Merge branch 'dygraph' into lite

README.md

@@ -93,7 +93,7 @@ For a new language request, please refer to [Guideline for new language_requests
     - [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
     - [Python Inference](./doc/doc_en/inference_en.md)
     - [C++ Inference](./deploy/cpp_infer/readme_en.md)
-    - [Serving](./deploy/hubserving/readme_en.md)
+    - [Serving](./deploy/pdserving/README.md)
     - [Mobile](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme_en.md)
     - [Benchmark](./doc/doc_en/benchmark_en.md)  
 - Data Annotation and Synthesis

README_ch.md

@@ -88,7 +88,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
     - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
     - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
     - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
-    - [服务化部署](./deploy/hubserving/readme.md)
+    - [服务化部署](./deploy/pdserving/README_CN.md)
     - [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
     - [Benchmark](./doc/doc_ch/benchmark.md)
 - 数据集

configs/det/det_r50_vd_sast_icdar15.yml

@@ -14,12 +14,13 @@ Global:
   load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
-  checkpoints: 
+  checkpoints:
   save_inference_dir:
   use_visualdl: False
-  infer_img: 
+  infer_img:
   save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
 
+
 Architecture:
   model_type: det
   algorithm: SAST

configs/e2e/e2e_r50_vd_pg.yml

+Global:
+  use_gpu: True
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/pgnet_r50_vd_totaltext/
+  save_epoch_step: 10
+  # evaluation is run every 0 iterationss after the 1000th iteration
+  eval_batch_step: [ 0, 1000 ]
+  # 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: False
+  cal_metric_during_train: False
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  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/ic15_dict.txt
+  character_type: EN
+  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
+  algorithm: PGNet
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: PGFPN
+  Head:
+    name: PGHead
+
+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
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+
+PostProcess:
+  name: PGPostProcess
+  score_thresh: 0.5
+Metric:
+  name: E2EMetric
+  character_dict_path: ppocr/utils/ic15_dict.txt
+  main_indicator: f_score_e2e
+
+Train:
+  dataset:
+    name: PGDataSet
+    label_file_list: [.././train_data/total_text/train/]
+    ratio_list: [1.0]
+    data_format: icdar #two data format: icdar/textnet
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - PGProcessTrain:
+          batch_size: 14  # same as loader: batch_size_per_card
+          min_crop_size: 24
+          min_text_size: 4
+          max_text_size: 512
+      - 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: 14
+    num_workers: 16
+
+Eval:
+  dataset:
+    name: PGDataSet
+    data_dir: ./train_data/
+    label_file_list: [./train_data/total_text/test/]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - E2ELabelEncode:
+      - E2EResizeForTest:
+          max_side_len: 768
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2
\ No newline at end of file

deploy/hubserving/readme.md

@@ -2,7 +2,7 @@
 
 PaddleOCR提供2种服务部署方式：
 - 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，按照本教程使用；
-- (coming soon)基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../../deploy/pdserving/readme.md)。
+- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../../deploy/pdserving/README_CN.md)。
 
 # 基于PaddleHub Serving的服务部署
 

deploy/hubserving/readme_en.md

@@ -2,7 +2,7 @@ English | [简体中文](readme.md)
 
 PaddleOCR provides 2 service deployment methods:
 - Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial.
-- (coming soon)Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../deploy/pdserving/readme.md) for usage.
+- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../deploy/pdserving/README.md) for usage.
 
 # Service deployment based on PaddleHub Serving  
 

deploy/pdserving/README.md

+# OCR Pipeline WebService
+
+(English|[简体中文](./README_CN.md))
+
+PaddleOCR provides two service deployment methods:
+- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please refer to the [tutorial](../../deploy/hubserving/readme_en.md)
+- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please follow this tutorial.
+
+# Service deployment based on PaddleServing  
+
+This document will introduce how to use the [PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README.md) to deploy the PPOCR dynamic graph model as a pipeline online service.
+
+Some Key Features of Paddle Serving:
+- Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed with one line command.
+- Industrial serving features supported, such as models management, online loading, online A/B testing etc.
+- Highly concurrent and efficient communication between clients and servers supported.
+
+The introduction and tutorial of Paddle Serving service deployment framework reference [document](https://github.com/PaddlePaddle/Serving/blob/develop/README.md).
+
+
+## Contents
+- [Environmental preparation](#environmental-preparation)
+- [Model conversion](#model-conversion)
+- [Paddle Serving pipeline deployment](#paddle-serving-pipeline-deployment)
+- [FAQ](#faq)
+
+<a name="environmental-preparation"></a>
+## Environmental preparation
+
+PaddleOCR operating environment and Paddle Serving operating environment are needed.
+
+1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md).
+
+2. The steps of PaddleServing operating environment prepare are as follows:
+
+    Install serving which used to start the service
+    ```
+    pip3 install paddle-serving-server==0.5.0 # for CPU
+    pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
+    # Other GPU environments need to confirm the environment and then choose to execute the following commands
+    pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
+    pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
+    pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
+    pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
+    ```
+
+3. Install the client to send requests to the service
+    ```
+    pip3 install paddle-serving-client==0.5.0 # for CPU
+
+    pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
+    ```
+
+4. Install serving-app
+    ```
+    pip3 install paddle-serving-app==0.3.0
+    # fix local_predict to support load dynamic model
+    # find the install directoory of paddle_serving_app
+    vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
+    # replace line 85 of local_predict.py config = AnalysisConfig(model_path) with:
+    if os.path.exists(os.path.join(model_path, "__params__")):
+        config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
+    else:
+        config = AnalysisConfig(model_path)
+    ```
+
+   **note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md).
+
+
+<a name="model-conversion"></a>
+## Model conversion
+When using PaddleServing for service deployment, you need to convert the saved inference model into a serving model that is easy to deploy.
+
+Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15) of PPOCR
+```
+# Download and unzip the OCR text detection model
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
+# Download and unzip the OCR text recognition model
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
+
+```
+Then, you can use installed paddle_serving_client tool to convert inference model to server model.
+```
+#  Detection model conversion
+python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
+                                         --model_filename inference.pdmodel          \
+                                         --params_filename inference.pdiparams       \
+                                         --serving_server ./ppocr_det_server_2.0_serving/ \
+                                         --serving_client ./ppocr_det_server_2.0_client/
+
+#  Recognition model conversion
+python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
+                                         --model_filename inference.pdmodel          \
+                                         --params_filename inference.pdiparams       \
+                                         --serving_server ./ppocr_rec_server_2.0_serving/  \
+                                         --serving_client ./ppocr_rec_server_2.0_client/
+
+```
+
+After the detection model is converted, there will be additional folders of `ppocr_det_server_2.0_serving` and `ppocr_det_server_2.0_client` in the current folder, with the following format:
+```
+|- ppocr_det_server_2.0_serving/
+   |- __model__
+   |- __params__
+   |- serving_server_conf.prototxt
+   |- serving_server_conf.stream.prototxt
+
+|- ppocr_det_server_2.0_client
+   |- serving_client_conf.prototxt
+   |- serving_client_conf.stream.prototxt
+
+```
+The recognition model is the same.
+
+<a name="paddle-serving-pipeline-deployment"></a>
+## Paddle Serving pipeline deployment
+
+1. Download the PaddleOCR code, if you have already downloaded it, you can skip this step.
+    ```
+    git clone https://github.com/PaddlePaddle/PaddleOCR
+
+    # Enter the working directory  
+    cd PaddleOCR/deploy/pdserver/
+    ```
+
+    The pdserver directory contains the code to start the pipeline service and send prediction requests, including:
+    ```
+    __init__.py
+    config.yml # Start the service configuration file
+    ocr_reader.py # OCR model pre-processing and post-processing code implementation
+    pipeline_http_client.py # Script to send pipeline prediction request
+    web_service.py # Start the script of the pipeline server
+    ```
+
+2. Run the following command to start the service.
+    ```
+    # Start the service and save the running log in log.txt
+    python3 web_service.py &>log.txt &
+    ```
+    After the service is successfully started, a log similar to the following will be printed in log.txt
+    ![](./imgs/start_server.png)
+
+3. Send service request
+    ```
+    python3 pipeline_http_client.py
+    ```
+    After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
+    ![](./imgs/results.png)  
+
+<a name="faq"></a>
+## FAQ
+**Q1**: No result return after sending the request.
+
+**A1**: Do not set the proxy when starting the service and sending the request. You can close the proxy before starting the service and before sending the request. The command to close the proxy is:
+```
+unset https_proxy
+unset http_proxy
+```  

deploy/pdserving/README_CN.md

+# PPOCR 服务化部署
+
+([English](./README.md)|简体中文)
+
+PaddleOCR提供2种服务部署方式：
+- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../../deploy/hubserving/readme.md)；
+- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。
+
+# 基于PaddleServing的服务部署
+
+本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PPOCR
+动态图模型的pipeline在线服务。
+
+相比较于hubserving部署，PaddleServing具备以下优点：
+- 支持客户端和服务端之间高并发和高效通信
+- 支持 工业级的服务能力 例如模型管理，在线加载，在线A/B测试等
+- 支持 多种编程语言 开发客户端，例如C++, Python和Java
+
+更多有关PaddleServing服务化部署框架介绍和使用教程参考[文档](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)。
+
+## 目录
+- [环境准备](#环境准备)
+- [模型转换](#模型转换)
+- [Paddle Serving pipeline部署](#部署)
+- [FAQ](#FAQ)
+
+<a name="环境准备"></a>
+## 环境准备
+
+需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
+
+- 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md)
+
+- 准备PaddleServing的运行环境，步骤如下
+
+1. 安装serving，用于启动服务
+    ```
+    pip3 install paddle-serving-server==0.5.0 # for CPU
+    pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
+    # 其他GPU环境需要确认环境再选择执行如下命令
+    pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
+    pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
+    pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
+    pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
+    ```
+
+2. 安装client，用于向服务发送请求
+    ```
+    pip3 install paddle-serving-client==0.5.0  # for CPU
+
+    pip3 install paddle-serving-client-gpu==0.5.0   # for GPU
+    ```
+
+3. 安装serving-app
+    ```
+    pip3 install paddle-serving-app==0.3.0
+    ```
+    **note:**  安装0.3.0版本的serving-app后，为了能加载动态图模型，需要修改serving_app的源码，具体为：
+    ```
+    # 找到paddle_serving_app的安装目录，找到并编辑local_predict.py文件
+    vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
+    # 将local_predict.py 的第85行 config = AnalysisConfig(model_path)  替换为：
+    if os.path.exists(os.path.join(model_path, "__params__")):
+        config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
+    else:
+        config = AnalysisConfig(model_path)
+    ```
+
+    **Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。
+
+<a name="模型转换"></a>
+## 模型转换
+
+使用PaddleServing做服务化部署时，需要将保存的inference模型转换为serving易于部署的模型。
+
+首先，下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15)
+```
+# 下载并解压 OCR 文本检测模型
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
+# 下载并解压 OCR 文本识别模型
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
+```
+
+接下来，用安装的paddle_serving_client把下载的inference模型转换成易于server部署的模型格式。
+
+```
+# 转换检测模型
+python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
+                                         --model_filename inference.pdmodel          \
+                                         --params_filename inference.pdiparams       \
+                                         --serving_server ./ppocr_det_server_2.0_serving/ \
+                                         --serving_client ./ppocr_det_server_2.0_client/
+
+# 转换识别模型
+python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
+                                         --model_filename inference.pdmodel          \
+                                         --params_filename inference.pdiparams       \
+                                         --serving_server ./ppocr_rec_server_2.0_serving/  \
+                                         --serving_client ./ppocr_rec_server_2.0_client/
+```
+
+检测模型转换完成后，会在当前文件夹多出`ppocr_det_server_2.0_serving` 和`ppocr_det_server_2.0_client`的文件夹，具备如下格式：
+```
+|- ppocr_det_server_2.0_serving/
+  |- __model__  
+  |- __params__
+  |- serving_server_conf.prototxt  
+  |- serving_server_conf.stream.prototxt
+
+|- ppocr_det_server_2.0_client
+  |- serving_client_conf.prototxt  
+  |- serving_client_conf.stream.prototxt
+
+```
+识别模型同理。
+
+<a name="部署"></a>
+## Paddle Serving pipeline部署
+
+1. 下载PaddleOCR代码，若已下载可跳过此步骤
+    ```
+    git clone https://github.com/PaddlePaddle/PaddleOCR
+
+    # 进入到工作目录
+    cd PaddleOCR/deploy/pdserver/
+    ```
+    pdserver目录包含启动pipeline服务和发送预测请求的代码，包括：
+    ```
+    __init__.py
+    config.yml            # 启动服务的配置文件
+    ocr_reader.py         # OCR模型预处理和后处理的代码实现
+    pipeline_http_client.py   # 发送pipeline预测请求的脚本
+    web_service.py        # 启动pipeline服务端的脚本
+    ```
+
+2. 启动服务可运行如下命令：
+    ```
+    # 启动服务，运行日志保存在log.txt
+    python3 web_service.py &>log.txt &
+    ```
+    成功启动服务后，log.txt中会打印类似如下日志
+    ![](./imgs/start_server.png)
+
+3. 发送服务请求：
+    ```
+    python3 pipeline_http_client.py
+    ```
+    成功运行后，模型预测的结果会打印在cmd窗口中，结果示例为：
+    ![](./imgs/results.png)
+
+
+<a name="FAQ"></a>
+## FAQ
+**Q1**： 发送请求后没有结果返回或者提示输出解码报错
+
+**A1**： 启动服务和发送请求时不要设置代理，可以在启动服务前和发送请求前关闭代理，关闭代理的命令是：
+```
+unset https_proxy
+unset http_proxy
+```

deploy/pdserving/__init__.py

+# 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.
+# 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.

deploy/pdserving/config.yml

+#rpc端口, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时，会自动将rpc_port设置为http_port+1
+rpc_port: 18090
+
+#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时，不自动生成http_port
+http_port: 9999
+
+#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程，每个进程内构建grpcSever和DAG
+##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
+worker_num: 20
+
+#build_dag_each_worker, False，框架在进程内创建一条DAG；True，框架会每个进程内创建多个独立的DAG
+build_dag_each_worker: false
+
+dag:
+    #op资源类型, True, 为线程模型；False，为进程模型
+    is_thread_op: False
+
+    #重试次数
+    retry: 1
+
+    #使用性能分析, True，生成Timeline性能数据，对性能有一定影响；False为不使用
+    use_profile: False
+    
+    tracer:
+        interval_s: 10
+op:
+    det:
+        #并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 4
+
+        #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
+        local_service_conf:
+            #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测
+            client_type: local_predictor
+
+            #det模型路径
+            model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model
+
+            #Fetch结果列表，以client_config中fetch_var的alias_name为准
+            fetch_list: ["save_infer_model/scale_0.tmp_1"]
+
+            #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
+            devices: "2"
+            
+            ir_optim: True
+    rec:
+        #并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 1
+
+        #超时时间, 单位ms
+        timeout: -1
+ 
+        #Serving交互重试次数，默认不重试
+        retry: 1
+
+        #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
+        local_service_conf:
+
+            #client类型，包括brpc, grpc和local_predictor。local_predictor不启动Serving服务，进程内预测
+            client_type: local_predictor
+
+            #rec模型路径
+            model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model
+
+            #Fetch结果列表，以client_config中fetch_var的alias_name为准
+            fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] 
+
+            #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
+            devices: "2"
+            
+            ir_optim: True

