add visual png

configs/e2e/e2e_r50_vd_pg.yml

@@ -18,11 +18,13 @@ Global:
   save_inference_dir:
   use_visualdl: False
   infer_img:
-  valid_set: totaltext #two mode: totaltext valid curved words, partvgg valid non-curved words
+  valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
   save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
-  character_dict_path: ppocr/utils/pgnet_dict.txt
+  character_dict_path: ppocr/utils/ic15_dict.txt
   character_type: EN
-  max_text_length: 50
+  max_text_length: 50 # the max length in seq
+  max_text_nums: 30 # the max seq nums in a pic
+  tcl_len: 64
 
 Architecture:
   model_type: e2e
@@ -33,13 +35,15 @@ Architecture:
     layers: 50
   Neck:
     name: PGFPN
-    model_name: large
   Head:
     name: PGHead
-    model_name: large
 
 Loss:
   name: PGLoss
+  tcl_bs: 64
+  max_text_length: 50 # the same as Global: max_text_length
+  max_text_nums: 30 # the same as Global：max_text_nums
+  pad_num: 36 # the length of dict for pad
 
 Optimizer:
   name: Adam
@@ -54,10 +58,10 @@ Optimizer:
 
 PostProcess:
   name: PGPostProcess
-  score_thresh: 0.8
+  score_thresh: 0.5
 Metric:
   name: E2EMetric
-  character_dict_path: ppocr/utils/pgnet_dict.txt
+  character_dict_path: ppocr/utils/ic15_dict.txt
   main_indicator: f_score_e2e
 
 Train:

doc/doc_ch/e2e.md

@@ -9,8 +9,10 @@
 解压数据集和下载标注文件后，PaddleOCR/train_data/part_vgg_synth/train/ 有一个文件夹和一个文件，分别是：
 ```
 /PaddleOCR/train_data/part_vgg_synth/train/
-  └─ image/         partvgg数据集的训练数据
-  └─ train_annotation_info.txt     partvgg数据集的测试标注
+  |- image/         partvgg数据集的训练数据
+      |- 119_nile_110_31.png
+      | ...
+  |- train_annotation_info.txt     partvgg数据集的测试标注
 ```
 
 提供的标注文件格式如下，中间用"\t"分隔：
@@ -18,7 +20,7 @@
 " 图像文件名      图像标注信息--四点标注                                         图像标注信息--识别标注  
 119_nile_110_31    140.2    222.5    266.0    194.6    278.7    251.8    152.9    279.7    Path:    32.9    133.1    106.0    130.8    106.4    143.8    33.3    146.1    were    21.8    81.9    106.9    80.4    107.7    123.2    22.6    124.7    why
 ```
-标注文件txt当中，其中每一行代表一组数据，以第一行为例。第一个代表同级目录image/下面的文件名， 后面每9个代表一组标注信息，前8个代表文本框的四个点坐标（x,y)，从左上角的点开始顺时针排列。
+标注文件txt当中，其中每一行代表一组数据，以第一行为例。第一个代表同级目录image/下面的文件名前缀， 后面每9个代表一组标注信息，前8个代表文本框的四个点坐标（x,y)，从左上角的点开始顺时针排列。
 最后一个代表文字的识别结果，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
 
 
@@ -26,8 +28,12 @@
 解压数据集和下载标注文件后，PaddleOCR/train_data/total_text/train/ 有两个文件夹，分别是：
 ```
 /PaddleOCR/train_data/total_text/train/
-  └─ rgb/         total_text数据集的训练数据
-  └─ poly/        total_text数据集的测试标注
+  |- rgb/           total_text数据集的训练数据
+      |- gt_0.png
+      | ...  
+  |-poly/           total_text数据集的测试标注
+      |- gt_0.txt
+      | ...
 ```
 
 提供的标注文件格式如下，中间用"\t"分隔：
@@ -36,7 +42,7 @@
 1004.0,689.0,1019.0,698.0,1034.0,708.0,1049.0,718.0,1064.0,728.0,1079.0,738.0,1095.0,748.0,1094.0,774.0,1079.0,765.0,1065.0,756.0,1050.0,747.0,1036.0,738.0,1021.0,729.0,1007.0,721.0    EST
 1102.0,755.0,1116.0,764.0,1131.0,773.0,1146.0,783.0,1161.0,792.0,1176.0,801.0,1191.0,811.0,1193.0,837.0,1178.0,828.0,1164.0,819.0,1150.0,810.0,1135.0,801.0,1121.0,792.0,1107.0,784.0    1972
 ```
-标注文件当中，其中每一个txt文件代表一组数据，文件名同级目录rgb/下面的文件名。以第一行为例，前面28个代表文本框的十四个点坐标（x,y)，从左上角的点开始顺时针排列。
+标注文件当中，其中每一个txt文件代表一组数据，文件名就是同级目录rgb/下面的文件名。以第一行为例，前面28个代表文本框的十四个点坐标（x,y)，从左上角的点开始顺时针排列。
 最后一个代表文字的识别结果，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
 如果您想在其他数据集上训练，可以按照上述形式构建标注文件。
 

doc/doc_ch/inference.md

@@ -29,7 +29,7 @@ inference 模型（`paddle.jit.save`保存的模型）
     - [5. 多语言模型的推理](#多语言模型的推理)
 
 - [四、端到端模型推理](#端到端模型推理)
-    - [1. PGNet端到端模型推理](#SAST文本检测模型推理)
+    - [1. PGNet端到端模型推理](#PGNet端到端模型推理)
 
 - [五、方向分类模型推理](#方向识别模型推理)
     - [1. 方向分类模型推理](#方向分类模型推理)
@@ -366,7 +366,7 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
 ## 四、端到端模型推理
 
