Add pre-commit and add TODO

coco_2_labelImg.py

@@ -10,83 +10,85 @@
 from tqdm import tqdm
 
 
-class COCO2labelImg():
+class COCO2labelImg:
     def __init__(self, data_dir: str = None):
         # coco dir
         self.data_dir = Path(data_dir)
         self.verify_exists(self.data_dir)
 
-        anno_dir = self.data_dir / 'annotations'
+        anno_dir = self.data_dir / "annotations"
         self.verify_exists(anno_dir)
 
-        self.train_json = anno_dir / 'instances_train2017.json'
-        self.val_json = anno_dir / 'instances_val2017.json'
+        self.train_json = anno_dir / "instances_train2017.json"
+        self.val_json = anno_dir / "instances_val2017.json"
         self.verify_exists(self.train_json)
         self.verify_exists(self.val_json)
 
-        self.train2017_dir = self.data_dir / 'train2017'
-        self.val2017_dir = self.data_dir / 'val2017'
+        self.train2017_dir = self.data_dir / "train2017"
+        self.val2017_dir = self.data_dir / "val2017"
         self.verify_exists(self.train2017_dir)
         self.verify_exists(self.val2017_dir)
 
         # save dir
-        self.save_dir = self.data_dir.parent / 'COCO_labelImg_format'
+        self.save_dir = self.data_dir.parent / "COCO_labelImg_format"
         self.mkdir(self.save_dir)
 
-        self.save_train_dir = self.save_dir / 'train'
+        self.save_train_dir = self.save_dir / "train"
         self.mkdir(self.save_train_dir)
 
-        self.save_val_dir = self.save_dir / 'val'
+        self.save_val_dir = self.save_dir / "val"
         self.mkdir(self.save_val_dir)
 
-    def __call__(self, ):
+    def __call__(
+        self,
+    ):
         train_list = [self.train_json, self.save_train_dir, self.train2017_dir]
         self.convert(train_list)
 
         val_list = [self.val_json, self.save_val_dir, self.val2017_dir]
         self.convert(val_list)
 
-        print(f'Successfully convert, detail in {self.save_dir}')
+        print(f"Successfully convert, detail in {self.save_dir}")
 
     def convert(self, info_list: list):
         json_path, save_dir, img_dir = info_list
 
         data = self.read_json(str(json_path))
-        self.gen_classes_txt(save_dir, data.get('categories'))
+        self.gen_classes_txt(save_dir, data.get("categories"))
 
-        id_img_dict = {v['id']: v for v in data.get('images')}
-        all_annotaions = data.get('annotations')
+        id_img_dict = {v["id"]: v for v in data.get("images")}
+        all_annotaions = data.get("annotations")
         for one_anno in tqdm(all_annotaions):
-            image_info = id_img_dict.get(one_anno['image_id'])
-            img_name = image_info.get('file_name')
-            img_height = image_info.get('height')
-            img_width = image_info.get('width')
+            image_info = id_img_dict.get(one_anno["image_id"])
+            img_name = image_info.get("file_name")
+            img_height = image_info.get("height")
+            img_width = image_info.get("width")
 
-            seg_info = one_anno.get('segmentation')
+            seg_info = one_anno.get("segmentation")
             if seg_info:
                 bbox = self.get_bbox(seg_info)
                 xywh = self.xyxy_to_xywh(bbox, img_width, img_height)
-                category_id = int(one_anno.get('category_id')) - 1
-                xywh_str = ' '.join([str(v) for v in xywh])
-                label_str = f'{category_id} {xywh_str}'
+                category_id = int(one_anno.get("category_id")) - 1
+                xywh_str = " ".join([str(v) for v in xywh])
+                label_str = f"{category_id} {xywh_str}"
 
                 # 写入标注的txt文件
-                txt_full_path = save_dir / f'{Path(img_name).stem}.txt'
-                self.write_txt(txt_full_path, label_str, mode='a')
+                txt_full_path = save_dir / f"{Path(img_name).stem}.txt"
+                self.write_txt(txt_full_path, label_str, mode="a")
 
             # 复制图像到转换后目录
             img_full_path = img_dir / img_name
             shutil.copy2(img_full_path, save_dir)
 
     @staticmethod
     def read_json(json_path):
-        with open(json_path, 'r', encoding='utf-8') as f:
+        with open(json_path, "r", encoding="utf-8") as f:
             data = json.load(f)
         return data
 
     def gen_classes_txt(self, save_dir, categories_dict):
-        class_info = [value['name'] for value in categories_dict]
-        self.write_txt(save_dir / 'classes.txt', class_info)
+        class_info = [value["name"] for value in categories_dict]
+        self.write_txt(save_dir / "classes.txt", class_info)
 
     def get_bbox(self, seg_info):
         seg_info = np.array(seg_info[0]).reshape(4, 2)
@@ -96,20 +98,20 @@ def get_bbox(self, seg_info):
         return bbox
 
     @staticmethod
-    def write_txt(save_path: str, content: list, mode='w'):
+    def write_txt(save_path: str, content: list, mode="w"):
         if not isinstance(save_path, str):
             save_path = str(save_path)
 
         if isinstance(content, str):
             content = [content]
-        with open(save_path, mode, encoding='utf-8') as f:
+        with open(save_path, mode, encoding="utf-8") as f:
             for value in content:
-                f.write(f'{value}\n')
+                f.write(f"{value}\n")
 
     @staticmethod
-    def xyxy_to_xywh(xyxy: list,
-                     img_width: int,
-                     img_height: int) -> tuple([float, float, float, float]):
+    def xyxy_to_xywh(
+        xyxy: list, img_width: int, img_height: int
+    ) -> tuple([float, float, float, float]):
         """
         xyxy: (list), [x1, y1, x2, y2]
         """
@@ -127,18 +129,21 @@ def xyxy_to_xywh(xyxy: list,
     def verify_exists(file_path):
         file_path = Path(file_path)
         if not file_path.exists():
-            raise FileNotFoundError(f'The {file_path} is not exists!!!')
+            raise FileNotFoundError(f"The {file_path} is not exists!!!")
 
