fix config add e2e_ch.md e2e_res_img623_pg

configs/e2e/e2e_r50_vd_pg.yml

 Global:
-  use_gpu: False
+  use_gpu: True
   epoch_num: 600
   log_smooth_window: 20
-  print_batch_step: 2
+  print_batch_step: 10
   save_model_dir: ./output/pg_r50_vd_tt/
-  save_epoch_step: 1
-  # evaluation is run every 5000 iterationss after the 4000th iteration
+  save_epoch_step: 10
+  # evaluation is run every 0 iterationss after the 1000th iteration
   eval_batch_step: [ 0, 1000 ]
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: False
+  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
+  #    from static branch, load_static_weights must be set as True.
+  # 2. If you want to finetune the pretrained models we provide in the docs,
+  #    you should set load_static_weights as False.
+  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model:
   checkpoints:
@@ -19,7 +22,7 @@ Global:
 
 Architecture:
   model_type: e2e
-  algorithm: PG
+  algorithm: PGNet
   Transform:
   Backbone:
     name: ResNet
@@ -34,28 +37,16 @@ Architecture:
 Loss:
   name: PGLoss
 
-#Optimizer:
-#  name: Adam
-#  beta1: 0.9
-#  beta2: 0.999
-#  lr:
-#    name: Cosine
-#    learning_rate: 0.001
-#    warmup_epoch: 1
-#  regularizer:
-#    name: 'L2'
-#    factor: 0
-
 Optimizer:
-  name: RMSProp
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
   lr:
-    name: Piecewise
     learning_rate: 0.001
-    decay_epochs: [ 40, 80, 120, 160, 200 ]
-    values: [ 0.001, 0.00033, 0.0001, 0.000033, 0.00001 ]
   regularizer:
     name: 'L2'
-    factor: 0.00005
+    factor: 0
+
 
 PostProcess:
   name: PGPostProcess
@@ -65,45 +56,45 @@ PostProcess:
 
 Metric:
   name: E2EMetric
+  Lexicon_Table: [ '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' ]
   main_indicator: f_score_e2e
 
 Train:
   dataset:
     name: PGDateSet
-    label_file_list:
-    ratio_list:
-    data_format: textnet  # textnet/partvgg
-    Lexicon_Table: [ '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' ]
+    label_file_list: [./train_data/total_text/train/]
+    ratio_list: [1.0]
+    data_format: icdar
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
       - PGProcessTrain:
           batch_size: 14
-          data_format: icdar
-          tcl_len: 64
           min_crop_size: 24
           min_text_size: 4
           max_text_size: 512
+          Lexicon_Table: [ '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' ]
       - KeepKeys:
           keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: True
-    batch_size_per_card: 1
-    num_workers: 8
+    batch_size_per_card: 14
+    num_workers: 16
 
 Eval:
   dataset:
-    name: PGDateSet
+    name: PGDataSet
     data_dir: ./train_data/
-    label_file_list:
+    label_file_list: [./train_data/total_text/test/]
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
       - E2ELabelEncode:
-          label_list: [ '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' ]
+          Lexicon_Table: [ '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' ]
+          max_len: 50
       - E2EResizeForTest:
           valid_set: totaltext
           max_side_len: 768

doc/doc_ch/e2e.md

+# 端到端文字识别
+
+本节以partvgg/totaltext数据集为例，介绍PaddleOCR中端到端模型的训练、评估与测试。
+
+## 数据准备
+支持两种不同的数据形式textnet / icdar ，分别为四点标注数据和十四点标注数据，十四点标注数据效果要比四点标注效果好
+###数据形式为textnet
+
+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
+```
+
+提供的标注文件格式如下，中间用"\t"分隔：
+```
+" 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。
+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。**
+
+
+###数据形式为icdar
+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
+```
+
+提供的标注文件格式如下，中间用"\t"分隔：
+```
+" 图像标注信息--十四点标注数据                                                                                                                                                              图像标注信息--识别标注  
+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。
+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。
+
+## 快速启动训练
+
+首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列，
+您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
+```shell
+cd PaddleOCR/
+下载ResNet50_vd的预训练模型
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
+
+# 解压预训练模型文件，以ResNet50_vd为例
+tar -xf ./pretrain_models/ResNet50_vd_ssld_pretrained.tar ./pretrain_models/
+
+# 注：正确解压backbone预训练权重文件后，文件夹下包含众多以网络层命名的权重文件，格式如下：
+./pretrain_models/ResNet50_vd_ssld_pretrained/
+  └─ conv_last_bn_mean
+  └─ conv_last_bn_offset
+  └─ conv_last_bn_scale
+  └─ conv_last_bn_variance
+  └─ ......
+
+```
+
+#### 启动训练
+
+*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
+
+```shell
+# 单机单卡训练 e2e 模型
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml \
+     -o Global.pretrain_weights=./pretrain_models/ResNet50_vd_ssld_pretrained/ Global.load_static_weights=True
+# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml \
+     -o Global.pretrain_weights=./pretrain_models/ResNet50_vd_ssld_pretrained/  Global.load_static_weights=True
+```
+
+
+上述指令中，通过-c 选择训练使用configs/e2e/e2e_r50_vd_pg.yml配置文件。
+有关配置文件的详细解释，请参考[链接](./config.md)。
+
+您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
+```shell
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0001
+```
+
+#### 断点训练
+
+如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
+```shell
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints=./your/trained/model
+```
+
+**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
+
+## 指标评估
+
+PaddleOCR计算三个OCR端到端相关的指标，分别是：Precision、Recall、Hmean。
+
+运行如下代码，根据配置文件`e2e_r50_vd_pg.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
+
+评估时设置后处理参数`max_side_len=768`，使用不同数据集、不同模型训练，可调整参数进行优化
+训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。
+```shell
+python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy"
+```
+
+
+
+## 测试端到端效果
+
+测试单张图像的端到端识别效果
+```shell
+python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
+```
+
+测试文件夹下所有图像的端到端识别效果
+```shell
+python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
+```