 端到端模型推理，默认使用PGNet模型的配置参数。当不使用PGNet模型时，在推理时，需要通过传入相应的参数进行算法适配，细节参考下文。
-<a name="SAST文本检测模型推理"></a>
+<a name="PGNet端到端模型推理"></a>
 ### 1. PGNet端到端模型推理
 #### (1). 四边形文本检测模型（ICDAR2015）  
 首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar))，可以使用如下命令进行转换：
@@ -375,28 +375,26 @@ python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrai
 ```
 **PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`**，可以执行如下命令：
 ```
-python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e_pgnet_ic15/"
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/"  --e2e_pgnet_polygon=False
 ```
 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
 
-![](../imgs_results/det_res_img_10_sast.jpg)
+![](../imgs_results/e2e_res_img_10_pgnet.jpg)
 
 #### (2). 弯曲文本检测模型（Total-Text）  
 首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar))，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e_pgnet_tt
+python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
 ```
 
 **PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`，同时，还需要增加参数`--e2e_pgnet_polygon=True`，**可以执行如下命令：
 ```
-python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_pgnet_tt/" --e2e_pgnet_polygon=True
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
 ```
 可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
 
-![](../imgs_results/e2e_res_img623_pg.jpg)
-
-**注意**：本代码库中，SAST后处理Locality-Aware NMS有python和c++两种版本，c++版速度明显快于python版。由于c++版本nms编译版本问题，只有python3.5环境下会调用c++版nms，其他情况将调用python版nms。
+![](../imgs_results/e2e_res_img623_pgnet.jpg)
 
 
 <a name="方向分类模型推理"></a>

ppocr/data/imaug/label_ops.py

@@ -197,17 +197,17 @@ class E2ELabelEncode(BaseRecLabelEncode):
         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
 
     def __call__(self, data):
         texts = data['strs']
         temp_texts = []
         for text in texts:
-            text = text.upper()
+            text = text.lower()
             text = self.encode(text)
             if text is None:
                 return None
-            text = text + [36] * (self.max_text_len - len(text)
-                                  )  # use 36 to pad
+            text = text + [self.pad_num] * (self.max_text_len - len(text))
             temp_texts.append(text)
         data['strs'] = np.array(temp_texts)
         return data

ppocr/data/imaug/pg_process.py

@@ -22,16 +22,23 @@ __all__ = ['PGProcessTrain']
 class PGProcessTrain(object):
     def __init__(self,
                  character_dict_path,
+                 max_text_length,
+                 max_text_nums,
+                 tcl_len,
                  batch_size=14,
                  min_crop_size=24,
                  min_text_size=10,
                  max_text_size=512,
                  **kwargs):
+        self.tcl_len = tcl_len
+        self.max_text_length = max_text_length
+        self.max_text_nums = max_text_nums
         self.batch_size = batch_size
         self.min_crop_size = min_crop_size
         self.min_text_size = min_text_size
         self.max_text_size = max_text_size
         self.Lexicon_Table = self.get_dict(character_dict_path)
+        self.pad_num = len(self.Lexicon_Table)
         self.img_id = 0
 
     def get_dict(self, character_dict_path):
@@ -290,7 +297,7 @@ class PGProcessTrain(object):
             height_list.append(quad_h)
         norm_width = max(sum(width_list) / n_char, 1.0)
         average_height = max(sum(height_list) / len(height_list), 1.0)
-
+        k = 1
         for quad in poly_quads:
             direct_vector_full = (
                 (quad[1] + quad[2]) - (quad[0] + quad[3])) / 2.0
@@ -302,6 +309,8 @@ class PGProcessTrain(object):
             cv2.fillPoly(direction_map,
                          quad.round().astype(np.int32)[np.newaxis, :, :],
                          direction_label)
+            cv2.imwrite("output/{}.png".format(k), direction_map * 255.0)
+            k += 1
         return direction_map
 
     def calculate_average_height(self, poly_quads):
@@ -371,7 +380,6 @@ class PGProcessTrain(object):
                 continue
 
             if tag:
-                # continue
                 cv2.fillPoly(training_mask,
                              poly.astype(np.int32)[np.newaxis, :, :], 0.15)
             else:
@@ -577,7 +585,7 @@ class PGProcessTrain(object):
         Prepare text lablel by given Lexicon_Table.
         """
         if len(Lexicon_Table) == 36:
-            return label_str.upper()
+            return label_str.lower()
         else:
             return label_str
 
@@ -846,23 +854,23 @@ class PGProcessTrain(object):
             return None
         pos_list_temp = np.zeros([64, 3])
         pos_mask_temp = np.zeros([64, 1])
-        label_list_temp = np.zeros([50, 1]) + 36
+        label_list_temp = np.zeros([self.max_text_length, 1]) + self.pad_num
 
         for i, label in enumerate(label_list):
             n = len(label)
-            if n > 50:
-                label_list[i] = label[:50]
+            if n > self.max_text_length:
+                label_list[i] = label[:self.max_text_length]
                 continue
-            while n < 50:
-                label.append([36])
+            while n < self.max_text_length:
+                label.append([self.pad_num])
                 n += 1
 
         for i in range(len(label_list)):
             label_list[i] = np.array(label_list[i])
 
-        if len(pos_list) <= 0 or len(pos_list) > 30:  #一张图片中最多存在30行文本
+        if len(pos_list) <= 0 or len(pos_list) > self.max_text_nums:
             return None
-        for __ in range(30 - len(pos_list), 0, -1):
+        for __ in range(self.max_text_nums - len(pos_list), 0, -1):
             pos_list.append(pos_list_temp)
             pos_mask.append(pos_mask_temp)
             label_list.append(label_list_temp)

ppocr/data/pgnet_dataset.py

@@ -156,6 +156,7 @@ class PGDataSet(Dataset):
                 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/losses/e2e_pg_loss.py

