Created json generator for annotations of Fluo-N2DH-SIM+ dataset

training_dataset/BF-C2DL-MuSC/gen_json.py

@@ -5,6 +5,7 @@ import glob
 import json
 import cv2
 
+data_path = 'data'
 video_list = ['01', '02']
 
 snippets = dict()
@@ -20,7 +21,7 @@ for video in video_list:
     frames = sorted([''.join(filter(str.isdigit, frame)) for frame in os.listdir(os.path.join('data', video)) if frame.endswith('.tif')])
 
     # Load tracking file and get information about objects and frames they appear in
-    with open(os.path.join('data', video + '_GT', 'TRA', 'man_track.txt'), 'r') as track_file:
+    with open(os.path.join(data_path, video + '_GT', 'TRA', 'man_track.txt'), 'r') as track_file:
         for line in track_file:
             parts = line.split(' ')
             obj_id = parts[0]
@@ -30,9 +31,9 @@ for video in video_list:
 
     for frame in frames:
         # Load segmented image, gold truth if available, silver truth otherwise
-        seg = glob.glob(os.path.join('data', video + '_GT', 'SEG', '*' + frame + '*'))
+        seg = glob.glob(os.path.join(data_path, video + '_GT', 'SEG', '*' + frame + '*'))
         if len(seg) == 0:
-            seg = glob.glob(os.path.join('data', video + '_ST', 'SEG', '*' + frame + '*'))
+            seg = glob.glob(os.path.join(data_path, video + '_ST', 'SEG', '*' + frame + '*'))
         seg_image = cv2.imread(seg[0], cv2.IMREAD_ANYDEPTH)
 
         for obj_id in range(1, seg_image.max() + 1):

training_dataset/Fluo-N2DH-SIM+/gen_json.py

+# Author: Kristyna Janku, Snippets creation and structure inspired by gen_json.py for VID dataset
+
+import os
+import glob
+import json
+import cv2
+
+data_path = 'data'
+video_list = ['01', '02']
+
+snippets = dict()
+num_videos = 0
+
+for video in video_list:
+    num_videos += 1
+    id_set = []
+    id_frames = {}
+    snippets[video] = dict()
+
+    # Load numbers of all frames from filenames
+    frames = sorted([''.join(filter(str.isdigit, frame)) for frame in os.listdir(os.path.join(data_path, video)) if frame.endswith('.tif')])
+
+    # Load tracking file and get information about objects and frames they appear in
+    with open(os.path.join(data_path, video + '_GT', 'TRA', 'man_track.txt'), 'r') as track_file:
+        for line in track_file:
+            parts = line.split(' ')
+            obj_id = parts[0]
+            id_set.append(int(obj_id))
+            id_frames[int(obj_id)] = list(range(int(parts[1]), (int(parts[2]) + 1)))
+            snippets[video][obj_id] = dict()
+
+    for frame in frames:
+        # Load segmented image, gold truth if available, silver truth otherwise
+        seg = glob.glob(os.path.join(data_path, video + '_GT', 'SEG', '*' + frame + '*'))
+        seg_image = cv2.imread(seg[0], cv2.IMREAD_ANYDEPTH)
+
+        for obj_id in range(1, seg_image.max() + 1):
+            if obj_id in id_set and int(frame) in id_frames[obj_id]:
+                # Mask only pixels with current object, multiply by 1 to change True/False to 1/0
+                mask = (seg_image == obj_id) * 1
+                # Convert to 8-bit image to enable further transformations
+                mask = cv2.convertScaleAbs(mask)
+                # Checking if tracked object really is in segmented image
+                if mask.max() == 0:
+                    print('Video: ' + str(num_videos) + ' Frame: ' + frame + ' Missing object id in seg: ' + str(obj_id))
+                else:
+                    # Get bounding box of object from contour
+                    cnt = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
+                    x, y, w, h = cv2.boundingRect(cnt[0])
+                    snippets[video][str(obj_id)][frame] = [x, y, x+w, y+h]
+
+    print('Video number: {:d} Snippets count: {:d}'.format(num_videos, len(snippets[video])))
+
+# Divide into training and validation data
+train = {k: v for (k, v) in snippets.items() if '02' in k}
+val = {k: v for (k, v) in snippets.items() if '01' in k}
+
+# Save to json files
+json.dump(train, open('train.json', 'w'), indent=4, sort_keys=True)
+json.dump(val, open('val.json', 'w'), indent=4, sort_keys=True)
+print('All videos done!')