ppocr/data/__init__.py

@@ -34,7 +34,7 @@ import paddle.distributed as dist
 from ppocr.data.imaug import transform, create_operators
 from ppocr.data.simple_dataset import SimpleDataSet
 from ppocr.data.lmdb_dataset import LMDBDataSet
-from ppocr.data.pgnet_dataset import PGDateSet
+from ppocr.data.pgnet_dataset import PGDataSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -55,8 +55,7 @@ signal.signal(signal.SIGTERM, term_mp)
 def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
-
-    support_dict = ['SimpleDataSet', 'LMDBDateSet', 'PGDateSet']
+    support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet']
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))

ppocr/data/imaug/label_ops.py

@@ -35,9 +35,10 @@ class ClsLabelEncode(object):
 
 
 class E2ELabelEncode(object):
-    def __init__(self, label_list, **kwargs):
-        self.label_list = label_list
-        self.max_len = 50
+    def __init__(self, Lexicon_Table, max_len, **kwargs):
+        self.Lexicon_Table = Lexicon_Table
+        self.max_len = max_len
+        self.pad_num = len(self.Lexicon_Table)
 
     def __call__(self, data):
         text_label_index_list, temp_text = [], []
@@ -46,9 +47,10 @@ class E2ELabelEncode(object):
             text = text.upper()
             temp_text = []
             for c_ in text:
-                if c_ in self.label_list:
-                    temp_text.append(self.label_list.index(c_))
-            temp_text = temp_text + [36] * (self.max_len - len(temp_text))
+                if c_ in self.Lexicon_Table:
+                    temp_text.append(self.Lexicon_Table.index(c_))
+            temp_text = temp_text + [self.pad_num] * (self.max_len -
+                                                      len(temp_text))
             text_label_index_list.append(temp_text)
         data['strs'] = np.array(text_label_index_list)
         return data

ppocr/data/imaug/operators.py

@@ -197,7 +197,6 @@ class DetResizeForTest(object):
             sys.exit(0)
         ratio_h = resize_h / float(h)
         ratio_w = resize_w / float(w)
-        # return img, np.array([h, w])
         return img, [ratio_h, ratio_w]
 
     def resize_image_type2(self, img):
@@ -206,7 +205,6 @@ class DetResizeForTest(object):
         resize_w = w
         resize_h = h
 
-        # Fix the longer side
         if resize_h > resize_w:
             ratio = float(self.resize_long) / resize_h
         else:
@@ -245,10 +243,8 @@ class E2EResizeForTest(object):
         return data
 
     def resize_image_for_totaltext(self, im, max_side_len=512):
-        """
-        """
-        h, w, _ = im.shape
 
+        h, w, _ = im.shape
         resize_w = w
         resize_h = h
         ratio = 1.25

ppocr/data/imaug/pg_process.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,7 +15,6 @@
 import math
 import cv2
 import numpy as np
-import os
 
 __all__ = ['PGProcessTrain']
 
@@ -23,15 +22,11 @@ __all__ = ['PGProcessTrain']
 class PGProcessTrain(object):
     def __init__(self,
                  batch_size=14,
-                 data_format='icdar',
-                 tcl_len=64,
                  min_crop_size=24,
                  min_text_size=10,
                  max_text_size=512,
                  **kwargs):
         self.batch_size = batch_size
-        self.data_format = data_format
-        self.tcl_len = tcl_len
         self.min_crop_size = min_crop_size
         self.min_text_size = min_text_size
         self.max_text_size = max_text_size
@@ -60,24 +55,22 @@ class PGProcessTrain(object):
         """
         point_num = poly.shape[0]
         min_area_quad = np.zeros((4, 2), dtype=np.float32)
-        if True:
-            rect = cv2.minAreaRect(poly.astype(
-                np.int32))  # (center (x,y), (width, height), angle of rotation)
-            center_point = rect[0]
-            box = np.array(cv2.boxPoints(rect))
-
-            first_point_idx = 0
-            min_dist = 1e4
-            for i in range(4):
-                dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
-                       np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
-                       np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
-                       np.linalg.norm(box[(i + 3) % 4] - poly[-1])
-                if dist < min_dist:
-                    min_dist = dist
-                    first_point_idx = i
-            for i in range(4):
-                min_area_quad[i] = box[(first_point_idx + i) % 4]
+        rect = cv2.minAreaRect(poly.astype(
+            np.int32))  # (center (x,y), (width, height), angle of rotation)
+        box = np.array(cv2.boxPoints(rect))
+
+        first_point_idx = 0
+        min_dist = 1e4
+        for i in range(4):
+            dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
+                   np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
+                   np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
+                   np.linalg.norm(box[(i + 3) % 4] - poly[-1])
+            if dist < min_dist:
+                min_dist = dist
+                first_point_idx = i
+        for i in range(4):
+            min_area_quad[i] = box[(first_point_idx + i) % 4]
 
         return min_area_quad
 
@@ -235,8 +228,6 @@ class PGProcessTrain(object):
         ys, xs = np.where(tmp_image > 0)
         xy_text = np.array(list(zip(xs, ys)), dtype='float32')
 
