fix

modules/image/text_recognition/ppocrv3_det_ch/module.py

@@ -34,14 +34,14 @@ def base64_to_cv2(b64str):
 
 
 @moduleinfo(
-    name="ppocrv3_det_ch",
+    name="ch_pp-ocrv3_det",
     version="1.0.0",
     summary=
     "The module aims to detect chinese text position in the image, which is based on differentiable_binarization algorithm.",
     author="paddle-dev",
     author_email="paddle-dev@baidu.com",
     type="cv/text_recognition")
-class ChineseTextDetectionDB(hub.Module):
+class ChPPOCRv3Det(hub.Module):
 
     def _initialize(self, enable_mkldnn=False):
         """
@@ -155,7 +155,8 @@ class ChineseTextDetectionDB(hub.Module):
                     use_gpu=False,
                     output_dir='detection_result',
                     visualization=False,
-                    box_thresh=0.5):
+                    box_thresh=0.5,
+                    det_db_unclip_ratio=1.5):
         """
         Get the text box in the predicted images.
         Args:
@@ -165,6 +166,7 @@ class ChineseTextDetectionDB(hub.Module):
             output_dir (str): The directory to store output images.
             visualization (bool): Whether to save image or not.
             box_thresh(float): the threshold of the detected text box's confidence
+            det_db_unclip_ratio(float): unclip ratio for post processing in DB detection.
         Returns:
             res (list): The result of text detection box and save path of images.
         """
@@ -195,7 +197,7 @@ class ChineseTextDetectionDB(hub.Module):
             'thresh': 0.3,
             'box_thresh': 0.6,
             'max_candidates': 1000,
-            'unclip_ratio': 1.5
+            'unclip_ratio': det_db_unclip_ratio
         })
 
         all_imgs = []
@@ -204,7 +206,6 @@ class ChineseTextDetectionDB(hub.Module):
         for original_image in predicted_data:
             ori_im = original_image.copy()
             im, ratio_list = preprocessor(original_image)
-            print('after preprocess int det, shape{}'.format(im.shape))
             res = {'save_path': ''}
             if im is None:
                 res['data'] = []
@@ -222,15 +223,10 @@ class ChineseTextDetectionDB(hub.Module):
                 outs_dict = {}
                 outs_dict['maps'] = outputs[0]
 
-                # data_out = self.output_tensors[0].copy_to_cpu()
-                print('Outputs[0] in det, shape: {}'.format(outputs[0].shape))
                 dt_boxes_list = postprocessor(outs_dict, [ratio_list])
                 dt_boxes = dt_boxes_list[0]
-                print('after postprocess int det, shape{}'.format(dt_boxes.shape))
                 boxes = self.filter_tag_det_res(dt_boxes_list[0], original_image.shape)
-                print('after fitler tag int det, shape{}'.format(boxes.shape))
                 res['data'] = boxes.astype(np.int).tolist()
-                print('boxes: {}'.format(boxes))
                 all_imgs.append(im)
                 all_ratios.append(ratio_list)
                 if visualization:
@@ -278,6 +274,7 @@ class ChineseTextDetectionDB(hub.Module):
         results = self.detect_text(paths=[args.input_path],
                                    use_gpu=args.use_gpu,
                                    output_dir=args.output_dir,
+                                   det_db_unclip_ratio=args.det_db_unclip_ratio,
                                    visualization=args.visualization)
         return results
 
@@ -297,6 +294,10 @@ class ChineseTextDetectionDB(hub.Module):
                                            type=ast.literal_eval,
                                            default=False,
                                            help="whether to save output as images.")
+        self.arg_config_group.add_argument('--det_db_unclip_ratio',
+                                           type=float,
+                                           default=1.5,
+                                           help="unclip ratio for post processing in DB detection.")
 
     def add_module_input_arg(self):
         """

modules/image/text_recognition/ppocrv3_det_ch/processor.py

@@ -25,7 +25,6 @@ class DBProcessTest(object):
         self.resize_type = 0
         if 'test_image_shape' in params:
             self.image_shape = params['test_image_shape']
-            # print(self.image_shape)
             self.resize_type = 1
         if 'max_side_len' in params:
             self.max_side_len = params['max_side_len']
@@ -54,15 +53,14 @@ class DBProcessTest(object):
         resize_h = int(h * ratio)
         resize_w = int(w * ratio)
 