deploy/pdserving/ocr_reader.py

+# 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.
+# 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 cv2
+import copy
+import numpy as np
+import math
+import re
+import sys
+import argparse
+import string
+from copy import deepcopy
+import paddle
+
+
+class DetResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(DetResizeForTest, self).__init__()
+        self.resize_type = 0
+        if 'image_shape' in kwargs:
+            self.image_shape = kwargs['image_shape']
+            self.resize_type = 1
+        elif 'limit_side_len' in kwargs:
+            self.limit_side_len = kwargs['limit_side_len']
+            self.limit_type = kwargs.get('limit_type', 'min')
+        elif 'resize_long' in kwargs:
+            self.resize_type = 2
+            self.resize_long = kwargs.get('resize_long', 960)
+        else:
+            self.limit_side_len = 736
+            self.limit_type = 'min'
+
+    def __call__(self, data):
+        img = deepcopy(data)
+        src_h, src_w, _ = img.shape
+
+        if self.resize_type == 0:
+            img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+        elif self.resize_type == 2:
+            img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+        else:
+            img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+
+        return img
+
+    def resize_image_type1(self, img):
+        resize_h, resize_w = self.image_shape
+        ori_h, ori_w = img.shape[:2]  # (h, w, c)
+        ratio_h = float(resize_h) / ori_h
+        ratio_w = float(resize_w) / ori_w
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type0(self, img):
+        """
+        resize image to a size multiple of 32 which is required by the network
+        args:
+            img(array): array with shape [h, w, c]
+        return(tuple):
+            img, (ratio_h, ratio_w)
+        """
+        limit_side_len = self.limit_side_len
+        h, w, _ = img.shape
+
+        # limit the max side
+        if self.limit_type == 'max':
+            if max(h, w) > limit_side_len:
+                if h > w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        else:
+            if min(h, w) < limit_side_len:
+                if h < w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        resize_h = int(h * ratio)
+        resize_w = int(w * ratio)
+
+        resize_h = int(round(resize_h / 32) * 32)
+        resize_w = int(round(resize_w / 32) * 32)
+
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None, (None, None)
+            img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        except:
+            print(img.shape, resize_w, resize_h)
+            sys.exit(0)
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        # return img, np.array([h, w])
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type2(self, img):
+        h, w, _ = img.shape
+
+        resize_w = w
+        resize_h = h
+
+        # Fix the longer side
+        if resize_h > resize_w:
+            ratio = float(self.resize_long) / resize_h
+        else:
+            ratio = float(self.resize_long) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return img, [ratio_h, ratio_w]
+
+
+class BaseRecLabelDecode(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, config):
+        support_character_type = [
+            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
+            'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
+            'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
+            'ne', 'EN'
+        ]
+        character_type = config['character_type']
+        character_dict_path = config['character_dict_path']
+        use_space_char = True
+        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
+            support_character_type, character_type)
+
+        self.beg_str = "sos"
+        self.end_str = "eos"
+
+        if character_type == "en":
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        elif character_type == "EN_symbol":
+            # same with ASTER setting (use 94 char).
+            self.character_str = string.printable[:-6]
+            dict_character = list(self.character_str)
+        elif character_type in support_character_type:
+            self.character_str = ""
+            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
+                character_type)
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n").strip("\r\n")
+                    self.character_str += line
+            if use_space_char:
+                self.character_str += " "
+            dict_character = list(self.character_str)
+
+        else:
+            raise NotImplementedError
+        self.character_type = character_type
+        dict_character = self.add_special_char(dict_character)
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = dict_character
+
+    def add_special_char(self, dict_character):
+        return dict_character
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = self.get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] in ignored_tokens:
+                    continue
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+                            batch_idx][idx]:
+                        continue
+                char_list.append(self.character[int(text_index[batch_idx][
+                    idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text, np.mean(conf_list)))
+        return result_list
+
+    def get_ignored_tokens(self):
+        return [0]  # for ctc blank
+
+
+class CTCLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(
+            self,
+            config,
+            #character_dict_path=None,
+            #character_type='ch',
+            #use_space_char=False,
+            **kwargs):
+        super(CTCLabelDecode, self).__init__(config)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if isinstance(preds, paddle.Tensor):
+            preds = preds.numpy()
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
+        if label is None:
+            return text
+        label = self.decode(label)
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['blank'] + dict_character
+        return dict_character
+
+
+class CharacterOps(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, config):
+        self.character_type = config['character_type']
+        self.loss_type = config['loss_type']
+        if self.character_type == "en":
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        elif self.character_type == "ch":
+            character_dict_path = config['character_dict_path']
+            self.character_str = ""
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n").strip("\r\n")
+                    self.character_str += line
+            dict_character = list(self.character_str)
+        elif self.character_type == "en_sensitive":
+            # same with ASTER setting (use 94 char).
+            self.character_str = string.printable[:-6]
+            dict_character = list(self.character_str)
+        else:
+            self.character_str = None
+        assert self.character_str is not None, \
+            "Nonsupport type of the character: {}".format(self.character_str)
+        self.beg_str = "sos"
+        self.end_str = "eos"
+        if self.loss_type == "attention":
+            dict_character = [self.beg_str, self.end_str] + dict_character
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = dict_character
+
+    def encode(self, text):
+        """convert text-label into text-index.
+        input:
+            text: text labels of each image. [batch_size]
+
+        output:
+            text: concatenated text index for CTCLoss.
+                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
+            length: length of each text. [batch_size]
+        """
+        if self.character_type == "en":
+            text = text.lower()
+
+        text_list = []
+        for char in text:
+            if char not in self.dict:
+                continue
+            text_list.append(self.dict[char])
+        text = np.array(text_list)
+        return text
+
+    def decode(self, text_index, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        char_list = []
+        char_num = self.get_char_num()
+
+        if self.loss_type == "attention":
+            beg_idx = self.get_beg_end_flag_idx("beg")
+            end_idx = self.get_beg_end_flag_idx("end")
+            ignored_tokens = [beg_idx, end_idx]
+        else:
+            ignored_tokens = [char_num]
+
+        for idx in range(len(text_index)):
+            if text_index[idx] in ignored_tokens:
+                continue
+            if is_remove_duplicate:
+                if idx > 0 and text_index[idx - 1] == text_index[idx]:
+                    continue
+            char_list.append(self.character[text_index[idx]])
+        text = ''.join(char_list)
+        return text
+
+    def get_char_num(self):
+        return len(self.character)
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if self.loss_type == "attention":
+            if beg_or_end == "beg":
+                idx = np.array(self.dict[self.beg_str])
+            elif beg_or_end == "end":
+                idx = np.array(self.dict[self.end_str])
+            else:
+                assert False, "Unsupport type %s in get_beg_end_flag_idx"\
+                    % beg_or_end
+            return idx
+        else:
+            err = "error in get_beg_end_flag_idx when using the loss %s"\
+                % (self.loss_type)
+            assert False, err
+
+
+class OCRReader(object):
+    def __init__(self,
+                 algorithm="CRNN",
+                 image_shape=[3, 32, 320],
+                 char_type="ch",
+                 batch_num=1,
+                 char_dict_path="./ppocr_keys_v1.txt"):
+        self.rec_image_shape = image_shape
+        self.character_type = char_type
+        self.rec_batch_num = batch_num
+        char_ops_params = {}
+        char_ops_params["character_type"] = char_type
+        char_ops_params["character_dict_path"] = char_dict_path
+        char_ops_params['loss_type'] = 'ctc'
+        self.char_ops = CharacterOps(char_ops_params)
+        self.label_ops = CTCLabelDecode(char_ops_params)
+
+    def resize_norm_img(self, img, max_wh_ratio):
+        imgC, imgH, imgW = self.rec_image_shape
+        if self.character_type == "ch":
+            imgW = int(32 * max_wh_ratio)
+        h = img.shape[0]
+        w = img.shape[1]
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        resized_image = cv2.resize(img, (resized_w, imgH))
+        resized_image = resized_image.astype('float32')
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+
+        padding_im[:, :, 0:resized_w] = resized_image
+        return padding_im
+
+    def preprocess(self, img_list):
+        img_num = len(img_list)
+        norm_img_batch = []
+        max_wh_ratio = 0
+        for ino in range(img_num):
+            h, w = img_list[ino].shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+
+        for ino in range(img_num):
+            norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
+            norm_img = norm_img[np.newaxis, :]
+            norm_img_batch.append(norm_img)
+        norm_img_batch = np.concatenate(norm_img_batch)
+        norm_img_batch = norm_img_batch.copy()
+
+        return norm_img_batch[0]
+
+    def postprocess_old(self, outputs, with_score=False):
+        rec_res = []
+        rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
+        rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
+        if with_score:
+            predict_lod = outputs["softmax_0.tmp_0.lod"]
+        for rno in range(len(rec_idx_lod) - 1):
+            beg = rec_idx_lod[rno]
+            end = rec_idx_lod[rno + 1]
+            if isinstance(rec_idx_batch, list):
+                rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
+            else:  #nd array
+                rec_idx_tmp = rec_idx_batch[beg:end, 0]
+            preds_text = self.char_ops.decode(rec_idx_tmp)
+            if with_score:
+                beg = predict_lod[rno]
+                end = predict_lod[rno + 1]
+                if isinstance(outputs["softmax_0.tmp_0"], list):
+                    outputs["softmax_0.tmp_0"] = np.array(outputs[
+                        "softmax_0.tmp_0"]).astype(np.float32)
+                probs = outputs["softmax_0.tmp_0"][beg:end, :]
+                ind = np.argmax(probs, axis=1)
+                blank = probs.shape[1]
+                valid_ind = np.where(ind != (blank - 1))[0]
+                score = np.mean(probs[valid_ind, ind[valid_ind]])
+                rec_res.append([preds_text, score])
+            else:
+                rec_res.append([preds_text])
+        return rec_res
+
+    def postprocess(self, outputs, with_score=False):
+        preds = outputs["save_infer_model/scale_0.tmp_1"]
+        try:
+            preds = preds.numpy()
+        except:
+            pass
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.label_ops.decode(
+            preds_idx, preds_prob, is_remove_duplicate=True)
+        return text

deploy/pdserving/pipeline_http_client.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
+import requests
+import json
+import base64
+import os
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+url = "http://127.0.0.1:9999/ocr/prediction"
+test_img_dir = "../doc/imgs/"
+for idx, img_file in enumerate(os.listdir(test_img_dir)):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data1 = file.read()
+
+    image = cv2_to_base64(image_data1)
+
+    for i in range(1):
+        data = {"key": ["image"], "value": [image]}
+        r = requests.post(url=url, data=json.dumps(data))
+        print(r.json())
+
+test_img_dir = "../doc/imgs/"
+print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))

deploy/pdserving/pipeline_rpc_client.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.
+try:
+    from paddle_serving_server_gpu.pipeline import PipelineClient
+except ImportError:
+    from paddle_serving_server.pipeline import PipelineClient
+import numpy as np
+import requests
+import json
+import cv2
+import base64
+import os
+
+client = PipelineClient()
+client.connect(['127.0.0.1:18090'])
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+test_img_dir = "imgs/"
+for img_file in os.listdir(test_img_dir):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data = file.read()
+    image = cv2_to_base64(image_data)
+
+for i in range(1):
+    ret = client.predict(feed_dict={"image": image}, fetch=["res"])
+    print(ret)
+    #print(ret)

deploy/pdserving/web_service.py

+# 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.
+# 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.
+try:
+    from paddle_serving_server_gpu.web_service import WebService, Op
+except ImportError:
+    from paddle_serving_server.web_service import WebService, Op
+
+import logging
+import numpy as np
+import cv2
+import base64
+# from paddle_serving_app.reader import OCRReader
+from ocr_reader import OCRReader, DetResizeForTest
+from paddle_serving_app.reader import Sequential, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+
+_LOGGER = logging.getLogger()
+
+
+class DetOp(Op):
+    def init_op(self):
+        self.det_preprocess = Sequential([
+            DetResizeForTest(), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.filter_func = FilterBoxes(10, 10)
+        self.post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+
+    def preprocess(self, input_dicts, data_id, log_id):
+        (_, input_dict), = input_dicts.items()
+        data = base64.b64decode(input_dict["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        # Note: class variables(self.var) can only be used in process op mode
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.im = im
+        self.ori_h, self.ori_w, _ = im.shape
+
+        det_img = self.det_preprocess(self.im)
+        _, self.new_h, self.new_w = det_img.shape
+        print("det image shape", det_img.shape)
+        return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
+
+    def postprocess(self, input_dicts, fetch_dict, log_id):
+        print("input_dicts: ", input_dicts)
+        det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
+        ratio_list = [
+            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
+        ]
+        dt_boxes_list = self.post_func(det_out, [ratio_list])
+        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
+        out_dict = {"dt_boxes": dt_boxes, "image": self.im}
+
+        print("out dict", out_dict["dt_boxes"])
+        return out_dict, None, ""
+
+
+class RecOp(Op):
+    def init_op(self):
+        self.ocr_reader = OCRReader(
+            char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
+
+        self.get_rotate_crop_image = GetRotateCropImage()
+        self.sorted_boxes = SortedBoxes()
+
+    def preprocess(self, input_dicts, data_id, log_id):
+        (_, input_dict), = input_dicts.items()
+        im = input_dict["image"]
+        dt_boxes = input_dict["dt_boxes"]
+        dt_boxes = self.sorted_boxes(dt_boxes)
+        feed_list = []
+        img_list = []
+        max_wh_ratio = 0
+        for i, dtbox in enumerate(dt_boxes):
+            boximg = self.get_rotate_crop_image(im, dt_boxes[i])
+            img_list.append(boximg)
+            h, w = boximg.shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        _, w, h = self.ocr_reader.resize_norm_img(img_list[0],
+                                                  max_wh_ratio).shape
+
+        imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
+        for id, img in enumerate(img_list):
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            imgs[id] = norm_img
+        print("rec image shape", imgs.shape)
+        feed = {"x": imgs.copy()}
+        return feed, False, None, ""
+
+    def postprocess(self, input_dicts, fetch_dict, log_id):
+        rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": str(res_lst)}
+        return res, None, ""
+
+
+class OcrService(WebService):
+    def get_pipeline_response(self, read_op):
+        det_op = DetOp(name="det", input_ops=[read_op])
+        rec_op = RecOp(name="rec", input_ops=[det_op])
+        return rec_op
+
+
+uci_service = OcrService(name="ocr")
+uci_service.prepare_pipeline_config("config.yml")
+uci_service.run_service()

doc/doc_ch/algorithm_overview.md

@@ -28,7 +28,9 @@ PaddleOCR开源的文本检测算法列表：
 | --- | --- | --- | --- | --- | --- |
 |SAST|ResNet50_vd|89.63%|78.44%|83.66%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
 
-**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
+**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：
+* [百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
+* [Google Drive下载地址](https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing)
 
 PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./detection.md)。
 

doc/doc_ch/inference.md

@@ -12,7 +12,8 @@ inference 模型（`paddle.jit.save`保存的模型）
 - [一、训练模型转inference模型](#训练模型转inference模型)
     - [检测模型转inference模型](#检测模型转inference模型)
     - [识别模型转inference模型](#识别模型转inference模型)  
-    - [方向分类模型转inference模型](#方向分类模型转inference模型)  
+    - [方向分类模型转inference模型](#方向分类模型转inference模型)
+    - [端到端模型转inference模型](#端到端模型转inference模型)
 
 - [二、文本检测模型推理](#文本检测模型推理)
     - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
@@ -27,10 +28,13 @@ inference 模型（`paddle.jit.save`保存的模型）
     - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
     - [5. 多语言模型的推理](#多语言模型的推理)
 
-- [四、方向分类模型推理](#方向识别模型推理)
+- [四、端到端模型推理](#端到端模型推理)
+    - [1. PGNet端到端模型推理](#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)
 
-- [五、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
+- [六、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
     - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
     - [2. 其他模型推理](#其他模型推理)
 
@@ -118,6 +122,32 @@ python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_mo
     ├── inference.pdiparams.info    # 分类inference模型的参数信息，可忽略
     └── inference.pdmodel           # 分类inference模型的program文件
 ```
+<a name="端到端模型转inference模型"></a>
+### 端到端模型转inference模型
+
+下载端到端模型：
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
+```
+
+端到端模型转inference模型与检测的方式相同，如下：
+```
+# -c 后面设置训练算法的yml配置文件
+# -o 配置可选参数
+# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
+# Global.load_static_weights 参数需要设置为 False。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e/
+```
+
+转换成功后，在目录下有三个文件：
+```
+/inference/e2e/
+    ├── inference.pdiparams         # 分类inference模型的参数文件
+    ├── inference.pdiparams.info    # 分类inference模型的参数信息，可忽略
+    └── inference.pdmodel           # 分类inference模型的program文件
+```
 
 <a name="文本检测模型推理"></a>
 ## 二、文本检测模型推理
@@ -332,8 +362,38 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
 Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
 ```
 
+<a name="端到端模型推理"></a>
+## 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="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/pgnet/en_server_pgnetA.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=./en_server_pgnetA/iter_epoch_450 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"`**，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/"  --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/e2e_res_img_10_pgnet.jpg)
+
+#### (2). 弯曲文本检测模型（Total-Text）  
+和四边形文本检测模型共用一个推理模型
+**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：
+```
+python3.7 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_pgnet.jpg)
+
+
 <a name="方向分类模型推理"></a>
-## 四、方向分类模型推理
+## 五、方向分类模型推理
 