-        # left_center_pt = np.array(key_point_xys[0]).reshape(1, 2)
-        # right_center_pt = np.array(key_point_xys[-1]).reshape(1, 2)
         left_center_pt = (
             (min_area_quad[0] - min_area_quad[1]) / 2.0).reshape(1, 2)
         right_center_pt = (
@@ -317,16 +308,6 @@ class PGProcessTrain(object):
         average_height = max(sum(height_list) / len(height_list), 1.0)
         return average_height
 
-    def encode(self, text):
-        text_list = []
-        for char in text:
-            if char not in self.dict:
-                continue
-            text_list.append([self.dict[char]])
-        if len(text_list) == 0:
-            return None
-        return text_list
-
     def generate_tcl_ctc_label(self,
                                h,
                                w,
@@ -390,8 +371,6 @@ class PGProcessTrain(object):
                 text_label = text_strs[poly_idx]
                 text_label = self.prepare_text_label(text_label,
                                                      self.Lexicon_Table)
-                # text = text.decode('utf-8')
-                # text_label_index_list = self.encode(text)
 
                 text_label_index_list = [[self.Lexicon_Table.index(c_)]
                                          for c_ in text_label
@@ -402,22 +381,18 @@ class PGProcessTrain(object):
                 tcl_poly = self.poly2tcl(poly, tcl_ratio)
                 tcl_quads = self.poly2quads(tcl_poly)
                 poly_quads = self.poly2quads(poly)
-                # stcl map
+
                 stcl_quads, quad_index = self.shrink_poly_along_width(
                     tcl_quads,
                     shrink_ratio_of_width=shrink_ratio_of_width,
                     expand_height_ratio=1.0 / tcl_ratio)
-                # generate tcl map
+
                 cv2.fillPoly(score_map,
                              np.round(stcl_quads).astype(np.int32), 1.0)
                 cv2.fillPoly(score_map_big,
                              np.round(stcl_quads / ds_ratio).astype(np.int32),
                              1.0)
 
-                # generate tbo map
-                # tbo_tcl_poly = poly2tcl(poly, 0.5)
-                # tbo_tcl_quads = poly2quads(tbo_tcl_poly)
-                # for idx, quad in enumerate(tbo_tcl_quads):
                 for idx, quad in enumerate(stcl_quads):
                     quad_mask = np.zeros((h, w), dtype=np.float32)
                     quad_mask = cv2.fillPoly(
@@ -432,7 +407,6 @@ class PGProcessTrain(object):
                     score_label_map_text_label_list.append(text_pos_list_)
 
                 label_idx += 1
-                # cv2.fillPoly(score_label_map, np.round(poly_quads[np.newaxis, :, :]).astype(np.int32), label_idx)
                 cv2.fillPoly(score_label_map,
                              np.round(poly_quads).astype(np.int32), label_idx)
                 score_label_map_text_label_list.append(text_label_index_list)
@@ -641,8 +615,6 @@ class PGProcessTrain(object):
         d = a1 * b2 - a2 * b1
 
         if d == 0:
-            # print("line1", line1)
-            # print("line2", line2)
             print('Cross point does not exist')
             return np.array([0, 0], dtype=np.float32)
         else:

ppocr/data/pgnet_dataset.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,9 +18,9 @@ from .imaug import transform, create_operators
 import random
 
 
-class PGDateSet(Dataset):
+class PGDataSet(Dataset):
     def __init__(self, config, mode, logger, seed=None):
-        super(PGDateSet, self).__init__()
+        super(PGDataSet, self).__init__()
 
         self.logger = logger
         self.seed = seed
@@ -81,7 +81,9 @@ class PGDateSet(Dataset):
         """
         info_list = im_fn.split('\t')
         img_path = ''
-        for ext in ['.jpg', '.png', '.jpeg', '.JPG']:
+        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
@@ -111,11 +113,12 @@ class PGDateSet(Dataset):
         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', 'png', 'jpeg', 'JPG']
-                ]
+                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())

ppocr/losses/e2e_pg_loss.py

-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -21,20 +21,12 @@ import paddle
 import numpy as np
 import copy
 