-        resize_h = int(round(resize_h / 32) * 32)
-        resize_w = int(round(resize_w / 32) * 32)
+        resize_h = max(int(round(resize_h / 32) * 32), 32)
+        resize_w = max(int(round(resize_w / 32) * 32), 32)
 
         try:
             if int(resize_w) <= 0 or int(resize_h) <= 0:
                 return None, (None, None)
             img = cv2.resize(img, (int(resize_w), int(resize_h)))
         except:
-            print(img.shape, resize_w, resize_h)
             sys.exit(0)
         ratio_h = resize_h / float(h)
         ratio_w = resize_w / float(w)
@@ -93,13 +91,14 @@ class DBProcessTest(object):
         return im
 
     def __call__(self, im):
+        src_h, src_w, _ = im.shape
         if self.resize_type == 0:
             im, (ratio_h, ratio_w) = self.resize_image_type0(im)
         else:
             im, (ratio_h, ratio_w) = self.resize_image_type1(im)
         im = self.normalize(im)
         im = im[np.newaxis, :]
-        return [im, (ratio_h, ratio_w)]
+        return [im, (src_h, src_w, ratio_h, ratio_w)]
 
 
 class DBPostProcess(object):
@@ -228,7 +227,7 @@ class DBPostProcess(object):
         cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
-    def __call__(self, outs_dict, ratio_list):
+    def __call__(self, outs_dict, shape_list):
         pred = outs_dict['maps']
 
         pred = pred[:, 0, :, :]
@@ -236,10 +235,10 @@ class DBPostProcess(object):
 
         boxes_batch = []
         for batch_index in range(pred.shape[0]):
-            height, width = pred.shape[-2:]
+            src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
 
             mask = segmentation[batch_index]
-            tmp_boxes, tmp_scores = self.boxes_from_bitmap(pred[batch_index], mask, width, height)
+            tmp_boxes, tmp_scores = self.boxes_from_bitmap(pred[batch_index], mask, src_w, src_h)
 
             boxes_batch.append(tmp_boxes)
         return boxes_batch

modules/image/text_recognition/ppocrv3_rec_ch/character.py

@@ -59,6 +59,7 @@ class CharacterOps(object):
         self.character = dict_character
 
     def add_special_char(self, dict_character):
+        dict_character = ['blank'] + dict_character
         return dict_character
 
     def encode(self, text):
@@ -93,12 +94,6 @@ class CharacterOps(object):
                 selection[1:] = text_index[batch_idx][1:] != text_index[batch_idx][:-1]
             for ignored_token in ignored_tokens:
                 selection &= text_index[batch_idx] != ignored_token
-            # print(text_index)
-            # print(batch_idx)
-            # print(selection)
-            # for text_id in text_index[batch_idx][selection]:
-            #     print(text_id)
-            #     print(self.character[text_id])
             char_list = [self.character[text_id] for text_id in text_index[batch_idx][selection]]
             if text_prob is not None:
                 conf_list = text_prob[batch_idx][selection]

modules/image/text_recognition/ppocrv3_rec_ch/module.py

@@ -29,14 +29,14 @@ from paddlehub.module.module import serving
 
 
 @moduleinfo(
-    name="ppocrv3_rec_ch",
+    name="ch_pp-ocrv3",
     version="1.0.0",
     summary="The module can recognize the chinese texts in an image. Firstly, it will detect the text box positions \
         based on the differentiable_binarization_chn module. Then it classifies the text angle and recognizes the chinese texts. ",
     author="paddle-dev",
     author_email="paddle-dev@baidu.com",
     type="cv/text_recognition")
-class ChineseOCRDBCRNN(hub.Module):
+class ChPPOCRv3(hub.Module):
 