 下面将介绍方向分类模型推理。
 
@@ -358,7 +418,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
 ```
 
 <a name="文本检测、方向分类和文字识别串联推理"></a>
-## 五、文本检测、方向分类和文字识别串联推理
+## 六、文本检测、方向分类和文字识别串联推理
 <a name="超轻量中文OCR模型推理"></a>
 ### 1. 超轻量中文OCR模型推理
 

doc/doc_ch/multi_languages.md

+# 多语言模型
+
+**近期更新**
+
+- 2021.4.9 支持**80种**语言的检测和识别
+- 2021.4.9 支持**轻量高精度**英文模型检测识别
+
+- [1 安装](#安装)
+    - [1.1 paddle 安装](#paddle安装)
+    - [1.2 paddleocr package 安装](#paddleocr_package_安装)  
+
+- [2 快速使用](#快速使用)
+    - [2.1 命令行运行](#命令行运行)
+     - [2.1.1 整图预测](#bash_检测+识别)
+     - [2.1.2 识别预测](#bash_识别)
+     - [2.1.3 检测预测](#bash_检测)
+    - [2.2 python 脚本运行](#python_脚本运行)
+     - [2.2.1 整图预测](#python_检测+识别)
+     - [2.2.2 识别预测](#python_识别)
+     - [2.2.3 检测预测](#python_检测)
+- [3 自定义训练](#自定义训练)
+- [4 支持语种及缩写](#语种缩写)
+
+<a name="安装"></a>
+## 1 安装
+
+<a name="paddle安装"></a>
+### 1.1 paddle 安装
+```
+# cpu
+pip install paddlepaddle
+
+# gpu
+pip instll paddlepaddle-gpu
+```
+
+<a name="paddleocr_package_安装"></a>
+### 1.2 paddleocr package 安装
+
+
+pip 安装
+```
+pip install "paddleocr>=2.0.4" # 推荐使用2.0.4版本
+```
+本地构建并安装
+```
+python3 setup.py bdist_wheel
+pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本号
+```
+
+<a name="快速使用"></a>
+## 2 快速使用
+
+<a name="命令行运行"></a>
+### 2.1 命令行运行
+
+查看帮助信息
+
+```
+paddleocr -h
+```
+
+* 整图预测（检测+识别）
+
+Paddleocr目前支持80个语种，可以通过修改--lang参数进行切换，具体支持的[语种](#语种缩写)可查看表格。
+
+``` bash
+
+paddleocr --image_dir doc/imgs/japan_2.jpg --lang=japan
+```
+![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs/japan_2.jpg)
+
+结果是一个list，每个item包含了文本框，文字和识别置信度
+```text
+[[[671.0, 60.0], [847.0, 63.0], [847.0, 104.0], [671.0, 102.0]], ('もちもち', 0.9993342)]
+[[[394.0, 82.0], [536.0, 77.0], [538.0, 127.0], [396.0, 132.0]], ('天然の', 0.9919842)]
+[[[880.0, 89.0], [1014.0, 93.0], [1013.0, 127.0], [879.0, 124.0]], ('とろっと', 0.9976762)]
+[[[1067.0, 101.0], [1294.0, 101.0], [1294.0, 138.0], [1067.0, 138.0]], ('後味のよい', 0.9988712)]
+......
+```
+
+* 识别预测
+
+```bash
+paddleocr --image_dir doc/imgs_words/japan/1.jpg   --det false --lang=japan
+```
+
+![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_words/japan/1.jpg)
+
+结果是一个tuple，返回识别结果和识别置信度
+
+```text
+('したがって', 0.99965394)
+```
+
+* 检测预测
+
+```
+paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
+```
+
+结果是一个list，每个item只包含文本框
+
+```
+[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
+[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
+[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
+......
+```
+
+<a name="python_脚本运行"></a>
+### 2.2 python 脚本运行
+
+ppocr 也支持在python脚本中运行，便于嵌入到您自己的代码中：
+
+* 整图预测（检测+识别）
+
+```
+from paddleocr import PaddleOCR, draw_ocr
+
+# 同样也是通过修改 lang 参数切换语种
+ocr = PaddleOCR(lang="korean") # 首次执行会自动下载模型文件
+img_path = 'doc/imgs/korean_1.jpg '
+result = ocr.ocr(img_path)
+# 打印检测框和识别结果
+for line in result:
+    print(line)
+
+# 可视化
+from PIL import Image
+image = Image.open(img_path).convert('RGB')
+boxes = [line[0] for line in result]
+txts = [line[1][0] for line in result]
+scores = [line[1][1] for line in result]
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/korean.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+结果可视化:
+![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/korean.jpg)
+
+
+* 识别预测
+
+```
+from paddleocr import PaddleOCR
+ocr = PaddleOCR(lang="german")
+img_path = 'PaddleOCR/doc/imgs_words/german/1.jpg'
+result = ocr.ocr(img_path, det=False, cls=True)
+for line in result:
+    print(line)
+```
+
+![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_words/german/1.jpg)
+
+结果是一个tuple，只包含识别结果和识别置信度
+
+```
+('leider auch jetzt', 0.97538936)
+```
+
+* 检测预测
+
+```python
+from paddleocr import PaddleOCR, draw_ocr
+ocr = PaddleOCR() # need to run only once to download and load model into memory
+img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
+result = ocr.ocr(img_path, rec=False)
+for line in result:
+    print(line)
+
+# 显示结果
+from PIL import Image
+
+image = Image.open(img_path).convert('RGB')
+im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+结果是一个list，每个item只包含文本框
+```bash
+[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
+[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
+[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
+......
+```
+
+结果可视化 ：
+![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/whl/12_det.jpg)
+
+ppocr 还支持方向分类， 更多使用方式请参考：[whl包使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_ch/whl.md)。
+
+<a name="自定义训练"></a>
+## 3 自定义训练
+
+ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别模型可以参考 [法语配置文件](../../configs/rec/multi_language/rec_french_lite_train.yml)
+修改训练数据路径、字典等参数。
+
+具体数据准备、训练过程可参考：[文本检测](../doc_ch/detection.md)、[文本识别](../doc_ch/recognition.md)，更多功能如预测部署、
+数据标注等功能可以阅读完整的[文档教程](../../README_ch.md)。
+
+<a name="语种缩写"></a>
+## 4 支持语种及缩写
+
+| 语种 | 描述 | 缩写 |
+| --- | --- | --- |
+|中文|chinese and english|ch|
+|英文|english|en|
+|法文|french|fr|
+|德文|german|german|
+|日文|japan|japan|
+|韩文|korean|korean|
+|中文繁体|chinese traditional |ch_tra|
+|意大利文| Italian |it|
+|西班牙文|Spanish |es|
+|葡萄牙文| Portuguese|pt|
+|俄罗斯文|Russia|ru|
+|阿拉伯文|Arabic|ar|
+|印地文|Hindi|hi|
+|维吾尔|Uyghur|ug|
+|波斯文|Persian|fa|
+|乌尔都文|Urdu|ur|
+|塞尔维亚文（latin)| Serbian(latin) |rs_latin|
+|欧西坦文|Occitan |oc|
+|马拉地文|Marathi|mr|
+|尼泊尔文|Nepali|ne|
+|塞尔维亚文（cyrillic)|Serbian(cyrillic)|rs_cyrillic|
+|保加利亚文|Bulgarian |bg|
+|乌克兰文|Ukranian|uk|
+|白俄罗斯文|Belarusian|be|
+|泰卢固文|Telugu |te|
+|卡纳达文|Kannada |kn|
+|泰米尔文|Tamil |ta|
+|南非荷兰文 |Afrikaans |af|
+|阿塞拜疆文 |Azerbaijani	|az|
+|波斯尼亚文|Bosnian|bs|
+|捷克文|Czech|cs|
+|威尔士文 |Welsh |cy|
+|丹麦文 |Danish|da|
+|爱沙尼亚文 |Estonian |et|
+|爱尔兰文 |Irish |ga|
+|克罗地亚文|Croatian |hr|
+|匈牙利文|Hungarian |hu|
+|印尼文|Indonesian|id|
+|冰岛文 |Icelandic|is|
+|库尔德文 |Kurdish|ku|
+|立陶宛文|Lithuanian |lt|
+|拉脱维亚文 |Latvian |lv|
+|毛利文|Maori|mi|
+|马来文 |Malay|ms|
+|马耳他文 |Maltese |mt|
+|荷兰文 |Dutch |nl|
+|挪威文 |Norwegian |no|
+|波兰文|Polish |pl|
+| 罗马尼亚文|Romanian |ro|
+| 斯洛伐克文|Slovak |sk|
+| 斯洛文尼亚文|Slovenian |sl|
+| 阿尔巴尼亚文|Albanian |sq|
+| 瑞典文|Swedish |sv|
+| 西瓦希里文|Swahili |sw|
+| 塔加洛文|Tagalog |tl|
+| 土耳其文|Turkish |tr|
+| 乌兹别克文|Uzbek |uz|
+| 越南文|Vietnamese |vi|
+| 蒙古文|Mongolian |mn|
+| 阿巴扎文|Abaza |abq|
+| 阿迪赫文|Adyghe |ady|
+| 卡巴丹文|Kabardian |kbd|
+| 阿瓦尔文|Avar |ava|
+| 达尔瓦文|Dargwa |dar|
+| 因古什文|Ingush |inh|
+| 拉克文|Lak |lbe|
+| 莱兹甘文|Lezghian |lez|
+|塔巴萨兰文 |Tabassaran |tab|
+| 比尔哈文|Bihari |bh|
+| 迈蒂利文|Maithili |mai|
+| 昂加文|Angika |ang|
+| 孟加拉文|Bhojpuri |bho|
+| 摩揭陀文 |Magahi |mah|
+| 那格浦尔文|Nagpur |sck|
+| 尼瓦尔文|Newari |new|
+| 保加利亚文 |Goan Konkani|gom|
+| 沙特阿拉伯文|Saudi Arabia|sa|

doc/doc_ch/pgnet.md

+# 端对端OCR算法-PGNet
+- [一、简介](#简介)
+- [二、环境配置](#环境配置)
+- [三、快速使用](#快速使用)
+- [四、快速训练](#开始训练)
+- [五、预测推理](#预测推理)
+
+
+<a name="简介"></a>
+##简介
+OCR算法可以分为两阶段算法和端对端的算法。二阶段OCR算法一般分为两个部分，文本检测和文本识别算法，文件检测算法从图像中得到文本行的检测框，然后识别算法去识别文本框中的内容。而端对端OCR算法可以在一个算法中完成文字检测和文字识别，其基本思想是设计一个同时具有检测单元和识别模块的模型，共享其中两者的CNN特征，并联合训练。由于一个算法即可完成文字识别，端对端模型更小，速度更快。
+
+### PGNet算法介绍
+近些年来，端对端OCR算法得到了良好的发展，包括MaskTextSpotter系列、TextSnake、TextDragon、PGNet系列等算法。在这些算法中，PGNet算法具备其他算法不具备的优势，包括：
+- 设计PGNet loss指导训练，不需要字符级别的标注
+- 不需要NMS和ROI相关操作，加速预测
+- 提出预测文本行内的阅读顺序模块；
+- 提出基于图的修正模块（GRM）来进一步提高模型识别性能
+- 精度更高，预测速度更快
+
+PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf)， 算法原理图如下所示：
+![](../pgnet_framework.png)
+输入图像经过特征提取送入四个分支，分别是：文本边缘偏移量预测TBO模块，文本中心线预测TCL模块，文本方向偏移量预测TDO模块，以及文本字符分类图预测TCC模块。
+其中TBO以及TCL的输出经过后处理后可以得到文本的检测结果，TCL、TDO、TCC负责文本识别。
+其检测识别效果图如下：
+![](../imgs_results/e2e_res_img293_pgnet.png)
+![](../imgs_results/e2e_res_img295_pgnet.png)
+
+<a name="环境配置"></a>
+##环境配置
+请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
+
+*注意：也可以通过 whl 包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./whl.md)。*
+
+<a name="快速使用"></a>
+##快速使用
+### inference模型下载
+本节以训练好的端到端模型为例，快速使用模型预测，首先下载训练好的端到端inference模型[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar)
+```
+mkdir inference && cd inference
+# 下载英文端到端模型并解压
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar && tar xf e2e_server_pgnetA_infer.tar
+```
+* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
+
+解压完毕后应有如下文件结构：
+```
+├── e2e_server_pgnetA_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+```
+### 单张图像或者图像集合预测
+```bash
+# 预测image_dir指定的单张图像
+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
+
+# 预测image_dir指定的图像集合
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
+
+# 如果想使用CPU进行预测，需设置use_gpu参数为False
+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 --use_gpu=False
+```
+<a name="开始训练"></a>
+##开始训练
+本节以totaltext数据集为例，介绍PaddleOCR中端到端模型的训练、评估与测试。
+###数据形式为icdar, 十六点标注数据
+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
+      |- gt_0.png
+      | ...  
+  |- total_text.txt  total_text数据集的训练标注
+```
+
+提供的标注文件格式如下，中间用"\t"分隔：
+```
+" 图像文件名                    json.dumps编码的图像标注信息"
+rgb/gt_0.png    [{"transcription": "EST", "points": [[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]]}, {...}]
+```
+json.dumps编码前的图像标注信息是包含多个字典的list，字典中的 `points` 表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。
+`transcription` 表示当前文本框的文字，**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
+
+模型训练一般分两步骤进行，第一步可以选择用合成数据训练，第二步加载第一步训练好的模型训练，这边我们提供了第一步训练好的模型，可以直接加载，从第二步开始训练
+[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar)
+```shell
+cd PaddleOCR/
+下载ResNet50_vd的动态图预训练模型
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar
+可以得到以下的文件格式
+./pretrain_models/train_step1/
+  └─ best_accuracy.pdopt
+  └─ best_accuracy.states
+  └─ best_accuracy.pdparams
+
+```
+
+*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.pretrained_model=./pretrain_models/train_step1/best_accuracy Global.load_static_weights=False
+# 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.pretrained_model=./pretrain_models/train_step1/best_accuracy  Global.load_static_weights=False
+```
+
+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。
+
+<a name="预测推理"></a>
+## 预测推理
+
+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
+```
+
+###转为推理模型
+### (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，以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/pgnet/en_server_pgnetA.tar) ，可以使用如下命令进行转换：
+```
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar
+python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 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"`**，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/"  --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/e2e_res_img_10_pgnet.jpg)
+
+### (2). 弯曲文本检测模型（Total-Text）
+对于弯曲文本样例
+
+**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/" --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_pgnet.jpg)

doc/doc_ch/whl.md

@@ -36,7 +36,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -69,7 +69,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -114,7 +114,7 @@ for line in result:
 from PIL import Image
 
 image = Image.open(img_path).convert('RGB')
-im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -253,7 +253,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -285,7 +285,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -314,7 +314,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```

doc/doc_en/algorithm_overview_en.md

@@ -31,7 +31,9 @@ On Total-Text dataset, the text detection result is as follows:
 | --- | --- | --- | --- | --- | --- |
 |SAST|ResNet50_vd|89.63%|78.44%|83.66%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
 
-**Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
+**Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
+* [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
+* [Google Drive](https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing)
 
 For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./detection_en.md)
 

doc/doc_en/whl_en.md

@@ -35,7 +35,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -69,7 +69,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -116,7 +116,7 @@ for line in result:
 from PIL import Image
 
 image = Image.open(img_path).convert('RGB')
-im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -262,7 +262,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -292,7 +292,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -320,7 +320,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```

paddleocr.py

@@ -66,6 +66,46 @@ model_urls = {
             'url':
             'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/dict/japan_dict.txt'
+        },
+        'chinese_cht': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
+        },
+        'ta': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/ta_dict.txt'
+        },
+        'te': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/te_dict.txt'
+        },
+        'ka': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/ka_dict.txt'
+        },
+        'latin': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/latin_dict.txt'
+        },
+        'arabic': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/arabic_dict.txt'
+        },
+        'cyrillic': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
+        },
+        'devanagari': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
         }
     },
     'cls':
@@ -233,10 +273,35 @@ class PaddleOCR(predict_system.TextSystem):
         postprocess_params.__dict__.update(**kwargs)
         self.use_angle_cls = postprocess_params.use_angle_cls
         lang = postprocess_params.lang
+        latin_lang = [
+            'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'en', 'es', 'et', 'fr',
+            'ga', 'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi',
+            'ms', 'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin',
+            'sk', 'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
+        ]
+        arabic_lang = ['ar', 'fa', 'ug', 'ur']
+        cyrillic_lang = [
+            'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
+            'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
+        ]
+        devanagari_lang = [
+            'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
+            'gom', 'sa', 'bgc'
+        ]
+        if lang in latin_lang:
+            lang = "latin"
+        elif lang in arabic_lang:
+            lang = "arabic"
+        elif lang in cyrillic_lang:
+            lang = "cyrillic"
+        elif lang in devanagari_lang:
+            lang = "devanagari"
         assert lang in model_urls[
             'rec'], 'param lang must in {}, but got {}'.format(
                 model_urls['rec'].keys(), lang)
+        use_inner_dict = False
         if postprocess_params.rec_char_dict_path is None:
+            use_inner_dict = True
             postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
                 'dict_path']
 
@@ -263,9 +328,9 @@ class PaddleOCR(predict_system.TextSystem):
         if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL:
             logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
             sys.exit(0)
-
-        postprocess_params.rec_char_dict_path = str(
-            Path(__file__).parent / postprocess_params.rec_char_dict_path)
+        if use_inner_dict:
+            postprocess_params.rec_char_dict_path = str(
+                Path(__file__).parent / postprocess_params.rec_char_dict_path)
 
         # init det_model and rec_model
         super().__init__(postprocess_params)
@@ -282,8 +347,13 @@ class PaddleOCR(predict_system.TextSystem):
         if isinstance(img, list) and det == True:
             logger.error('When input a list of images, det must be false')
             exit(0)
+        if cls == False:
+            self.use_angle_cls = False
+        elif cls == True and self.use_angle_cls == False:
+            logger.warning(
+                'Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process'
+            )
 
-        self.use_angle_cls = cls
         if isinstance(img, str):
             # download net image
             if img.startswith('http'):

ppocr/data/__init__.py

@@ -34,6 +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 PGDataSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -54,7 +55,7 @@ signal.signal(signal.SIGTERM, term_mp)
 def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
-    support_dict = ['SimpleDataSet', 'LMDBDataSet']
+    support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet']
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))
@@ -72,14 +73,14 @@ def build_dataloader(config, mode, device, logger, seed=None):
     else:
         use_shared_memory = True
     if mode == "Train":
-        #Distribute data to multiple cards
+        # Distribute data to multiple cards
         batch_sampler = DistributedBatchSampler(
             dataset=dataset,
             batch_size=batch_size,
             shuffle=shuffle,
             drop_last=drop_last)
     else:
-        #Distribute data to single card
+        # Distribute data to single card
         batch_sampler = BatchSampler(
             dataset=dataset,
             batch_size=batch_size,

ppocr/data/imaug/__init__.py

@@ -28,6 +28,7 @@ from .label_ops import *
 
 from .east_process import *
 from .sast_process import *
+from .pg_process import *
 
 
 def transform(data, ops=None):

ppocr/data/imaug/label_ops.py

@@ -187,6 +187,34 @@ class CTCLabelEncode(BaseRecLabelEncode):
         return dict_character
 
 
+class E2ELabelEncode(BaseRecLabelEncode):
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='EN',
+                 use_space_char=False,
+                 **kwargs):
+        super(E2ELabelEncode,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+        self.pad_num = len(self.dict)  # the length to pad
+
+    def __call__(self, data):
+        text_label_index_list, temp_text = [], []
+        texts = data['strs']
+        for text in texts:
+            text = text.lower()
+            temp_text = []
+            for c_ in text:
+                if c_ in self.dict:
+                    temp_text.append(self.dict[c_])
+            temp_text = temp_text + [self.pad_num] * (self.max_text_len -
+                                                      len(temp_text))
+            text_label_index_list.append(temp_text)
+        data['strs'] = np.array(text_label_index_list)
+        return data
+
+
 class AttnLabelEncode(BaseRecLabelEncode):
     """ Convert between text-label and text-index """
 

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:
@@ -223,3 +221,72 @@ class DetResizeForTest(object):
         ratio_w = resize_w / float(w)
 
         return img, [ratio_h, ratio_w]
+
+
+class E2EResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(E2EResizeForTest, self).__init__()
+        self.max_side_len = kwargs['max_side_len']
+        self.valid_set = kwargs['valid_set']
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+        if self.valid_set == 'totaltext':
+            im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
+                img, max_side_len=self.max_side_len)
+        else:
+            im_resized, (ratio_h, ratio_w) = self.resize_image(
+                img, max_side_len=self.max_side_len)
+        data['image'] = im_resized
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+    def resize_image_for_totaltext(self, im, max_side_len=512):
+
+        h, w, _ = im.shape
+        resize_w = w
+        resize_h = h
+        ratio = 1.25
+        if h * ratio > max_side_len:
+            ratio = float(max_side_len) / resize_h
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return im, (ratio_h, ratio_w)
+
+    def resize_image(self, im, max_side_len=512):
+        """
+        resize image to a size multiple of max_stride which is required by the network
+        :param im: the resized image
+        :param max_side_len: limit of max image size to avoid out of memory in gpu
+        :return: the resized image and the resize ratio
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+
+        # Fix the longer side
+        if resize_h > resize_w:
+            ratio = float(max_side_len) / resize_h
+        else:
+            ratio = float(max_side_len) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return im, (ratio_h, ratio_w)

ppocr/data/imaug/pg_process.py

+# 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.
+# 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 math
+import cv2
+import numpy as np
+
+__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=4,
+                 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):
+        character_str = ""
+        with open(character_dict_path, "rb") as fin:
+            lines = fin.readlines()
+            for line in lines:
+                line = line.decode('utf-8').strip("\n").strip("\r\n")
+                character_str += line
+            dict_character = list(character_str)
+        return dict_character
+
+    def quad_area(self, poly):
+        """
+        compute area of a polygon
+        :param poly:
+        :return:
+        """
+        edge = [(poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]),
+                (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]),
+                (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]),
+                (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1])]
+        return np.sum(edge) / 2.
+
+    def gen_quad_from_poly(self, poly):
+        """
+        Generate min area quad from poly.
+        """
+        point_num = poly.shape[0]
+        min_area_quad = np.zeros((4, 2), dtype=np.float32)
+        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
+
+    def check_and_validate_polys(self, polys, tags, xxx_todo_changeme):
+        """
+        check so that the text poly is in the same direction,
+        and also filter some invalid polygons
+        :param polys:
+        :param tags:
+        :return:
+        """
+        (h, w) = xxx_todo_changeme
+        if polys.shape[0] == 0:
+            return polys, np.array([]), np.array([])
+        polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1)
+        polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1)
+
+        validated_polys = []
+        validated_tags = []
+        hv_tags = []
+        for poly, tag in zip(polys, tags):
+            quad = self.gen_quad_from_poly(poly)
+            p_area = self.quad_area(quad)
+            if abs(p_area) < 1:
+                print('invalid poly')
+                continue
+            if p_area > 0:
+                if tag == False:
+                    print('poly in wrong direction')
+                    tag = True  # reversed cases should be ignore
+                poly = poly[(0, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2,
+                             1), :]
