added cristae eval script

Author: lufre1

scripts/cooper/training/evaluate_cristae.py

@@ -0,0 +1,148 @@
+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
+
+
+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
+    labels = open_file(labels_path)
+    seg = open_file(seg_path)
+    if segment_key is not None:
+        seg = seg[segment_key][:]
+    if anno_key is not None:
+        labels = labels[anno_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, mask_key, output_folder, ext=".tif"):
+    print(f"Evaluating folder {segmentation_path}")
+    print(f"Using labels stored in {labels_path}")
+
+    label_paths = get_file_paths(labels_path, ext=ext)
+    seg_paths = get_file_paths(segmentation_path, ext=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
+
+    for label_path in label_paths:
+        label_base = os.path.splitext(os.path.basename(label_path))[0]  # Remove extension
+        if raw_base in label_base:  # Ensure raw name is contained in label name
+            return label_path
+
+    return None  # No match found
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-sp", "--segmentation_path", required=True,
+                        default="/scratch-grete/projects/nim00007/data/mitochondria/cooper/cristae_test_segmentations/")
+    parser.add_argument("-gp", "--groundtruth_path", required=True,
+                        default="/scratch-grete/projects/nim00007/data/mitochondria/cooper/cristae_test_segmentations/")
+    parser.add_argument("-n", "--model_name", required=True)
+    parser.add_argument("-sk", "--segmentation_key", default=None, default="labels/new_cristae_seg")
+    parser.add_argument("-gk", "--groundtruth_key", default=None, default="labels/cristae")
+    parser.add_argument("-m", "--mask_key", default=None)
+    parser.add_argument(
+        "-o", "--output_folder", required=True,
+        default="/scratch-grete/projects/nim00007/data/mitochondria/cooper/cristae_test_segmentations/eval"
+        )
+    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.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()