-from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
+from .det_basic_loss import DiceLoss
 
 
 class PGLoss(nn.Layer):
-    """
-    Differentiable Binarization (DB) Loss Function
-    args:
-        param (dict): the super paramter for DB Loss
-    """
-
-    def __init__(self, alpha=5, beta=10, eps=1e-6, **kwargs):
+    def __init__(self, eps=1e-6, **kwargs):
         super(PGLoss, self).__init__()
-        self.alpha = alpha
-        self.beta = beta
         self.dice_loss = DiceLoss(eps=eps)
 
     def org_tcl_rois(self, batch_size, pos_lists, pos_masks, label_lists):
@@ -86,27 +78,30 @@ class PGLoss(nn.Layer):
         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()
-        b, h, w, c = label_list.shape
+        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(b):
-            for j in range(30):
+        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(
-            b, pos_list_t, pos_mask_t, label_list_t)
+            batch, pos_list_t, pos_mask_t, label_list_t)
         label = []
         tt = [l.tolist() for l in label_list]
-        for i in range(64):
+        for i in range(batch):
             k = 0
-            for j in range(50):
-                if tt[i][j][0] != 36:
+            for j in range(max_str_len):
+                if tt[i][j][0] != pad_num:
                     k += 1
                 else:
                     break

ppocr/metrics/e2e_metric.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -22,12 +22,9 @@ from ppocr.utils.e2e_metric.Deteval import *
 
 
 class E2EMetric(object):
-    def __init__(self, main_indicator='f_score_e2e', **kwargs):
-        self.label_list = [
-            '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'
-        ]
+    def __init__(self, Lexicon_Table, main_indicator='f_score_e2e', **kwargs):
+        self.label_list = Lexicon_Table
+        self.max_index = len(self.label_list)
         self.main_indicator = main_indicator
         self.reset()
 
@@ -40,12 +37,12 @@ class E2EMetric(object):
         for temp_list in temp_gt_strs_batch:
             t = ""
             for index in temp_list:
-                if index < 36:
+                if index < self.max_index:
                     t += self.label_list[index]
             gt_strs_batch.append(t)
 
         for pred, gt_polyons, gt_strs, ignore_tags in zip(
-                preds, gt_polyons_batch, gt_strs_batch, ignore_tags_batch):
+            [preds], gt_polyons_batch, [gt_strs_batch], ignore_tags_batch):
             # prepare gt
             gt_info_list = [{
                 'points': gt_polyon,
@@ -57,7 +54,7 @@ class E2EMetric(object):
             e2e_info_list = [{
                 'points': det_polyon,
                 'text': pred_str
-            } for det_polyon, pred_str in zip(pred['points'], preds['strs'])]
+            } for det_polyon, pred_str in zip(pred['points'], pred['strs'])]
             result = get_socre(gt_info_list, e2e_info_list)
             self.results.append(result)
 

ppocr/modeling/backbones/e2e_resnet_vd_pg.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -62,8 +62,6 @@ class ConvBNLayer(nn.Layer):
             moving_variance_name=bn_name + '_variance')
 
     def forward(self, inputs):
-        # if self.is_vd_mode:
-        #     inputs = self._pool2d_avg(inputs)
         y = self._conv(inputs)
         y = self._batch_norm(y)
         return y

ppocr/modeling/heads/e2e_pg_head.py

-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -179,7 +179,7 @@ class PGHead(nn.Layer):
             name="conv_f_char{}".format(5))
         self.conv3 = nn.Conv2D(
             in_channels=256,
-            out_channels=6625,
+            out_channels=37,
             kernel_size=3,
             stride=1,
             padding=1,

ppocr/modeling/necks/pg_fpn.py

-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -60,8 +60,6 @@ class ConvBNLayer(nn.Layer):
             use_global_stats=False)
 
     def forward(self, inputs):
-        # if self.is_vd_mode:
-        #     inputs = self._pool2d_avg(inputs)
         y = self._conv(inputs)
         y = self._batch_norm(y)
         return y
@@ -112,7 +110,6 @@ class PGFPN(nn.Layer):
         num_inputs = [2048, 2048, 1024, 512, 256]
         num_outputs = [256, 256, 192, 192, 128]
         self.out_channels = 128
-        # print(in_channels)
         self.conv_bn_layer_1 = ConvBNLayer(
             in_channels=3,
             out_channels=32,

ppocr/postprocess/pg_postprocess.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -23,14 +23,9 @@ __dir__ = os.path.dirname(__file__)
 sys.path.append(__dir__)
 sys.path.append(os.path.join(__dir__, '..'))
 
-import numpy as np
-from .locality_aware_nms import nms_locality
 from ppocr.utils.e2e_utils.extract_textpoint import *
-from ppocr.utils.e2e_utils.ski_thin import *
 from ppocr.utils.e2e_utils.visual import *
 import paddle
-import cv2
-import time
 
 
 class PGPostProcess(object):
@@ -115,7 +110,6 @@ class PGPostProcess(object):
             if len(yx_center_line) == 1:
                 yx_center_line.append(yx_center_line[-1])
 
-            # expand corresponding offset for total-text.
             offset_expand = 1.0
             if self.valid_set == 'totaltext':
                 offset_expand = 1.2
@@ -137,7 +131,6 @@ class PGPostProcess(object):
                     [ratio_w, ratio_h]).reshape(-1, 2)
                 point_pair_list.append(point_pair)
 
-                # for visualization
                 all_point_list.append([
                     int(round(x * 4.0 / ratio_w)),
                     int(round(y * 4.0 / ratio_h))
@@ -145,7 +138,6 @@ class PGPostProcess(object):
                 all_point_pair_list.append(point_pair.round().astype(np.int32)
                                            .tolist())
 
-            # ndarry: (x, 2)
             detected_poly, pair_length_info = point_pair2poly(point_pair_list)
             detected_poly = expand_poly_along_width(
                 detected_poly, shrink_ratio_of_width=0.2)

ppocr/utils/e2e_metric/Deteval.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,42 +11,27 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import numpy as np
 from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
 
