image cv2

main.py

@@ -49,11 +49,13 @@ def get_masks():
         if image_file.filename == '':
             return jsonify({"error": "No image file provided"}), 400
 
-        raw_image = Image.open(image_file).convert("RGB")
-        # Convert the PIL Image to a NumPy array
-        image_array = np.array(raw_image)
-        # Since OpenCV expects BGR, convert RGB to BGR
-        image = image_array[:, :, ::-1]
+        # Read image file using OpenCV-style approach (similar to cv2.imread)
+        # Convert the image file to a NumPy array using OpenCV
+        file_bytes = np.fromstring(image_file.read(), np.uint8)
+        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+
+        # Convert BGR to RGB using OpenCV (similar to cv2.cvtColor)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
         if image is None:
             raise ValueError("Image not found or unable to read.")
@@ -73,20 +75,20 @@ def get_masks():
                    segmentation[10, -10] or segmentation[-10, -10])
             return val
 
-        # masks = [mask for mask in masks if not is_background(mask['segmentation'])]
+        masks = [mask for mask in masks if not is_background(mask['segmentation'])]
 
-        # for i in range(0, len(masks) - 1)[::-1]:
-        #     large_mask = masks[i]['segmentation']
-        #     for j in range(i+1, len(masks)):
-        #         not_small_mask = np.logical_not(masks[j]['segmentation'])
-        #         masks[i]['segmentation'] = np.logical_and(large_mask, not_small_mask)
-        #         masks[i]['area'] = masks[i]['segmentation'].sum()
-        #         large_mask = masks[i]['segmentation']
+        for i in range(0, len(masks) - 1)[::-1]:
+            large_mask = masks[i]['segmentation']
+            for j in range(i+1, len(masks)):
+                not_small_mask = np.logical_not(masks[j]['segmentation'])
+                masks[i]['segmentation'] = np.logical_and(large_mask, not_small_mask)
+                masks[i]['area'] = masks[i]['segmentation'].sum()
+                large_mask = masks[i]['segmentation']
 
-        # def sum_under_threshold(segmentation, threshold):
-        #     return segmentation.sum() / segmentation.size < 0.0015
+        def sum_under_threshold(segmentation, threshold):
+            return segmentation.sum() / segmentation.size < 0.0015
 
-        # masks = [mask for mask in masks if not sum_under_threshold(mask['segmentation'], 100)]
+        masks = [mask for mask in masks if not sum_under_threshold(mask['segmentation'], 100)]
         masks = sorted(masks, key=(lambda x: x['area']), reverse=True)
 
         # Create a zip file in memory