@@ -18,102 +18,26 @@ from __future__ import print_function
 
 from paddle import nn
 import paddle
-import numpy as np
-import copy
 
 from .det_basic_loss import DiceLoss
+from ppocr.utils.e2e_utils.extract_batchsize import *
 
 
 class PGLoss(nn.Layer):
-    def __init__(self, eps=1e-6, **kwargs):
+    def __init__(self,
+                 tcl_bs,
+                 max_text_length,
+                 max_text_nums,
+                 pad_num,
+                 eps=1e-6,
+                 **kwargs):
         super(PGLoss, self).__init__()
+        self.tcl_bs = tcl_bs
+        self.max_text_nums = max_text_nums
+        self.max_text_length = max_text_length
+        self.pad_num = pad_num
         self.dice_loss = DiceLoss(eps=eps)
 
-    def org_tcl_rois(self, batch_size, pos_lists, pos_masks, label_lists):
-        """
-        """
-        pos_lists_, pos_masks_, label_lists_ = [], [], []
-        img_bs = batch_size
-        tcl_bs = 64
-        ngpu = int(batch_size / img_bs)
-        img_ids = np.array(pos_lists, dtype=np.int32)[:, 0, 0].copy()
-        pos_lists_split, pos_masks_split, label_lists_split = [], [], []
-        for i in range(ngpu):
-            pos_lists_split.append([])
-            pos_masks_split.append([])
-            label_lists_split.append([])
-
-        for i in range(img_ids.shape[0]):
-            img_id = img_ids[i]
-            gpu_id = int(img_id / img_bs)
-            img_id = img_id % img_bs
-            pos_list = pos_lists[i].copy()
-            pos_list[:, 0] = img_id
-            pos_lists_split[gpu_id].append(pos_list)
-            pos_masks_split[gpu_id].append(pos_masks[i].copy())
-            label_lists_split[gpu_id].append(copy.deepcopy(label_lists[i]))
-        # repeat or delete
-        for i in range(ngpu):
-            vp_len = len(pos_lists_split[i])
-            if vp_len <= tcl_bs:
-                for j in range(0, tcl_bs - vp_len):
-                    pos_list = pos_lists_split[i][j].copy()
-                    pos_lists_split[i].append(pos_list)
-                    pos_mask = pos_masks_split[i][j].copy()
-                    pos_masks_split[i].append(pos_mask)
-                    label_list = copy.deepcopy(label_lists_split[i][j])
-                    label_lists_split[i].append(label_list)
-            else:
-                for j in range(0, vp_len - tcl_bs):
-                    c_len = len(pos_lists_split[i])
-                    pop_id = np.random.permutation(c_len)[0]
-                    pos_lists_split[i].pop(pop_id)
-                    pos_masks_split[i].pop(pop_id)
-                    label_lists_split[i].pop(pop_id)
-        # merge
-        for i in range(ngpu):
-            pos_lists_.extend(pos_lists_split[i])
-            pos_masks_.extend(pos_masks_split[i])
-            label_lists_.extend(label_lists_split[i])
-        return pos_lists_, pos_masks_, label_lists_
-
-    def pre_process(self, label_list, pos_list, pos_mask):
-        max_len = 30  # the max texts in a single image
-        max_str_len = 50  # the max len in a single text
-        pad_num = 36  # padding num
-        label_list = label_list.numpy()
-        batch, _, _, _ = label_list.shape
-        pos_list = pos_list.numpy()
-        pos_mask = pos_mask.numpy()
-        pos_list_t = []
-        pos_mask_t = []
-        label_list_t = []
-        for i in range(batch):
-            for j in range(max_len):
-                if pos_mask[i, j].any():
-                    pos_list_t.append(pos_list[i][j])
-                    pos_mask_t.append(pos_mask[i][j])
-                    label_list_t.append(label_list[i][j])
-        pos_list, pos_mask, label_list = self.org_tcl_rois(
-            batch, pos_list_t, pos_mask_t, label_list_t)
-        label = []
-        tt = [l.tolist() for l in label_list]
-        for i in range(batch):
-            k = 0
-            for j in range(max_str_len):
-                if tt[i][j][0] != pad_num:
-                    k += 1
-                else:
-                    break
-            label.append(k)
-        label = paddle.to_tensor(label)
-        label = paddle.cast(label, dtype='int64')
-        pos_list = paddle.to_tensor(pos_list)
-        pos_mask = paddle.to_tensor(pos_mask)
-        label_list = paddle.squeeze(paddle.to_tensor(label_list), axis=2)
-        label_list = paddle.cast(label_list, dtype='int32')
-        return pos_list, pos_mask, label_list, label
-
     def border_loss(self, f_border, l_border, l_score, l_mask):
         l_border_split, l_border_norm = paddle.tensor.split(
             l_border, num_or_sections=[4, 1], axis=1)
@@ -183,7 +107,7 @@ class PGLoss(nn.Layer):
             labels=tcl_label,
             input_lengths=input_lengths,
             label_lengths=label_t,
-            blank=36,
+            blank=self.pad_num,
             reduction='none')
         cost = cost.mean()
         return cost
@@ -192,12 +116,14 @@ class PGLoss(nn.Layer):
         images, tcl_maps, tcl_label_maps, border_maps \
             , direction_maps, training_masks, label_list, pos_list, pos_mask = labels
         # for all the batch_size
-        pos_list, pos_mask, label_list, label_t = self.pre_process(
-            label_list, pos_list, pos_mask)
+        pos_list, pos_mask, label_list, label_t = pre_process(
+            label_list, pos_list, pos_mask, self.max_text_length,
+            self.max_text_nums, self.pad_num, self.tcl_bs)
 
-        f_score, f_boder, f_direction, f_char = predicts
+        f_score, f_border, f_direction, f_char = predicts['f_score'], predicts['f_border'], predicts['f_direction'], \
+                                                 predicts['f_char']
         score_loss = self.dice_loss(f_score, tcl_maps, training_masks)
-        border_loss = self.border_loss(f_boder, border_maps, tcl_maps,
+        border_loss = self.border_loss(f_border, border_maps, tcl_maps,
                                        training_masks)
         direction_loss = self.direction_loss(f_direction, direction_maps,
                                              tcl_maps, training_masks)

ppocr/modeling/heads/e2e_pg_head.py

@@ -66,9 +66,8 @@ class PGHead(nn.Layer):
     """
     """
 
-    def __init__(self, in_channels, model_name, **kwargs):
+    def __init__(self, in_channels, **kwargs):
         super(PGHead, self).__init__()
-        self.model_name = model_name
         self.conv_f_score1 = ConvBNLayer(
             in_channels=in_channels,
             out_channels=64,

ppocr/postprocess/pg_postprocess.py

@@ -23,8 +23,7 @@ __dir__ = os.path.dirname(__file__)
 sys.path.append(__dir__)
 sys.path.append(os.path.join(__dir__, '..'))
 