-try:  # python2
-    range = xrange
-except Exception:
-    # python3
-    range = range
-"""
-Input format: y0,x0, ..... yn,xn. Each detection is separated by the end of line token ('\n')'
-"""
-
-# if len(sys.argv) != 4:
-#     print('\n usage: test.py pred_dir gt_dir savefile')
-#     sys.exit()
-
 
 def get_socre(gt_dict, pred_dict):
-    # allInputs = listdir(input_dir)
     allInputs = 1
 
-    def input_reading_mod(pred_dict, input):
+    def input_reading_mod(pred_dict):
         """This helper reads input from txt files"""
         det = []
         n = len(pred_dict)
         for i in range(n):
             points = pred_dict[i]['points']
             text = pred_dict[i]['text']
-            # for i in range(len(points)):
             point = ",".join(map(str, points.reshape(-1, )))
             det.append([point, text])
         return det
 
-    def gt_reading_mod(gt_dict, gt_id):
+    def gt_reading_mod(gt_dict):
         """This helper reads groundtruths from mat files"""
-        # gt_id = gt_id.split('.')[0]
         gt = []
         n = len(gt_dict)
         for i in range(n):
@@ -74,23 +59,12 @@ def get_socre(gt_dict, pred_dict):
 
     def detection_filtering(detections, groundtruths, threshold=0.5):
         for gt_id, gt in enumerate(groundtruths):
-            print
-            "liushanshan gt[1] = {}".format(gt[1])
-            print
-            "liushanshan gt[2] = {}".format(gt[2])
-            print
-            "liushanshan gt[3] = {}".format(gt[3])
-            print
-            "liushanshan gt[4] = {}".format(gt[4])
-            print
-            "liushanshan gt[5] = {}".format(gt[5])
             if (gt[5] == '#') and (gt[1].shape[1] > 1):
                 gt_x = list(map(int, np.squeeze(gt[1])))
                 gt_y = list(map(int, np.squeeze(gt[3])))
                 for det_id, detection in enumerate(detections):
                     detection_orig = detection
                     detection = [float(x) for x in detection[0].split(',')]
-                    # detection = detection.split(',')
                     detection = list(map(int, detection))
                     det_x = detection[0::2]
                     det_y = detection[1::2]
@@ -105,18 +79,10 @@ def get_socre(gt_dict, pred_dict):
         """
         sigma = inter_area / gt_area
         """
-        # print(area_of_intersection(det_x, det_y, gt_x, gt_y))
         return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
                          area(gt_x, gt_y)), 2)
 
     def tau_calculation(det_x, det_y, gt_x, gt_y):
-        """
-        tau = inter_area / det_area
-        """
-        # print "liushanshan det_x {}".format(det_x)
-        # print "liushanshan det_y {}".format(det_y)
-        # print "liushanshan area {}".format(area(det_x, det_y))
-        # print "liushanshan tau = {}".format(np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) / area(det_x, det_y)), 2))
         if area(det_x, det_y) == 0.0:
             return 0
         return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
@@ -141,10 +107,8 @@ def get_socre(gt_dict, pred_dict):
                 input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
                 input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
                 and (input_id != 'Deteval_result_non_curved.txt'):
-            print(input_id)
-            detections = input_reading_mod(pred_dict, input_id)
-            # print "liushanshan detections = {}".format(detections)
-            groundtruths = gt_reading_mod(gt_dict, input_id)
+            detections = input_reading_mod(pred_dict)
+            groundtruths = gt_reading_mod(gt_dict)
             detections = detection_filtering(
                 detections,
                 groundtruths)  # filters detections overlapping with DC area
@@ -187,10 +151,6 @@ def get_socre(gt_dict, pred_dict):
             global_tau.append(local_tau_table)
             global_pred_str.append(local_pred_str)
             global_gt_str.append(local_gt_str)
-            print
-            "liushanshan global_pred_str = {}".format(global_pred_str)
-            print
-            "liushanshan global_gt_str = {}".format(global_gt_str)
 
