fix rabbit consumer

preprocessing.py

@@ -28,6 +28,7 @@ transform = A.Compose([
     A.HueSaturationValue(p=0.3, hue_shift_limit=8, sat_shift_limit=8, val_shift_limit=8)
 ], bbox_params=A.BboxParams(format='yolo'))
 
+
 def correct_bboxes(labels):
     margin = 0.0005
     min_size = 0.01
@@ -40,11 +41,11 @@ def correct_bboxes(labels):
 
         # calc how much bboxes are outside borders ( +small margin ).
         # value should be negative. If it's positive, then put 0, as no correction
-        w_diff = min( (1 - margin) - (x + half_width), (x - half_width) - margin, 0 )
+        w_diff = min((1 - margin) - (x + half_width), (x - half_width) - margin, 0)
         w = bboxes[2] + 2*w_diff
         if w < min_size:
             continue
-        h_diff = min( (1 - margin) - (y + half_height), ((y - half_height) - margin), 0)
+        h_diff = min((1 - margin) - (y + half_height), ((y - half_height) - margin), 0)
         h = bboxes[3] + 2 * h_diff
         if h < min_size:
             continue
@@ -125,10 +126,11 @@ def preprocess_annotations():
     with concurrent.futures.ThreadPoolExecutor() as executor:
         executor.map(process_image_file, images)
 
-def process_image_file(image_file): # this function will be executed in thread
+
+def process_image_file(image_file):
     try:
         image_path = os.path.join(data_images_dir, image_file.name)
-        labels_path = os.path.join(data_labels_dir, f'{Path(image_path).stem}.txt')
+        labels_path = os.path.join(data_labels_dir, f'{Path(str(image_path)).stem}.txt')
         image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
 
         img_ann = ImageLabel(