-from ppocr.utils.e2e_utils.extract_textpoint import *
-from ppocr.utils.e2e_utils.visual import *
+from ppocr.utils.e2e_utils.extract_textpoint import get_dict, generate_pivot_list, restore_poly
 import paddle
 
 
@@ -34,16 +33,10 @@ class PGPostProcess(object):
     """
 
     def __init__(self, character_dict_path, valid_set, score_thresh, **kwargs):
-
         self.Lexicon_Table = get_dict(character_dict_path)
         self.valid_set = valid_set
         self.score_thresh = score_thresh
 
-        # c++ la-nms is faster, but only support python 3.5
-        self.is_python35 = False
-        if sys.version_info.major == 3 and sys.version_info.minor == 5:
-            self.is_python35 = True
-
     def __call__(self, outs_dict, shape_list):
         p_score = outs_dict['f_score']
         p_border = outs_dict['f_border']
@@ -61,96 +54,15 @@ class PGPostProcess(object):
             p_char = p_char[0]
 
         src_h, src_w, ratio_h, ratio_w = shape_list[0]
-        is_curved = self.valid_set == "totaltext"
-        instance_yxs_list = generate_pivot_list(
+        instance_yxs_list, seq_strs = generate_pivot_list(
             p_score,
             p_char,
             p_direction,
-            score_thresh=self.score_thresh,
-            is_backbone=True,
-            is_curved=is_curved)
-        p_char = np.expand_dims(p_char, axis=0)
-        p_char = paddle.to_tensor(p_char)
-        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])
-            featyre_seq = paddle.gather_nd(f_char_map, gather_info_lod)
-            featyre_seq = np.expand_dims(featyre_seq.numpy(), axis=0)
-            t = len(featyre_seq[0])
-            featyre_seq = paddle.to_tensor(featyre_seq)
-            l = np.array([[t]]).astype(np.int64)
-            length = paddle.to_tensor(l)
-            seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
-                input=featyre_seq, blank=36, input_length=length)
-            seq_pred1 = seq_pred[0].numpy().tolist()[0]
-            seq_len = seq_pred[1].numpy()[0][0]
-            temp_t = []
-            for x in seq_pred1[:seq_len]:
-                temp_t.append(x)
-            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])
-            seq_strs.append(pr_str)
-        poly_list = []
-        keep_str_list = []
-        all_point_list = []
-        all_point_pair_list = []
-        for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
-            if len(yx_center_line) == 1:
-                yx_center_line.append(yx_center_line[-1])
-
-            offset_expand = 1.0
-            if self.valid_set == 'totaltext':
-                offset_expand = 1.2
-
-            point_pair_list = []
-            for batch_id, y, x in yx_center_line:
-                offset = p_border[:, y, x].reshape(2, 2)
-                if offset_expand != 1.0:
-                    offset_length = np.linalg.norm(
-                        offset, axis=1, keepdims=True)
-                    expand_length = np.clip(
-                        offset_length * (offset_expand - 1),
-                        a_min=0.5,
-                        a_max=3.0)
-                    offset_detal = offset / offset_length * expand_length
-                    offset = offset + offset_detal
-                ori_yx = np.array([y, x], dtype=np.float32)
-                point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
-                    [ratio_w, ratio_h]).reshape(-1, 2)
-                point_pair_list.append(point_pair)
-
-                all_point_list.append([
-                    int(round(x * 4.0 / ratio_w)),
-                    int(round(y * 4.0 / ratio_h))
-                ])
-                all_point_pair_list.append(point_pair.round().astype(np.int32)
-                                           .tolist())
-
-            detected_poly, pair_length_info = point_pair2poly(point_pair_list)
-            detected_poly = expand_poly_along_width(
-                detected_poly, shrink_ratio_of_width=0.2)
-            detected_poly[:, 0] = np.clip(
-                detected_poly[:, 0], a_min=0, a_max=src_w)
-            detected_poly[:, 1] = np.clip(
-                detected_poly[:, 1], a_min=0, a_max=src_h)
-
-            if len(keep_str) < 2:
-                continue
-
-            keep_str_list.append(keep_str)
-            if self.valid_set == 'partvgg':
-                middle_point = len(detected_poly) // 2
-                detected_poly = detected_poly[
-                    [0, middle_point - 1, middle_point, -1], :]
-                poly_list.append(detected_poly)
-            elif self.valid_set == 'totaltext':
-                poly_list.append(detected_poly)
-            else:
-                print('--> Not supported format.')
-                exit(-1)
+            self.Lexicon_Table,
+            score_thresh=self.score_thresh)
+        poly_list, keep_str_list = restore_poly(instance_yxs_list, seq_strs,
+                                                p_border, ratio_w, ratio_h,
+                                                src_w, src_h, self.valid_set)
         data = {
             'points': poly_list,
             'strs': keep_str_list,

