# HG changeset patch # User imgteam # Date 1665180359 0 # Node ID 0729657d9e4e635e8343997d6d70f2a8e0003998 planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/segmetrics/ commit 3b911df716a7b42115c6cd773f666bc90a2bb10f diff -r 000000000000 -r 0729657d9e4e run-segmetrics.py --- /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) diff -r 000000000000 -r 0729657d9e4e segmetrics.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/segmetrics.xml Fri Oct 07 22:05:59 2022 +0000 @@ -0,0 +1,96 @@ + + image segmentation and object detection performance measures + + segmetrics + scikit-image + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + Image segmentation and object detection performance measures for 2-D image data. + + + 10.1093/bioinformatics/btu080 + 10.1109/ISBI.2009.5193098 + 10.1109/ICIP.2003.1246871 + + diff -r 000000000000 -r 0729657d9e4e test-data/input1.png Binary file test-data/input1.png has changed diff -r 000000000000 -r 0729657d9e4e test-data/input1.zip Binary file test-data/input1.zip has changed diff -r 000000000000 -r 0729657d9e4e test-data/input2.png Binary file test-data/input2.png has changed diff -r 000000000000 -r 0729657d9e4e test-data/input2.zip Binary file test-data/input2.zip has changed diff -r 000000000000 -r 0729657d9e4e test-data/results1.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/results1.csv Fri Oct 07 22:05:59 2022 +0000 @@ -0,0 +1,2 @@ +;Dice;SEG;Jaccard index;Ob. HSD (sym);Ob. NSD;Split;Merge;Spurious;Missing +;0.5473684210526316;0.291005291005291;1.6238374627624792;141.68949443548658;0.8175405481022531;0;1;2;0 diff -r 000000000000 -r 0729657d9e4e test-data/results2.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/results2.csv Fri Oct 07 22:05:59 2022 +0000 @@ -0,0 +1,4 @@ +;Dice;SEG;Jaccard index;Ob. HSD (sym);Ob. NSD;Split;Merge;Spurious;Missing +directory/img1.png;0.5473684210526316;0.14285714285714285;1.6238374627624792;142.42099468121046;0.7765050343914003;1;0;0;2 +directory/img2.png;0.5473684210526316;0.291005291005291;1.6238374627624792;141.68949443548658;0.8175405481022531;0;1;2;0 +;0.5473684210526316;0.2169312169312169;1.6238374627624792;142.0552445583485;0.7970227912468267;1;1;2;2