Update SGN evaluation

Author: constantinpape

flamingo_tools/validation.py

@@ -35,12 +35,10 @@ def parse_annotation_path(annotation_path):
     return cochlea, slice_id
 
 
-# TODO enable table component filtering with MoBIE table
-# NOTE: the main component is always #1
 def fetch_data_for_evaluation(
     annotation_path: str,
     cache_path: Optional[str] = None,
-    seg_name: str = "SGN",
+    seg_name: str = "SGN_v2",
     z_extent: int = 0,
     components_for_postprocessing: Optional[List[int]] = None,
 ) -> Tuple[np.ndarray, pd.DataFrame]:

scripts/sgn_stain_predictions/check_segmentation.py

@@ -6,7 +6,8 @@
 
 
 ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops"
-SAVE_ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops/segmentations"
+SAVE_ROOT1 = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops/segmentations"  # noqa
+SAVE_ROOT2 = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops/segmentations_v2"  # noqa
 
 
 def main():
@@ -16,18 +17,22 @@ def main():
             return
         print("Visualizing", ff)
         rel_path = os.path.relpath(ff, ROOT)
-        seg_path = os.path.join(SAVE_ROOT, rel_path)
+        seg_path1 = os.path.join(SAVE_ROOT1, rel_path)
+        seg_path2 = os.path.join(SAVE_ROOT2, rel_path)
 
+        print("Load raw")
         image = imageio.imread(ff)
-        if os.path.exists(seg_path):
-            seg = imageio.imread(seg_path)
-        else:
-            seg = None
+        print("Load segmentation 1")
+        seg1 = imageio.imread(seg_path1) if os.path.exists(seg_path1) else None
+        print("Load segmentation 2")
+        seg2 = imageio.imread(seg_path2) if os.path.exists(seg_path2) else None
 
         v = napari.Viewer()
         v.add_image(image)
-        if seg is not None:
-            v.add_labels(seg)
+        if seg1 is not None:
+            v.add_labels(seg1, name="original")
+        if seg2 is not None:
+            v.add_labels(seg2, name="adapted")
         napari.run()
 
 

scripts/sgn_stain_predictions/measure_intensities.py

@@ -6,12 +6,12 @@
 
 
 ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops"
-SAVE_ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops/segmentations"
+SAVE_ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops/segmentations_v2"  # noqa
 
 
 def measure_intensities(ff):
     rel_path = os.path.relpath(ff, ROOT)
-    out_path = os.path.join("./measurements", rel_path.replace(".tif", ".xlsx"))
+    out_path = os.path.join("./measurements_v2", rel_path.replace(".tif", ".xlsx"))
     if os.path.exists(out_path):
         return
 

scripts/sgn_stain_predictions/run_prediction.py

@@ -7,9 +7,10 @@
 from flamingo_tools.segmentation import run_unet_prediction
 
 ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops"
-MODEL_PATH = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/SGN/cochlea_distance_unet_SGN_March2025Model"  # noqa
+# MODEL_PATH = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/SGN/cochlea_distance_unet_SGN_March2025Model"  # noqa
+MODEL_PATH = "/mnt/vast-nhr/home/pape41/u12086/Work/my_projects/flamingo-tools/scripts/training/sgn_model.pt"  # noqa
 
-SAVE_ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops/segmentations"
+SAVE_ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/LS_sampleprepcomparison_crops/segmentations_v2"  # noqa
 
 
 def check_data():

scripts/validation/analyze.py renamed to scripts/validation/SGNs/analyze.py

@@ -1,8 +1,14 @@
+import argparse
 import pandas as pd
 
 # TODO more logic to separate by annotator etc.
 # For now this is just a simple script for global eval
-table = pd.read_csv("./results.csv")
+
+parser = argparse.ArgumentParser()
+parser.add_argument("file")
+args = parser.parse_args()
+
+table = pd.read_csv(args.file)
 print("Table:")
 print(table)
 

scripts/validation/check_annotations.py renamed to scripts/validation/SGNs/compare_annotations.py

@@ -4,8 +4,7 @@
 import napari
 import pandas as pd
 
-# ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/F1Validation"
-ROOT = "annotation_data"
+ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/F1Validation"
 TEST_ANNOTATION = os.path.join(ROOT, "AnnotationsEK/MAMD58L_PV_z771_base_full_annotationsEK.csv")
 