     def _initialize(self, text_detector_module=None, enable_mkldnn=False):
         """
@@ -51,7 +51,7 @@ class ChineseOCRDBCRNN(hub.Module):
             'use_space_char': True
         }
         self.char_ops = CharacterOps(char_ops_params)
-        self.rec_image_shape = [3, 32, 320]
+        self.rec_image_shape = [3, 48, 320]
         self._text_detector_module = text_detector_module
         self.font_file = os.path.join(self.directory, 'assets', 'simfang.ttf')
         self.enable_mkldnn = enable_mkldnn
@@ -109,7 +109,7 @@ class ChineseOCRDBCRNN(hub.Module):
         text detect module
         """
         if not self._text_detector_module:
-            self._text_detector_module = hub.Module(name='ppocrv3_det_ch',
+            self._text_detector_module = hub.Module(name='ch_pp-ocrv3_det',
                                                     enable_mkldnn=self.enable_mkldnn,
                                                     version='1.0.0')
         return self._text_detector_module
@@ -152,7 +152,7 @@ class ChineseOCRDBCRNN(hub.Module):
     def resize_norm_img_rec(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
         assert imgC == img.shape[2]
-        imgW = int((32 * max_wh_ratio))
+        imgW = int((imgH * max_wh_ratio))
         h, w = img.shape[:2]
         ratio = w / float(h)
         if math.ceil(imgH * ratio) > imgW:
@@ -199,7 +199,8 @@ class ChineseOCRDBCRNN(hub.Module):
                        visualization=False,
                        box_thresh=0.5,
                        text_thresh=0.5,
-                       angle_classification_thresh=0.9):
+                       angle_classification_thresh=0.9,
+                       det_db_unclip_ratio=1.5):
         """
         Get the chinese texts in the predicted images.
         Args:
@@ -212,7 +213,7 @@ class ChineseOCRDBCRNN(hub.Module):
             box_thresh(float): the threshold of the detected text box's confidence
             text_thresh(float): the threshold of the chinese text recognition confidence
             angle_classification_thresh(float): the threshold of the angle classification confidence
-
+            det_db_unclip_ratio(float): unclip ratio for post processing in DB detection.
         Returns:
             res (list): The result of chinese texts and save path of images.
         """
@@ -238,10 +239,10 @@ class ChineseOCRDBCRNN(hub.Module):
 
         detection_results = self.text_detector_module.detect_text(images=predicted_data,
                                                                   use_gpu=self.use_gpu,
-                                                                  box_thresh=box_thresh)
+                                                                  box_thresh=box_thresh,
+                                                                  det_db_unclip_ratio=det_db_unclip_ratio)
 
         boxes = [np.array(item['data']).astype(np.float32) for item in detection_results]
-        print("dt_boxes num : {}".format(len(boxes[0])))
         all_results = []
         for index, img_boxes in enumerate(boxes):
             original_image = predicted_data[index].copy()
@@ -255,7 +256,6 @@ class ChineseOCRDBCRNN(hub.Module):
                     tmp_box = copy.deepcopy(boxes[num_box])
                     img_crop = self.get_rotate_crop_image(original_image, tmp_box)
                     img_crop_list.append(img_crop)
-                    print('img_crop shape {}'.format(img_crop.shape))
                 img_crop_list, angle_list = self._classify_text(img_crop_list,
                                                                 angle_classification_thresh=angle_classification_thresh)
                 rec_results = self._recognize_text(img_crop_list)
@@ -371,7 +371,8 @@ class ChineseOCRDBCRNN(hub.Module):
         for beg_img_no in range(0, img_num, batch_num):
             end_img_no = min(img_num, beg_img_no + batch_num)
             norm_img_batch = []
-            max_wh_ratio = 0
+            imgC, imgH, imgW = self.rec_image_shape
+            max_wh_ratio = imgW / imgH
             for ino in range(beg_img_no, end_img_no):
                 h, w = img_list[indices[ino]].shape[0:2]
                 wh_ratio = w * 1.0 / h
@@ -400,10 +401,8 @@ class ChineseOCRDBCRNN(hub.Module):
                 preds = preds[-1]
             if isinstance(preds, paddle.Tensor):
                 preds = preds.numpy()
-            print('preds.shape: {}', preds.shape)
             preds_idx = preds.argmax(axis=2)
             preds_prob = preds.max(axis=2)
-            # print('preds_idx: {} \n preds_prob: {}'.format(preds_idx, preds_prob) )
             rec_result = self.char_ops.decode(preds_idx, preds_prob, is_remove_duplicate=True)
             for rno in range(len(rec_result)):
                 rec_res[indices[beg_img_no + rno]] = rec_result[rno]
@@ -431,6 +430,7 @@ class ChineseOCRDBCRNN(hub.Module):
         results = self.recognize_text(paths=[args.input_path],
                                       use_gpu=args.use_gpu,
                                       output_dir=args.output_dir,
+                                      det_db_unclip_ratio=args.det_db_unclip_ratio,
                                       visualization=args.visualization)
         return results
 
@@ -450,6 +450,10 @@ class ChineseOCRDBCRNN(hub.Module):
                                            type=ast.literal_eval,
                                            default=False,
                                            help="whether to save output as images.")
+        self.arg_config_group.add_argument('--det_db_unclip_ratio',
+                                           type=float,
+                                           default=1.5,
+                                           help="unclip ratio for post processing in DB detection.")
 
     def add_module_input_arg(self):
         """