Merge pull request #2497 from JetHong/rel/fix_data_input_format

Rel/fix data input format

configs/e2e/e2e_r50_vd_pg.yml

@@ -62,20 +62,21 @@ PostProcess:
   mode: fast   # fast or slow two ways
 Metric:
   name: E2EMetric
-  gt_mat_dir:    # the dir of gt_mat
+  gt_mat_dir:  ./train_data/total_text/gt  # the dir of gt_mat
   character_dict_path: ppocr/utils/ic15_dict.txt
   main_indicator: f_score_e2e
 
 Train:
   dataset:
     name: PGDataSet
-    label_file_list: [.././train_data/total_text/train/]
+    data_dir: ./train_data/total_text/train
+    label_file_list: [./train_data/total_text/train/]
     ratio_list: [1.0]
-    data_format: icdar #two data format: icdar/textnet
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
+      - E2ELabelEncode:
       - PGProcessTrain:
           batch_size: 14  # same as loader: batch_size_per_card
           min_crop_size: 24
@@ -92,13 +93,12 @@ Train:
 Eval:
   dataset:
     name: PGDataSet
-    data_dir: ./train_data/
+    data_dir: ./train_data/total_text/test
     label_file_list: [./train_data/total_text/test/]
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
           channel_first: False
-      - E2ELabelEncode:
       - E2EResizeForTest:
           max_side_len: 768
       - NormalizeImage:
@@ -108,7 +108,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags', 'img_id']
+          keep_keys: [ 'image', 'shape', 'img_id']
   loader:
     shuffle: False
     drop_last: False

doc/doc_ch/pgnet.md

@@ -30,6 +30,7 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang
 测试集：Total-Text
 
 测试环境: NVIDIA Tesla V100-SXM2-16GB
+
 |PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|下载|
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 |Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|

ppocr/data/imaug/label_ops.py

@@ -187,29 +187,31 @@ class CTCLabelEncode(BaseRecLabelEncode):
         return dict_character
 
 
-class E2ELabelEncode(BaseRecLabelEncode):
-    def __init__(self,
-                 max_text_length,
-                 character_dict_path=None,
-                 character_type='EN',
-                 use_space_char=False,
-                 **kwargs):
-        super(E2ELabelEncode,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
-        self.pad_num = len(self.dict)  # the length to pad
+class E2ELabelEncode(object):
+    def __init__(self, **kwargs):
+        pass
 
     def __call__(self, data):
-        texts = data['strs']
-        temp_texts = []
-        for text in texts:
-            text = text.lower()
-            text = self.encode(text)
-            if text is None:
-                return None
-            text = text + [self.pad_num] * (self.max_text_len - len(text))
-            temp_texts.append(text)
-        data['strs'] = np.array(temp_texts)
+        import json
+        label = data['label']
+        label = json.loads(label)
+        nBox = len(label)
+        boxes, txts, txt_tags = [], [], []
+        for bno in range(0, nBox):
+            box = label[bno]['points']
+            txt = label[bno]['transcription']
+            boxes.append(box)
+            txts.append(txt)
+            if txt in ['*', '###']:
+                txt_tags.append(True)
+            else:
+                txt_tags.append(False)
+        boxes = np.array(boxes, dtype=np.float32)
+        txt_tags = np.array(txt_tags, dtype=np.bool)
+
+        data['polys'] = boxes
+        data['texts'] = txts
+        data['ignore_tags'] = txt_tags
         return data
 
 

ppocr/data/imaug/pg_process.py

@@ -88,7 +88,7 @@ class PGProcessTrain(object):
 
         return min_area_quad
 
-    def check_and_validate_polys(self, polys, tags, xxx_todo_changeme):
+    def check_and_validate_polys(self, polys, tags, im_size):
         """
         check so that the text poly is in the same direction,
         and also filter some invalid polygons
@@ -96,7 +96,7 @@ class PGProcessTrain(object):
         :param tags:
         :return:
         """
-        (h, w) = xxx_todo_changeme
+        (h, w) = im_size
         if polys.shape[0] == 0:
             return polys, np.array([]), np.array([])
         polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1)
@@ -750,8 +750,8 @@ class PGProcessTrain(object):
         input_size = 512
         im = data['image']
         text_polys = data['polys']
-        text_tags = data['tags']
-        text_strs = data['strs']
+        text_tags = data['ignore_tags']
+        text_strs = data['texts']
         h, w, _ = im.shape
         text_polys, text_tags, hv_tags = self.check_and_validate_polys(
             text_polys, text_tags, (h, w))

ppocr/data/pgnet_dataset.py

@@ -29,20 +29,20 @@ class PGDataSet(Dataset):
         dataset_config = config[mode]['dataset']
         loader_config = config[mode]['loader']
 
+        self.delimiter = dataset_config.get('delimiter', '\t')
         label_file_list = dataset_config.pop('label_file_list')
         data_source_num = len(label_file_list)
         ratio_list = dataset_config.get("ratio_list", [1.0])
         if isinstance(ratio_list, (float, int)):
             ratio_list = [float(ratio_list)] * int(data_source_num)
-        self.data_format = dataset_config.get('data_format', 'icdar')
         assert len(
             ratio_list
         ) == data_source_num, "The length of ratio_list should be the same as the file_list."
+        self.data_dir = dataset_config['data_dir']
         self.do_shuffle = loader_config['shuffle']
 
