diff 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

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/2d_filter_segmentation_by_features.py	Mon Jul 22 05:00:03 2019 -0400
@@ -0,0 +1,29 @@
+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")