added eval script for mitochondria based on tif files (for now)

scripts/cooper/run_mitochondria_segmentation.py

@@ -8,7 +8,7 @@
 
 def run_mitochondria_segmentation(args):
     if args.model is None:
-        model_path = get_model_path("mitochondria2")
+        model_path = get_model_path("mitochondria")
     else:
         model_path = args.model
 

scripts/cooper/training/evaluate_mitochondria.py

@@ -0,0 +1,167 @@
+import argparse
+from glob import glob
+import os
+
+# import h5py
+# from elf.io import open_file
+from tifffile import imread
+import pandas as pd
+
+from elf.evaluation import matching, symmetric_best_dice_score
+
+from elf.io.files import open_file
+
+
+def evaluate(labels, vesicles):
+    assert labels.shape == vesicles.shape
+    stats = matching(vesicles, labels)
+    sbd = symmetric_best_dice_score(vesicles, labels)
+    return [stats["f1"], stats["precision"], stats["recall"], sbd]
+
+
+def summarize_eval(results):
+    summary = (
+        results[["dataset", "f1-score", "precision", "recall", "SBD score"]]
+        .groupby("dataset")
+        .mean()
+        .reset_index("dataset")
+    )
+    total = results[["f1-score", "precision", "recall", "SBD score"]].mean().values.tolist()
+    summary.iloc[-1] = ["all"] + total
+    table = summary.to_markdown(index=False)
+    print(table)
+
+
+def evaluate_file(labels_path, seg_path, model_name, segment_key, anno_key, mask_key, output_folder):
+    print(f"Evaluate labels \n{labels_path} and segmentations \n{seg_path}")
+    labels, seg = None, None
+    if ".tif" in labels_path:
+        labels = imread(labels_path)
+    elif anno_key is not None:
+        labels = open_file(labels_path, "r")[anno_key][:]
+    if ".tif" in seg_path:
+        seg = imread(seg_path)
+    elif segment_key is not None:
+        seg = open_file(seg_path, "r")[segment_key][:]
+    if labels is None or seg is None:
+        print("Could not find label file for", seg_path)
+        print("Skipping...")
+
+    # evaluate the match of ground truth and vesicles
+    scores = evaluate(labels, seg)
+
+    # store results
+    result_folder = output_folder
+    os.makedirs(result_folder, exist_ok=True)
+    result_path = os.path.join(result_folder, f"evaluation_{model_name}.csv")
+    print("Evaluation results are saved to:", result_path)
+
+    if os.path.exists(result_path):
+        results = pd.read_csv(result_path)
+    else:
+        results = None
+    ds_name = os.path.basename(os.path.dirname(labels_path))
+    tomo = os.path.basename(labels_path)
+    res = pd.DataFrame(
+        [[ds_name, tomo] + scores], columns=["dataset", "tomogram", "f1-score", "precision", "recall", "SBD score"]
+    )
+    if results is None:
+        results = res
+    else:
+        results = pd.concat([results, res])
+    results.to_csv(result_path, index=False)
+
+    # print results
+    summarize_eval(results)
+
+
+def evaluate_folder(labels_path, segmentation_path, model_name, segment_key, anno_key,
+                    anno_ext, segment_ext, mask_key, output_folder):
+    print(f"Evaluating folder {segmentation_path}")
+    print(f"Using labels stored in {labels_path}")
+    if labels_path is not None:
+        label_paths = get_file_paths(labels_path, ext=anno_ext)
+    else:
+        label_paths = _get_default_label_paths()
+    seg_paths = get_file_paths(segmentation_path, ext=segment_ext)
+    if label_paths is None or seg_paths is None:
+        print("Could not find label file or segmentation file")
+        return
+
+    for seg_path in seg_paths:
+        label_path = find_label_file(seg_path, label_paths)
+        if label_path is not None:
+            evaluate_file(label_path, seg_path, model_name, segment_key, anno_key, mask_key, output_folder)
+        else:
+            print("Could not find label file for", seg_path)
+            print("Skipping...")
+
+
+def get_file_paths(path, ext=".h5", reverse=False):
+    if ext in path:
+        return [path]
+    else:
+        paths = sorted(glob(os.path.join(path, "**", f"*{ext}"), recursive=True), reverse=reverse)
+        return paths
+
+
+def find_label_file(given_path: str, label_paths: list) -> str:
+    """
+    Find the corresponding label file for a given raw file.
+
+    Args:
+        given_path (str): The path we want to find label file to.
+        label_paths (list): A list of label file paths.
+
+    Returns:
+        str: The path to the matching label file, or None if no match is found.
+    """
+    raw_base = os.path.splitext(os.path.basename(given_path))[0]  # Remove extension
+    raw_base = raw_base.replace("prediction", "").replace("pred", "")
+    raw_base = raw_base.replace("segmentation", "").replace("seg", "")
+    raw_base = raw_base.replace("mito-v3_sd18_bt015_with__", "")
+    raw_base = raw_base.rstrip("_")
+    print("raw_base", raw_base)
+    for label_path in label_paths:
+        label_base = os.path.splitext(os.path.basename(label_path))[0]  # Remove extension
+        if raw_base.strip().lower() in label_base.strip().lower():  # Ensure raw name is contained in label name
+            return label_path
+
+    return None  # No match found
+
+
+def _get_default_label_paths():
+    return ['/scratch-grete/projects/nim00007/data/mitochondria/wichmann/refined_mitos/M2_eb10_model.h5',
+            '/scratch-grete/projects/nim00007/data/mitochondria/wichmann/refined_mitos/WT21_eb3_model2.h5',
+            '/scratch-grete/projects/nim00007/data/mitochondria/wichmann/refined_mitos/M10_eb9_model.h5',
+            '/scratch-grete/projects/nim00007/data/mitochondria/wichmann/refined_mitos/KO9_eb4_model.h5',
+            '/scratch-grete/projects/nim00007/data/mitochondria/wichmann/refined_mitos/M7_eb11_model.h5',
+            '/scratch-grete/projects/nim00007/data/mitochondria/cooper/fidi_down_s2/36859_J1_66K_TS_CA3_PS_25_rec_2Kb1dawbp_crop_downscaled.h5'
+            ]
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-sp", "--segmentation_path", required=True)
+    parser.add_argument("-gp", "--groundtruth_path", default=None)
+    parser.add_argument("-n", "--model_name", required=True)
+    parser.add_argument("-sk", "--segmentation_key", default=None)
+    parser.add_argument("-gk", "--groundtruth_key", default=None)
+    parser.add_argument("-ae", "--annotation_extension", default=None)
+    parser.add_argument("-se", "--segmentation_extension", default=None)
+    parser.add_argument("-m", "--mask_key", default=None)
+    parser.add_argument("-o", "--output_folder", required=True)
+    args = parser.parse_args()
+
+    if os.path.isdir(args.segmentation_path):
+        evaluate_folder(args.groundtruth_path, args.segmentation_path, args.model_name, args.segmentation_key,
+                        args.groundtruth_key, args.annotation_extension, args.segmentation_extension,
+                        args.mask_key, args.output_folder)
+    else:
+        evaluate_file(args.groundtruth_path, args.segmentation_path, args.model_name, args.segmentation_key,
+                      args.groundtruth_key,
+                      args.mask_key, args.output_folder)
+
+
+if __name__ == "__main__":
+    main()

scripts/cooper/training/evaluation.py

@@ -39,7 +39,7 @@ def evaluate_file(labels_path, vesicles_path, model_name, segment_key, anno_key,
 
     with h5py.File(vesicles_path) as seg_file:
         segmentation = seg_file["vesicles"]
-        vesicles = segmentation[segment_key][:] 
+        vesicles = segmentation[segment_key][:]
 
     if mask_key is not None:
         gt[mask == 0] = 0