Don't break overall processing on a bad image (#10216)

paddleocr.py

@@ -512,20 +512,20 @@ def __init__(self, **kwargs):
 
     def ocr(self, img, det=True, rec=True, cls=True):
         """
-        ocr with paddleocr
+        OCR with PaddleOCR
         args：
-            img: img for ocr, support ndarray, img_path and list or ndarray
-            det: use text detection or not. If false, only rec will be exec. Default is True
-            rec: use text recognition or not. If false, only det will be exec. Default is True
-            cls: use angle classifier or not. Default is True. If true, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False.
+            img: img for OCR, support ndarray, img_path and list or ndarray
+            det: use text detection or not. If False, only rec will be exec. Default is True
+            rec: use text recognition or not. If False, only det will be exec. Default is True
+            cls: use angle classifier or not. Default is True. If True, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False.
         """
         assert isinstance(img, (np.ndarray, list, str, bytes))
         if isinstance(img, list) and det == True:
             logger.error('When input a list of images, det must be false')
             exit(0)
         if cls == True and self.use_angle_cls == False:
             logger.warning(
-                'Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process'
+                'Since the angle classifier is not initialized, it will not be used during the forward process'
             )
 
         img = check_img(img)
@@ -541,6 +541,9 @@ def ocr(self, img, det=True, rec=True, cls=True):
             ocr_res = []
             for idx, img in enumerate(imgs):
                 dt_boxes, rec_res, _ = self.__call__(img, cls)
+                if not dt_boxes and not rec_res:
+                    ocr_res.append(None)
+                    continue
                 tmp_res = [[box.tolist(), res]
                            for box, res in zip(dt_boxes, rec_res)]
                 ocr_res.append(tmp_res)
@@ -549,6 +552,9 @@ def ocr(self, img, det=True, rec=True, cls=True):
             ocr_res = []
             for idx, img in enumerate(imgs):
                 dt_boxes, elapse = self.text_detector(img)
+                if not dt_boxes:
+                    ocr_res.append(None)
+                    continue
                 tmp_res = [box.tolist() for box in dt_boxes]
                 ocr_res.append(tmp_res)
             return ocr_res

tools/infer/predict_system.py

@@ -65,15 +65,25 @@ def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res):
         self.crop_image_res_index += bbox_num
 
     def __call__(self, img, cls=True):
-        time_dict = {'det': 0, 'rec': 0, 'csl': 0, 'all': 0}
+        time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
+
+        if img is None:
+            logger.debug("no valid image provided")
+            return None, None, time_dict
+
         start = time.time()
         ori_im = img.copy()
         dt_boxes, elapse = self.text_detector(img)
         time_dict['det'] = elapse
-        logger.debug("dt_boxes num : {}, elapse : {}".format(
-            len(dt_boxes), elapse))
+
         if dt_boxes is None:
-            return None, None
+            logger.debug("no dt_boxes found, elapsed : {}".format(elapse))
+            end = time.time()
+            time_dict['all'] = end - start
+            return None, None, time_dict
+        else:
+            logger.debug("dt_boxes num : {}, elapsed : {}".format(
+                len(dt_boxes), elapse))
         img_crop_list = []
 
         dt_boxes = sorted_boxes(dt_boxes)
@@ -89,12 +99,12 @@ def __call__(self, img, cls=True):
             img_crop_list, angle_list, elapse = self.text_classifier(
                 img_crop_list)
             time_dict['cls'] = elapse
-            logger.debug("cls num  : {}, elapse : {}".format(
+            logger.debug("cls num  : {}, elapsed : {}".format(
                 len(img_crop_list), elapse))
 
         rec_res, elapse = self.text_recognizer(img_crop_list)
         time_dict['rec'] = elapse
-        logger.debug("rec_res num  : {}, elapse : {}".format(
+        logger.debug("rec_res num  : {}, elapsed : {}".format(
             len(rec_res), elapse))
         if self.args.save_crop_res:
             self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,