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6_zoo_workflow_with_coregistration.py
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import os
from coastseg import coastseg_logs
from coastseg import zoo_model
from coastseg.tide_correction import compute_tidal_corrections
from coastseg import file_utilities
# The Zoo Model is a machine learning model that can be used to extract shorelines from satellite imagery.
# This script will only run a single ROI at a time. If you want to run multiple ROIs, you will need to run this script multiple times.
# Extract Shoreline Settings
settings ={
'min_length_sl': 100, # minimum length (m) of shoreline perimeter to be valid
'max_dist_ref':500, # maximum distance (m) from reference shoreline to search for valid shorelines. This detrmines the width of the buffer around the reference shoreline
'cloud_thresh': 0.5, # threshold on maximum cloud cover (0-1). If the cloud cover is above this threshold, no shorelines will be extracted from that image
'dist_clouds': 100, # distance(m) around clouds where shoreline will not be mapped
'min_beach_area': 50, # minimum area (m^2) for an object to be labelled as a beach
'sand_color': 'default', # 'default', 'latest', 'dark' (for grey/black sand beaches) or 'bright' (for white sand beaches)
"apply_cloud_mask": True, # apply cloud mask to the imagery. If False, the cloud mask will not be applied.
}
# The model can be run using the following settings:
model_setting = {
"sample_direc": None, # directory of jpgs ex. C:/Users/username/CoastSeg/data/ID_lla12_datetime11-07-23__08_14_11/jpg_files/preprocessed/RGB/",
"use_GPU": "0", # 0 or 1 0 means no GPU
"implementation": "BEST", # BEST or ENSEMBLE
"model_type": "global_segformer_RGB_4class_14036903", # model name from the zoo
"otsu": False, # Otsu Thresholding
"tta": False, # Test Time Augmentation
"apply_segmentation_filter": True, # apply segmentation filter to the model outputs to sort them into good or bad
}
# Available models can run input "RGB" # or "MNDWI" or "NDWI"
img_type = "RGB" # make sure the model name is compatible with the image type
# percentage of no data allowed in the image eg. 0.75 means 75% of the image can be no data
percent_no_data = 0.75
# 1. Set the User configuration Settings
# ---------------------------
# a. ENTER THE NAME OF THE SESSION TO SAVE THE MODEL PREDICTIONS TO
session_name = "coreg_session5"
# b. ENTER THE DIRECTORY WHERE THE INPUT IMAGES ARE STORED
# - Example of the directory where the input images are stored ( this should be the /data folder in the CoastSeg directory)
# sample_directory = r"C:\development\doodleverse\coastseg\CoastSeg\data\ID_1_datetime11-04-24__04_30_52\jpg_files\preprocessed\RGB"
sample_directory = r"C:\development\doodleverse\coastseg\CoastSeg\data\ID_1_datetime11-04-24__04_30_52_original_mess_with\coregistered\jpg_files\preprocessed\RGB"
# 2. Save the settings to the model instance
# -----------------
# Create an instance of the zoo model to run the model predictions
zoo_model_instance = zoo_model.Zoo_Model()
# Set the model settings to read the input images from the sample directory
model_setting["sample_direc"] = sample_directory
model_setting["img_type"] = img_type
# save settings to the zoo model instance
settings.update(model_setting)
# save the settings to the model instance
zoo_model_instance.set_settings(**settings)
# OPTIONAL: If you have a transects and shoreline file, you can extract shorelines from the zoo model outputs
transects_path = r"C:\development\doodleverse\coastseg\CoastSeg\data\ID_1_datetime11-04-24__04_30_52\transects.geojson" # path to the transects geojson file (optional, default will be loaded if not provided)
shoreline_path = "" # path to the shoreline geojson file (optional, default will be loaded if not provided)
shoreline_extraction_area_path= "" # path to the shoreline extraction area geojson file (optional)
# 3. Run the model and extract shorelines
# -------------------------------------
zoo_model_instance.run_model_and_extract_shorelines(
model_setting["sample_direc"],
session_name=session_name,
shoreline_path=shoreline_path,
transects_path=transects_path,
shoreline_extraction_area_path = shoreline_extraction_area_path,
coregistered=True,
)
# 4. OPTIONAL: Run Tide Correction
# ------------------------------------------
# Tide Correction (optional)
# WARNING: Before running this snippet, you must download the tide model to the CoastSeg/tide_model folder.
# WE RECOMMEND USING FES2022.
#
# Tutorial on How to Download the Tide Model:
# https://github.com/Doodleverse/CoastSeg/wiki/09.-How-to-Download-and-clip-Tide-Model
#
# The Tide Model must be downloaded to CoastSeg/tide_model.
# Two Tide Models are available: 'FES2014' or 'FES2022'.
#
# Parameters:
beach_slope = 0.02 # Slope of the beach (m/m)
reference_elevation = 0 # Reference elevation (m, relative to user-specified vertical datum)
tides_file = '' #(Optional) Enter the full path to the CSV file containing the tide data if you don't want to use the tide model. See accepted formats : https://satelliteshorelines.github.io/CoastSeg/tide-file-format/
slopes_file ='' #(Optional) Enter the full path to the CSV file containing the beach slopes if you don't want to use a constant slope. See accepted formats: https://satelliteshorelines.github.io/CoastSeg/slope-file-format/
if slopes_file:
beach_slope = slopes_file
# UNCOMMENT THESE 2 LINES TO RUN THE TIDE CORRECTION
# roi_id = file_utilities.get_ROI_ID_from_session(session_name) # read ROI ID from the config.json file found in the extracted shoreline session directory
# compute_tidal_corrections(session_name, [roi_id], beach_slope, reference_elevation,model='FES2022',tides_file=tides_file)