--- a/run-segmetrics.py	Sat Oct 08 21:54:40 2022 +0000
+++ b/run-segmetrics.py	Tue Jun 20 21:40:31 2023 +0000
@@ -1,5 +1,5 @@
 """
-Copyright 2022 Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University.
+Copyright 2022-2023 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
@@ -7,127 +7,50 @@
 """
 
 import argparse
-import csv
-import itertools
 import pathlib
+import subprocess
 import tempfile
 import zipfile
 
-import numpy as np
-import segmetrics as sm
-import skimage.io
+import pandas as pd
 
 
-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))
+def process_batch(seg_dir, seg_file, gt_file, tsv_output_file, recursive, gt_unique, seg_unique, measures):
+    with tempfile.NamedTemporaryFile() as csv_output_file:
+        cmd = ['python', '-m', 'segmetrics.cli', str(seg_dir), str(seg_file), str(gt_file), str(csv_output_file.name), '--semicolon']
+        if recursive:
+            cmd.append('--recursive')
+        if gt_unique:
+            cmd.append('--gt-unique')
+        if seg_unique:
+            cmd.append('--seg-unique')
+        cmd += measures
+        subprocess.run(cmd, check=True)
+        df = pd.read_csv(csv_output_file.name, sep=';')
+        df.to_csv(str(tsv_output_file), sep='\t', index=False)
 
 
 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('results', help='Path to the results file (TSV)')
     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]}')
-
+    parser.add_argument('measures', nargs='+', type=str, help='list of performance measures')
     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)
+            process_batch(seg_dir=seg_path, seg_file=rf'^{seg_path}/(.+\.(?:png|PNG|tif|TIF|tiff|TIFF))$', gt_file=gt_path / r'\1', tsv_output_file=args.results, recursive=True, gt_unique=args.gt_unique, seg_unique=args.seg_unique, measures=args.measures)
 
     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='\t', quotechar='"', quoting=csv.QUOTE_MINIMAL)
-        for row in rows:
-            csv_writer.writerow(row)
+        seg_path = pathlib.Path(args.input_seg)
+        process_batch(seg_dir=seg_path.parent, seg_file=seg_path, gt_file=args.input_gt, tsv_output_file=args.results, recursive=False, gt_unique=args.gt_unique, seg_unique=args.seg_unique, measures=args.measures)