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
-        self.data_lines = self.get_image_info_list(label_file_list, ratio_list,
-                                                   self.data_format)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
         self.data_idx_order_list = list(range(len(self.data_lines)))
         if mode.lower() == "train":
             self.shuffle_data_random()
@@ -55,108 +55,40 @@ class PGDataSet(Dataset):
             random.shuffle(self.data_lines)
         return
 
-    def extract_polys(self, poly_txt_path):
-        """
-        Read text_polys, txt_tags, txts from give txt file.
-        """
-        text_polys, txt_tags, txts = [], [], []
-        with open(poly_txt_path) as f:
-            for line in f.readlines():
-                poly_str, txt = line.strip().split('\t')
-                poly = list(map(float, poly_str.split(',')))
-                text_polys.append(
-                    np.array(
-                        poly, dtype=np.float32).reshape(-1, 2))
-                txts.append(txt)
-                txt_tags.append(txt == '###')
-
-        return np.array(list(map(np.array, text_polys))), \
-               np.array(txt_tags, dtype=np.bool), txts
-
-    def extract_info_textnet(self, im_fn, img_dir=''):
-        """
-        Extract information from line in textnet format.
-        """
-        info_list = im_fn.split('\t')
-        img_path = ''
-        for ext in [
-                'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'JPG'
-        ]:
-            if os.path.exists(os.path.join(img_dir, info_list[0] + "." + ext)):
-                img_path = os.path.join(img_dir, info_list[0] + "." + ext)
-                break
-
-        if img_path == '':
-            print('Image {0} NOT found in {1}, and it will be ignored.'.format(
-                info_list[0], img_dir))
-
-        nBox = (len(info_list) - 1) // 9
-        wordBBs, txts, txt_tags = [], [], []
-        for n in range(0, nBox):
-            wordBB = list(map(float, info_list[n * 9 + 1:(n + 1) * 9]))
-            txt = info_list[(n + 1) * 9]
-            wordBBs.append([[wordBB[0], wordBB[1]], [wordBB[2], wordBB[3]],
-                            [wordBB[4], wordBB[5]], [wordBB[6], wordBB[7]]])
-            txts.append(txt)
-            if txt == '###':
-                txt_tags.append(True)
-            else:
-                txt_tags.append(False)
-        return img_path, np.array(wordBBs, dtype=np.float32), txt_tags, txts
-
-    def get_image_info_list(self, file_list, ratio_list, data_format='textnet'):
+    def get_image_info_list(self, file_list, ratio_list):
         if isinstance(file_list, str):
             file_list = [file_list]
         data_lines = []
-        for idx, data_source in enumerate(file_list):
-            image_files = []
-            if data_format == 'icdar':
-                image_files = [(data_source, x) for x in
-                               os.listdir(os.path.join(data_source, 'rgb'))
-                               if x.split('.')[-1] in [
-                                   'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif',
-                                   'tiff', 'gif', 'JPG'
-                               ]]
-            elif data_format == 'textnet':
-                with open(data_source) as f:
-                    image_files = [(data_source, x.strip())
-                                   for x in f.readlines()]
-            else:
-                print("Unrecognized data format...")
-                exit(-1)
-            random.seed(self.seed)
-            image_files = random.sample(
-                image_files, round(len(image_files) * ratio_list[idx]))
-            data_lines.extend(image_files)
+        for idx, file in enumerate(file_list):
+            with open(file, "rb") as f:
+                lines = f.readlines()
+                if self.mode == "train" or ratio_list[idx] < 1.0:
+                    random.seed(self.seed)
+                    lines = random.sample(lines,
+                                          round(len(lines) * ratio_list[idx]))
+                data_lines.extend(lines)
         return data_lines
 
     def __getitem__(self, idx):
         file_idx = self.data_idx_order_list[idx]
-        data_path, data_line = self.data_lines[file_idx]
+        data_line = self.data_lines[file_idx]
         try:
-            if self.data_format == 'icdar':
-                im_path = os.path.join(data_path, 'rgb', data_line)
-                poly_path = os.path.join(data_path, 'poly',
-                                         data_line.split('.')[0] + '.txt')
-                text_polys, text_tags, text_strs = self.extract_polys(poly_path)
+            data_line = data_line.decode('utf-8')
+            substr = data_line.strip("\n").split(self.delimiter)
+            file_name = substr[0]
+            label = substr[1]
+            img_path = os.path.join(self.data_dir, file_name)
+            if self.mode.lower() == 'eval':
+                img_id = int(data_line.split(".")[0][7:])
             else:
-                image_dir = os.path.join(os.path.dirname(data_path), 'image')
-                im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
-                    data_line, image_dir)
-            img_id = int(data_line.split(".")[0][3:])
-
-            data = {
-                'img_path': im_path,
-                'polys': text_polys,
-                'tags': text_tags,
-                'strs': text_strs,
-                'img_id': img_id
-            }
+                img_id = 0
+            data = {'img_path': img_path, 'label': label, 'img_id': img_id}
+            if not os.path.exists(img_path):
+                raise Exception("{} does not exist!".format(img_path))
             with open(data['img_path'], 'rb') as f:
                 img = f.read()
                 data['image'] = img
             outs = transform(data, self.ops)
-
         except Exception as e:
             self.logger.error(
                 "When parsing line {}, error happened with msg: {}".format(

