Mercurial > repos > imgteam > 2d_filter_segmentation_by_features
view 2d_filter_segmentation_by_features.py @ 0:e576b73a2e2f draft
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/2d_filter_segmentation_by_features/ commit b2acc1845a25828181597fe5b6982fe116a7796d
author | imgteam |
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date | Mon, 22 Jul 2019 05:00:03 -0400 |
parents | |
children | 6ad1d3cfdea1 |
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import argparse import sys import skimage.io import skimage.util import pandas as pd if __name__ == "__main__": parser = argparse.ArgumentParser(description='Filter segmentation by features') parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file') parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)') parser.add_argument('feature_file', type=argparse.FileType('r'), default=sys.stdin, help='feature file (cols: label, f1, f2)') parser.add_argument('rule_file', type=argparse.FileType('r'), default=sys.stdin, help='file with rules per feature (cols: ,f1,2, rows: feature_name, min, max)') args = parser.parse_args() img_in = skimage.io.imread(args.input_file.name) features = pd.read_csv(args.feature_file, delimiter="\t") rules = pd.read_csv(args.rule_file, delimiter="\t") cols = [a for a in rules.columns if not 'Unnamed' in a] for a_c in cols: a_min = rules[rules.ix[:, 0] == 'min'][a_c] a_max = rules[rules.ix[:, 0] == 'max'][a_c] for a_l in features.label: a_val = float(features[features['label'] == a_l][a_c]) if a_val < float(a_min) or a_val > float(a_max): img_in[img_in == int(a_l)] = 0 res = skimage.util.img_as_uint(img_in) skimage.io.imsave(args.out_file.name, res, plugin="tifffile")