Mercurial > repos > imgteam > 2d_filter_segmentation_by_features

comparison 2d_filter_segmentation_by_features.py @ 3:9d47aabda459 draft default tip

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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_filter_segmentation_by_features/ commit c86a1b93cb7732f7331a981d13465653cc1a2790
author imgteam
date Wed, 24 Apr 2024 08:11:47 +0000
parents 6ad1d3cfdea1
children
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2:8f76184ca03f 3:9d47aabda459
1 import argparse 1 import argparse
2 import sys 2 import sys
3 3
4 import giatools.io
4 import pandas as pd 5 import pandas as pd
5 import skimage.io 6 import skimage.io
6 import skimage.util 7 import skimage.util
7 8
8 9
12 parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)') 13 parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)')
13 parser.add_argument('feature_file', type=argparse.FileType('r'), default=sys.stdin, help='feature file (cols: label, f1, f2)') 14 parser.add_argument('feature_file', type=argparse.FileType('r'), default=sys.stdin, help='feature file (cols: label, f1, f2)')
14 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)') 15 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)')
15 args = parser.parse_args() 16 args = parser.parse_args()
16 17
17 img_in = skimage.io.imread(args.input_file.name) 18 img_in = giatools.io.imread(args.input_file.name)
18 features = pd.read_csv(args.feature_file, delimiter="\t") 19 features = pd.read_csv(args.feature_file, delimiter="\t")
19 rules = pd.read_csv(args.rule_file, delimiter="\t") 20 rules = pd.read_csv(args.rule_file, delimiter="\t")
20 21
21 cols = [a for a in rules.columns if 'Unnamed' not in a] 22 cols = [a for a in rules.columns if 'Unnamed' not in a]
22 for a_c in cols: 23 for a_c in cols:
23 a_min = rules[rules.ix[:, 0] == 'min'][a_c] 24 a_min = rules[rules.iloc[:, 0] == 'min'][a_c]
24 a_max = rules[rules.ix[:, 0] == 'max'][a_c] 25 a_max = rules[rules.iloc[:, 0] == 'max'][a_c]
25 for a_l in features.label: 26 for a_l in features.label:
26 a_val = float(features[features['label'] == a_l][a_c]) 27 a_val = float(features[features['label'] == a_l][a_c])
27 if a_val < float(a_min) or a_val > float(a_max): 28 if a_val < float(a_min) or a_val > float(a_max):
28 img_in[img_in == int(a_l)] = 0 29 img_in[img_in == int(a_l)] = 0
29 30