     global_accumulative_recall = 0
     global_accumulative_precision = 0
@@ -236,17 +196,11 @@ def get_socre(gt_dict, pred_dict):
                 gt_flag[0, gt_id] = 1
                 matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
                 # recg start
-                print
-                "liushanshan one to one det_id = {}".format(matched_det_id)
-                print
-                "liushanshan one to one gt_id = {}".format(gt_id)
+
                 gt_str_cur = global_gt_str[idy][gt_id]
                 pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
                     0]]
-                print
-                "liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
-                print
-                "liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
+
                 if pred_str_cur == gt_str_cur:
                     hit_str_num += 1
                 else:
@@ -290,20 +244,10 @@ def get_socre(gt_dict, pred_dict):
                         gt_flag[0, gt_id] = 1
                         det_flag[0, qualified_tau_candidates] = 1
                         # recg start
-                        print
-                        "liushanshan one to many det_id = {}".format(
-                            qualified_tau_candidates)
-                        print
-                        "liushanshan one to many gt_id = {}".format(gt_id)
                         gt_str_cur = global_gt_str[idy][gt_id]
                         pred_str_cur = global_pred_str[idy][
                             qualified_tau_candidates[0].tolist()[0]]
-                        print
-                        "liushanshan one to many gt_str_cur = {}".format(
-                            gt_str_cur)
-                        print
-                        "liushanshan one to many pred_str_cur = {}".format(
-                            pred_str_cur)
+
                         if pred_str_cur == gt_str_cur:
                             hit_str_num += 1
                         else:
@@ -315,19 +259,11 @@ def get_socre(gt_dict, pred_dict):
                     gt_flag[0, gt_id] = 1
                     det_flag[0, qualified_tau_candidates] = 1
                     # recg start
-                    print
-                    "liushanshan one to many det_id = {}".format(
-                        qualified_tau_candidates)
-                    print
-                    "liushanshan one to many gt_id = {}".format(gt_id)
+
                     gt_str_cur = global_gt_str[idy][gt_id]
                     pred_str_cur = global_pred_str[idy][
                         qualified_tau_candidates[0].tolist()[0]]
-                    print
-                    "liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
-                    print
-                    "liushanshan one to many pred_str_cur = {}".format(
-                        pred_str_cur)
+
                     if pred_str_cur == gt_str_cur:
                         hit_str_num += 1
                     else:
@@ -377,25 +313,14 @@ def get_socre(gt_dict, pred_dict):
                         gt_flag[0, qualified_sigma_candidates] = 1
                         det_flag[0, det_id] = 1
                         # recg start
-                        print
-                        "liushanshan many to one det_id = {}".format(det_id)
-                        print
-                        "liushanshan many to one gt_id = {}".format(
-                            qualified_sigma_candidates)
                         pred_str_cur = global_pred_str[idy][det_id]
                         gt_len = len(qualified_sigma_candidates[0])
                         for idx in range(gt_len):
                             ele_gt_id = qualified_sigma_candidates[0].tolist()[
                                 idx]
-                            if not global_gt_str[idy].has_key(ele_gt_id):
+                            if ele_gt_id not in global_gt_str[idy]:
                                 continue
                             gt_str_cur = global_gt_str[idy][ele_gt_id]
-                            print
-                            "liushanshan many to one gt_str_cur = {}".format(
-                                gt_str_cur)
-                            print
-                            "liushanshan many to one pred_str_cur = {}".format(
-                                pred_str_cur)
                             if pred_str_cur == gt_str_cur:
                                 hit_str_num += 1
                                 break
@@ -409,24 +334,14 @@ def get_socre(gt_dict, pred_dict):
                     det_flag[0, det_id] = 1
                     gt_flag[0, qualified_sigma_candidates] = 1
                     # recg start
-                    print
-                    "liushanshan many to one det_id = {}".format(det_id)
-                    print
-                    "liushanshan many to one gt_id = {}".format(
-                        qualified_sigma_candidates)
+
                     pred_str_cur = global_pred_str[idy][det_id]
                     gt_len = len(qualified_sigma_candidates[0])
                     for idx in range(gt_len):
                         ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
-                        if not global_gt_str[idy].has_key(ele_gt_id):
+                        if ele_gt_id not in global_gt_str[idy]:
                             continue
                         gt_str_cur = global_gt_str[idy][ele_gt_id]
-                        print
-                        "liushanshan many to one gt_str_cur = {}".format(
-                            gt_str_cur)
-                        print
-                        "liushanshan many to one pred_str_cur = {}".format(
-                            pred_str_cur)
                         if pred_str_cur == gt_str_cur:
                             hit_str_num += 1
                             break
@@ -434,9 +349,6 @@ def get_socre(gt_dict, pred_dict):
                             if pred_str_cur.lower() == gt_str_cur.lower():
                                 hit_str_num += 1
                                 break
-                            else:
-                                print
-                                'no match'
                     # recg end
 
                     global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
@@ -448,7 +360,6 @@ def get_socre(gt_dict, pred_dict):
 
     single_data = {}
     for idx in range(len(global_sigma)):
-        # print(allInputs[idx])
         local_sigma_table = global_sigma[idx]
         local_tau_table = global_tau[idx]
 
@@ -504,8 +415,6 @@ def get_socre(gt_dict, pred_dict):
         except ZeroDivisionError:
             local_f_score = 0
 