ppocr/utils/e2e_utils/extract_batchsize.py

+import paddle
+import numpy as np
+import copy
+
+
+def org_tcl_rois(batch_size, pos_lists, pos_masks, label_lists, tcl_bs):
+    """
+    """
+    pos_lists_, pos_masks_, label_lists_ = [], [], []
+    img_bs = batch_size
+    ngpu = int(batch_size / img_bs)
+    img_ids = np.array(pos_lists, dtype=np.int32)[:, 0, 0].copy()
+    pos_lists_split, pos_masks_split, label_lists_split = [], [], []
+    for i in range(ngpu):
+        pos_lists_split.append([])
+        pos_masks_split.append([])
+        label_lists_split.append([])
+
+    for i in range(img_ids.shape[0]):
+        img_id = img_ids[i]
+        gpu_id = int(img_id / img_bs)
+        img_id = img_id % img_bs
+        pos_list = pos_lists[i].copy()
+        pos_list[:, 0] = img_id
+        pos_lists_split[gpu_id].append(pos_list)
+        pos_masks_split[gpu_id].append(pos_masks[i].copy())
+        label_lists_split[gpu_id].append(copy.deepcopy(label_lists[i]))
+    # repeat or delete
+    for i in range(ngpu):
+        vp_len = len(pos_lists_split[i])
+        if vp_len <= tcl_bs:
+            for j in range(0, tcl_bs - vp_len):
+                pos_list = pos_lists_split[i][j].copy()
+                pos_lists_split[i].append(pos_list)
+                pos_mask = pos_masks_split[i][j].copy()
+                pos_masks_split[i].append(pos_mask)
+                label_list = copy.deepcopy(label_lists_split[i][j])
+                label_lists_split[i].append(label_list)
+        else:
+            for j in range(0, vp_len - tcl_bs):
+                c_len = len(pos_lists_split[i])
+                pop_id = np.random.permutation(c_len)[0]
+                pos_lists_split[i].pop(pop_id)
+                pos_masks_split[i].pop(pop_id)
+                label_lists_split[i].pop(pop_id)
+    # merge
+    for i in range(ngpu):
+        pos_lists_.extend(pos_lists_split[i])
+        pos_masks_.extend(pos_masks_split[i])
+        label_lists_.extend(label_lists_split[i])
+    return pos_lists_, pos_masks_, label_lists_
+
+
+def pre_process(label_list, pos_list, pos_mask, max_text_length, max_text_nums,
+                pad_num, tcl_bs):
+    label_list = label_list.numpy()
+    batch, _, _, _ = label_list.shape
+    pos_list = pos_list.numpy()
+    pos_mask = pos_mask.numpy()
+    pos_list_t = []
+    pos_mask_t = []
+    label_list_t = []
+    for i in range(batch):
+        for j in range(max_text_nums):
+            if pos_mask[i, j].any():
+                pos_list_t.append(pos_list[i][j])
+                pos_mask_t.append(pos_mask[i][j])
+                label_list_t.append(label_list[i][j])
+    pos_list, pos_mask, label_list = org_tcl_rois(batch, pos_list_t, pos_mask_t,
+                                                  label_list_t, tcl_bs)
+    label = []
+    tt = [l.tolist() for l in label_list]
+    for i in range(tcl_bs):
+        k = 0
+        for j in range(max_text_length):
+            if tt[i][j][0] != pad_num:
+                k += 1
+            else:
+                break
+        label.append(k)
+    label = paddle.to_tensor(label)
+    label = paddle.cast(label, dtype='int64')
+    pos_list = paddle.to_tensor(pos_list)
+    pos_mask = paddle.to_tensor(pos_mask)
+    label_list = paddle.squeeze(paddle.to_tensor(label_list), axis=2)
+    label_list = paddle.cast(label_list, dtype='int32')
+    return pos_list, pos_mask, label_list, label

ppocr/utils/e2e_utils/extract_textpoint.py

@@ -17,11 +17,9 @@ from __future__ import division
 from __future__ import print_function
 
 import cv2
-import math
-
 import numpy as np
 from itertools import groupby
-from skimage.morphology._skeletonize import thin
+from cv2.ximgproc import thinning as thin
 
 
 def get_dict(character_dict_path):
@@ -35,87 +33,39 @@ def get_dict(character_dict_path):
     return dict_character
 
 
-def softmax(logits):
-    """
-    logits: N x d
-    """
-    max_value = np.max(logits, axis=1, keepdims=True)
-    exp = np.exp(logits - max_value)
-    exp_sum = np.sum(exp, axis=1, keepdims=True)
-    dist = exp / exp_sum
-    return dist
-
-
-def get_keep_pos_idxs(labels, remove_blank=None):
-    """
-    Remove duplicate and get pos idxs of keep items.
-    The value of keep_blank should be [None, 95].
-    """
-    duplicate_len_list = []
-    keep_pos_idx_list = []
-    keep_char_idx_list = []
-    for k, v_ in groupby(labels):
-        current_len = len(list(v_))
-        if k != remove_blank:
-            current_idx = int(sum(duplicate_len_list) + current_len // 2)
-            keep_pos_idx_list.append(current_idx)
-            keep_char_idx_list.append(k)
-        duplicate_len_list.append(current_len)
-    return keep_char_idx_list, keep_pos_idx_list
-
-
-def remove_blank(labels, blank=0):
-    new_labels = [x for x in labels if x != blank]
-    return new_labels
-
-
-def insert_blank(labels, blank=0):
-    new_labels = [blank]
-    for l in labels:
-        new_labels += [l, blank]
-    return new_labels
-
-
-def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
-    """
-    CTC greedy (best path) decoder.
-    """
-    raw_str = np.argmax(np.array(probs_seq), axis=1)
-    remove_blank_in_pos = None if keep_blank_in_idxs else blank
-    dedup_str, keep_idx_list = get_keep_pos_idxs(
-        raw_str, remove_blank=remove_blank_in_pos)
-    dst_str = remove_blank(dedup_str, blank=blank)
-    return dst_str, keep_idx_list
-
-
-def instance_ctc_greedy_decoder(gather_info,
-                                logits_map,
-                                keep_blank_in_idxs=True):
-    """
-    gather_info: [[x, y], [x, y] ...]
-    logits_map: H x W X (n_chars + 1)
-    """
+def instance_ctc_greedy_decoder(gather_info, logits_map, pts_num=4):
     _, _, C = logits_map.shape
     ys, xs = zip(*gather_info)
-    logits_seq = logits_map[list(ys), list(xs)]  # n x 96
-    probs_seq = softmax(logits_seq)
-    dst_str, keep_idx_list = ctc_greedy_decoder(
-        probs_seq, blank=C - 1, keep_blank_in_idxs=keep_blank_in_idxs)
+    logits_seq = logits_map[list(ys), list(xs)]
+    probs_seq = logits_seq
+    labels = np.argmax(probs_seq, axis=1)
+    dst_str = [k for k, v_ in groupby(labels) if k != C - 1]
+    detal = len(gather_info) // (pts_num - 1)
+    keep_idx_list = [0] + [detal * (i + 1) for i in range(pts_num - 2)] + [-1]
     keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
     return dst_str, keep_gather_list
 