+                quad = quad[(0, 3, 2, 1), :]
+
+            len_w = np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[3] -
+                                                                       quad[2])
+            len_h = np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] -
+                                                                       quad[2])
+            hv_tag = 1
+
+            if len_w * 2.0 < len_h:
+                hv_tag = 0
+
+            validated_polys.append(poly)
+            validated_tags.append(tag)
+            hv_tags.append(hv_tag)
+        return np.array(validated_polys), np.array(validated_tags), np.array(
+            hv_tags)
+
+    def crop_area(self,
+                  im,
+                  polys,
+                  tags,
+                  hv_tags,
+                  txts,
+                  crop_background=False,
+                  max_tries=25):
+        """
+        make random crop from the input image
+        :param im:
+        :param polys:  [b,4,2]
+        :param tags:
+        :param crop_background:
+        :param max_tries: 50 -> 25
+        :return:
+        """
+        h, w, _ = im.shape
+        pad_h = h // 10
+        pad_w = w // 10
+        h_array = np.zeros((h + pad_h * 2), dtype=np.int32)
+        w_array = np.zeros((w + pad_w * 2), dtype=np.int32)
+        for poly in polys:
+            poly = np.round(poly, decimals=0).astype(np.int32)
+            minx = np.min(poly[:, 0])
+            maxx = np.max(poly[:, 0])
+            w_array[minx + pad_w:maxx + pad_w] = 1
+            miny = np.min(poly[:, 1])
+            maxy = np.max(poly[:, 1])
+            h_array[miny + pad_h:maxy + pad_h] = 1
+        # ensure the cropped area not across a text
+        h_axis = np.where(h_array == 0)[0]
+        w_axis = np.where(w_array == 0)[0]
+        if len(h_axis) == 0 or len(w_axis) == 0:
+            return im, polys, tags, hv_tags, txts
+        for i in range(max_tries):
+            xx = np.random.choice(w_axis, size=2)
+            xmin = np.min(xx) - pad_w
+            xmax = np.max(xx) - pad_w
+            xmin = np.clip(xmin, 0, w - 1)
+            xmax = np.clip(xmax, 0, w - 1)
+            yy = np.random.choice(h_axis, size=2)
+            ymin = np.min(yy) - pad_h
+            ymax = np.max(yy) - pad_h
+            ymin = np.clip(ymin, 0, h - 1)
+            ymax = np.clip(ymax, 0, h - 1)
+            if xmax - xmin < self.min_crop_size or \
+                    ymax - ymin < self.min_crop_size:
+                continue
+            if polys.shape[0] != 0:
+                poly_axis_in_area = (polys[:, :, 0] >= xmin) & (polys[:, :, 0] <= xmax) \
+                                    & (polys[:, :, 1] >= ymin) & (polys[:, :, 1] <= ymax)
+                selected_polys = np.where(
+                    np.sum(poly_axis_in_area, axis=1) == 4)[0]
+            else:
+                selected_polys = []
+            if len(selected_polys) == 0:
+                # no text in this area
+                if crop_background:
+                    txts_tmp = []
+                    for selected_poly in selected_polys:
+                        txts_tmp.append(txts[selected_poly])
+                    txts = txts_tmp
+                    return im[ymin: ymax + 1, xmin: xmax + 1, :], \
+                           polys[selected_polys], tags[selected_polys], hv_tags[selected_polys], txts
+                else:
+                    continue
+            im = im[ymin:ymax + 1, xmin:xmax + 1, :]
+            polys = polys[selected_polys]
+            tags = tags[selected_polys]
+            hv_tags = hv_tags[selected_polys]
+            txts_tmp = []
+            for selected_poly in selected_polys:
+                txts_tmp.append(txts[selected_poly])
+            txts = txts_tmp
+            polys[:, :, 0] -= xmin
+            polys[:, :, 1] -= ymin
+            return im, polys, tags, hv_tags, txts
+
+        return im, polys, tags, hv_tags, txts
+
+    def fit_and_gather_tcl_points_v2(self,
+                                     min_area_quad,
+                                     poly,
+                                     max_h,
+                                     max_w,
+                                     fixed_point_num=64,
+                                     img_id=0,
+                                     reference_height=3):
+        """
+        Find the center point of poly as key_points, then fit and gather.
+        """
+        key_point_xys = []
+        point_num = poly.shape[0]
+        for idx in range(point_num // 2):
+            center_point = (poly[idx] + poly[point_num - 1 - idx]) / 2.0
+            key_point_xys.append(center_point)
+
+        tmp_image = np.zeros(
+            shape=(
+                max_h,
+                max_w, ), dtype='float32')
+        cv2.polylines(tmp_image, [np.array(key_point_xys).astype('int32')],
+                      False, 1.0)
+        ys, xs = np.where(tmp_image > 0)
+        xy_text = np.array(list(zip(xs, ys)), dtype='float32')
+
+        left_center_pt = (
+            (min_area_quad[0] - min_area_quad[1]) / 2.0).reshape(1, 2)
+        right_center_pt = (
+            (min_area_quad[1] - min_area_quad[2]) / 2.0).reshape(1, 2)
+        proj_unit_vec = (right_center_pt - left_center_pt) / (
+            np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
+        proj_unit_vec_tile = np.tile(proj_unit_vec,
+                                     (xy_text.shape[0], 1))  # (n, 2)
+        left_center_pt_tile = np.tile(left_center_pt,
+                                      (xy_text.shape[0], 1))  # (n, 2)
+        xy_text_to_left_center = xy_text - left_center_pt_tile
+        proj_value = np.sum(xy_text_to_left_center * proj_unit_vec_tile, axis=1)
+        xy_text = xy_text[np.argsort(proj_value)]
+
+        # convert to np and keep the num of point not greater then fixed_point_num
+        pos_info = np.array(xy_text).reshape(-1, 2)[:, ::-1]  # xy-> yx
+        point_num = len(pos_info)
+        if point_num > fixed_point_num:
+            keep_ids = [
+                int((point_num * 1.0 / fixed_point_num) * x)
+                for x in range(fixed_point_num)
+            ]
+            pos_info = pos_info[keep_ids, :]
+
+        keep = int(min(len(pos_info), fixed_point_num))
+        if np.random.rand() < 0.2 and reference_height >= 3:
+            dl = (np.random.rand(keep) - 0.5) * reference_height * 0.3
+            random_float = np.array([1, 0]).reshape([1, 2]) * dl.reshape(
+                [keep, 1])
+            pos_info += random_float
+            pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1)
+            pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1)
+
+        # padding to fixed length
+        pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32)
+        pos_l[:, 0] = np.ones((self.tcl_len, )) * img_id
+        pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32)
+        pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32)
+        pos_m[:keep] = 1.0
+        return pos_l, pos_m
+
+    def generate_direction_map(self, poly_quads, n_char, direction_map):
+        """
+        """
+        width_list = []
+        height_list = []
+        for quad in poly_quads:
+            quad_w = (np.linalg.norm(quad[0] - quad[1]) +
+                      np.linalg.norm(quad[2] - quad[3])) / 2.0
+            quad_h = (np.linalg.norm(quad[0] - quad[3]) +
+                      np.linalg.norm(quad[2] - quad[1])) / 2.0
+            width_list.append(quad_w)
+            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
+            direct_vector = direct_vector_full / (
+                np.linalg.norm(direct_vector_full) + 1e-6) * norm_width
+            direction_label = tuple(
+                map(float,
+                    [direct_vector[0], direct_vector[1], 1.0 / average_height]))
+            cv2.fillPoly(direction_map,
+                         quad.round().astype(np.int32)[np.newaxis, :, :],
+                         direction_label)
+            k += 1
+        return direction_map
+
+    def calculate_average_height(self, poly_quads):
+        """
+        """
+        height_list = []
+        for quad in poly_quads:
+            quad_h = (np.linalg.norm(quad[0] - quad[3]) +
+                      np.linalg.norm(quad[2] - quad[1])) / 2.0
+            height_list.append(quad_h)
+        average_height = max(sum(height_list) / len(height_list), 1.0)
+        return average_height
+
+    def generate_tcl_ctc_label(self,
+                               h,
+                               w,
+                               polys,
+                               tags,
+                               text_strs,
+                               ds_ratio,
+                               tcl_ratio=0.3,
+                               shrink_ratio_of_width=0.15):
+        """
+        Generate polygon.
+        """
+        score_map_big = np.zeros(
+            (
+                h,
+                w, ), dtype=np.float32)
+        h, w = int(h * ds_ratio), int(w * ds_ratio)
+        polys = polys * ds_ratio
+
+        score_map = np.zeros(
+            (
+                h,
+                w, ), dtype=np.float32)
+        score_label_map = np.zeros(
+            (
+                h,
+                w, ), dtype=np.float32)
+        tbo_map = np.zeros((h, w, 5), dtype=np.float32)
+        training_mask = np.ones(
+            (
+                h,
+                w, ), dtype=np.float32)
+        direction_map = np.ones((h, w, 3)) * np.array([0, 0, 1]).reshape(
+            [1, 1, 3]).astype(np.float32)
+
+        label_idx = 0
+        score_label_map_text_label_list = []
+        pos_list, pos_mask, label_list = [], [], []
+        for poly_idx, poly_tag in enumerate(zip(polys, tags)):
+            poly = poly_tag[0]
+            tag = poly_tag[1]
+
+            # generate min_area_quad
+            min_area_quad, center_point = self.gen_min_area_quad_from_poly(poly)
+            min_area_quad_h = 0.5 * (
+                np.linalg.norm(min_area_quad[0] - min_area_quad[3]) +
+                np.linalg.norm(min_area_quad[1] - min_area_quad[2]))
+            min_area_quad_w = 0.5 * (
+                np.linalg.norm(min_area_quad[0] - min_area_quad[1]) +
+                np.linalg.norm(min_area_quad[2] - min_area_quad[3]))
+
+            if min(min_area_quad_h, min_area_quad_w) < self.min_text_size * ds_ratio \
+                    or min(min_area_quad_h, min_area_quad_w) > self.max_text_size * ds_ratio:
+                continue
+
+            if tag:
+                cv2.fillPoly(training_mask,
+                             poly.astype(np.int32)[np.newaxis, :, :], 0.15)
+            else:
+                text_label = text_strs[poly_idx]
+                text_label = self.prepare_text_label(text_label,
+                                                     self.Lexicon_Table)
+
+                text_label_index_list = [[self.Lexicon_Table.index(c_)]
+                                         for c_ in text_label
+                                         if c_ in self.Lexicon_Table]
+                if len(text_label_index_list) < 1:
+                    continue
+
+                tcl_poly = self.poly2tcl(poly, tcl_ratio)
+                tcl_quads = self.poly2quads(tcl_poly)
+                poly_quads = self.poly2quads(poly)
+
+                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)
+
+                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)
+
+                for idx, quad in enumerate(stcl_quads):
+                    quad_mask = np.zeros((h, w), dtype=np.float32)
+                    quad_mask = cv2.fillPoly(
+                        quad_mask,
+                        np.round(quad[np.newaxis, :, :]).astype(np.int32), 1.0)
+                    tbo_map = self.gen_quad_tbo(poly_quads[quad_index[idx]],
+                                                quad_mask, tbo_map)
+
+                # score label map and score_label_map_text_label_list for refine
+                if label_idx == 0:
+                    text_pos_list_ = [[len(self.Lexicon_Table)], ]
+                    score_label_map_text_label_list.append(text_pos_list_)
+
+                label_idx += 1
+                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)
+
+                # direction info, fix-me
+                n_char = len(text_label_index_list)
+                direction_map = self.generate_direction_map(poly_quads, n_char,
+                                                            direction_map)
+
+                # pos info
+                average_shrink_height = self.calculate_average_height(
+                    stcl_quads)
+                pos_l, pos_m = self.fit_and_gather_tcl_points_v2(
+                    min_area_quad,
+                    poly,
+                    max_h=h,
+                    max_w=w,
+                    fixed_point_num=64,
+                    img_id=self.img_id,
+                    reference_height=average_shrink_height)
+
+                label_l = text_label_index_list
+                if len(text_label_index_list) < 2:
+                    continue
+
+                pos_list.append(pos_l)
+                pos_mask.append(pos_m)
+                label_list.append(label_l)
+
+        # use big score_map for smooth tcl lines
+        score_map_big_resized = cv2.resize(
+            score_map_big, dsize=None, fx=ds_ratio, fy=ds_ratio)
+        score_map = np.array(score_map_big_resized > 1e-3, dtype='float32')
+
+        return score_map, score_label_map, tbo_map, direction_map, training_mask, \
+               pos_list, pos_mask, label_list, score_label_map_text_label_list
+
+    def adjust_point(self, poly):
+        """
+        adjust point order.
+        """
+        point_num = poly.shape[0]
+        if point_num == 4:
+            len_1 = np.linalg.norm(poly[0] - poly[1])
+            len_2 = np.linalg.norm(poly[1] - poly[2])
+            len_3 = np.linalg.norm(poly[2] - poly[3])
+            len_4 = np.linalg.norm(poly[3] - poly[0])
+
+            if (len_1 + len_3) * 1.5 < (len_2 + len_4):
+                poly = poly[[1, 2, 3, 0], :]
+
+        elif point_num > 4:
+            vector_1 = poly[0] - poly[1]
+            vector_2 = poly[1] - poly[2]
+            cos_theta = np.dot(vector_1, vector_2) / (
+                np.linalg.norm(vector_1) * np.linalg.norm(vector_2) + 1e-6)
+            theta = np.arccos(np.round(cos_theta, decimals=4))
+
+            if abs(theta) > (70 / 180 * math.pi):
+                index = list(range(1, point_num)) + [0]
+                poly = poly[np.array(index), :]
+        return poly
+
+    def gen_min_area_quad_from_poly(self, poly):
+        """
+        Generate min area quad from poly.
+        """
+        point_num = poly.shape[0]
+        min_area_quad = np.zeros((4, 2), dtype=np.float32)
+        if point_num == 4:
+            min_area_quad = poly
+            center_point = np.sum(poly, axis=0) / 4
+        else:
+            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]
+
+        return min_area_quad, center_point
+
+    def shrink_quad_along_width(self,
+                                quad,
+                                begin_width_ratio=0.,
+                                end_width_ratio=1.):
+        """
+        Generate shrink_quad_along_width.
+        """
+        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 shrink_poly_along_width(self,
+                                quads,
+                                shrink_ratio_of_width,
+                                expand_height_ratio=1.0):
+        """
+        shrink poly with given length.
+        """
+        upper_edge_list = []
+
+        def get_cut_info(edge_len_list, cut_len):
+            for idx, edge_len in enumerate(edge_len_list):
+                cut_len -= edge_len
+                if cut_len <= 0.000001:
+                    ratio = (cut_len + edge_len_list[idx]) / edge_len_list[idx]
+                    return idx, ratio
+
+        for quad in quads:
+            upper_edge_len = np.linalg.norm(quad[0] - quad[1])
+            upper_edge_list.append(upper_edge_len)
+
+        # length of left edge and right edge.
+        left_length = np.linalg.norm(quads[0][0] - quads[0][
+            3]) * expand_height_ratio
+        right_length = np.linalg.norm(quads[-1][1] - quads[-1][
+            2]) * expand_height_ratio
+
+        shrink_length = min(left_length, right_length,
+                            sum(upper_edge_list)) * shrink_ratio_of_width
+        # shrinking length
+        upper_len_left = shrink_length
+        upper_len_right = sum(upper_edge_list) - shrink_length
+
+        left_idx, left_ratio = get_cut_info(upper_edge_list, upper_len_left)
+        left_quad = self.shrink_quad_along_width(
+            quads[left_idx], begin_width_ratio=left_ratio, end_width_ratio=1)
+        right_idx, right_ratio = get_cut_info(upper_edge_list, upper_len_right)
+        right_quad = self.shrink_quad_along_width(
+            quads[right_idx], begin_width_ratio=0, end_width_ratio=right_ratio)
+
+        out_quad_list = []
+        if left_idx == right_idx:
+            out_quad_list.append(
+                [left_quad[0], right_quad[1], right_quad[2], left_quad[3]])
+        else:
+            out_quad_list.append(left_quad)
+            for idx in range(left_idx + 1, right_idx):
+                out_quad_list.append(quads[idx])
+            out_quad_list.append(right_quad)
+
+        return np.array(out_quad_list), list(range(left_idx, right_idx + 1))
+
+    def prepare_text_label(self, label_str, Lexicon_Table):
+        """
+        Prepare text lablel by given Lexicon_Table.
+        """
+        if len(Lexicon_Table) == 36:
+            return label_str.lower()
+        else:
+            return label_str
+
+    def vector_angle(self, A, B):
+        """
+        Calculate the angle between vector AB and x-axis positive direction.
+        """
+        AB = np.array([B[1] - A[1], B[0] - A[0]])
+        return np.arctan2(*AB)
+
+    def theta_line_cross_point(self, theta, point):
+        """
+        Calculate the line through given point and angle in ax + by + c =0 form.
+        """
+        x, y = point
+        cos = np.cos(theta)
+        sin = np.sin(theta)
+        return [sin, -cos, cos * y - sin * x]
+
+    def line_cross_two_point(self, A, B):
+        """
+        Calculate the line through given point A and B in ax + by + c =0 form.
+        """
+        angle = self.vector_angle(A, B)
+        return self.theta_line_cross_point(angle, A)
+
+    def average_angle(self, poly):
+        """
+        Calculate the average angle between left and right edge in given poly.
+        """
+        p0, p1, p2, p3 = poly
+        angle30 = self.vector_angle(p3, p0)
+        angle21 = self.vector_angle(p2, p1)
+        return (angle30 + angle21) / 2
+
+    def line_cross_point(self, line1, line2):
+        """
+        line1 and line2 in  0=ax+by+c form, compute the cross point of line1 and line2
+        """
+        a1, b1, c1 = line1
+        a2, b2, c2 = line2
+        d = a1 * b2 - a2 * b1
+
+        if d == 0:
+            print('Cross point does not exist')
+            return np.array([0, 0], dtype=np.float32)
+        else:
+            x = (b1 * c2 - b2 * c1) / d
+            y = (a2 * c1 - a1 * c2) / d
+
+        return np.array([x, y], dtype=np.float32)
+
+    def quad2tcl(self, poly, ratio):
+        """
+        Generate center line by poly clock-wise point. (4, 2)
+        """
+        ratio_pair = np.array(
+            [[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
+        p0_3 = poly[0] + (poly[3] - poly[0]) * ratio_pair
+        p1_2 = poly[1] + (poly[2] - poly[1]) * ratio_pair
+        return np.array([p0_3[0], p1_2[0], p1_2[1], p0_3[1]])
+
+    def poly2tcl(self, poly, ratio):
+        """
+        Generate center line by poly clock-wise point.
+        """
+        ratio_pair = np.array(
+            [[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
+        tcl_poly = np.zeros_like(poly)
+        point_num = poly.shape[0]
+
+        for idx in range(point_num // 2):
+            point_pair = poly[idx] + (poly[point_num - 1 - idx] - poly[idx]
+                                      ) * ratio_pair
+            tcl_poly[idx] = point_pair[0]
+            tcl_poly[point_num - 1 - idx] = point_pair[1]
+        return tcl_poly
+
+    def gen_quad_tbo(self, quad, tcl_mask, tbo_map):
+        """
+        Generate tbo_map for give quad.
+        """
+        # upper and lower line function: ax + by + c = 0;
+        up_line = self.line_cross_two_point(quad[0], quad[1])
+        lower_line = self.line_cross_two_point(quad[3], quad[2])
+
+        quad_h = 0.5 * (np.linalg.norm(quad[0] - quad[3]) +
+                        np.linalg.norm(quad[1] - quad[2]))
+        quad_w = 0.5 * (np.linalg.norm(quad[0] - quad[1]) +
+                        np.linalg.norm(quad[2] - quad[3]))
+
+        # average angle of left and right line.
+        angle = self.average_angle(quad)
+
+        xy_in_poly = np.argwhere(tcl_mask == 1)
+        for y, x in xy_in_poly:
+            point = (x, y)
+            line = self.theta_line_cross_point(angle, point)
+            cross_point_upper = self.line_cross_point(up_line, line)
+            cross_point_lower = self.line_cross_point(lower_line, line)
+            ##FIX, offset reverse
+            upper_offset_x, upper_offset_y = cross_point_upper - point
+            lower_offset_x, lower_offset_y = cross_point_lower - point
+            tbo_map[y, x, 0] = upper_offset_y
+            tbo_map[y, x, 1] = upper_offset_x
+            tbo_map[y, x, 2] = lower_offset_y
+            tbo_map[y, x, 3] = lower_offset_x
+            tbo_map[y, x, 4] = 1.0 / max(min(quad_h, quad_w), 1.0) * 2
+        return tbo_map
+
+    def poly2quads(self, poly):
+        """
+        Split poly into quads.
+        """
+        quad_list = []
+        point_num = poly.shape[0]
+
+        # point pair
+        point_pair_list = []
+        for idx in range(point_num // 2):
+            point_pair = [poly[idx], poly[point_num - 1 - idx]]
+            point_pair_list.append(point_pair)
+
+        quad_num = point_num // 2 - 1
+        for idx in range(quad_num):
+            # reshape and adjust to clock-wise
+            quad_list.append((np.array(point_pair_list)[[idx, idx + 1]]
+                              ).reshape(4, 2)[[0, 2, 3, 1]])
+
+        return np.array(quad_list)
+
+    def rotate_im_poly(self, im, text_polys):
