Mercurial > repos > imgteam > 2d_filter_segmentation_by_features

comparison 2d_filter_segmentation_by_features.py @ 1:6ad1d3cfdea1 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_filter_segmentation_by_features/ commit 2286a6c9da88596349ed9d967c51541409c0a7bf
author imgteam
date Mon, 13 Nov 2023 22:10:21 +0000
parents e576b73a2e2f
children 9d47aabda459
comparison
equal deleted inserted replaced
0:e576b73a2e2f 1:6ad1d3cfdea1
1 import argparse 1 import argparse
2 import sys 2 import sys
3 import skimage.io 3
4 import pandas as pd
5 import skimage.io
4 import skimage.util 6 import skimage.util
5 import pandas as pd 7
6 8
7 if __name__ == "__main__": 9 if __name__ == "__main__":
8 parser = argparse.ArgumentParser(description='Filter segmentation by features') 10 parser = argparse.ArgumentParser(description='Filter segmentation by features')
9 parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file') 11 parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file')
10 parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)') 12 parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)')
13 args = parser.parse_args() 15 args = parser.parse_args()
14 16
15 img_in = skimage.io.imread(args.input_file.name) 17 img_in = skimage.io.imread(args.input_file.name)
16 features = pd.read_csv(args.feature_file, delimiter="\t") 18 features = pd.read_csv(args.feature_file, delimiter="\t")
17 rules = pd.read_csv(args.rule_file, delimiter="\t") 19 rules = pd.read_csv(args.rule_file, delimiter="\t")
18 20
19 cols = [a for a in rules.columns if not 'Unnamed' in a] 21 cols = [a for a in rules.columns if 'Unnamed' not in a]
20 for a_c in cols: 22 for a_c in cols:
21 a_min = rules[rules.ix[:, 0] == 'min'][a_c] 23 a_min = rules[rules.ix[:, 0] == 'min'][a_c]
22 a_max = rules[rules.ix[:, 0] == 'max'][a_c] 24 a_max = rules[rules.ix[:, 0] == 'max'][a_c]
23 for a_l in features.label: 25 for a_l in features.label:
24 a_val = float(features[features['label'] == a_l][a_c]) 26 a_val = float(features[features['label'] == a_l][a_c])