 
-def ctc_decoder_for_image(gather_info_list, logits_map,
-                          keep_blank_in_idxs=True):
+def ctc_decoder_for_image(gather_info_list,
+                          logits_map,
+                          Lexicon_Table,
+                          pts_num=6):
     """
     CTC decoder using multiple processes.
     """
-    decoder_results = []
+    decoder_str = []
+    decoder_xys = []
     for gather_info in gather_info_list:
-        res = instance_ctc_greedy_decoder(
-            gather_info, logits_map, keep_blank_in_idxs=keep_blank_in_idxs)
-        decoder_results.append(res)
-    return decoder_results
+        if len(gather_info) < pts_num:
+            continue
+        dst_str, xys_list = instance_ctc_greedy_decoder(
+            gather_info, logits_map, pts_num=pts_num)
+        dst_str_readable = ''.join([Lexicon_Table[idx] for idx in dst_str])
+        if len(dst_str_readable) < 2:
+            continue
+        decoder_str.append(dst_str_readable)
+        decoder_xys.append(xys_list)
+    return decoder_str, decoder_xys
 
 
 def sort_with_direction(pos_list, f_direction):
@@ -157,57 +107,6 @@ def sort_with_direction(pos_list, f_direction):
     return sorted_point, np.array(sorted_direction)
 
 
-def add_id(pos_list, image_id=0):
-    """
-    Add id for gather feature, for inference.
-    """
-    new_list = []
-    for item in pos_list:
-        new_list.append((image_id, item[0], item[1]))
-    return new_list
-
-
-def sort_and_expand_with_direction(pos_list, f_direction):
-    """
-    f_direction: h x w x 2
-    pos_list: [[y, x], [y, x], [y, x] ...]
-    """
-    h, w, _ = f_direction.shape
-    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
-
-    point_num = len(sorted_list)
-    sub_direction_len = max(point_num // 3, 2)
-    left_direction = point_direction[:sub_direction_len, :]
-    right_dirction = point_direction[point_num - sub_direction_len:, :]
-
-    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
-    left_average_len = np.linalg.norm(left_average_direction)
-    left_start = np.array(sorted_list[0])
-    left_step = left_average_direction / (left_average_len + 1e-6)
-
-    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
-    right_average_len = np.linalg.norm(right_average_direction)
-    right_step = right_average_direction / (right_average_len + 1e-6)
-    right_start = np.array(sorted_list[-1])
-
-    append_num = max(
-        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
-    left_list = []
-    right_list = []
-    for i in range(append_num):
-        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
-            'int32').tolist()
-        if ly < h and lx < w and (ly, lx) not in left_list:
-            left_list.append((ly, lx))
-        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
-            'int32').tolist()
-        if ry < h and rx < w and (ry, rx) not in right_list:
-            right_list.append((ry, rx))
-
-    all_list = left_list[::-1] + sorted_list + right_list
-    return all_list
-
-
 def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
     """
     f_direction: h x w x 2
@@ -260,262 +159,125 @@ def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
     return all_list
 
 
-def generate_pivot_list_curved(p_score,
-                               p_char_maps,
-                               f_direction,
-                               score_thresh=0.5,
-                               is_expand=True,
-                               is_backbone=False,
-                               image_id=0):
-    """
-    return center point and end point of TCL instance; filter with the char maps;
-    """
-    p_score = p_score[0]
-    f_direction = f_direction.transpose(1, 2, 0)
-    p_tcl_map = (p_score > score_thresh) * 1.0
-    skeleton_map = thin(p_tcl_map)
-    instance_count, instance_label_map = cv2.connectedComponents(
-        skeleton_map.astype(np.uint8), connectivity=8)
-
-    all_pos_yxs = []
-    center_pos_yxs = []
-    end_points_yxs = []
-    instance_center_pos_yxs = []
-    if instance_count > 0:
-        for instance_id in range(1, instance_count):
-            pos_list = []
-            ys, xs = np.where(instance_label_map == instance_id)
-            pos_list = list(zip(ys, xs))
-
-            if len(pos_list) < 3:
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    point_num = len(point_pair_list) * 2
+    point_list = [0] * point_num
+    for idx, point_pair in enumerate(point_pair_list):
+        point_list[idx] = point_pair[0]
+        point_list[point_num - 1 - idx] = point_pair[1]
+    return np.array(point_list).reshape(-1, 2)
+
+
+def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
+    ratio_pair = np.array(
+        [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+    p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
+    p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
+    return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
+
+
+def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
+    """
+    expand poly along width.
+    """
+    point_num = poly.shape[0]
+    left_quad = np.array(
+        [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+    left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
+                 (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+    left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
+    right_quad = np.array(
+        [
+            poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+            poly[point_num // 2], poly[point_num // 2 + 1]
+        ],
+        dtype=np.float32)
+    right_ratio = 1.0 + shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                  (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+    right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
+    poly[0] = left_quad_expand[0]
+    poly[-1] = left_quad_expand[-1]
+    poly[point_num // 2 - 1] = right_quad_expand[1]
+    poly[point_num // 2] = right_quad_expand[2]
+    return poly
+
+
+def restore_poly(instance_yxs_list, seq_strs, p_border, ratio_w, ratio_h, src_w,
+                 src_h, valid_set):
+    poly_list = []
+    keep_str_list = []
+    for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
+        if len(keep_str) < 2:
+            print('--> too short, {}'.format(keep_str))
             continue
 