+        """
+        rotate image with 90 / 180 / 270 degre
+        """
+        im_w, im_h = im.shape[1], im.shape[0]
+        dst_im = im.copy()
+        dst_polys = []
+        rand_degree_ratio = np.random.rand()
+        rand_degree_cnt = 1
+        if rand_degree_ratio > 0.5:
+            rand_degree_cnt = 3
+        for i in range(rand_degree_cnt):
+            dst_im = np.rot90(dst_im)
+        rot_degree = -90 * rand_degree_cnt
+        rot_angle = rot_degree * math.pi / 180.0
+        n_poly = text_polys.shape[0]
+        cx, cy = 0.5 * im_w, 0.5 * im_h
+        ncx, ncy = 0.5 * dst_im.shape[1], 0.5 * dst_im.shape[0]
+        for i in range(n_poly):
+            wordBB = text_polys[i]
+            poly = []
+            for j in range(4):  # 16->4
+                sx, sy = wordBB[j][0], wordBB[j][1]
+                dx = math.cos(rot_angle) * (sx - cx) - math.sin(rot_angle) * (
+                    sy - cy) + ncx
+                dy = math.sin(rot_angle) * (sx - cx) + math.cos(rot_angle) * (
+                    sy - cy) + ncy
+                poly.append([dx, dy])
+            dst_polys.append(poly)
+        return dst_im, np.array(dst_polys, dtype=np.float32)
+
+    def __call__(self, data):
+        input_size = 512
+        im = data['image']
+        text_polys = data['polys']
+        text_tags = data['tags']
+        text_strs = data['strs']
+        h, w, _ = im.shape
+        text_polys, text_tags, hv_tags = self.check_and_validate_polys(
+            text_polys, text_tags, (h, w))
+        if text_polys.shape[0] <= 0:
+            return None
+        # set aspect ratio and keep area fix
+        asp_scales = np.arange(1.0, 1.55, 0.1)
+        asp_scale = np.random.choice(asp_scales)
+        if np.random.rand() < 0.5:
+            asp_scale = 1.0 / asp_scale
+        asp_scale = math.sqrt(asp_scale)
+
+        asp_wx = asp_scale
+        asp_hy = 1.0 / asp_scale
+        im = cv2.resize(im, dsize=None, fx=asp_wx, fy=asp_hy)
+        text_polys[:, :, 0] *= asp_wx
+        text_polys[:, :, 1] *= asp_hy
+
+        h, w, _ = im.shape
+        if max(h, w) > 2048:
+            rd_scale = 2048.0 / max(h, w)
+            im = cv2.resize(im, dsize=None, fx=rd_scale, fy=rd_scale)
+            text_polys *= rd_scale
+        h, w, _ = im.shape
+        if min(h, w) < 16:
+            return None
+
+        # no background
+        im, text_polys, text_tags, hv_tags, text_strs = self.crop_area(
+            im,
+            text_polys,
+            text_tags,
+            hv_tags,
+            text_strs,
+            crop_background=False)
+
+        if text_polys.shape[0] == 0:
+            return None
+        # # continue for all ignore case
+        if np.sum((text_tags * 1.0)) >= text_tags.size:
+            return None
+        new_h, new_w, _ = im.shape
+        if (new_h is None) or (new_w is None):
+            return None
+        # resize image
+        std_ratio = float(input_size) / max(new_w, new_h)
+        rand_scales = np.array(
+            [0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0, 1.0, 1.0, 1.0, 1.0])
+        rz_scale = std_ratio * np.random.choice(rand_scales)
+        im = cv2.resize(im, dsize=None, fx=rz_scale, fy=rz_scale)
+        text_polys[:, :, 0] *= rz_scale
+        text_polys[:, :, 1] *= rz_scale
+
+        # add gaussian blur
+        if np.random.rand() < 0.1 * 0.5:
+            ks = np.random.permutation(5)[0] + 1
+            ks = int(ks / 2) * 2 + 1
+            im = cv2.GaussianBlur(im, ksize=(ks, ks), sigmaX=0, sigmaY=0)
+        # add brighter
+        if np.random.rand() < 0.1 * 0.5:
+            im = im * (1.0 + np.random.rand() * 0.5)
+            im = np.clip(im, 0.0, 255.0)
+        # add darker
+        if np.random.rand() < 0.1 * 0.5:
+            im = im * (1.0 - np.random.rand() * 0.5)
+            im = np.clip(im, 0.0, 255.0)
+
+        # Padding the im to [input_size, input_size]
+        new_h, new_w, _ = im.shape
+        if min(new_w, new_h) < input_size * 0.5:
+            return None
+        im_padded = np.ones((input_size, input_size, 3), dtype=np.float32)
+        im_padded[:, :, 2] = 0.485 * 255
+        im_padded[:, :, 1] = 0.456 * 255
+        im_padded[:, :, 0] = 0.406 * 255
+
+        # Random the start position
+        del_h = input_size - new_h
+        del_w = input_size - new_w
+        sh, sw = 0, 0
+        if del_h > 1:
+            sh = int(np.random.rand() * del_h)
+        if del_w > 1:
+            sw = int(np.random.rand() * del_w)
+
+        # Padding
+        im_padded[sh:sh + new_h, sw:sw + new_w, :] = im.copy()
+        text_polys[:, :, 0] += sw
+        text_polys[:, :, 1] += sh
+
+        score_map, score_label_map, border_map, direction_map, training_mask, \
+        pos_list, pos_mask, label_list, score_label_map_text_label = self.generate_tcl_ctc_label(input_size,
+                                                                                                 input_size,
+                                                                                                 text_polys,
+                                                                                                 text_tags,
+                                                                                                 text_strs, 0.25)
+        if len(label_list) <= 0:  # eliminate negative samples
+            return None
+        pos_list_temp = np.zeros([64, 3])
+        pos_mask_temp = np.zeros([64, 1])
+        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 > self.max_text_length:
+                label_list[i] = label[:self.max_text_length]
+                continue
+            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) > self.max_text_nums:
+            return None
+        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)
+
+        if self.img_id == self.batch_size - 1:
+            self.img_id = 0
+        else:
+            self.img_id += 1
+
+        im_padded[:, :, 2] -= 0.485 * 255
+        im_padded[:, :, 1] -= 0.456 * 255
+        im_padded[:, :, 0] -= 0.406 * 255
+        im_padded[:, :, 2] /= (255.0 * 0.229)
+        im_padded[:, :, 1] /= (255.0 * 0.224)
+        im_padded[:, :, 0] /= (255.0 * 0.225)
+        im_padded = im_padded.transpose((2, 0, 1))
+        images = im_padded[::-1, :, :]
+        tcl_maps = score_map[np.newaxis, :, :]
+        tcl_label_maps = score_label_map[np.newaxis, :, :]
+        border_maps = border_map.transpose((2, 0, 1))
+        direction_maps = direction_map.transpose((2, 0, 1))
+        training_masks = training_mask[np.newaxis, :, :]
+        pos_list = np.array(pos_list)
+        pos_mask = np.array(pos_mask)
+        label_list = np.array(label_list)
+        data['images'] = images
+        data['tcl_maps'] = tcl_maps
+        data['tcl_label_maps'] = tcl_label_maps
+        data['border_maps'] = border_maps
+        data['direction_maps'] = direction_maps
+        data['training_masks'] = training_masks
+        data['label_list'] = label_list
+        data['pos_list'] = pos_list
+        data['pos_mask'] = pos_mask
+        return data

ppocr/data/pgnet_dataset.py

+# 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.
+# 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
+import os
+from paddle.io import Dataset
+from .imaug import transform, create_operators
+import random
+
+
+class PGDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(PGDataSet, self).__init__()
+
+        self.logger = logger
+        self.seed = seed
+        self.mode = mode
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+
+        label_file_list = dataset_config.pop('label_file_list')
+        data_source_num = len(label_file_list)
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * int(data_source_num)
+        self.data_format = dataset_config.get('data_format', 'icdar')
+        assert len(
+            ratio_list
+        ) == data_source_num, "The length of ratio_list should be the same as the file_list."
+        self.do_shuffle = loader_config['shuffle']
+
+        logger.info("Initialize indexs of datasets:%s" % label_file_list)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list,
+                                                   self.data_format)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
+            self.shuffle_data_random()
+
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+
+    def shuffle_data_random(self):
+        if self.do_shuffle:
+            random.seed(self.seed)
+            random.shuffle(self.data_lines)
+        return
+
+    def extract_polys(self, poly_txt_path):
+        """
+        Read text_polys, txt_tags, txts from give txt file.
+        """
+        text_polys, txt_tags, txts = [], [], []
+        with open(poly_txt_path) as f:
+            for line in f.readlines():
+                poly_str, txt = line.strip().split('\t')
+                poly = list(map(float, poly_str.split(',')))
+                if self.mode.lower() == "eval":
+                    while len(poly) < 100:
+                        poly.append(-1)
+                text_polys.append(
+                    np.array(
+                        poly, dtype=np.float32).reshape(-1, 2))
+                txts.append(txt)
+                txt_tags.append(txt == '###')
+
+        return np.array(list(map(np.array, text_polys))), \
+               np.array(txt_tags, dtype=np.bool), txts
+
+    def extract_info_textnet(self, im_fn, img_dir=''):
+        """
+        Extract information from line in textnet format.
+        """
+        info_list = im_fn.split('\t')
+        img_path = ''
+        for ext in [
+                'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'JPG'
+        ]:
+            if os.path.exists(os.path.join(img_dir, info_list[0] + "." + ext)):
+                img_path = os.path.join(img_dir, info_list[0] + "." + ext)
+                break
+
+        if img_path == '':
+            print('Image {0} NOT found in {1}, and it will be ignored.'.format(
+                info_list[0], img_dir))
+
+        nBox = (len(info_list) - 1) // 9
+        wordBBs, txts, txt_tags = [], [], []
+        for n in range(0, nBox):
+            wordBB = list(map(float, info_list[n * 9 + 1:(n + 1) * 9]))
+            txt = info_list[(n + 1) * 9]
+            wordBBs.append([[wordBB[0], wordBB[1]], [wordBB[2], wordBB[3]],
+                            [wordBB[4], wordBB[5]], [wordBB[6], wordBB[7]]])
+            txts.append(txt)
+            if txt == '###':
+                txt_tags.append(True)
+            else:
+                txt_tags.append(False)
+        return img_path, np.array(wordBBs, dtype=np.float32), txt_tags, txts
+
+    def get_image_info_list(self, file_list, ratio_list, data_format='textnet'):
+        if isinstance(file_list, str):
+            file_list = [file_list]
+        data_lines = []
+        for idx, data_source in enumerate(file_list):
+            image_files = []
+            if data_format == 'icdar':
+                image_files = [(data_source, x) for x in
+                               os.listdir(os.path.join(data_source, 'rgb'))
+                               if x.split('.')[-1] in [
+                                   'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif',
+                                   'tiff', 'gif', 'JPG'
+                               ]]
+            elif data_format == 'textnet':
+                with open(data_source) as f:
+                    image_files = [(data_source, x.strip())
+                                   for x in f.readlines()]
+            else:
+                print("Unrecognized data format...")
+                exit(-1)
+            random.seed(self.seed)
+            image_files = random.sample(
+                image_files, round(len(image_files) * ratio_list[idx]))
+            data_lines.extend(image_files)
+        return data_lines
+
+    def __getitem__(self, idx):
+        file_idx = self.data_idx_order_list[idx]
+        data_path, data_line = self.data_lines[file_idx]
+        try:
+            if self.data_format == 'icdar':
+                im_path = os.path.join(data_path, 'rgb', data_line)
+                if self.mode.lower() == "eval":
+                    poly_path = os.path.join(data_path, 'poly_gt',
+                                             data_line.split('.')[0] + '.txt')
+                else:
+                    poly_path = os.path.join(data_path, 'poly',
+                                             data_line.split('.')[0] + '.txt')
+                text_polys, text_tags, text_strs = self.extract_polys(poly_path)
+            else:
+                image_dir = os.path.join(os.path.dirname(data_path), 'image')
+                im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
+                    data_line, image_dir)
+
+            data = {
+                'img_path': im_path,
+                'polys': text_polys,
+                'tags': text_tags,
+                'strs': text_strs
+            }
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            outs = transform(data, self.ops)
+
+        except Exception as e:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    self.data_idx_order_list[idx], e))
+            outs = None
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return len(self.data_idx_order_list)

ppocr/data/simple_dataset.py

@@ -23,6 +23,7 @@ class SimpleDataSet(Dataset):
     def __init__(self, config, mode, logger, seed=None):
         super(SimpleDataSet, self).__init__()
         self.logger = logger
+        self.mode = mode.lower()
 
         global_config = config['Global']
         dataset_config = config[mode]['dataset']
@@ -45,7 +46,7 @@ class SimpleDataSet(Dataset):
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
         self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
         self.data_idx_order_list = list(range(len(self.data_lines)))
-        if mode.lower() == "train":
+        if self.mode == "train" and self.do_shuffle:
             self.shuffle_data_random()
         self.ops = create_operators(dataset_config['transforms'], global_config)
 
@@ -56,16 +57,16 @@ class SimpleDataSet(Dataset):
         for idx, file in enumerate(file_list):
             with open(file, "rb") as f:
                 lines = f.readlines()
-                random.seed(self.seed)
-                lines = random.sample(lines,
-                                      round(len(lines) * ratio_list[idx]))
+                if self.mode == "train" or ratio_list[idx] < 1.0:
+                    random.seed(self.seed)
+                    lines = random.sample(lines,
+                                          round(len(lines) * ratio_list[idx]))
                 data_lines.extend(lines)
         return data_lines
 
     def shuffle_data_random(self):
-        if self.do_shuffle:
-            random.seed(self.seed)
-            random.shuffle(self.data_lines)
+        random.seed(self.seed)
+        random.shuffle(self.data_lines)
         return
 
     def __getitem__(self, idx):
@@ -90,7 +91,10 @@ class SimpleDataSet(Dataset):
                     data_line, e))
             outs = None
         if outs is None:
-            return self.__getitem__(np.random.randint(self.__len__()))
+            # during evaluation, we should fix the idx to get same results for many times of evaluation.
+            rnd_idx = np.random.randint(self.__len__(
+            )) if self.mode == "train" else (idx + 1) % self.__len__()
+            return self.__getitem__(rnd_idx)
         return outs
 
     def __len__(self):

ppocr/losses/__init__.py

@@ -29,10 +29,11 @@ def build_loss(config):
     # cls loss
     from .cls_loss import ClsLoss
 
+    # e2e loss
+    from .e2e_pg_loss import PGLoss
     support_dict = [
         'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
-        'SRNLoss'
-    ]
+        'SRNLoss', 'PGLoss']
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/e2e_pg_loss.py

+# 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.
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import nn
+import paddle
+
+from .det_basic_loss import DiceLoss
+from ppocr.utils.e2e_utils.extract_batchsize import pre_process
+
+
+class PGLoss(nn.Layer):
+    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 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)
+        f_border_split = f_border
+        b, c, h, w = l_border_norm.shape
+        l_border_norm_split = paddle.expand(
+            x=l_border_norm, shape=[b, 4 * c, h, w])
+        b, c, h, w = l_score.shape
+        l_border_score = paddle.expand(x=l_score, shape=[b, 4 * c, h, w])
+        b, c, h, w = l_mask.shape
+        l_border_mask = paddle.expand(x=l_mask, shape=[b, 4 * c, h, w])
+        border_diff = l_border_split - f_border_split
+        abs_border_diff = paddle.abs(border_diff)
+        border_sign = abs_border_diff < 1.0
+        border_sign = paddle.cast(border_sign, dtype='float32')
+        border_sign.stop_gradient = True
+        border_in_loss = 0.5 * abs_border_diff * abs_border_diff * border_sign + \
+                         (abs_border_diff - 0.5) * (1.0 - border_sign)
+        border_out_loss = l_border_norm_split * border_in_loss
+        border_loss = paddle.sum(border_out_loss * l_border_score * l_border_mask) / \
+                      (paddle.sum(l_border_score * l_border_mask) + 1e-5)
+        return border_loss
+
+    def direction_loss(self, f_direction, l_direction, l_score, l_mask):
+        l_direction_split, l_direction_norm = paddle.tensor.split(
+            l_direction, num_or_sections=[2, 1], axis=1)
+        f_direction_split = f_direction
+        b, c, h, w = l_direction_norm.shape
+        l_direction_norm_split = paddle.expand(
+            x=l_direction_norm, shape=[b, 2 * c, h, w])
+        b, c, h, w = l_score.shape
+        l_direction_score = paddle.expand(x=l_score, shape=[b, 2 * c, h, w])
+        b, c, h, w = l_mask.shape
+        l_direction_mask = paddle.expand(x=l_mask, shape=[b, 2 * c, h, w])
+        direction_diff = l_direction_split - f_direction_split
+        abs_direction_diff = paddle.abs(direction_diff)
+        direction_sign = abs_direction_diff < 1.0
+        direction_sign = paddle.cast(direction_sign, dtype='float32')
+        direction_sign.stop_gradient = True
+        direction_in_loss = 0.5 * abs_direction_diff * abs_direction_diff * direction_sign + \
+                            (abs_direction_diff - 0.5) * (1.0 - direction_sign)
+        direction_out_loss = l_direction_norm_split * direction_in_loss
+        direction_loss = paddle.sum(direction_out_loss * l_direction_score * l_direction_mask) / \
+                         (paddle.sum(l_direction_score * l_direction_mask) + 1e-5)
+        return direction_loss
+
+    def ctcloss(self, f_char, tcl_pos, tcl_mask, tcl_label, label_t):
+        f_char = paddle.transpose(f_char, [0, 2, 3, 1])
+        tcl_pos = paddle.reshape(tcl_pos, [-1, 3])
+        tcl_pos = paddle.cast(tcl_pos, dtype=int)
+        f_tcl_char = paddle.gather_nd(f_char, tcl_pos)
+        f_tcl_char = paddle.reshape(f_tcl_char,
+                                    [-1, 64, 37])  # len(Lexicon_Table)+1
+        f_tcl_char_fg, f_tcl_char_bg = paddle.split(f_tcl_char, [36, 1], axis=2)
+        f_tcl_char_bg = f_tcl_char_bg * tcl_mask + (1.0 - tcl_mask) * 20.0
+        b, c, l = tcl_mask.shape
+        tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, 36 * l])
+        tcl_mask_fg.stop_gradient = True
+        f_tcl_char_fg = f_tcl_char_fg * tcl_mask_fg + (1.0 - tcl_mask_fg) * (
+            -20.0)
+        f_tcl_char_mask = paddle.concat([f_tcl_char_fg, f_tcl_char_bg], axis=2)
+        f_tcl_char_ld = paddle.transpose(f_tcl_char_mask, (1, 0, 2))
+        N, B, _ = f_tcl_char_ld.shape
+        input_lengths = paddle.to_tensor([N] * B, dtype='int64')
+        cost = paddle.nn.functional.ctc_loss(
+            log_probs=f_tcl_char_ld,
+            labels=tcl_label,
+            input_lengths=input_lengths,
+            label_lengths=label_t,
+            blank=self.pad_num,
+            reduction='none')
+        cost = cost.mean()
+        return cost
+
+    def forward(self, predicts, labels):
+        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 = 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_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_border, border_maps, tcl_maps,
+                                       training_masks)
+        direction_loss = self.direction_loss(f_direction, direction_maps,
+                                             tcl_maps, training_masks)
+        ctc_loss = self.ctcloss(f_char, pos_list, pos_mask, label_list, label_t)
+        loss_all = score_loss + border_loss + direction_loss + 5 * ctc_loss
+
+        losses = {
+            'loss': loss_all,
+            "score_loss": score_loss,
+            "border_loss": border_loss,
+            "direction_loss": direction_loss,
+            "ctc_loss": ctc_loss
+        }
+        return losses

ppocr/metrics/__init__.py

@@ -26,8 +26,9 @@ def build_metric(config):
     from .det_metric import DetMetric
     from .rec_metric import RecMetric
     from .cls_metric import ClsMetric
+    from .e2e_metric import E2EMetric
 
-    support_dict = ['DetMetric', 'RecMetric', 'ClsMetric']
+    support_dict = ['DetMetric', 'RecMetric', 'ClsMetric', 'E2EMetric']
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/metrics/e2e_metric.py

+# 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.
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+__all__ = ['E2EMetric']
+
+from ppocr.utils.e2e_metric.Deteval import get_socre, combine_results
+from ppocr.utils.e2e_utils.extract_textpoint import get_dict
+
+
+class E2EMetric(object):
+    def __init__(self,
+                 character_dict_path,
+                 main_indicator='f_score_e2e',
+                 **kwargs):
+        self.label_list = get_dict(character_dict_path)
+        self.max_index = len(self.label_list)
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, preds, batch, **kwargs):
+        temp_gt_polyons_batch = batch[2]
+        temp_gt_strs_batch = batch[3]
+        ignore_tags_batch = batch[4]
+        gt_polyons_batch = []
+        gt_strs_batch = []
+
+        temp_gt_polyons_batch = temp_gt_polyons_batch[0].tolist()
+        for temp_list in temp_gt_polyons_batch:
+            t = []
+            for index in temp_list:
+                if index[0] != -1 and index[1] != -1:
+                    t.append(index)