     @staticmethod
     def mkdir(dir_path):
         Path(dir_path).mkdir(parents=True, exist_ok=True)
 
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser('Datasets convert from COCO to labelImg')
-    parser.add_argument('--data_dir', type=str,
-                        default='dataset/YOLOV5_COCO_format',
-                        help='Dataset root path')
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Datasets convert from COCO to labelImg")
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        default="dataset/YOLOV5_COCO_format",
+        help="Dataset root path",
+    )
     args = parser.parse_args()
 
     converter = COCO2labelImg(args.data_dir)

coco_visual.py

@@ -10,68 +10,80 @@
 
 
 def visualization_bbox(num_image, json_path, img_path):
-    with open(json_path, 'r', encoding='utf-8') as annos:
+    with open(json_path, "r", encoding="utf-8") as annos:
         annotation_json = json.load(annos)
-    print('The annotation_json num_key is:', len(annotation_json))
-    print('The annotation_json key is:', annotation_json.keys())
-    print('The annotation_json num_images is:', len(annotation_json['images']))
+    print("The annotation_json num_key is:", len(annotation_json))
+    print("The annotation_json key is:", annotation_json.keys())
+    print("The annotation_json num_images is:", len(annotation_json["images"]))
 
-    categories = annotation_json['categories']
-    categories_dict = {c['id']: c['name'] for c in categories}
+    categories = annotation_json["categories"]
+    categories_dict = {c["id"]: c["name"] for c in categories}
     class_nums = len(categories_dict.keys())
-    color = [(random.randint(0, 255), random.randint(0, 255),
-              random.randint(0, 255)) for _ in range(class_nums)]
+    color = [
+        (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+        for _ in range(class_nums)
+    ]
 
-    image_name = annotation_json['images'][num_image - 1]['file_name']
-    img_id = annotation_json['images'][num_image - 1]['id']
+    image_name = annotation_json["images"][num_image - 1]["file_name"]
+    img_id = annotation_json["images"][num_image - 1]["id"]
     image_path = os.path.join(img_path, str(image_name).zfill(5))
     image = cv2.imread(image_path, 1)
 
-    annotations = annotation_json['annotations']
+    annotations = annotation_json["annotations"]
     num_bbox = 0
     for anno in annotations:
-        if anno['image_id'] == img_id:
+        if anno["image_id"] == img_id:
             num_bbox = num_bbox + 1
 
-            class_id = anno['category_id']
+            class_id = anno["category_id"]
             class_name = categories_dict[class_id]
-            class_color = color[class_id-1]
+            class_color = color[class_id - 1]
 
-            x, y, w, h = list(map(int, anno['bbox']))
-            cv2.rectangle(image, (int(x), int(y)),
-                          (int(x + w), int(y + h)),
-                          class_color, 2)
+            x, y, w, h = list(map(int, anno["bbox"]))
+            cv2.rectangle(
+                image, (int(x), int(y)), (int(x + w), int(y + h)), class_color, 2
+            )
 
             font_size = 0.7
-            txt_size = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX,
-                                       font_size, 1)[0]
-            cv2.rectangle(image, (x, y + 1),
-                          (x + txt_size[0] + 10, y - int(2 * txt_size[1])),
-                          class_color, -1)
-            cv2.putText(image, class_name, (x + 5, y - 5),
-                        cv2.FONT_HERSHEY_SIMPLEX,
-                        font_size, (255, 255, 255), 1)
+            txt_size = cv2.getTextSize(
+                class_name, cv2.FONT_HERSHEY_SIMPLEX, font_size, 1
+            )[0]
+            cv2.rectangle(
+                image,
+                (x, y + 1),
+                (x + txt_size[0] + 10, y - int(2 * txt_size[1])),
+                class_color,
+                -1,
+            )
+            cv2.putText(
+                image,
+                class_name,
+                (x + 5, y - 5),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                font_size,
+                (255, 255, 255),
+                1,
+            )
 
-    print('The unm_bbox of the display image is:', num_bbox)
+    print("The unm_bbox of the display image is:", num_bbox)
 
     cur_os = platform.system()
-    if cur_os == 'Windows':
+    if cur_os == "Windows":
         cv2.namedWindow(image_name, 0)
         cv2.resizeWindow(image_name, 1000, 1000)
         cv2.imshow(image_name, image)
         cv2.waitKey(0)
     else:
-        save_path = f'visul_{num_image}.jpg'
+        save_path = f"visul_{num_image}.jpg"
         cv2.imwrite(save_path, image)
-        print(f'The {save_path} has been saved the current director.')
+        print(f"The {save_path} has been saved the current director.")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--vis_num', type=int, default=1,
-                        help="visual which one")
-    parser.add_argument('--json_path', type=str, required=True)
-    parser.add_argument('--img_dir', type=str, required=True)
+    parser.add_argument("--vis_num", type=int, default=1, help="visual which one")
+    parser.add_argument("--json_path", type=str, required=True)
+    parser.add_argument("--img_dir", type=str, required=True)
     args = parser.parse_args()
 
     visualization_bbox(args.vis_num, args.json_path, args.img_dir)