Merge branch 'classifier_model' into main_copy_2

3_zoo_workflow.py

@@ -27,6 +27,7 @@
             "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
@@ -39,7 +40,7 @@
 model_session_name = "sample_session_demo1"
 # 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 = "C:\development\doodleverse\coastseg\CoastSeg\data\ID_wra5_datetime03-04-24__03_43_01\jpg_files\preprocessed\RGB"
+sample_directory = r"C:\development\doodleverse\coastseg\CoastSeg\data\ID_wra5_datetime03-04-24__03_43_01\jpg_files\preprocessed\RGB"
 
 
 # 2. Save the settings to the model instance 

src/coastseg/classifier.py

@@ -14,6 +14,7 @@
 
 def filter_segmentations(
     session_path: str,
+    threshold: float = 0.40,
 ) -> str:
     """
     Sort model output files into "good" and "bad" folders based on the satellite name in the filename.
@@ -31,7 +32,7 @@ def filter_segmentations(
                     session_path,
                     session_path,
                     good_path=good_path,
-                    threshold=0.40)
+                    threshold=threshold)
     # if the good folder does not exist then this means the classifier could not find any png files at the session path and something went wrong
     if not os.path.exists(good_path):
         raise FileNotFoundError(f"No model output files found at {session_path}. Shoreline Filtering failed.")

src/coastseg/extracted_shoreline.py

@@ -1843,6 +1843,7 @@ def create_extracted_shorelines_from_session(
         new_session_path: str = None,
         output_directory: str = None, 
         shoreline_extraction_area : gpd.GeoDataFrame = None,  
+        apply_segmentation_filter: bool = True,
         **kwargs: dict,
     ) -> "Extracted_Shoreline":
         """
@@ -1948,8 +1949,11 @@ def create_extracted_shorelines_from_session(
             return self 
 
         # Filter the segmentations to only include the good segmentations, then update the metadata to only include the files with the good segmentations
-        good_directory = classifier.filter_segmentations(session_path)
-        metadata= common.filter_metadata_with_dates(metadata,good_directory,file_type="npz") 
+        good_directory = session_path
+        if apply_segmentation_filter:
+            good_directory = classifier.filter_segmentations(session_path)
+        # Filter the metadata to only include the files with segmentations that are in the good_directory
+        metadata= common.filter_metadata_with_dates(metadata,good_directory,file_type="npz")
 
         extracted_shorelines_dict = extract_shorelines_with_dask(
             session_path,

src/coastseg/filters.py

@@ -252,44 +252,5 @@ def apply_land_mask( directory_path: str) -> None:
 
     return directory_path
 
-
-
-def filter_model_outputs(
-    satname: str, files: list, dest_folder_good: str, dest_folder_bad: str
-) -> None:
-    """
-    Filter model outputs based on KMeans clustering of RMSE values and organize into 'good' and 'bad'.
-
-    Args:
-        label (str): Label used for categorizing.
-        files (list): List of file paths.
-        dest_folder_good (str): Destination folder for 'good' files.
-        dest_folder_bad (str): Destination folder for 'bad' files.
-    """
-
-    count_shapes = count_files_with_same_shape(files)
-    # get the most common shape
-    # modal_shape = mode(get_image_shapes(files))
-    for shape, count in count_shapes.items():
-        print(f"Shape: {shape} Count: {count}")
-        valid_files = get_files_with_shape(files,shape)
-        if len(valid_files) <3:
-            # if there are not enough valid files to perform the analysis, move all files to the good folder
-            handle_files_and_directories(
-            [], valid_files, dest_folder_bad, dest_folder_good
-            ) 
-        else:
-            times, time_var = get_time_vectors(valid_files)
-            da = xr.concat([load_xarray_data(f) for f in valid_files], dim=time_var)
-            timeav = da.mean(dim="time")
-
-            rmse, input_rmse = measure_rmse(da, times, timeav)
-            labels, scores = get_kmeans_clusters(input_rmse, rmse)
-            files_bad, files_good = get_good_bad_files(valid_files, labels, scores)
-            handle_files_and_directories(
-                files_bad, files_good, dest_folder_bad, dest_folder_good
-            ) 
-            # apply_land_mask(dest_folder_good)    
-
 
 

src/coastseg/zoo_model.py

@@ -639,6 +639,7 @@ def set_settings(self, **kwargs):
             "apply_cloud_mask": True,  # whether to apply cloud mask to images or not
             "drop_intersection_pts": False, # whether to drop intersection points not on the transect
             "coastseg_version": __version__,  # version of coastseg used to generate the data
+            "apply_segmentation_filter": True,  # whether to apply to sort the segmentations as good or bad
         }
         if kwargs:
             self.settings.update({key: value for key, value in kwargs.items()})
@@ -890,6 +891,7 @@ def extract_shorelines_with_unet(
             shoreline_extraction_area_gdf = shoreline_extraction_area_gdf,
         )
 
+
         # extract shorelines
         extracted_shorelines = extracted_shoreline.Extracted_Shoreline()
         extracted_shorelines = (
@@ -901,6 +903,7 @@ def extract_shorelines_with_unet(
                 session_path,
                 new_session_path,
                 shoreline_extraction_area=shoreline_extraction_area_gdf,
+                apply_segmentation_filter=settings.get("apply_segmentation_filter", True),
                 **kwargs,
             )
         )