-        # temp = ('%s: Recall=%.4f, Precision=%.4f, f_score=%.4f\n' % (
-        #     allInputs[idx], local_recall, local_precision, local_f_score))
     single_data['sigma'] = global_sigma
     single_data['global_tau'] = global_tau
     single_data['global_pred_str'] = global_pred_str
@@ -575,17 +484,9 @@ def combine_results(all_data):
                 gt_flag[0, gt_id] = 1
                 matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
                 # recg start
-                print
-                "liushanshan one to one det_id = {}".format(matched_det_id)
-                print
-                "liushanshan one to one gt_id = {}".format(gt_id)
                 gt_str_cur = global_gt_str[idy][gt_id]
                 pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
                     0]]
-                print
-                "liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
-                print
-                "liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
                 if pred_str_cur == gt_str_cur:
                     hit_str_num += 1
                 else:
@@ -629,20 +530,9 @@ def combine_results(all_data):
                         gt_flag[0, gt_id] = 1
                         det_flag[0, qualified_tau_candidates] = 1
                         # recg start
-                        print
-                        "liushanshan one to many det_id = {}".format(
-                            qualified_tau_candidates)
-                        print
-                        "liushanshan one to many gt_id = {}".format(gt_id)
                         gt_str_cur = global_gt_str[idy][gt_id]
                         pred_str_cur = global_pred_str[idy][
                             qualified_tau_candidates[0].tolist()[0]]
-                        print
-                        "liushanshan one to many gt_str_cur = {}".format(
-                            gt_str_cur)
-                        print
-                        "liushanshan one to many pred_str_cur = {}".format(
-                            pred_str_cur)
                         if pred_str_cur == gt_str_cur:
                             hit_str_num += 1
                         else:
@@ -654,19 +544,9 @@ def combine_results(all_data):
                     gt_flag[0, gt_id] = 1
                     det_flag[0, qualified_tau_candidates] = 1
                     # recg start
-                    print
-                    "liushanshan one to many det_id = {}".format(
-                        qualified_tau_candidates)
-                    print
-                    "liushanshan one to many gt_id = {}".format(gt_id)
                     gt_str_cur = global_gt_str[idy][gt_id]
                     pred_str_cur = global_pred_str[idy][
                         qualified_tau_candidates[0].tolist()[0]]
-                    print
-                    "liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
-                    print
-                    "liushanshan one to many pred_str_cur = {}".format(
-                        pred_str_cur)
                     if pred_str_cur == gt_str_cur:
                         hit_str_num += 1
                     else:
@@ -716,11 +596,6 @@ def combine_results(all_data):
                         gt_flag[0, qualified_sigma_candidates] = 1
                         det_flag[0, det_id] = 1
                         # recg start
-                        print
-                        "liushanshan many to one det_id = {}".format(det_id)
-                        print
-                        "liushanshan many to one gt_id = {}".format(
-                            qualified_sigma_candidates)
                         pred_str_cur = global_pred_str[idy][det_id]
                         gt_len = len(qualified_sigma_candidates[0])
                         for idx in range(gt_len):
@@ -729,12 +604,6 @@ def combine_results(all_data):
                             if ele_gt_id not in global_gt_str[idy]:
                                 continue
                             gt_str_cur = global_gt_str[idy][ele_gt_id]
-                            print
-                            "liushanshan many to one gt_str_cur = {}".format(
-                                gt_str_cur)
-                            print
-                            "liushanshan many to one pred_str_cur = {}".format(
-                                pred_str_cur)
                             if pred_str_cur == gt_str_cur:
                                 hit_str_num += 1
                                 break
@@ -748,24 +617,13 @@ def combine_results(all_data):
                     det_flag[0, det_id] = 1
                     gt_flag[0, qualified_sigma_candidates] = 1
                     # recg start
-                    print
-                    "liushanshan many to one det_id = {}".format(det_id)
-                    print
-                    "liushanshan many to one gt_id = {}".format(
-                        qualified_sigma_candidates)
                     pred_str_cur = global_pred_str[idy][det_id]
                     gt_len = len(qualified_sigma_candidates[0])
                     for idx in range(gt_len):
                         ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
-                        if not global_gt_str[idy].has_key(ele_gt_id):
+                        if ele_gt_id not in global_gt_str[idy]:
                             continue
                         gt_str_cur = global_gt_str[idy][ele_gt_id]
-                        print
-                        "liushanshan many to one gt_str_cur = {}".format(
-                            gt_str_cur)
-                        print
-                        "liushanshan many to one pred_str_cur = {}".format(
-                            pred_str_cur)
                         if pred_str_cur == gt_str_cur:
                             hit_str_num += 1
                             break
@@ -773,9 +631,6 @@ def combine_results(all_data):
                             if pred_str_cur.lower() == gt_str_cur.lower():
                                 hit_str_num += 1
                                 break
-                            else:
-                                print
-                                'no match'
                     # recg end
 
                     global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k

ppocr/utils/e2e_utils/extract_textpoint.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,14 +16,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
 import cv2
-import time
 import math
 
 import numpy as np
 from itertools import groupby
-from ppocr.utils.e2e_utils.ski_thin import thin
+from skimage.morphology._skeletonize import thin
 
 
 def softmax(logits):
@@ -518,28 +516,6 @@ def generate_pivot_list_tt_inference(p_score,
                 continue
             pos_list_sorted = sort_and_expand_with_direction_v2(
                 pos_list, f_direction, p_tcl_map)
-            # pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
             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
-
-
-if __name__ == '__main__':
-    np.random.seed(0)
-    import time
-
-    logits_map = np.random.random([10, 20, 33])
-    # a list of [x, y]
-    instance_gather_info_1 = [(2, 3), (2, 4), (3, 5)]
-    instance_gather_info_2 = [(15, 6), (15, 7), (18, 8)]
-    instance_gather_info_3 = [(8, 8), (8, 8), (8, 8)]
-    gather_info_list = [
-        instance_gather_info_1, instance_gather_info_2, instance_gather_info_3
-    ]
-
-    time0 = time.time()
-    res = ctc_decoder_for_image(
-        gather_info_list, logits_map, keep_blank_in_idxs=True)
-    print(res)
-    print('cost {}'.format(time.time() - time0))
-    print('--' * 20)

