add debugging scripts

7_zoo_workflow_with_coreg_run_model_only.py

@@ -0,0 +1,112 @@
+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
+        }
+# 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
+model_session_name = "coreg_session3"
+# 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
+# -------------------------------------
+
+
+
+model_implementation = settings.get('implementation', "BEST")
+use_GPU = settings.get('use_GPU', "0")
+use_otsu = settings.get('otsu', False)
+use_tta = settings.get('tta', False)
+
+
+zoo_model_instance.run_model(
+            img_type,
+            model_implementation,
+            session_name,
+            input_directory,
+            model_name=model_name,
+            use_GPU=use_GPU,
+            use_otsu=use_otsu,
+            use_tta=use_tta,
+            percent_no_data=percent_no_data,
+            coregistered = True
+)
+
+# zoo_model_instance.run_model_and_extract_shorelines(
+#             model_setting["sample_direc"],
+#             session_name=model_session_name,
+#             shoreline_path=shoreline_path,
+#             transects_path=transects_path,
+#             shoreline_extraction_area_path = shoreline_extraction_area_path
+#         )
+
+# 4. OPTIONAL: Run Tide Correction
+# ------------------------------------------
+# Tide Correction (optional)
+# Before running this snippet, you must download the tide model to the CoastSeg/tide_model folder
+# Tutorial: https://github.com/Doodleverse/CoastSeg/wiki/09.-How-to-Download-and-clip-Tide-Model
+#  You will need to uncomment the line below to run the tide correction
+
+beach_slope = 0.02 # Slope of the beach (m)
+reference_elevation = 0 # Elevation of the beach Mean Sea Level (M.S.L) (m)
+
+# UNCOMMENT THESE 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
+# )

8_extract_shorelines_planet.py

@@ -53,7 +53,7 @@
 
 # This script assumes the site directory is in the format of the above structure and is provided
 session_dir = r'C:\development\coastseg-planet\downloads\UNALAKLEET_pier_cloud_0.7_TOAR_enabled_2020-06-01_to_2023-08-01\e2821741-0677-435a-a93a-d4f060f3adf4\coregistered'
-
+good_dir = r'C:\development\coastseg-planet\downloads\UNALAKLEET_pier_cloud_0.7_TOAR_enabled_2020-06-01_to_2023-08-01\e2821741-0677-435a-a93a-d4f060f3adf4\coregistered\segmentations\good'
 # Make a simpler version of run model that doesn't need config
 
 # Make a simpler version of extract shorelines that doesn't need config
@@ -77,84 +77,3 @@
 
 # Now run the segmentation classifier on the output directory
 classifier.filter_segmentations(output_directory,threshold=0.50)
-
-# Extract shorelines from directory
-satellite = 'PS' # planet
-meta_dir = os.path.join(session_dir, satellite, 'meta')
-ms_dir = os.path.join(session_dir, satellite, 'ms')
-udm2_dir = os.path.join(session_dir, satellite, 'udm2')
-filepath =  [ms_dir,udm2_dir] # get the directories of the ms and udm2 files
-
-pixel_size = 3.0 # meters For PlanetScope
-
-# get the minimum beach area in number of pixels depending on the satellite
-settings["min_length_sl"] 
-
-# whatever file is currently being processed
-filename =''
-
-# get date
-date = filename[:19]
-
-# get filepath of ms and udm2 of this particular filenmae
-fn = [os.path.join(ms_dir, filename), os.path.join(udm2_dir, filename)]
-
-
-# basically this function should do what preprocess_single does
-
-# filepaths to .tif files
-fn_ms = fn[0]
-fn_mask = fn[1]
-
-im_ms, georef = raster_utils.read_ms_planet(fn_ms)
-cloud_mask = raster_utils.read_cloud_mask_planet(fn_mask)
-im_nodata = raster_utils.read_nodata_mask_planet(fn_mask)
-combined_mask = np.logical_or(cloud_mask, im_nodata)
-
-# Note may need to add code to add 0 pixels to the no data mask later
-
-# Only do this is apply_cloud_mask is True
-cloud_mask = np.logical_or(cloud_mask, im_nodata)
-
-# Here is where we would get rid of the image if cloud cover or no data was above a certain threshold
-
-# this is the next step in the process find the shorelines
-    # ref_shoreline_buffer = SDS_shoreline.create_shoreline_buffer(
-    #     cloud_mask.shape, georef, image_epsg, pixel_size, settings
-    # )
-    # # read the model outputs from the npz file for this image
-    # npz_file = find_matching_npz(filename, os.path.join(session_path, "good"))
-    # if npz_file is None:
-    #     npz_file = find_matching_npz(filename, session_path)
-    # if npz_file is None:
-    #     logger.warning(f"npz file not found for {filename}")
-    #     return None
-
-    # # get the labels for water and land
-    # land_mask = load_merged_image_labels(npz_file, class_indices=class_indices)
-    # all_labels = load_image_labels(npz_file)
-
-    # min_beach_area = settings["min_beach_area"]
-    # land_mask = remove_small_objects_and_binarize(land_mask, min_beach_area)
-
-    # # get the shoreline from the image
-    # shoreline = find_shoreline(
-    #     fn,
-    #     image_epsg,
-    #     settings,
-    #     np.logical_xor(cloud_mask, im_nodata),
-    #     cloud_mask,
-    #     im_nodata,
-    #     georef,
-    #     land_mask,
-    #     ref_shoreline_buffer,
-    # )
-
-# zoo_model_instance.run_model_and_extract_shorelines(
-#             model_setting["sample_direc"],
-#             session_name=model_session_name,
-#             shoreline_path=shoreline_path,
-#             transects_path=transects_path,
-#             shoreline_extraction_area_path = shoreline_extraction_area_path,
-#             coregistered=True,
-#         )