8a6a0_3274"    ><li class="list__item" id="c6b8a6a0_3275"><p><a href="load-dataset.html#frdcdataset" id="c6b8a6a0_3276" data-tooltip="Initializes the dataset downloader. This doesn't immediately download the dataset, but only when you call the get_* functions. The site, date, version must match the dataset path on GCS. For example if the dataset is at gs://frdc-scan/my-site/20201218/90deg/map, site='my-site'&hellip;"  >FRDCDataset</a></p></li><li class="list__item" id="c6b8a6a0_3277"><p><a href="preprocessing-extract-segments.html" id="c6b8a6a0_3278" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >Segmentation</a></p></li></ul></li><li class="list__item" id="c6b8a6a0_3279"><p><code class="code" id="c6b8a6a0_3280">preprocess</code> is a function that takes in a segment and returns a preprocessed segment. In particular, it should accept a list of NumPy NDArrays and return a single stacked PyToch Tensor.</p></li><li class="list__item" id="c6b8a6a0_3281"><p><code class="code" id="c6b8a6a0_3282">augmentation</code> is a function that takes in a segment and returns an augmented segment. In particular, it takes in a PyTorch Tensor and returns another.</p></li><li class="list__item" id="c6b8a6a0_3283"><p><code class="code" id="c6b8a6a0_3284">train_val_test_split</code> is a function that takes a TensorDataset and returns a list of 3 TensorDatasets, for train, val and test respectively.</p></li><li class="list__item" id="c6b8a6a0_3285"><p><code class="code" id="c6b8a6a0_3286">batch_size</code> is the batch size.</p></li></ul><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_3287"><p>For now, the <code class="code" id="c6b8a6a0_3288">augmentation</code> is only applied to training data</p></aside></dd><dt id="c6b8a6a0_3289" data-expandable="false">FRDCModule(model_cls, model_kwargs, optim_cls, optim_kwargs)</dt><dd><p><b id="c6b8a6a0_3290">Initializes the FRDC PyTorch Lightning Module.</b></p><br><ul class="list _ul" id="c6b8a6a0_3292"    ><li class="list__item" id="c6b8a6a0_3293"><p><code class="code" id="c6b8a6a0_3294">model_cls</code> is the Class of the model.</p></li><li class="list__item" id="c6b8a6a0_3295"><p><code class="code" id="c6b8a6a0_3296">model_kwargs</code> is the kwargs to pass to the model.</p></li><li class="list__item" id="c6b8a6a0_3297"><p><code class="code" id="c6b8a6a0_3298">optim_cls</code> is the Class of the optimizer.</p></li><li class="list__item" id="c6b8a6a0_3299"><p><code class="code" id="c6b8a6a0_3300">optim_kwargs</code> is the kwargs to pass to the optimizer.</p></li></ul><p> Internally, the module will initialize the model and optimizer as follows: </p><div class="code-block" data-lang="python"         >
 model = model_cls(**model_kwargs)
 optim = optim_cls(model.parameters(), **optim_kwargs)
-</div><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_1146"><p>We do not accept the instances of the Model and Optimizer so that we can pickle them.</p></aside></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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+</div><aside class="prompt" data-type="note" data-title="" id="c6b8a6a0_3302"><p>We do not accept the instances of the Model and Optimizer so that we can pickle them.</p></aside></dd></dl></section><div class="last-modified"> Last modified: 26 October 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.1.5-b176/app.js"></script></body></html>
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