ppocr/utils/e2e_utils/ski_thin.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-from scipy import ndimage as ndi
-
-G123_LUT = np.array(
-    [
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,
-        0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
-        1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
-        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0
-    ],
-    dtype=np.bool)
-
-G123P_LUT = np.array(
-    [
-        0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,
-        0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-    ],
-    dtype=np.bool)
-
-
-def thin(image, max_iter=None):
-    """
-    Perform morphological thinning of a binary image.
-    Parameters
-    ----------
-    image : binary (M, N) ndarray
-        The image to be thinned.
-    max_iter : int, number of iterations, optional
-        Regardless of the value of this parameter, the thinned image
-        is returned immediately if an iteration produces no change.
-        If this parameter is specified it thus sets an upper bound on
-        the number of iterations performed.
-    Returns
-    -------
-    out : ndarray of bool
-        Thinned image.
-    See also
-    --------
-    skeletonize, medial_axis
-    Notes
-    -----
-    This algorithm [1]_ works by making multiple passes over the image,
-    removing pixels matching a set of criteria designed to thin
-    connected regions while preserving eight-connected components and
-    2 x 2 squares [2]_. In each of the two sub-iterations the algorithm
-    correlates the intermediate skeleton image with a neighborhood mask,
-    then looks up each neighborhood in a lookup table indicating whether
-    the central pixel should be deleted in that sub-iteration.
-    References
-    ----------
-    .. [1] Z. Guo and R. W. Hall, "Parallel thinning with
-           two-subiteration algorithms," Comm. ACM, vol. 32, no. 3,
-           pp. 359-373, 1989. :DOI:`10.1145/62065.62074`
-    .. [2] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning
-           Methodologies-A Comprehensive Survey," IEEE Transactions on
-           Pattern Analysis and Machine Intelligence, Vol 14, No. 9,
-           p. 879, 1992. :DOI:`10.1109/34.161346`
-    Examples
-    --------
-    >>> square = np.zeros((7, 7), dtype=np.uint8)
-    >>> square[1:-1, 2:-2] = 1
-    >>> square[0, 1] =  1
-    >>> square
-    array([[0, 1, 0, 0, 0, 0, 0],
-           [0, 0, 1, 1, 1, 0, 0],
-           [0, 0, 1, 1, 1, 0, 0],
-           [0, 0, 1, 1, 1, 0, 0],
-           [0, 0, 1, 1, 1, 0, 0],
-           [0, 0, 1, 1, 1, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
-    >>> skel = thin(square)
-    >>> skel.astype(np.uint8)
-    array([[0, 1, 0, 0, 0, 0, 0],
-           [0, 0, 1, 0, 0, 0, 0],
-           [0, 0, 0, 1, 0, 0, 0],
-           [0, 0, 0, 1, 0, 0, 0],
-           [0, 0, 0, 1, 0, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
-    """
-    # convert image to uint8 with values in {0, 1}
-    skel = np.asanyarray(image, dtype=bool).astype(np.uint8)
-
-    # neighborhood mask
-    mask = np.array([[8, 4, 2], [16, 0, 1], [32, 64, 128]], dtype=np.uint8)
-
-    # iterate until convergence, up to the iteration limit
-    max_iter = max_iter or np.inf
-    n_iter = 0
-    n_pts_old, n_pts_new = np.inf, np.sum(skel)
-    while n_pts_old != n_pts_new and n_iter < max_iter:
-        n_pts_old = n_pts_new
-
-        # perform the two "subiterations" described in the paper
-        for lut in [G123_LUT, G123P_LUT]:
-            # correlate image with neighborhood mask
-            N = ndi.correlate(skel, mask, mode='constant')
-            # take deletion decision from this subiteration's LUT
-            D = np.take(lut, N)
-            # perform deletion
-            skel[D] = 0
-
-        n_pts_new = np.sum(skel)  # count points after thinning
-        n_iter += 1
-
-    return skel.astype(np.bool)

ppocr/utils/e2e_utils/visual.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -50,7 +50,7 @@ def resize_image(im, max_side_len=512):
 def resize_image_min(im, max_side_len=512):
     """
     """
-    print('--> Using resize_image_min')
+    # print('--> Using resize_image_min')
     h, w, _ = im.shape
 
     resize_w = w

tools/infer_e2e.py

@@ -45,8 +45,14 @@ def draw_e2e_res(dt_boxes, strs, config, img, img_name):
         for box, str in zip(dt_boxes, strs):
             box = box.astype(np.int32).reshape((-1, 1, 2))
             cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
-            cv2.putText(src_im, str, org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
-                    fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.7, color=(0, 255, 0), thickness=1)
+            cv2.putText(
+                src_im,
+                str,
+                org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+                fontFace=cv2.FONT_HERSHEY_COMPLEX,
+                fontScale=0.7,
+                color=(0, 255, 0),
+                thickness=1)
         save_det_path = os.path.dirname(config['Global'][
             'save_res_path']) + "/e2e_results/"
         if not os.path.exists(save_det_path):
@@ -55,6 +61,7 @@ def draw_e2e_res(dt_boxes, strs, config, img, img_name):
         cv2.imwrite(save_path, src_im)
         logger.info("The e2e Image saved in {}".format(save_path))
 
+
 def main():
     global_config = config['Global']
 
@@ -111,4 +118,4 @@ def main():
 
 if __name__ == '__main__':
     config, device, logger, vdl_writer = program.preprocess()
-    main()
\ No newline at end of file
+    main()

tools/program.py

@@ -375,7 +375,7 @@ def preprocess(is_train=False):
     alg = config['Architecture']['algorithm']
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
-        'CLS', 'PG'
+        'CLS', 'PGNet'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'