--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/run-segmetrics.py	Fri Oct 07 22:05:59 2022 +0000
@@ -0,0 +1,133 @@
+"""
+Copyright 2022 Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University.
+
+Distributed under the MIT license.
+See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+
+"""
+
+import argparse
+import csv
+import itertools
+import pathlib
+import tempfile
+import zipfile
+
+import numpy as np
+import segmetrics as sm
+import skimage.io
+
+
+measures = [
+    ('dice', 'Dice', sm.regional.Dice()),
+    ('seg', 'SEG', sm.regional.ISBIScore()),
+    ('jc', 'Jaccard coefficient', sm.regional.JaccardSimilarityIndex()),
+    ('ji', 'Jaccard index', sm.regional.JaccardIndex()),
+    ('ri', 'Rand index', sm.regional.RandIndex()),
+    ('ari', 'Adjusted Rand index', sm.regional.AdjustedRandIndex()),
+    ('hsd_sym', 'HSD (sym)', sm.boundary.Hausdorff('sym')),
+    ('hsd_e2a', 'HSD (e2a)', sm.boundary.Hausdorff('e2a')),
+    ('hsd_a2e', 'HSD (a2e)', sm.boundary.Hausdorff('a2e')),
+    ('nsd', 'NSD', sm.boundary.NSD()),
+    ('o_hsd_sym', 'Ob. HSD (sym)', sm.boundary.ObjectBasedDistance(sm.boundary.Hausdorff('sym'))),
+    ('o_hsd_e2a', 'Ob. HSD (e2a)', sm.boundary.ObjectBasedDistance(sm.boundary.Hausdorff('e2a'))),
+    ('o_hsd_a2e', 'Ob. HSD (a2e)', sm.boundary.ObjectBasedDistance(sm.boundary.Hausdorff('a2e'))),
+    ('o_nsd', 'Ob. NSD', sm.boundary.ObjectBasedDistance(sm.boundary.NSD())),
+    ('fs', 'Split', sm.detection.FalseSplit()),
+    ('fm', 'Merge', sm.detection.FalseMerge()),
+    ('fp', 'Spurious', sm.detection.FalsePositive()),
+    ('fn', 'Missing', sm.detection.FalseNegative()),
+]
+
+
+def process_batch(study, gt_filelist, seg_filelist, namelist, gt_is_unique, seg_is_unique):
+    for gt_filename, seg_filename, name in zip(gt_filelist, seg_filelist, namelist):
+        img_ref = skimage.io.imread(gt_filename)
+        img_seg = skimage.io.imread(seg_filename)
+        study.set_expected(img_ref, unique=gt_is_unique)
+        study.process(img_seg, unique=seg_is_unique, chunk_id=name)
+
+
+def aggregate(measure, values):
+    fnc = np.sum if measure.ACCUMULATIVE else np.mean
+    return fnc(values)
+
+
+def is_zip_filepath(filepath):
+    return filepath.lower().endswith('.zip')
+
+
+def is_image_filepath(filepath):
+    suffixes = ['png', 'tif', 'tiff']
+    return any((filepath.lower().endswith(f'.{suffix}') for suffix in suffixes))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Image segmentation and object detection performance measures for 2-D image data')
+    parser.add_argument('input_seg', help='Path to the segmented image or image archive (ZIP)')
+    parser.add_argument('input_gt', help='Path to the ground truth image or image archive (ZIP)')
+    parser.add_argument('results', help='Path to the results file (CSV)')
+    parser.add_argument('-unzip', action='store_true')
+    parser.add_argument('-seg_unique', action='store_true')
+    parser.add_argument('-gt_unique', action='store_true')
+    for measure in measures:
+        parser.add_argument(f'-measure-{measure[0]}', action='store_true', help=f'Include {measure[1]}')
+
+    args = parser.parse_args()
+    study = sm.study.Study()
+
+    used_measures = []
+    for measure in measures:
+        if getattr(args, f'measure_{measure[0]}'):
+            used_measures.append(measure)
+            study.add_measure(measure[2], measure[1])
+
+    if args.unzip:
+        zipfile_seg = zipfile.ZipFile(args.input_seg)
+        zipfile_gt = zipfile.ZipFile(args.input_gt)
+        namelist = [filepath for filepath in zipfile_seg.namelist() if is_image_filepath(filepath) and filepath in zipfile_gt.namelist()]
+        print('namelist:', namelist)
+        with tempfile.TemporaryDirectory() as tmpdir:
+            basepath = pathlib.Path(tmpdir)
+            gt_path, seg_path = basepath / 'gt', basepath / 'seg'
+            zipfile_seg.extractall(str(seg_path))
+            zipfile_gt.extractall(str(gt_path))
+            gt_filelist, seg_filelist = list(), list()
+            for filepath in namelist:
+                seg_filelist.append(str(seg_path / filepath))
+                gt_filelist.append(str(gt_path / filepath))
+            process_batch(study, gt_filelist, seg_filelist, namelist, args.gt_unique, args.seg_unique)
+
+    else:
+        namelist = ['']
+        process_batch(study, [args.input_gt], [args.input_seg], namelist, args.gt_unique, args.seg_unique)
+
+    # define header
+    rows = [[''] + [measure[1] for measure in used_measures]]
+
+    # define rows
+    if len(namelist) > 1:
+        for chunk_id in namelist:
+            row = [chunk_id]
+            for measure in used_measures:
+                measure_name = measure[1]
+                measure = study.measures[measure_name]
+                chunks = study.results[measure_name]
+                row += [aggregate(measure, chunks[chunk_id])]
+            rows.append(row)
+
+    # define footer
+    rows.append([''])
+    for measure in used_measures:
+        measure_name = measure[1]
+        measure = study.measures[measure_name]
+        chunks = study.results[measure_name]
+        values = list(itertools.chain(*[chunks[chunk_id] for chunk_id in chunks]))
+        val = aggregate(measure, values)
+        rows[-1].append(val)
+
+    # write results
+    with open(args.results, 'w', newline='') as fout:
+        csv_writer = csv.writer(fout, delimiter=';', quotechar='|', quoting=csv.QUOTE_MINIMAL)
+        for row in rows:
+            csv_writer.writerow(row)