ppocr/metrics/e2e_metric.py

@@ -35,11 +35,11 @@ class E2EMetric(object):
         self.reset()
 
     def __call__(self, preds, batch, **kwargs):
-        img_id = batch[5][0]
+        img_id = batch[2][0]
         e2e_info_list = [{
             'points': det_polyon,
-            'text': pred_str
-        } for det_polyon, pred_str in zip(preds['points'], preds['strs'])]
+            'texts': pred_str
+        } for det_polyon, pred_str in zip(preds['points'], preds['texts'])]
         result = get_socre(self.gt_mat_dir, img_id, e2e_info_list)
         self.results.append(result)
 

ppocr/utils/e2e_metric/Deteval.py

@@ -26,7 +26,7 @@ def get_socre(gt_dir, img_id, pred_dict):
         n = len(pred_dict)
         for i in range(n):
             points = pred_dict[i]['points']
-            text = pred_dict[i]['text']
+            text = pred_dict[i]['texts']
             point = ",".join(map(str, points.reshape(-1, )))
             det.append([point, text])
         return det

ppocr/utils/e2e_utils/extract_textpoint_slow.py

@@ -342,6 +342,7 @@ def generate_pivot_list_curved(p_score,
     center_pos_yxs = []
     end_points_yxs = []
     instance_center_pos_yxs = []
+    pred_strs = []
     if instance_count > 0:
         for instance_id in range(1, instance_count):
             pos_list = []
@@ -367,12 +368,13 @@ def generate_pivot_list_curved(p_score,
         if is_backbone:
             keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
             instance_center_pos_yxs.append(keep_yxs_list_with_id)
+            pred_strs.append(decoded_str)
         else:
             end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
             center_pos_yxs.extend(keep_yxs_list)
 
     if is_backbone:
-        return instance_center_pos_yxs
+        return pred_strs, instance_center_pos_yxs
     else:
         return center_pos_yxs, end_points_yxs
 

ppocr/utils/e2e_utils/pgnet_pp_utils.py

@@ -64,7 +64,7 @@ class PGNet_PostProcess(object):
                                                 src_w, src_h, self.valid_set)
         data = {
             'points': poly_list,
-            'strs': keep_str_list,
+            'texts': keep_str_list,
         }
         return data
 
@@ -85,32 +85,13 @@ class PGNet_PostProcess(object):
             p_char = p_char[0]
         src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
         is_curved = self.valid_set == "totaltext"
-        instance_yxs_list = generate_pivot_list_slow(
+        char_seq_idx_set, instance_yxs_list = generate_pivot_list_slow(
             p_score,
             p_char,
             p_direction,
             score_thresh=self.score_thresh,
             is_backbone=True,
             is_curved=is_curved)
-        p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
-        char_seq_idx_set = []
-        for i in range(len(instance_yxs_list)):
-            gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
-            f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
-            feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
-            feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
-            feature_len = [len(feature_seq[0])]
-            featyre_seq = paddle.to_tensor(feature_seq)
-            feature_len = np.array([feature_len]).astype(np.int64)
-            length = paddle.to_tensor(feature_len)
-            seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
-                input=featyre_seq, blank=36, input_length=length)
-            seq_pred_str = seq_pred[0].numpy().tolist()[0]
-            seq_len = seq_pred[1].numpy()[0][0]
-            temp_t = []
-            for c in seq_pred_str[:seq_len]:
-                temp_t.append(c)
-            char_seq_idx_set.append(temp_t)
         seq_strs = []
         for char_idx_set in char_seq_idx_set:
             pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
@@ -176,6 +157,6 @@ class PGNet_PostProcess(object):
                 exit(-1)
         data = {
             'points': poly_list,
-            'strs': keep_str_list,
+            'texts': keep_str_list,
         }
         return data

tools/infer/predict_e2e.py

@@ -122,7 +122,7 @@ class TextE2E(object):
         else:
             raise NotImplementedError
         post_result = self.postprocess_op(preds, shape_list)
-        points, strs = post_result['points'], post_result['strs']
+        points, strs = post_result['points'], post_result['texts']
         dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
         elapse = time.time() - starttime
         return dt_boxes, strs, elapse

tools/infer_e2e.py

@@ -103,7 +103,7 @@ def main():
             images = paddle.to_tensor(images)
             preds = model(images)
             post_result = post_process_class(preds, shape_list)
-            points, strs = post_result['points'], post_result['strs']
+            points, strs = post_result['points'], post_result['texts']
             # write resule
             dt_boxes_json = []
             for poly, str in zip(points, strs):