+            gt_polyons_batch.append(t)
+
+        temp_gt_strs_batch = temp_gt_strs_batch[0].tolist()
+        for temp_list in temp_gt_strs_batch:
+            t = ""
+            for index in temp_list:
+                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):
+            # prepare gt
+            gt_info_list = [{
+                'points': gt_polyon,
+                'text': gt_str,
+                'ignore': ignore_tag
+            } for gt_polyon, gt_str, ignore_tag in
+                            zip(gt_polyons, gt_strs, ignore_tags)]
+            # prepare det
+            e2e_info_list = [{
+                'points': det_polyon,
+                'text': pred_str
+            } for det_polyon, pred_str in zip(pred['points'], pred['strs'])]
+            result = get_socre(gt_info_list, e2e_info_list)
+            self.results.append(result)
+
+    def get_metric(self):
+        metircs = combine_results(self.results)
+        self.reset()
+        return metircs
+
+    def reset(self):
+        self.results = []  # clear results

ppocr/metrics/eval_det_iou.py

@@ -150,7 +150,7 @@ class DetectionIoUEvaluator(object):
                             pairs.append({'gt': gtNum, 'det': detNum})
                             detMatchedNums.append(detNum)
                             evaluationLog += "Match GT #" + \
-                                str(gtNum) + " with Det #" + str(detNum) + "\n"
+                                             str(gtNum) + " with Det #" + str(detNum) + "\n"
 
         numGtCare = (len(gtPols) - len(gtDontCarePolsNum))
         numDetCare = (len(detPols) - len(detDontCarePolsNum))
@@ -162,7 +162,7 @@ class DetectionIoUEvaluator(object):
             precision = 0 if numDetCare == 0 else float(detMatched) / numDetCare
 
         hmean = 0 if (precision + recall) == 0 else 2.0 * \
-            precision * recall / (precision + recall)
+                                                    precision * recall / (precision + recall)
 
         matchedSum += detMatched
         numGlobalCareGt += numGtCare
@@ -200,7 +200,8 @@ class DetectionIoUEvaluator(object):
         methodPrecision = 0 if numGlobalCareDet == 0 else float(
             matchedSum) / numGlobalCareDet
         methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
-            methodRecall * methodPrecision / (methodRecall + methodPrecision)
+                                                                    methodRecall * methodPrecision / (
+                                                                            methodRecall + methodPrecision)
         # print(methodRecall, methodPrecision, methodHmean)
         # sys.exit(-1)
         methodMetrics = {

ppocr/modeling/backbones/__init__.py

@@ -26,6 +26,9 @@ def build_backbone(config, model_type):
         from .rec_resnet_vd import ResNet
         from .rec_resnet_fpn import ResNetFPN
         support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
+    elif model_type == 'e2e':
+        from .e2e_resnet_vd_pg import ResNet
+        support_dict = ['ResNet']
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/e2e_resnet_vd_pg.py

+# 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.
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+__all__ = ["ResNet"]
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            groups=1,
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = nn.BatchNorm(
+            out_channels,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+        self.conv2 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels * 4,
+            kernel_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels * 4,
+                kernel_size=1,
+                stride=stride,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv2)
+        y = F.relu(y)
+        return y
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            act=None,
+            name=name + "_branch2b")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv1)
+        y = F.relu(y)
+        return y
+
+
+class ResNet(nn.Layer):
+    def __init__(self, in_channels=3, layers=50, **kwargs):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152, 200]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            # depth = [3, 4, 6, 3]
+            depth = [3, 4, 6, 3, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512, 1024,
+                        2048] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512, 512]
+
+        self.conv1_1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=7,
+            stride=2,
+            act='relu',
+            name="conv1_1")
+        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
+
+        self.stages = []
+        self.out_channels = [3, 64]
+        # num_filters = [64, 128, 256, 512, 512]
+        if layers >= 50:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(bottleneck_block)
+                self.out_channels.append(num_filters[block] * 4)
+                self.stages.append(nn.Sequential(*block_list))
+        else:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(basic_block)
+                self.out_channels.append(num_filters[block])
+                self.stages.append(nn.Sequential(*block_list))
+
+    def forward(self, inputs):
+        out = [inputs]
+        y = self.conv1_1(inputs)
+        out.append(y)
+        y = self.pool2d_max(y)
+        for block in self.stages:
+            y = block(y)
+            out.append(y)
+        return out

ppocr/modeling/heads/__init__.py

@@ -20,6 +20,7 @@ def build_head(config):
     from .det_db_head import DBHead
     from .det_east_head import EASTHead
     from .det_sast_head import SASTHead
+    from .e2e_pg_head import PGHead
 
     # rec head
     from .rec_ctc_head import CTCHead
@@ -30,8 +31,8 @@ def build_head(config):
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-        'SRNHead'
-    ]
+        'SRNHead', 'PGHead']
+
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('head only support {}'.format(

ppocr/modeling/heads/e2e_pg_head.py

+# 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.
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 padding,
+                 groups=1,
+                 if_act=True,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+        self.if_act = if_act
+        self.act = act
+        self.conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=act,
+            param_attr=ParamAttr(name="bn_" + name + "_scale"),
+            bias_attr=ParamAttr(name="bn_" + name + "_offset"),
+            moving_mean_name="bn_" + name + "_mean",
+            moving_variance_name="bn_" + name + "_variance",
+            use_global_stats=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class PGHead(nn.Layer):
+    """
+    """
+
+    def __init__(self, in_channels, **kwargs):
+        super(PGHead, self).__init__()
+        self.conv_f_score1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_score{}".format(1))
+        self.conv_f_score2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_score{}".format(2))
+        self.conv_f_score3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_score{}".format(3))
+
+        self.conv1 = nn.Conv2D(
+            in_channels=128,
+            out_channels=1,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_score{}".format(4)),
+            bias_attr=False)
+
+        self.conv_f_boder1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_boder{}".format(1))
+        self.conv_f_boder2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_boder{}".format(2))
+        self.conv_f_boder3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_boder{}".format(3))
+        self.conv2 = nn.Conv2D(
+            in_channels=128,
+            out_channels=4,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_boder{}".format(4)),
+            bias_attr=False)
+        self.conv_f_char1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(1))
+        self.conv_f_char2 = ConvBNLayer(
+            in_channels=128,
+            out_channels=128,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_char{}".format(2))
+        self.conv_f_char3 = ConvBNLayer(
+            in_channels=128,
+            out_channels=256,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(3))
+        self.conv_f_char4 = ConvBNLayer(
+            in_channels=256,
+            out_channels=256,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_char{}".format(4))
+        self.conv_f_char5 = ConvBNLayer(
+            in_channels=256,
+            out_channels=256,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(5))
+        self.conv3 = nn.Conv2D(
+            in_channels=256,
+            out_channels=37,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_char{}".format(6)),
+            bias_attr=False)
+
+        self.conv_f_direc1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_direc{}".format(1))
+        self.conv_f_direc2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_direc{}".format(2))
+        self.conv_f_direc3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_direc{}".format(3))
+        self.conv4 = nn.Conv2D(
+            in_channels=128,
+            out_channels=2,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_direc{}".format(4)),
+            bias_attr=False)
+
+    def forward(self, x):
+        f_score = self.conv_f_score1(x)
+        f_score = self.conv_f_score2(f_score)
+        f_score = self.conv_f_score3(f_score)
+        f_score = self.conv1(f_score)
+        f_score = F.sigmoid(f_score)
+
+        # f_border
+        f_border = self.conv_f_boder1(x)
+        f_border = self.conv_f_boder2(f_border)
+        f_border = self.conv_f_boder3(f_border)
+        f_border = self.conv2(f_border)
+
+        f_char = self.conv_f_char1(x)
+        f_char = self.conv_f_char2(f_char)
+        f_char = self.conv_f_char3(f_char)
+        f_char = self.conv_f_char4(f_char)
+        f_char = self.conv_f_char5(f_char)
+        f_char = self.conv3(f_char)
+
+        f_direction = self.conv_f_direc1(x)
+        f_direction = self.conv_f_direc2(f_direction)
+        f_direction = self.conv_f_direc3(f_direction)
+        f_direction = self.conv4(f_direction)
+
+        predicts = {}
+        predicts['f_score'] = f_score
+        predicts['f_border'] = f_border
+        predicts['f_char'] = f_char
+        predicts['f_direction'] = f_direction
+        return predicts

ppocr/modeling/heads/rec_att_head.py

@@ -38,7 +38,7 @@ class AttentionHead(nn.Layer):
         return input_ont_hot
 
     def forward(self, inputs, targets=None, batch_max_length=25):
-        batch_size = inputs.shape[0]
+        batch_size = paddle.shape(inputs)[0]
         num_steps = batch_max_length
 
         hidden = paddle.zeros((batch_size, self.hidden_size))

ppocr/modeling/necks/__init__.py

@@ -14,12 +14,14 @@
 
 __all__ = ['build_neck']
 
+
 def build_neck(config):
     from .db_fpn import DBFPN
     from .east_fpn import EASTFPN
     from .sast_fpn import SASTFPN
     from .rnn import SequenceEncoder
-    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder']
+    from .pg_fpn import PGFPN
+    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/modeling/necks/pg_fpn.py

+# 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.
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 groups=1,
+                 is_vd_mode=False,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = nn.BatchNorm(
+            out_channels,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance',
+            use_global_stats=False)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class DeConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=4,
+                 stride=2,
+                 padding=1,
+                 groups=1,
+                 if_act=True,
+                 act=None,
+                 name=None):
+        super(DeConvBNLayer, self).__init__()
+
+        self.if_act = if_act
+        self.act = act
+        self.deconv = nn.Conv2DTranspose(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=act,
+            param_attr=ParamAttr(name="bn_" + name + "_scale"),
+            bias_attr=ParamAttr(name="bn_" + name + "_offset"),
+            moving_mean_name="bn_" + name + "_mean",
+            moving_variance_name="bn_" + name + "_variance",
+            use_global_stats=False)
+
+    def forward(self, x):
+        x = self.deconv(x)
+        x = self.bn(x)
+        return x
+
+
+class PGFPN(nn.Layer):
+    def __init__(self, in_channels, **kwargs):
+        super(PGFPN, self).__init__()
+        num_inputs = [2048, 2048, 1024, 512, 256]
+        num_outputs = [256, 256, 192, 192, 128]
+        self.out_channels = 128
+        self.conv_bn_layer_1 = ConvBNLayer(
+            in_channels=3,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            act=None,
+            name='FPN_d1')
+        self.conv_bn_layer_2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            act=None,
+            name='FPN_d2')
+        self.conv_bn_layer_3 = ConvBNLayer(
+            in_channels=256,
+            out_channels=128,
+            kernel_size=3,
+            stride=1,
+            act=None,
+            name='FPN_d3')
+        self.conv_bn_layer_4 = ConvBNLayer(
+            in_channels=32,
+            out_channels=64,
+            kernel_size=3,
+            stride=2,
+            act=None,
+            name='FPN_d4')
+        self.conv_bn_layer_5 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name='FPN_d5')
+        self.conv_bn_layer_6 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=3,
+            stride=2,
+            act=None,
+            name='FPN_d6')
+        self.conv_bn_layer_7 = ConvBNLayer(
+            in_channels=128,
+            out_channels=128,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name='FPN_d7')
+        self.conv_bn_layer_8 = ConvBNLayer(
+            in_channels=128,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name='FPN_d8')
+
+        self.conv_h0 = ConvBNLayer(
+            in_channels=num_inputs[0],
+            out_channels=num_outputs[0],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(0))
+        self.conv_h1 = ConvBNLayer(
+            in_channels=num_inputs[1],
+            out_channels=num_outputs[1],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(1))
+        self.conv_h2 = ConvBNLayer(
+            in_channels=num_inputs[2],
+            out_channels=num_outputs[2],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(2))
+        self.conv_h3 = ConvBNLayer(
+            in_channels=num_inputs[3],
+            out_channels=num_outputs[3],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(3))
+        self.conv_h4 = ConvBNLayer(
+            in_channels=num_inputs[4],
+            out_channels=num_outputs[4],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(4))
+
+        self.dconv0 = DeConvBNLayer(
+            in_channels=num_outputs[0],
+            out_channels=num_outputs[0 + 1],
+            name="dconv_{}".format(0))
+        self.dconv1 = DeConvBNLayer(
+            in_channels=num_outputs[1],
+            out_channels=num_outputs[1 + 1],
+            act=None,
+            name="dconv_{}".format(1))
+        self.dconv2 = DeConvBNLayer(
+            in_channels=num_outputs[2],
+            out_channels=num_outputs[2 + 1],
+            act=None,
+            name="dconv_{}".format(2))
+        self.dconv3 = DeConvBNLayer(
+            in_channels=num_outputs[3],
+            out_channels=num_outputs[3 + 1],
+            act=None,
+            name="dconv_{}".format(3))
+        self.conv_g1 = ConvBNLayer(
+            in_channels=num_outputs[1],
+            out_channels=num_outputs[1],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(1))
+        self.conv_g2 = ConvBNLayer(
+            in_channels=num_outputs[2],
+            out_channels=num_outputs[2],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(2))
+        self.conv_g3 = ConvBNLayer(
+            in_channels=num_outputs[3],
+            out_channels=num_outputs[3],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(3))
+        self.conv_g4 = ConvBNLayer(
+            in_channels=num_outputs[4],
+            out_channels=num_outputs[4],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(4))
+        self.convf = ConvBNLayer(
+            in_channels=num_outputs[4],
+            out_channels=num_outputs[4],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_f{}".format(4))
+
+    def forward(self, x):
+        c0, c1, c2, c3, c4, c5, c6 = x
+        # FPN_Down_Fusion
+        f = [c0, c1, c2]
+        g = [None, None, None]
+        h = [None, None, None]
+        h[0] = self.conv_bn_layer_1(f[0])
+        h[1] = self.conv_bn_layer_2(f[1])
+        h[2] = self.conv_bn_layer_3(f[2])
+
+        g[0] = self.conv_bn_layer_4(h[0])
+        g[1] = paddle.add(g[0], h[1])
+        g[1] = F.relu(g[1])
+        g[1] = self.conv_bn_layer_5(g[1])
+        g[1] = self.conv_bn_layer_6(g[1])
+
+        g[2] = paddle.add(g[1], h[2])
+        g[2] = F.relu(g[2])
+        g[2] = self.conv_bn_layer_7(g[2])
+        f_down = self.conv_bn_layer_8(g[2])
+
+        # FPN UP Fusion
+        f1 = [c6, c5, c4, c3, c2]
+        g = [None, None, None, None, None]
+        h = [None, None, None, None, None]
+        h[0] = self.conv_h0(f1[0])
+        h[1] = self.conv_h1(f1[1])
+        h[2] = self.conv_h2(f1[2])
+        h[3] = self.conv_h3(f1[3])
+        h[4] = self.conv_h4(f1[4])
+
+        g[0] = self.dconv0(h[0])
+        g[1] = paddle.add(g[0], h[1])
+        g[1] = F.relu(g[1])
+        g[1] = self.conv_g1(g[1])
+        g[1] = self.dconv1(g[1])
+
+        g[2] = paddle.add(g[1], h[2])
+        g[2] = F.relu(g[2])
+        g[2] = self.conv_g2(g[2])
+        g[2] = self.dconv2(g[2])
+
+        g[3] = paddle.add(g[2], h[3])
+        g[3] = F.relu(g[3])
+        g[3] = self.conv_g3(g[3])
+        g[3] = self.dconv3(g[3])
+
+        g[4] = paddle.add(x=g[3], y=h[4])
+        g[4] = F.relu(g[4])
+        g[4] = self.conv_g4(g[4])
+        f_up = self.convf(g[4])
+        f_common = paddle.add(f_down, f_up)
+        f_common = F.relu(f_common)
+        return f_common

ppocr/postprocess/__init__.py

@@ -28,10 +28,11 @@ def build_post_process(config, global_config=None):
     from .sast_postprocess import SASTPostProcess
     from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
     from .cls_postprocess import ClsPostProcess
+    from .pg_postprocess import PGPostProcess
 
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
-        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode'
+        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess'
     ]
 
     config = copy.deepcopy(config)

ppocr/postprocess/pg_postprocess.py

+# 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.
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+__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 *
+import paddle
+
+
+class PGPostProcess(object):
+    """
+    The post process for PGNet.
+    """
+
+    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']
+        p_char = outs_dict['f_char']
+        p_direction = outs_dict['f_direction']
+        if isinstance(p_score, paddle.Tensor):
+            p_score = p_score[0].numpy()
+            p_border = p_border[0].numpy()
+            p_direction = p_direction[0].numpy()
+            p_char = p_char[0].numpy()
+        else:
+            p_score = p_score[0]
+            p_border = p_border[0]
+            p_direction = p_direction[0]
+            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(
+            p_score,
+            p_char,
+            p_direction,
+            score_thresh=self.score_thresh,
+            is_backbone=True,
+            is_curved=is_curved)
+        p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
+        char_seq_idx_set = []
+        for i in range(len(instance_yxs_list)):
+            gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
+            f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
+            feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
+            feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
+            feature_len = [len(feature_seq[0])]
+            featyre_seq = paddle.to_tensor(feature_seq)
+            feature_len = np.array([feature_len]).astype(np.int64)
+            length = paddle.to_tensor(feature_len)
+            seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
+                input=featyre_seq, blank=36, input_length=length)
+            seq_pred_str = seq_pred[0].numpy().tolist()[0]
+            seq_len = seq_pred[1].numpy()[0][0]
+            temp_t = []
+            for c in seq_pred_str[:seq_len]:
+                temp_t.append(c)
+            char_seq_idx_set.append(temp_t)
+        seq_strs = []
+        for char_idx_set in char_seq_idx_set:
+            pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
+            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)
+        data = {
+            'points': poly_list,
+            'strs': keep_str_list,
+        }
+        return data

ppocr/postprocess/sast_postprocess.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import os
 import sys
+
 __dir__ = os.path.dirname(__file__)
 sys.path.append(__dir__)
 sys.path.append(os.path.join(__dir__, '..'))
@@ -49,12 +50,12 @@ class SASTPostProcess(object):
         self.shrink_ratio_of_width = shrink_ratio_of_width
         self.expand_scale = expand_scale
         self.tcl_map_thresh = tcl_map_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 point_pair2poly(self, point_pair_list):
         """
         Transfer vertical point_pairs into poly point in clockwise.
@@ -66,31 +67,42 @@ class SASTPostProcess(object):
             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(self, quad, begin_width_ratio=0., end_width_ratio=1.):
+
+    def shrink_quad_along_width(self,
+                                quad,
+                                begin_width_ratio=0.,
+                                end_width_ratio=1.):
         """ 
         Generate shrink_quad_along_width.
         """
-        ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+        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(self, 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_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 = self.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)