 

scripts/validation/run_evaluation.py renamed to scripts/validation/SGNs/run_evaluation.py

@@ -6,8 +6,8 @@
     fetch_data_for_evaluation, parse_annotation_path, compute_scores_for_annotated_slice
 )
 
-ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/F1Validation"
-ANNOTATION_FOLDERS = ["AnnotationsEK", "AnnotationsAMD"]
+ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/F1ValidationSGNs"
+ANNOTATION_FOLDERS = ["AnnotationsEK", "AnnotationsAMD", "AnnotationLR"]
 
 
 def run_evaluation(root, annotation_folders, result_file, cache_folder):
@@ -27,12 +27,13 @@ def run_evaluation(root, annotation_folders, result_file, cache_folder):
         annotator = folder[len("Annotations"):]
         annotations = sorted(glob(os.path.join(root, folder, "*.csv")))
         for annotation_path in annotations:
+            print(annotation_path)
             cochlea, slice_id = parse_annotation_path(annotation_path)
             # We don't have this cochlea in MoBIE yet
             if cochlea == "M_LR_000169_R":
                 continue
 
-            print("Run evaluation for", annotator, cochlea, slice_id)
+            print("Run evaluation for", annotator, cochlea, "z=", slice_id)
             segmentation, annotations = fetch_data_for_evaluation(
                 annotation_path, components_for_postprocessing=[1],
                 cache_path=None if cache_folder is None else os.path.join(cache_folder, f"{cochlea}_{slice_id}.tif")

scripts/validation/SGNs/visualize_validation.py

@@ -0,0 +1,79 @@
+import argparse
+import os
+from glob import glob
+
+import napari
+import tifffile
+
+from flamingo_tools.validation import (
+    fetch_data_for_evaluation, compute_matches_for_annotated_slice, for_visualization, parse_annotation_path
+)
+
+ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/AnnotatedImageCrops/F1ValidationSGNs"
+
+
+def _match_image_path(annotation_path):
+    all_files = glob(os.path.join(ROOT, "*.tif"))
+    prefix = os.path.basename(annotation_path).split("_")[:-3]
+    prefix = "_".join(prefix)
+    matches = [path for path in all_files if os.path.basename(path).startswith(prefix)]
+    # if len(matches) != 1:
+    #     breakpoint()
+    assert len(matches) == 1, f"{prefix}: {len(matches)}"
+    return matches[0]
+
+
+def visualize_anotation(annotation_path, cache_folder):
+    print("Checking", annotation_path)
+    cochlea, slice_id = parse_annotation_path(annotation_path)
+    cache_path = None if cache_folder is None else os.path.join(cache_folder, f"{cochlea}_{slice_id}.tif")
+
+    image_path = _match_image_path(annotation_path)
+    return
+
+    segmentation, annotations = fetch_data_for_evaluation(
+        annotation_path, cache_path=cache_path, components_for_postprocessing=[1],
+    )
+
+    image = tifffile.memmap(image_path)
+    if segmentation.ndim == 2:
+        image = image[image.shape[0] // 2]
+    assert image.shape == segmentation.shape, f"{image.shape}, {segmentation.shape}"
+
+    matches = compute_matches_for_annotated_slice(segmentation, annotations, matching_tolerance=5)
+    vis_segmentation, vis_points, seg_props, point_props = for_visualization(segmentation, annotations, matches)
+
+    # tps, fns = matches["tp_annotations"], matches["fn"]
+    # print("True positive annotations:")
+    # print(tps)
+    # print("False negative annotations:")
+    # print(fns)
+
+    v = napari.Viewer()
+    v.add_image(image)
+    v.add_labels(vis_segmentation, **seg_props)
+    v.add_points(vis_points, **point_props)
+    v.add_labels(segmentation, visible=False)
+    v.add_points(annotations, visible=False)
+    v.title = os.path.relpath(annotation_path, ROOT)
+    napari.run()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--annotations", nargs="+")
+    parser.add_argument("--cache_folder")
+    args = parser.parse_args()
+    cache_folder = args.cache_folder
+
+    if args.annotations is None:
+        annotation_paths = sorted(glob(os.path.join(ROOT, "**", "*.csv"), recursive=True))
+    else:
+        annotation_paths = args.annotations
+
+    for annotation_path in annotation_paths:
+        visualize_anotation(annotation_path, cache_folder)
+
+
+if __name__ == "__main__":
+    main()