-            if is_expand:
-                pos_list_sorted = sort_and_expand_with_direction_v2(
-                    pos_list, f_direction, p_tcl_map)
-            else:
-                pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
-            all_pos_yxs.append(pos_list_sorted)
-
-    p_char_maps = p_char_maps.transpose([1, 2, 0])
-    decode_res = ctc_decoder_for_image(
-        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
-    for decoded_str, keep_yxs_list in decode_res:
-        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)
-        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
+        offset_expand = 1.0
+        if valid_set == 'totaltext':
+            offset_expand = 1.2
+
+        point_pair_list = []
+        for y, x in yx_center_line:
+            offset = p_border[:, y, x].reshape(2, 2) * offset_expand
+            ori_yx = np.array([y, x], dtype=np.float32)
+            point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
+                [ratio_w, ratio_h]).reshape(-1, 2)
+            point_pair_list.append(point_pair)
+
+        detected_poly = point_pair2poly(point_pair_list)
+        detected_poly = expand_poly_along_width(
+            detected_poly, shrink_ratio_of_width=0.2)
+        detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
+        detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
+
+        keep_str_list.append(keep_str)
+        if valid_set == 'partvgg':
+            middle_point = len(detected_poly) // 2
+            detected_poly = detected_poly[
+                [0, middle_point - 1, middle_point, -1], :]
+            poly_list.append(detected_poly)
+        elif valid_set == 'totaltext':
+            poly_list.append(detected_poly)
         else:
-        return center_pos_yxs, end_points_yxs
+            print('--> Not supported format.')
+            exit(-1)
+    return poly_list, keep_str_list
 
 
-def generate_pivot_list_horizontal(p_score,
+def generate_pivot_list(p_score,
                         p_char_maps,
                         f_direction,
-                                   score_thresh=0.5,
-                                   is_backbone=False,
-                                   image_id=0):
+                        Lexicon_Table,
+                        score_thresh=0.5):
     """
     return center point and end point of TCL instance; filter with the char maps;
     """
     p_score = p_score[0]
     f_direction = f_direction.transpose(1, 2, 0)
-    p_tcl_map_bi = (p_score > score_thresh) * 1.0
+    ret, p_tcl_map = cv2.threshold(p_score, score_thresh, 255,
+                                   cv2.THRESH_BINARY)
+    skeleton_map = thin(p_tcl_map.astype('uint8'))
     instance_count, instance_label_map = cv2.connectedComponents(
-        p_tcl_map_bi.astype(np.uint8), connectivity=8)
+        skeleton_map, connectivity=8)
 
     # get TCL Instance
     all_pos_yxs = []
-    center_pos_yxs = []
-    end_points_yxs = []
-    instance_center_pos_yxs = []
-
     if instance_count > 0:
         for instance_id in range(1, instance_count):
             pos_list = []
             ys, xs = np.where(instance_label_map == instance_id)
             pos_list = list(zip(ys, xs))
 
-            if len(pos_list) < 5:
+            if len(pos_list) < 3:
                 continue
 
-            main_direction = extract_main_direction(pos_list,
-                                                    f_direction)  # y x
-            reference_directin = np.array([0, 1]).reshape([-1, 2])  # y x
-            is_h_angle = abs(np.sum(
-                main_direction * reference_directin)) < math.cos(math.pi / 180 *
-                                                                 70)
-
-            point_yxs = np.array(pos_list)
-            max_y, max_x = np.max(point_yxs, axis=0)
-            min_y, min_x = np.min(point_yxs, axis=0)
-            is_h_len = (max_y - min_y) < 1.5 * (max_x - min_x)
-
-            pos_list_final = []
-            if is_h_len:
-                xs = np.unique(xs)
-                for x in xs:
-                    ys = instance_label_map[:, x].copy().reshape((-1, ))
-                    y = int(np.where(ys == instance_id)[0].mean())
-                    pos_list_final.append((y, x))
-            else:
-                ys = np.unique(ys)
-                for y in ys:
-                    xs = instance_label_map[y, :].copy().reshape((-1, ))
-                    x = int(np.where(xs == instance_id)[0].mean())
-                    pos_list_final.append((y, x))
-
-            pos_list_sorted, _ = sort_with_direction(pos_list_final,
-                                                     f_direction)
+            pos_list_sorted = sort_and_expand_with_direction_v2(
+                pos_list, f_direction, p_tcl_map)
             all_pos_yxs.append(pos_list_sorted)
 
