added getpolygons function for Image segmentation with labelbox json (Sourcery refactored)

labelbox_json2yolo.py

@@ -9,6 +9,35 @@
 
 from utils import make_dirs
 
+def getPolygons(url):
+    # Download the image from the URL
+    with urllib.request.urlopen(url) as url_response:
+        img_array = np.asarray(bytearray(url_response.read()), dtype=np.uint8)
+    image = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+
+    h, w = image.shape[:2]
+    line_width = int((h + w) * 0.5 * 0.0025)
+    #convert to grayscale
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    contours, _ = cv2.findContours(gray_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
+
+    contours_approx = []
+    polygons = []
+    #Get polygons
+    for contour in contours:
+        epsilon = 0.001 * cv2.arcLength(contour, True)
+        contour_approx = cv2.approxPolyDP(contour, epsilon, True)
+
+        contours_approx.append(contour_approx)
+        polygon = (contour_approx.flatten().reshape(-1, 2) / np.array([w, h])).tolist()
+        polygons.append(polygon)
+    cv2.drawContours(image, contours_approx, -1, 128, line_width)
+    #format the data correctly
+    result = [[f"{x},{y}" for x, y in sublist] for sublist in polygons]
+    return [
+        [float(x) for pair in sublist for x in pair.split(',')]
+        for sublist in result
+    ]
 
 def convert(file, zip=True):
     # Convert Labelbox JSON labels to YOLO labels
@@ -26,6 +55,36 @@ def convert(file, zip=True):
         image_path = save_dir / 'images' / img['External ID']
         im.save(image_path, quality=95, subsampling=0)
 
+
+        ######################################
+        #for Image Segmentation 
+        ######################################
+
+        # for img in tqdm(data, desc=f'Converting {file}'):
+        # im_path = img['Labeled Data']
+        # im = Image.open(requests.get(im_path, stream=True).raw if im_path.startswith('http') else im_path)  # open
+        # width, height = im.size  # image size
+        # label_path =   save_dir / 'labels' / Path(img['External ID']).with_suffix('.txt').name
+        # print(label_path)
+        # image_path =   save_dir / 'images' / img['External ID']
+        # print(image_path)
+        # im.save(image_path, quality=95, subsampling=0)
+
+        # for label in img['Label']['objects']:
+        #     
+        #     polygon = getPolygons(label['instanceURI'])
+        #     # class
+        #     cls = label['value']  # class name
+        #     if cls not in names:
+        #         names.append(cls)
+
+        #     line = names.index(cls), *polygon[0]  # YOLO format (class_index, polygon)
+        #     with open(label_path, 'a') as f:
+        #         f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+        ######################################
+        #for Object detection 
+        ######################################
         for label in img['Label']['objects']:
             # box
             top, left, h, w = label['bbox'].values()  # top, left, height, width