+                     (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+        left_quad_expand = self.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 = self.shrink_quad_along_width(right_quad, 0.0, right_ratio)
+                      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 = self.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]
@@ -100,7 +112,7 @@ class SASTPostProcess(object):
     def restore_quad(self, tcl_map, tcl_map_thresh, tvo_map):
         """Restore quad."""
         xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
-        xy_text = xy_text[:, ::-1] # (n, 2)
+        xy_text = xy_text[:, ::-1]  # (n, 2)
 
         # Sort the text boxes via the y axis
         xy_text = xy_text[np.argsort(xy_text[:, 1])]
@@ -112,7 +124,7 @@ class SASTPostProcess(object):
         point_num = int(tvo_map.shape[-1] / 2)
         assert point_num == 4
         tvo_map = tvo_map[xy_text[:, 1], xy_text[:, 0], :]
-        xy_text_tile = np.tile(xy_text, (1, point_num)) # (n, point_num * 2)
+        xy_text_tile = np.tile(xy_text, (1, point_num))  # (n, point_num * 2)
         quads = xy_text_tile - tvo_map
 
         return scores, quads, xy_text
@@ -121,14 +133,12 @@ class SASTPostProcess(object):
         """
         compute area of a quad.
         """
-        edge = [
-            (quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
-            (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
-            (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
-            (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])
-        ]
+        edge = [(quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
+                (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
+                (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
+                (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])]
         return np.sum(edge) / 2.
-        
+
     def nms(self, dets):
         if self.is_python35:
             import lanms
@@ -141,7 +151,7 @@ class SASTPostProcess(object):
         """
         Cluster pixels in tcl_map based on quads.
         """
-        instance_count = quads.shape[0] + 1 # contain background
+        instance_count = quads.shape[0] + 1  # contain background
         instance_label_map = np.zeros(tcl_map.shape[:2], dtype=np.int32)
         if instance_count == 1:
             return instance_count, instance_label_map
@@ -149,18 +159,19 @@ class SASTPostProcess(object):
         # predict text center
         xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
         n = xy_text.shape[0]
-        xy_text = xy_text[:, ::-1] # (n, 2)
-        tco = tco_map[xy_text[:, 1], xy_text[:, 0], :] # (n, 2)
+        xy_text = xy_text[:, ::-1]  # (n, 2)
+        tco = tco_map[xy_text[:, 1], xy_text[:, 0], :]  # (n, 2)
         pred_tc = xy_text - tco
-        
+
         # get gt text center
         m = quads.shape[0]
-        gt_tc = np.mean(quads, axis=1) # (m, 2)
+        gt_tc = np.mean(quads, axis=1)  # (m, 2)
 
-        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :], (1, m, 1)) # (n, m, 2)
-        gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1)) # (n, m, 2)
-        dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2) # (n, m)
-        xy_text_assign = np.argmin(dist_mat, axis=1) + 1 # (n,)
+        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :],
+                               (1, m, 1))  # (n, m, 2)
+        gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1))  # (n, m, 2)
+        dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2)  # (n, m)
+        xy_text_assign = np.argmin(dist_mat, axis=1) + 1  # (n,)
 
         instance_label_map[xy_text[:, 1], xy_text[:, 0]] = xy_text_assign
         return instance_count, instance_label_map
@@ -169,26 +180,47 @@ class SASTPostProcess(object):
         """
         Estimate sample points number.
         """
-        eh = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) / 2.0
-        ew = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0
+        eh = (np.linalg.norm(quad[0] - quad[3]) +
+              np.linalg.norm(quad[1] - quad[2])) / 2.0
+        ew = (np.linalg.norm(quad[0] - quad[1]) +
+              np.linalg.norm(quad[2] - quad[3])) / 2.0
 
         dense_sample_pts_num = max(2, int(ew))
-        dense_xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, dense_sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
-
-        dense_xy_center_line_diff = dense_xy_center_line[1:] - dense_xy_center_line[:-1]
-        estimate_arc_len = np.sum(np.linalg.norm(dense_xy_center_line_diff, axis=1))
+        dense_xy_center_line = xy_text[np.linspace(
+            0,
+            xy_text.shape[0] - 1,
+            dense_sample_pts_num,
+            endpoint=True,
+            dtype=np.float32).astype(np.int32)]
+
+        dense_xy_center_line_diff = dense_xy_center_line[
+            1:] - dense_xy_center_line[:-1]
+        estimate_arc_len = np.sum(
+            np.linalg.norm(
+                dense_xy_center_line_diff, axis=1))
 
         sample_pts_num = max(2, int(estimate_arc_len / eh))
         return sample_pts_num
 
-    def detect_sast(self, tcl_map, tvo_map, tbo_map, tco_map, ratio_w, ratio_h, src_w, src_h, 
-                shrink_ratio_of_width=0.3, tcl_map_thresh=0.5, offset_expand=1.0, out_strid=4.0):
+    def detect_sast(self,
+                    tcl_map,
+                    tvo_map,
+                    tbo_map,
+                    tco_map,
+                    ratio_w,
+                    ratio_h,
+                    src_w,
+                    src_h,
+                    shrink_ratio_of_width=0.3,
+                    tcl_map_thresh=0.5,
+                    offset_expand=1.0,
+                    out_strid=4.0):
         """
         first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys
         """
         # restore quad
-        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh, tvo_map)
+        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh,
+                                                   tvo_map)
         dets = np.hstack((quads, scores)).astype(np.float32, copy=False)
         dets = self.nms(dets)
         if dets.shape[0] == 0:
@@ -202,7 +234,8 @@ class SASTPostProcess(object):
 
         # instance segmentation
         # instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8)
-        instance_count, instance_label_map = self.cluster_by_quads_tco(tcl_map, tcl_map_thresh, quads, tco_map)
+        instance_count, instance_label_map = self.cluster_by_quads_tco(
+            tcl_map, tcl_map_thresh, quads, tco_map)
 
         # restore single poly with tcl instance.
         poly_list = []
@@ -212,10 +245,10 @@ class SASTPostProcess(object):
             q_area = quad_areas[instance_idx - 1]
             if q_area < 5:
                 continue
-            
+
             #
-            len1 = float(np.linalg.norm(quad[0] -quad[1]))
-            len2 = float(np.linalg.norm(quad[1] -quad[2]))
+            len1 = float(np.linalg.norm(quad[0] - quad[1]))
+            len2 = float(np.linalg.norm(quad[1] - quad[2]))
             min_len = min(len1, len2)
             if min_len < 3:
                 continue
@@ -225,16 +258,18 @@ class SASTPostProcess(object):
                 continue
 
             # filter low confidence instance
-            xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0] 
+            xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0]
             if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.1:
-            # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
+                # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
                 continue
 
             # sort xy_text
-            left_center_pt = np.array([[(quad[0, 0] + quad[-1, 0]) / 2.0,
-                                        (quad[0, 1] + quad[-1, 1]) / 2.0]]) # (1, 2)
-            right_center_pt = np.array([[(quad[1, 0] + quad[2, 0]) / 2.0,
-                                        (quad[1, 1] + quad[2, 1]) / 2.0]]) # (1, 2)
+            left_center_pt = np.array(
+                [[(quad[0, 0] + quad[-1, 0]) / 2.0,
+                  (quad[0, 1] + quad[-1, 1]) / 2.0]])  # (1, 2)
+            right_center_pt = np.array(
+                [[(quad[1, 0] + quad[2, 0]) / 2.0,
+                  (quad[1, 1] + quad[2, 1]) / 2.0]])  # (1, 2)
             proj_unit_vec = (right_center_pt - left_center_pt) / \
                             (np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
             proj_value = np.sum(xy_text * proj_unit_vec, axis=1)
@@ -245,33 +280,45 @@ class SASTPostProcess(object):
                 sample_pts_num = self.estimate_sample_pts_num(quad, xy_text)
             else:
                 sample_pts_num = self.sample_pts_num
-            xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
+            xy_center_line = xy_text[np.linspace(
+                0,
+                xy_text.shape[0] - 1,
+                sample_pts_num,
+                endpoint=True,
+                dtype=np.float32).astype(np.int32)]
 
             point_pair_list = []
             for x, y in xy_center_line:
                 # get corresponding offset
                 offset = tbo_map[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_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                
-                # original point
+                    offset = offset + offset_detal
+                    # original point
                 ori_yx = np.array([y, x], dtype=np.float32)
-                point_pair = (ori_yx +  offset)[:, ::-1]* out_strid / np.array([ratio_w, ratio_h]).reshape(-1, 2) 
+                point_pair = (ori_yx + offset)[:, ::-1] * out_strid / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
                 point_pair_list.append(point_pair)
 
             # ndarry: (x, 2), expand poly along width
             detected_poly = self.point_pair2poly(point_pair_list)
-            detected_poly = self.expand_poly_along_width(detected_poly, shrink_ratio_of_width)
-            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)
+            detected_poly = self.expand_poly_along_width(detected_poly,
+                                                         shrink_ratio_of_width)
+            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)
             poly_list.append(detected_poly)
 
         return poly_list
 
-    def __call__(self, outs_dict, shape_list):                
+    def __call__(self, outs_dict, shape_list):
         score_list = outs_dict['f_score']
         border_list = outs_dict['f_border']
         tvo_list = outs_dict['f_tvo']
@@ -281,20 +328,28 @@ class SASTPostProcess(object):
             border_list = border_list.numpy()
             tvo_list = tvo_list.numpy()
             tco_list = tco_list.numpy()
-                    
+
         img_num = len(shape_list)
         poly_lists = []
         for ino in range(img_num):
-            p_score = score_list[ino].transpose((1,2,0))
-            p_border = border_list[ino].transpose((1,2,0))
-            p_tvo = tvo_list[ino].transpose((1,2,0))
-            p_tco = tco_list[ino].transpose((1,2,0))
+            p_score = score_list[ino].transpose((1, 2, 0))
+            p_border = border_list[ino].transpose((1, 2, 0))
+            p_tvo = tvo_list[ino].transpose((1, 2, 0))
+            p_tco = tco_list[ino].transpose((1, 2, 0))
             src_h, src_w, ratio_h, ratio_w = shape_list[ino]
 
-            poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h, 
-                                         shrink_ratio_of_width=self.shrink_ratio_of_width, 
-                                         tcl_map_thresh=self.tcl_map_thresh, offset_expand=self.expand_scale)
+            poly_list = self.detect_sast(
+                p_score,
+                p_tvo,
+                p_border,
+                p_tco,
+                ratio_w,
+                ratio_h,
+                src_w,
+                src_h,
+                shrink_ratio_of_width=self.shrink_ratio_of_width,
+                tcl_map_thresh=self.tcl_map_thresh,
+                offset_expand=self.expand_scale)
             poly_lists.append({'points': np.array(poly_list)})
 
         return poly_lists
-

ppocr/utils/dict/arabic_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
+_
+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
+É
+é
+ء
+آ
+أ
+ؤ
+إ
+ئ
+ا
+ب
+ة
+ت
+ث
+ج
+ح
+خ
+د
+ذ
+ر
+ز
+س
+ش
+ص
+ض
+ط
+ظ
+ع
+غ
+ف
+ق
+ك
+ل
+م
+ن
+ه
+و
+ى
+ي
+ً
+ٌ
+ٍ
+َ
+ُ
+ِ
+ّ
+ْ
+ٓ
+ٔ
+ٰ
+ٱ
+ٹ
+پ
+چ
+ڈ
+ڑ
+ژ
+ک
+ڭ
+گ
+ں
+ھ
+ۀ
+ہ
+ۂ
+ۃ
+ۆ
+ۇ
+ۈ
+ۋ
+ی
+ې
+ے
+ۓ
+ە
+١
+٢
+٣
+٤
+٥
+٦
+٧
+٨
+٩

ppocr/utils/dict/cyrillic_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
+_
+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
+É
+é
+Ё
+Є
+І
+Ј
+Љ
+Ў
+А
+Б
+В
+Г
+Д
+Е
+Ж
+З
+И
+Й
+К
+Л
+М
+Н
+О
+П
+Р
+С
+Т
+У
+Ф
+Х
+Ц
+Ч
+Ш
+Щ
+Ъ
+Ы
+Ь
+Э
+Ю
+Я
+а
+б
+в
+г
+д
+е
+ж
+з
+и
+й
+к
+л
+м
+н
+о
+п
+р
+с
+т
+у
+ф
+х
+ц
+ч
+ш
+щ
+ъ
+ы
+ь
+э
+ю
+я
+ё
+ђ
+є
+і
+ј
+љ
+њ
+ћ
+ў
+џ
+Ґ
+ґ

ppocr/utils/dict/devanagari_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
+_
+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
+É
+é
+ँ
+ं
+ः
+अ
+आ
+इ
+ई
+उ
+ऊ
+ऋ
+ए
+ऐ
+ऑ
+ओ
+औ
+क
+ख
+ग
+घ
+ङ
+च
+छ
+ज
+झ
+ञ
+ट
+ठ
+ड
+ढ
+ण
+त
+थ
+द
+ध
+न
+ऩ
+प
+फ
+ब
+भ
+म
+य
+र
+ऱ
+ल
+ळ
+व
+श
+ष
+स
+ह
+़
+ा
+ि
+ी
+ु
+ू
+ृ
+ॅ
+े
+ै
+ॉ
+ो
+ौ
+्
+॒
+क़
+ख़
+ग़
+ज़
+ड़
+ढ़
+फ़
+ॠ
+।
+०
+१
+२
+३
+४
+५
+६
+७
+८
+९
+॰

ppocr/utils/dict/latin_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
+[
+]
+_
+`
+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
+{
+}
+¡
+£
+§
+ª
+«
+­
+°
+²
+³
+´
+µ
+·
+º
+»
+¿
+À
+Á
+Â
+Ä
+Å
+Ç
+È
+É
+Ê
+Ë
+Ì
+Í
+Î
+Ï
+Ò
+Ó
+Ô
+Õ
+Ö
+Ú
+Ü
+Ý
+ß
+à
+á
+â
+ã
+ä
+å
+æ
+ç
+è
+é
+ê
+ë
+ì
+í
+î
+ï
+ñ
+ò
+ó
+ô
+õ
+ö
+ø
+ù
+ú
+û
+ü
+ý
+ą
+Ć
+ć
+Č
+č
+Đ
+đ
+ę
+ı
+Ł
+ł
+ō
+Œ
+œ
+Š
+š
+Ÿ
+Ž
+ž
+ʒ
+β
+δ
+ε
+з
+Ṡ
+‘
+€
+™

ppocr/utils/e2e_metric/Deteval.py

+# 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.
+# 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 ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
+
+
+def get_socre(gt_dict, pred_dict):
+    allInputs = 1
+
+    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']
+            point = ",".join(map(str, points.reshape(-1, )))
+            det.append([point, text])
+        return det
+
+    def gt_reading_mod(gt_dict):
+        """This helper reads groundtruths from mat files"""
+        gt = []
+        n = len(gt_dict)
+        for i in range(n):
+            points = gt_dict[i]['points']
+            h = len(points)
+            text = gt_dict[i]['text']
+            xx = [
+                np.array(
+                    ['x:'], dtype='<U2'), 0, np.array(
+                        ['y:'], dtype='<U2'), 0, np.array(
+                            ['#'], dtype='<U1'), np.array(
+                                ['#'], dtype='<U1')
+            ]
+            t_x, t_y = [], []
+            for j in range(h):
+                t_x.append(points[j][0])
+                t_y.append(points[j][1])
+            xx[1] = np.array([t_x], dtype='int16')
+            xx[3] = np.array([t_y], dtype='int16')
+            if text != "" and "#" not in text:
+                xx[4] = np.array([text], dtype='U{}'.format(len(text)))
+                xx[5] = np.array(['c'], dtype='<U1')
+            gt.append(xx)
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            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 = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        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):
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(det_x, det_y)), 2)
+
+    ##############################Initialization###################################
+    # global_sigma = []
+    # global_tau = []
+    # global_pred_str = []
+    # global_gt_str = []
+    ###############################################################################
+
+    for input_id in range(allInputs):
+        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
+                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'):
+            detections = input_reading_mod(pred_dict)
+            groundtruths = gt_reading_mod(gt_dict)
+            detections = detection_filtering(
+                detections,
+                groundtruths)  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == '#':
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(',')]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma = local_sigma_table
+            global_tau = local_tau_table
+            global_pred_str = local_pred_str
+            global_gt_str = local_gt_str
+
+    single_data = {}
+    single_data['sigma'] = global_sigma
+    single_data['global_tau'] = global_tau
+    single_data['global_pred_str'] = global_pred_str
+    single_data['global_gt_str'] = global_gt_str
+    return single_data
+
+
+def combine_results(all_data):
+    tr = 0.7
+    tp = 0.6
+    fsc_k = 0.8
+    k = 2
+    global_sigma = []
+    global_tau = []
+    global_pred_str = []
+    global_gt_str = []
+    for data in all_data:
+        global_sigma.append(data['sigma'])
+        global_tau.append(data['global_tau'])
+        global_pred_str.append(data['global_pred_str'])
+        global_gt_str.append(data['global_gt_str'])
+
+    global_accumulative_recall = 0
+    global_accumulative_precision = 0
+    total_num_gt = 0
+    total_num_det = 0
+    hit_str_count = 0
+    hit_count = 0
+
+    def one_to_one(local_sigma_table, local_tau_table,
+                   local_accumulative_recall, local_accumulative_precision,
+                   global_accumulative_recall, global_accumulative_precision,
+                   gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            gt_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[gt_id, :] > tr)
+            gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
+                0].shape[0]
+            gt_matching_qualified_tau_candidates = np.where(
+                local_tau_table[gt_id, :] > tp)
+            gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            det_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
+                > tr)
+            det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
+                0].shape[0]
+            det_matching_qualified_tau_candidates = np.where(
+                local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
+                tp)
+            det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
+                    (det_matching_num_qualified_sigma_candidates == 1) and (
+                    det_matching_num_qualified_tau_candidates == 1):
+                global_accumulative_recall = global_accumulative_recall + 1.0
+                global_accumulative_precision = global_accumulative_precision + 1.0
+                local_accumulative_recall = local_accumulative_recall + 1.0
+                local_accumulative_precision = local_accumulative_precision + 1.0
+
+                gt_flag[0, gt_id] = 1
+                matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
+                # recg start
+                gt_str_cur = global_gt_str[idy][gt_id]
+                pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
+                    0]]
+                if pred_str_cur == gt_str_cur:
+                    hit_str_num += 1
+                else:
+                    if pred_str_cur.lower() == gt_str_cur.lower():
+                        hit_str_num += 1
+                # recg end
+                det_flag[0, matched_det_id] = 1
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def one_to_many(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            # skip the following if the groundtruth was matched
+            if gt_flag[0, gt_id] > 0:
+                continue
+
+            non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
+            num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
+
+            if num_non_zero_in_sigma >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_tau_candidates = np.where((local_tau_table[
+                    gt_id, :] >= tp) & (det_flag[0, :] == 0))
+                num_qualified_tau_candidates = qualified_tau_candidates[
+                    0].shape[0]
+
+                if num_qualified_tau_candidates == 1:
+                    if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
+                            and
+                        (local_sigma_table[gt_id, qualified_tau_candidates] >=
+                         tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, gt_id] = 1
+                        det_flag[0, qualified_tau_candidates] = 1
+                        # recg start
+                        gt_str_cur = global_gt_str[idy][gt_id]
+                        pred_str_cur = global_pred_str[idy][
+                            qualified_tau_candidates[0].tolist()[0]]
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                        # recg end
+                elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
+                      >= tr):
+                    gt_flag[0, gt_id] = 1
+                    det_flag[0, qualified_tau_candidates] = 1
+                    # recg start
+                    gt_str_cur = global_gt_str[idy][gt_id]
+                    pred_str_cur = global_pred_str[idy][
+                        qualified_tau_candidates[0].tolist()[0]]
+                    if pred_str_cur == gt_str_cur:
+                        hit_str_num += 1
+                    else:
+                        if pred_str_cur.lower() == gt_str_cur.lower():
+                            hit_str_num += 1
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + fsc_k
+                    global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + fsc_k
+                    local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def many_to_one(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for det_id in range(num_det):
+            # skip the following if the detection was matched
+            if det_flag[0, det_id] > 0:
+                continue
+
+            non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
+            num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
+
+            if num_non_zero_in_tau >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_sigma_candidates = np.where((
+                    local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
+                num_qualified_sigma_candidates = qualified_sigma_candidates[