     p_char_maps = p_char_maps.transpose([1, 2, 0])
-    decode_res = ctc_decoder_for_image(
-        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
-    for decoded_str, keep_yxs_list in decode_res:
-        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)
-        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
-    else:
-        return center_pos_yxs, end_points_yxs
-
-
-def generate_pivot_list(p_score,
-                        p_char_maps,
-                        f_direction,
-                        score_thresh=0.5,
-                        is_backbone=False,
-                        is_curved=True,
-                        image_id=0):
-    """
-    Warp all the function together.
-    """
-    if is_curved:
-        return generate_pivot_list_curved(
-            p_score,
-            p_char_maps,
-            f_direction,
-            score_thresh=score_thresh,
-            is_expand=True,
-            is_backbone=is_backbone,
-            image_id=image_id)
-    else:
-        return generate_pivot_list_horizontal(
-            p_score,
-            p_char_maps,
-            f_direction,
-            score_thresh=score_thresh,
-            is_backbone=is_backbone,
-            image_id=image_id)
-
-
-def extract_main_direction(pos_list, f_direction):
-    """
-    f_direction: h x w x 2
-    pos_list: [[y, x], [y, x], [y, x] ...]
-    """
-    pos_list = np.array(pos_list)
-    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
-    point_direction = point_direction[:, ::-1]  # x, y -> y, x
-    average_direction = np.mean(point_direction, axis=0, keepdims=True)
-    average_direction = average_direction / (
-        np.linalg.norm(average_direction) + 1e-6)
-    return average_direction
-
-
-def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
-    """
-    f_direction: h x w x 2
-    pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
-    """
-    pos_list_full = np.array(pos_list).reshape(-1, 3)
-    pos_list = pos_list_full[:, 1:]
-    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
-    point_direction = point_direction[:, ::-1]  # x, y -> y, x
-    average_direction = np.mean(point_direction, axis=0, keepdims=True)
-    pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
-    sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
-    return sorted_list
-
-
-def sort_by_direction_with_image_id(pos_list, f_direction):
-    """
-    f_direction: h x w x 2
-    pos_list: [[y, x], [y, x], [y, x] ...]
-    """
-
-    def sort_part_with_direction(pos_list_full, point_direction):
-        pos_list_full = np.array(pos_list_full).reshape(-1, 3)
-        pos_list = pos_list_full[:, 1:]
-        point_direction = np.array(point_direction).reshape(-1, 2)
-        average_direction = np.mean(point_direction, axis=0, keepdims=True)
-        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
-        sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
-        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
-        return sorted_list, sorted_direction
-
-    pos_list = np.array(pos_list).reshape(-1, 3)
-    point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]]  # x, y
-    point_direction = point_direction[:, ::-1]  # x, y -> y, x
-    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
-                                                              point_direction)
-
-    point_num = len(sorted_point)
-    if point_num >= 16:
-        middle_num = point_num // 2
-        first_part_point = sorted_point[:middle_num]
-        first_point_direction = sorted_direction[:middle_num]
-        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
-            first_part_point, first_point_direction)
-
-        last_part_point = sorted_point[middle_num:]
-        last_point_direction = sorted_direction[middle_num:]
-        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
-            last_part_point, last_point_direction)
-        sorted_point = sorted_fist_part_point + sorted_last_part_point
-        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
-
-    return sorted_point
-
-
-def generate_pivot_list_tt_inference(p_score,
-                                     p_char_maps,
-                                     f_direction,
-                                     score_thresh=0.5,
-                                     is_backbone=False,
-                                     is_curved=True,
-                                     image_id=0):
-    """
-    return center point and end point of TCL instance; filter with the char maps;
-    """
-    p_score = p_score[0]
-    f_direction = f_direction.transpose(1, 2, 0)
-    p_tcl_map = (p_score > score_thresh) * 1.0
-    skeleton_map = thin(p_tcl_map)
-    instance_count, instance_label_map = cv2.connectedComponents(
-        skeleton_map.astype(np.uint8), connectivity=8)
-
-    all_pos_yxs = []
-    if instance_count > 0:
-        for instance_id in range(1, instance_count):
-            pos_list = []
-            ys, xs = np.where(instance_label_map == instance_id)
-            pos_list = list(zip(ys, xs))
-            if len(pos_list) < 3:
-                continue
-            pos_list_sorted = sort_and_expand_with_direction_v2(
-                pos_list, f_direction, p_tcl_map)
-            pos_list_sorted_with_id = add_id(pos_list_sorted, image_id=image_id)
-            all_pos_yxs.append(pos_list_sorted_with_id)
-    return all_pos_yxs
+    decoded_str, keep_yxs_list = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, Lexicon_Table=Lexicon_Table)
+    return keep_yxs_list, decoded_str

ppocr/utils/pgnet_dict.txt

-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
\ No newline at end of file

tools/infer/predict_e2e.py

@@ -39,10 +39,7 @@ class TextE2e(object):
         self.args = args
         self.e2e_algorithm = args.e2e_algorithm
         pre_process_list = [{
-            'E2EResizeForTest': {
-                'max_side_len': 768,
-                'valid_set': 'totaltext'
-            }
+            'E2EResizeForTest': {}
         }, {
             'NormalizeImage': {
                 'std': [0.229, 0.224, 0.225],
@@ -70,12 +67,6 @@ class TextE2e(object):
             postprocess_params["character_dict_path"] = args.e2e_char_dict_path
             postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
             self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
-            if self.e2e_pgnet_polygon:
-                postprocess_params["expand_scale"] = 1.2
-                postprocess_params["shrink_ratio_of_width"] = 0.2
-            else:
-                postprocess_params["expand_scale"] = 1.0
-                postprocess_params["shrink_ratio_of_width"] = 0.3
         else:
             logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
             sys.exit(0)
@@ -102,6 +93,7 @@ class TextE2e(object):
         return dt_boxes
 
     def __call__(self, img):
+
         ori_im = img.copy()
         data = {'image': img}
         data = transform(data, self.preprocess_op)
@@ -109,7 +101,6 @@ class TextE2e(object):
         if img is None:
             return None, 0
         img = np.expand_dims(img, axis=0)
-        print(img.shape)
         shape_list = np.expand_dims(shape_list, axis=0)
         img = img.copy()
         starttime = time.time()
@@ -123,13 +114,12 @@ class TextE2e(object):
 
         preds = {}
         if self.e2e_algorithm == 'PGNet':
-            preds['f_score'] = outputs[0]
-            preds['f_border'] = outputs[1]
+            preds['f_border'] = outputs[0]
+            preds['f_char'] = outputs[1]
             preds['f_direction'] = outputs[2]
-            preds['f_char'] = outputs[3]
+            preds['f_score'] = outputs[3]
         else:
             raise NotImplementedError
-
         post_result = self.postprocess_op(preds, shape_list)
         points, strs = post_result['points'], post_result['strs']
         dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)

tools/infer/utility.py

@@ -83,11 +83,9 @@ def parse_args():
     # PGNet parmas
     parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
     parser.add_argument(
-        "--e2e_char_dict_path",
-        type=str,
-        default="./ppocr/utils/pgnet_dict.txt")
+        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
     parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
-    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=False)
+    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
 
     # params for text classifier
     parser.add_argument("--use_angle_cls", type=str2bool, default=False)