Merge branch 'develop' into develop

README.md

@@ -142,7 +142,7 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
 |RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
 |SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[Download link](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
 
-**Note：** SRN model uses data expansion method to expand the two training sets mentioned above, and the expanded data can be downloaded from [Baidu Drive](todo).
+**Note：** SRN model uses data expansion method to expand the two training sets mentioned above, and the expanded data can be downloaded from [Baidu Drive](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), Extract the code：y3ry.
 
 The average accuracy of the two-stage training in the original paper is 89.74%, and that of one stage training in paddleocr is 88.33%. Both pre-trained weights can be downloaded [here](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar).
 
@@ -225,3 +225,4 @@ We welcome all the contributions to PaddleOCR and appreciate for your feedback v
 - Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
 - Thanks [authorfu](https://github.com/authorfu) for contributing Android demo  and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
 - Thanks [BeyondYourself](https://github.com/BeyondYourself) for contributing many great suggestions and simplifying part of the code style.
+- Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.

README_cn.md

@@ -148,7 +148,8 @@ PaddleOCR开源的文本识别算法列表：
 |RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
 |SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
 
-**说明：** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广，增广后的数据可以在[百度网盘](todo)上下载。原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
+**说明：** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广，增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载，提取码: y3ry。
+原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
 
 使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：  
 
@@ -224,3 +225,4 @@ PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训
 - 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集
 - 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
 - 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格。
+- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务。

deploy/android_demo/gradle/wrapper/gradle-wrapper.properties

-#Thu Aug 22 15:05:37 CST 2019
+#Wed Jul 22 23:48:44 CST 2020
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
 zipStoreBase=GRADLE_USER_HOME

doc/doc_ch/recognition.md

@@ -18,6 +18,8 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 
 若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
 
+如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
+
 * 使用自己数据集：
 
 若您希望使用自己的数据进行训练，请参考下文组织您的数据。
@@ -161,6 +163,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
 | rec_r34_vd_none_none_ctc.yml |  Rosetta |   Resnet34_vd |  None   |  None |  ctc  |
 | rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
 | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
+| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
 
 训练中文数据，推荐使用`rec_chinese_lite_train.yml`，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
 

doc/doc_en/recognition_en.md

@@ -18,6 +18,8 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 
 If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，download the lmdb format dataset required for benchmark
 
+If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path.
+
 * Use your own dataset:
 
 If you want to use your own data for training, please refer to the following to organize your data.

docker/hubserving/cpu/Dockerfile

+# Version: 1.0.0
+FROM hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev
+
+# PaddleOCR base on Python3.7
+RUN pip3.7 install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN python3.7 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN pip3.7 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN git clone https://gitee.com/PaddlePaddle/PaddleOCR
+
+WORKDIR /PaddleOCR
+
+RUN pip3.7 install -r requirments.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN mkdir -p /PaddleOCR/inference
+# Download orc detect model(light version). if you want to change normal version, you can change ch_det_mv3_db_infer to ch_det_r50_vd_db_infer, also remember change det_model_dir in deploy/hubserving/ocr_system/params.py）
+ADD https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar /PaddleOCR/inference
+RUN tar xf /PaddleOCR/inference/ch_det_mv3_db_infer.tar -C /PaddleOCR/inference
+
+# Download orc recognition model(light version). If you want to change normal version, you can change ch_rec_mv3_crnn_infer to ch_rec_r34_vd_crnn_enhance_infer, also remember change rec_model_dir in deploy/hubserving/ocr_system/params.py）
+ADD https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar /PaddleOCR/inference
+RUN tar xf /PaddleOCR/inference/ch_rec_mv3_crnn_infer.tar -C /PaddleOCR/inference
+
+EXPOSE 8866
+
+CMD ["/bin/bash","-c","export PYTHONPATH=. && hub install deploy/hubserving/ocr_system/ && hub serving start -m ocr_system"]
\ No newline at end of file

