move filter_segmentations to classifier.py

src/coastseg/classifier.py

@@ -12,6 +12,31 @@
 # Some of these functions were originally written by Mark Lundine and have been modified for this project.
 
 
+def filter_segmentations(
+    session_path: str,
+) -> str:
+    """
+    Sort model output files into "good" and "bad" folders based on the satellite name in the filename.
+    Applies the land mask to the model output files in the "good" folder.
+
+    Args:
+        session_path (str): The path to the session directory containing the model output files.
+
+    Returns:
+        str: The path to the "good" folder containing the sorted model output files.
+    """
+    segmentation_classifier = get_segmentation_classifier()
+    good_path = os.path.join(session_path, "good")
+    csv_path,good_path,bad_path = run_inference_segmentation_classifier(segmentation_classifier,
+                    session_path,
+                    session_path,
+                    good_path=good_path,
+                    threshold=0.40)
+    # 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.")
+    return good_path
+
 def move_matching_files(input_image_path, search_string, file_exts, target_dir):
     """
     Move files matching the given search string and file extensions to the target directory.

src/coastseg/extracted_shoreline.py

@@ -1486,30 +1486,6 @@ def extract_shorelines_with_dask(
     return combine_satellite_data(shoreline_dict)
 
 
-def filter_segmentations(
-    session_path: str,
-) -> str:
-    """
-    Sort model output files into "good" and "bad" folders based on the satellite name in the filename.
-    Applies the land mask to the model output files in the "good" folder.
-
-    Args:
-        session_path (str): The path to the session directory containing the model output files.
-
-    Returns:
-        str: The path to the "good" folder containing the sorted model output files.
-    """
-    segmentation_classifier = classifier.get_segmentation_classifier()
-    good_path = os.path.join(session_path, "good")
-    csv_path,good_path,bad_path = classifier.run_inference_segmentation_classifier(segmentation_classifier,
-                    session_path,
-                    session_path,
-                    good_path=good_path,
-                    threshold=0.40)
-    # 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.")
-    return good_path
 
 
 def get_min_shoreline_length(satname: str, default_min_length_sl: float) -> int:
@@ -1972,7 +1948,7 @@ 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 = filter_segmentations(session_path)
+        good_directory = classifier.filter_segmentations(session_path)
         metadata= common.filter_metadata_with_dates(metadata,good_directory,file_type="npz") 
 
         extracted_shorelines_dict = extract_shorelines_with_dask(