Image FastFormat DataType

Cargo.toml

@@ -2,13 +2,16 @@
 name = "fastformat"
 version = "0.1.0"
 edition = "2021"
-license = "MIT"
+license = "Apache-2.0"
 description = "Fast world data converter"
 
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 [lib]
 name = "fastformat"
-crate-type = ["cdylib"]
+crate-type = ["cdylib", "lib"]
 
 [dependencies]
 pyo3 = "0.21.1"
+ndarray = "0.15.4"
+arrow = "52.1.0"
+eyre = "0.6.12"

LICENSE

@@ -1,21 +1,201 @@
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-SOFTWARE.
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README.md

@@ -0,0 +1,28 @@
+## Project Objective
+
+The goal is to create an efficient way to reconstruct and process data in real-time, in specific formats like NDarray,
+Numpy, or Arrow, without unnecessary copies.
+
+- **Independent Library**: We are creating an independent library for our formats, which everyone could use even without
+  DORA. The current name is fastformat: <https://github.com/dora-rs/fastformat>
+
+- **Agnosticism**: We want our solution to be agnostic to Arrow, meaning it can work with Numpy and other data types
+  easily by implementing to_[format] functions.
+
+- **Rust and Python Code**: The main implementation will be in Rust for performance and portability reasons, with a
+  Python interface using PyO3. This will allow for fast vector manipulation and potentially GPU support in the future.
+
+- **Portability and Dependencies**: By using Rust, we aim to minimize dependencies and maximize portability. This will
+  allow us to achieve optimal performance across different platforms.
+
+- **Simplicity**: The goal is not to create yet another library for creating formats to store/process data. The aim here
+  is to create a simple interface to wrap data from one type and convert it to another.
+
+## DataTypes
+
+- **Image**: (Arrow representation is a **UnionArray**),
+    - Field "data': UintXArray (e.g [0, 255, 0, 255, 0, 255, ...])
+    - Field "width": Uint32Array (e.g [1280])
+    - Field "height": Uint32Array (e.g [720])
+    - Field "encoding": StringArray (e.g ["RGB8"])
+    - Field "name" (Optional): StringArray (e.g,["image.front_camera"] or [None])

examples/dummy-opencv-capture/main.rs

@@ -0,0 +1,68 @@
+extern crate fastformat;
+
+use fastformat::image::Image;
+
+fn camera_read() -> ndarray::Array<u8, ndarray::Ix3> {
+    // Dummy camera read
+
+    let flat_image = vec![0; 27];
+    println!(
+        "Generate a camera image at address: {:?}",
+        flat_image.as_ptr()
+    );
+
+    let image = Image::new_bgr8(flat_image, 3, 3, None).unwrap();
+
+    return image.bgr8_to_ndarray().unwrap();
+}
+
+fn image_show(_frame: ndarray::ArrayView<u8, ndarray::Ix3>) {
+    // Dummy image show
+
+    println!("Showing an image.");
+}
+
+fn send_output(arrow_array: arrow::array::UnionArray) {
+    // Dummy send output
+
+    let image = Image::from_arrow(arrow_array).unwrap();
+
+    println!(
+        "Sending an image to dataflow. Image address is: {:?}",
+        image.as_ptr()
+    );
+}
+
+fn main() {
+    // Read OpenCV Camera, default is ndarray BGR8
+    let frame = camera_read();
+
+    let image = Image::bgr8_from_ndarray(frame, Some("camera.left")).unwrap();
+
+    // Convert to RGB8, apply some filter (Black and White).
+    let mut frame = image.to_rgb8().unwrap().rgb8_to_ndarray().unwrap();
+
+    for i in 0..frame.shape()[0] {
+        for j in 0..frame.shape()[1] {
+            let mean =
+                (frame[[i, j, 0]] as f32 + frame[[i, j, 1]] as f32 + frame[[i, j, 2]] as f32) / 3.0;
+
+            if mean > 128.0 {
+                frame[[i, j, 0]] = 255;
+                frame[[i, j, 1]] = 255;
+                frame[[i, j, 2]] = 255;
+            } else {
+                frame[[i, j, 0]] = 0;
+                frame[[i, j, 1]] = 0;
+                frame[[i, j, 2]] = 0;
+            }
+        }
+    }
+
+    let image = Image::rgb8_from_ndarray(frame, Some("camera.left.baw")).unwrap();
+
+    // Plot the image, you may only need a ndarray_view
+    image_show(image.rgb8_to_ndarray_view().unwrap());
+
+    send_output(image.to_arrow().unwrap());
+}