+                    0].shape[0]
+
+                if num_qualified_sigma_candidates == 1:
+                    if ((local_tau_table[qualified_sigma_candidates, det_id] >=
+                         tp) and
+                        (local_sigma_table[qualified_sigma_candidates, det_id]
+                         >= tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, qualified_sigma_candidates] = 1
+                        det_flag[0, det_id] = 1
+                        # recg start
+                        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 ele_gt_id not in global_gt_str[idy]:
+                                continue
+                            gt_str_cur = global_gt_str[idy][ele_gt_id]
+                            if pred_str_cur == gt_str_cur:
+                                hit_str_num += 1
+                                break
+                            else:
+                                if pred_str_cur.lower() == gt_str_cur.lower():
+                                    hit_str_num += 1
+                                break
+                        # recg end
+                elif (np.sum(local_tau_table[qualified_sigma_candidates,
+                                             det_id]) >= tp):
+                    det_flag[0, det_id] = 1
+                    gt_flag[0, qualified_sigma_candidates] = 1
+                    # recg start
+                    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 ele_gt_id not in global_gt_str[idy]:
+                            continue
+                        gt_str_cur = global_gt_str[idy][ele_gt_id]
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                            break
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                                break
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    global_accumulative_precision = global_accumulative_precision + fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    local_accumulative_precision = local_accumulative_precision + fsc_k
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    for idx in range(len(global_sigma)):
+        local_sigma_table = np.array(global_sigma[idx])
+        local_tau_table = global_tau[idx]
+
+        num_gt = local_sigma_table.shape[0]
+        num_det = local_sigma_table.shape[1]
+
+        total_num_gt = total_num_gt + num_gt
+        total_num_det = total_num_det + num_det
+
+        local_accumulative_recall = 0
+        local_accumulative_precision = 0
+        gt_flag = np.zeros((1, num_gt))
+        det_flag = np.zeros((1, num_det))
+
+        #######first check for one-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
+                                                    local_accumulative_recall, local_accumulative_precision,
+                                                    global_accumulative_recall, global_accumulative_precision,
+                                                    gt_flag, det_flag, idx)
+
+        hit_str_count += hit_str_num
+        #######then check for one-to-many case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+        hit_str_count += hit_str_num
+        #######then check for many-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+        hit_str_count += hit_str_num
+
+    try:
+        recall = global_accumulative_recall / total_num_gt
+    except ZeroDivisionError:
+        recall = 0
+
+    try:
+        precision = global_accumulative_precision / total_num_det
+    except ZeroDivisionError:
+        precision = 0
+
+    try:
+        f_score = 2 * precision * recall / (precision + recall)
+    except ZeroDivisionError:
+        f_score = 0
+
+    try:
+        seqerr = 1 - float(hit_str_count) / global_accumulative_recall
+    except ZeroDivisionError:
+        seqerr = 1
+
+    try:
+        recall_e2e = float(hit_str_count) / total_num_gt
+    except ZeroDivisionError:
+        recall_e2e = 0
+
+    try:
+        precision_e2e = float(hit_str_count) / total_num_det
+    except ZeroDivisionError:
+        precision_e2e = 0
+
+    try:
+        f_score_e2e = 2 * precision_e2e * recall_e2e / (
+            precision_e2e + recall_e2e)
+    except ZeroDivisionError:
+        f_score_e2e = 0
+
+    final = {
+        'total_num_gt': total_num_gt,
+        'total_num_det': total_num_det,
+        'global_accumulative_recall': global_accumulative_recall,
+        'hit_str_count': hit_str_count,
+        'recall': recall,
+        'precision': precision,
+        'f_score': f_score,
+        'seqerr': seqerr,
+        'recall_e2e': recall_e2e,
+        'precision_e2e': precision_e2e,
+        'f_score_e2e': f_score_e2e
+    }
+    return final

ppocr/utils/e2e_metric/polygon_fast.py

+# 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.
+# 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 shapely.geometry import Polygon
+"""
+:param det_x: [1, N] Xs of detection's vertices 
+:param det_y: [1, N] Ys of detection's vertices
+:param gt_x: [1, N] Xs of groundtruth's vertices
+:param gt_y: [1, N] Ys of groundtruth's vertices
+
+##############
+All the calculation of 'AREA' in this script is handled by:
+1) First generating a binary mask with the polygon area filled up with 1's
+2) Summing up all the 1's
+"""
+
+
+def area(x, y):
+    polygon = Polygon(np.stack([x, y], axis=1))
+    return float(polygon.area)
+
+
+def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine if both polygons are intersecting with each others with an approximation method.
+    Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
+    """
+    det_ymax = np.max(det_y)
+    det_xmax = np.max(det_x)
+    det_ymin = np.min(det_y)
+    det_xmin = np.min(det_x)
+
+    gt_ymax = np.max(gt_y)
+    gt_xmax = np.max(gt_x)
+    gt_ymin = np.min(gt_y)
+    gt_xmin = np.min(gt_x)
+
+    all_min_ymax = np.minimum(det_ymax, gt_ymax)
+    all_max_ymin = np.maximum(det_ymin, gt_ymin)
+
+    intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
+
+    all_min_xmax = np.minimum(det_xmax, gt_xmax)
+    all_max_xmin = np.maximum(det_xmin, gt_xmin)
+    intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
+
+    return intersect_heights * intersect_widths
+
+
+def area_of_intersection(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.intersection(p2).area)
+
+
+def area_of_union(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.union(p2).area)
+
+
+def iou(det_x, det_y, gt_x, gt_y):
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area_of_union(det_x, det_y, gt_x, gt_y) + 1.0)
+
+
+def iod(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine the fraction of intersection area over detection area
+    """
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area(det_x, det_y) + 1.0)

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

+# 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.
+# 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.
+"""Contains various CTC decoders."""
+from __future__ import absolute_import
+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
+
+
+def get_dict(character_dict_path):
+    character_str = ""
+    with open(character_dict_path, "rb") as fin:
+        lines = fin.readlines()
+        for line in lines:
+            line = line.decode('utf-8').strip("\n").strip("\r\n")
+            character_str += line
+        dict_character = list(character_str)
+    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)
+    """
+    _, _, 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)
+    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):
+    """
+    CTC decoder using multiple processes.
+    """
+    decoder_results = []
+    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
+
+
+def sort_with_direction(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, point_direction):
+        pos_list = np.array(pos_list).reshape(-1, 2)
+        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[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, 2)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # 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, 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)
+
+    # expand along
+    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
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    binary_tcl_map: h x w
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    # expand along
+    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)
+    max_append_num = 2 * append_num
+
+    left_list = []
+    right_list = []
+    for i in range(max_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:
+            if binary_tcl_map[ly, lx] > 0.5:
+                left_list.append((ly, lx))
+            else:
+                break
+
+    for i in range(max_append_num):
+        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:
+            if binary_tcl_map[ry, rx] > 0.5:
+                right_list.append((ry, rx))
+            else:
+                break
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    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)
+
+    # 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))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 3:
+                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)
+
+    # use decoder to filter backgroud points.
+    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_horizontal(p_score,
+                                   p_char_maps,
+                                   f_direction,
+                                   score_thresh=0.5,
+                                   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_bi = (p_score > score_thresh) * 1.0
+    instance_count, instance_label_map = cv2.connectedComponents(
+        p_tcl_map_bi.astype(np.uint8), 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))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 5:
+                continue
+
+            # add rule here
+            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)
+            all_pos_yxs.append(pos_list_sorted)
+
+    # use decoder to filter backgroud points.
+    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)
+
+
+# for refine module
+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)
+
+    # get TCL Instance
+    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))
+            ### FIX-ME, eliminate outlier
+            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

ppocr/utils/e2e_utils/visual.py

+# 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.
+# 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
+import cv2
+import time
+
+
+def resize_image(im, max_side_len=512):
+    """
+    resize image to a size multiple of max_stride which is required by the network
+    :param im: the resized image
+    :param max_side_len: limit of max image size to avoid out of memory in gpu
+    :return: the resized image and the resize ratio
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    if resize_h > resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_min(im, max_side_len=512):
+    """
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    if resize_h < resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_for_totaltext(im, max_side_len=512):
+    """
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+    ratio = 1.25
+    if h * ratio > max_side_len:
+        ratio = float(max_side_len) / resize_h
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    pair_length_list = []
+    for point_pair in point_pair_list:
+        pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
+        pair_length_list.append(pair_length)
+    pair_length_list = np.array(pair_length_list)
+    pair_info = (pair_length_list.max(), pair_length_list.min(),
+                 pair_length_list.mean())
+
+    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), pair_info
+
+
+def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
+    """
+    Generate shrink_quad_along_width.
+    """
+    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 norm2(x, axis=None):
+    if axis:
+        return np.sqrt(np.sum(x**2, axis=axis))
+    return np.sqrt(np.sum(x**2))
+
+
+def cos(p1, p2):
+    return (p1 * p2).sum() / (norm2(p1) * norm2(p2))

requirements.txt

@@ -7,4 +7,5 @@ opencv-python==4.2.0.32
 tqdm
 numpy
 visualdl
-python-Levenshtein
+python-Levenshtein
\ No newline at end of file
+opencv-contrib-python
\ No newline at end of file

setup.py

@@ -32,7 +32,7 @@ setup(
     package_dir={'paddleocr': ''},
     include_package_data=True,
     entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
-    version='2.0.2',
+    version='2.0.4',
     install_requires=requirements,
     license='Apache License 2.0',
     description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',

tools/infer/predict_e2e.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 os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import numpy as np
+import time
+import sys
+
+import tools.infer.utility as utility
+from ppocr.utils.logging import get_logger
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppocr.data import create_operators, transform
+from ppocr.postprocess import build_post_process
+
+logger = get_logger()
+
+
+class TextE2E(object):
+    def __init__(self, args):
+        self.args = args
+        self.e2e_algorithm = args.e2e_algorithm
+        pre_process_list = [{
+            'E2EResizeForTest': {}
+        }, {
+            'NormalizeImage': {
+                'std': [0.229, 0.224, 0.225],
+                'mean': [0.485, 0.456, 0.406],
+                'scale': '1./255.',
+                'order': 'hwc'
+            }
+        }, {
+            'ToCHWImage': None
+        }, {
+            'KeepKeys': {
+                'keep_keys': ['image', 'shape']
+            }
+        }]
+        postprocess_params = {}
+        if self.e2e_algorithm == "PGNet":
+            pre_process_list[0] = {
+                'E2EResizeForTest': {
+                    'max_side_len': args.e2e_limit_side_len,
+                    'valid_set': 'totaltext'
+                }
+            }
+            postprocess_params['name'] = 'PGPostProcess'
+            postprocess_params["score_thresh"] = args.e2e_pgnet_score_thresh
+            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
+        else:
+            logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
+            sys.exit(0)
+
+        self.preprocess_op = create_operators(pre_process_list)
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
+            args, 'e2e', logger)  # paddle.jit.load(args.det_model_dir)
+        # self.predictor.eval()
+
+    def clip_det_res(self, points, img_height, img_width):
+        for pno in range(points.shape[0]):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+        return points
+
+    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.clip_det_res(box, img_height, img_width)
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def __call__(self, img):
+
+        ori_im = img.copy()
+        data = {'image': img}
+        data = transform(data, self.preprocess_op)
+        img, shape_list = data
+        if img is None:
+            return None, 0
+        img = np.expand_dims(img, axis=0)
+        shape_list = np.expand_dims(shape_list, axis=0)
+        img = img.copy()
+        starttime = time.time()
+
+        self.input_tensor.copy_from_cpu(img)
+        self.predictor.run()
+        outputs = []
+        for output_tensor in self.output_tensors:
+            output = output_tensor.copy_to_cpu()
+            outputs.append(output)
+
+        preds = {}
+        if self.e2e_algorithm == 'PGNet':
+            preds['f_border'] = outputs[0]
+            preds['f_char'] = outputs[1]
+            preds['f_direction'] = outputs[2]
+            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)
+        elapse = time.time() - starttime
+        return dt_boxes, strs, elapse
+
+
+if __name__ == "__main__":
+    args = utility.parse_args()
+    image_file_list = get_image_file_list(args.image_dir)
+    text_detector = TextE2E(args)
+    count = 0
+    total_time = 0
+    draw_img_save = "./inference_results"
+    if not os.path.exists(draw_img_save):
+        os.makedirs(draw_img_save)
+    for image_file in image_file_list:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        points, strs, elapse = text_detector(img)
+        if count > 0:
+            total_time += elapse
+        count += 1
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
+        src_im = utility.draw_e2e_res(points, strs, image_file)
+        img_name_pure = os.path.split(image_file)[-1]
+        img_path = os.path.join(draw_img_save,
+                                "e2e_res_{}".format(img_name_pure))
+        cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
+    if count > 1:
+        logger.info("Avg Time: {}".format(total_time / (count - 1)))

tools/infer/utility.py

@@ -74,6 +74,19 @@ def parse_args():
         "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
     parser.add_argument("--drop_score", type=float, default=0.5)
 
+    # params for e2e
+    parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
+    parser.add_argument("--e2e_model_dir", type=str)
+    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
+    parser.add_argument("--e2e_limit_type", type=str, default='max')
+
+    # 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/ic15_dict.txt")
+    parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
+    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
+
     # params for text classifier
     parser.add_argument("--use_angle_cls", type=str2bool, default=False)
     parser.add_argument("--cls_model_dir", type=str)
@@ -93,8 +106,10 @@ def create_predictor(args, mode, logger):
         model_dir = args.det_model_dir
     elif mode == 'cls':
         model_dir = args.cls_model_dir
-    else:
+    elif mode == 'rec':
         model_dir = args.rec_model_dir
+    else:
+        model_dir = args.e2e_model_dir
 
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
@@ -148,6 +163,22 @@ def create_predictor(args, mode, logger):
     return predictor, input_tensor, output_tensors
 
 
+def draw_e2e_res(dt_boxes, strs, img_path):
+    src_im = cv2.imread(img_path)
+    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)
+    return src_im
+
+
 def draw_text_det_res(dt_boxes, img_path):
     src_im = cv2.imread(img_path)
     for box in dt_boxes:

tools/infer_e2e.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import json
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import init_model
+from ppocr.utils.utility import get_image_file_list
+import tools.program as program
+
+
+def draw_e2e_res(dt_boxes, strs, config, img, img_name):
+    if len(dt_boxes) > 0:
+        src_im = img
+        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)
+        save_det_path = os.path.dirname(config['Global'][
+            'save_res_path']) + "/e2e_results/"
+        if not os.path.exists(save_det_path):
+            os.makedirs(save_det_path)
+        save_path = os.path.join(save_det_path, os.path.basename(img_name))
+        cv2.imwrite(save_path, src_im)
+        logger.info("The e2e Image saved in {}".format(save_path))
+
+
+def main():
+    global_config = config['Global']
+
+    # build model
+    model = build_model(config['Architecture'])
+
+    init_model(config, model, logger)
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # create data ops
+    transforms = []
+    for op in config['Eval']['dataset']['transforms']:
+        op_name = list(op)[0]
+        if 'Label' in op_name:
+            continue
+        elif op_name == 'KeepKeys':
+            op[op_name]['keep_keys'] = ['image', 'shape']
+        transforms.append(op)
+
+    ops = create_operators(transforms, global_config)
+
+    save_res_path = config['Global']['save_res_path']
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
+    model.eval()
+    with open(save_res_path, "wb") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            images = np.expand_dims(batch[0], axis=0)
+            shape_list = np.expand_dims(batch[1], axis=0)
+            images = paddle.to_tensor(images)
+            preds = model(images)
+            post_result = post_process_class(preds, shape_list)
+            points, strs = post_result['points'], post_result['strs']
+            # write resule
+            dt_boxes_json = []
+            for poly, str in zip(points, strs):
+                tmp_json = {"transcription": str}
+                tmp_json['points'] = poly.tolist()
+                dt_boxes_json.append(tmp_json)
+            otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
+            fout.write(otstr.encode())
+            src_img = cv2.imread(file)
+            draw_e2e_res(points, strs, config, src_img, file)
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()

tools/program.py

@@ -237,8 +237,9 @@ def train(config,
                     vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
                 vdl_writer.add_scalar('TRAIN/lr', lr, global_step)
 
-            if dist.get_rank(
-            ) == 0 and global_step > 0 and global_step % print_batch_step == 0:
+            if dist.get_rank() == 0 and (
+                (global_step > 0 and global_step % print_batch_step == 0) or
+                (idx >= len(train_dataloader) - 1)):
                 logs = train_stats.log()
                 strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
                     epoch, epoch_num, global_step, logs, train_reader_cost /
@@ -374,7 +375,8 @@ def preprocess(is_train=False):
 
     alg = config['Architecture']['algorithm']
     assert alg in [
-        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
+        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
+        'CLS', 'PGNet'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'

tools/train.py

@@ -52,7 +52,10 @@ def main(config, device, logger, vdl_writer):
     train_dataloader = build_dataloader(config, 'Train', device, logger)
     if len(train_dataloader) == 0:
         logger.error(
-            'No Images in train dataset, please check annotation file and path in the configuration file'
+            "No Images in train dataset, please ensure\n" +
+            "\t1. The images num in the train label_file_list should be larger than or equal with batch size.\n"
+            +
+            "\t2. The annotation file and path in the configuration file are provided normally."
         )
         return