docker/hubserving/gpu/Dockerfile

+# Version: 1.0.0
+FROM hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev
+
+# PaddleOCR base on Python3.7
+RUN pip3.7 install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN python3.7 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN pip3.7 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN git clone https://gitee.com/PaddlePaddle/PaddleOCR
+
+WORKDIR /home/PaddleOCR
+
+RUN pip3.7 install -r requirments.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN mkdir -p /PaddleOCR/inference
+# Download orc detect model(light version). if you want to change normal version, you can change ch_det_mv3_db_infer to ch_det_r50_vd_db_infer, also remember change det_model_dir in deploy/hubserving/ocr_system/params.py）
+ADD https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar /PaddleOCR/inference
+RUN tar xf /PaddleOCR/inference/ch_det_mv3_db_infer.tar -C /PaddleOCR/inference
+
+# Download orc recognition model(light version). If you want to change normal version, you can change ch_rec_mv3_crnn_infer to ch_rec_r34_vd_crnn_enhance_infer, also remember change rec_model_dir in deploy/hubserving/ocr_system/params.py）
+ADD https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar /PaddleOCR/inference
+RUN tar xf /PaddleOCR/inference/ch_rec_mv3_crnn_infer.tar -C /PaddleOCR/inference
+
+EXPOSE 8866
+
+CMD ["/bin/bash","-c","export PYTHONPATH=. && hub install deploy/hubserving/ocr_system/ && hub serving start -m ocr_system"]
\ No newline at end of file

docker/hubserving/readme.md

+# Docker化部署服务
+在日常项目应用中，相信大家一般都会希望能通过Docker技术，把PaddleOCR服务打包成一个镜像，以便在Docker或k8s环境里，快速发布上线使用。
+
+本文将提供一些标准化的代码来实现这样的目标。大家通过如下步骤可以把PaddleOCR项目快速发布成可调用的Restful API服务。（目前暂时先实现了基于HubServing模式的部署，后续作者计划增加PaddleServing模式的部署）
+
+## 1.实施前提准备
+
+需要先完成如下基本组件的安装：
+a. Docker环境
+b. 显卡驱动和CUDA 10.0+（GPU）
+c. NVIDIA Container Toolkit（GPU，Docker 19.03以上版本可以跳过此步）
+d. cuDNN 7.6+（GPU）
+
+## 2.制作镜像
+a.下载PaddleOCR项目代码
+```
+git clone https://github.com/PaddlePaddle/PaddleOCR.git
+```
+b.切换至Dockerfile目录（注：需要区分cpu或gpu版本，下文以cpu为例，gpu版本需要替换一下关键字即可）
+```
+cd docker/cpu
+```
+c.生成镜像
+```
+docker build -t paddleocr:cpu . 
+```
+
+## 3.启动Docker容器
+a. CPU 版本
+```
+sudo docker run -dp 8866:8866 --name paddle_ocr paddleocr:cpu
+```
+b. GPU 版本 (通过NVIDIA Container Toolkit)
+```
+sudo nvidia-docker run -dp 8866:8866 --name paddle_ocr paddleocr:gpu
+```
+c. GPU 版本 (Docker 19.03以上版本，可以直接用如下命令)
+```
+sudo docker run -dp 8866:8866 --gpus all --name paddle_ocr paddleocr:gpu
+```
+d. 检查服务运行情况（出现：Successfully installed ocr_system和Running on http://0.0.0.0:8866/等信息，表示运行成功）
+```
+docker logs -f paddle_ocr
+```
+
+## 4.测试服务
+a. 计算待识别图片的Base64编码（如果只是测试一下效果，可以通过免费的在线工具实现，如：http://tool.chinaz.com/tools/imgtobase/）
+b. 发送服务请求（可参见sample_request.txt中的值）
+```
+curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"填入图片Base64编码(需要删除'data:image/jpg;base64,'）\"]}" http://localhost:8866/predict/ocr_system
+```
+c. 返回结果（如果调用成功，会返回如下结果）
+```
+{"msg":"","results":[[{"confidence":0.8403433561325073,"text":"约定","text_region":[[345,377],[641,390],[634,540],[339,528]]},{"confidence":0.8131805658340454,"text":"最终相遇","text_region":[[356,532],[624,530],[624,596],[356,598]]}]],"status":"0"}
+```

ppocr/modeling/architectures/rec_model.py

@@ -58,7 +58,7 @@ class RecModel(object):
         self.loss_type = global_params['loss_type']
         self.image_shape = global_params['image_shape']
         self.max_text_length = global_params['max_text_length']
-        if "num_heads" in params:
+        if "num_heads" in global_params:
             self.num_heads = global_params["num_heads"]
         else:
             self.num_heads = None