Merge pull request #6 from PaddlePaddle/develop

update-2020-7-27

deploy/lite/readme_en.md

@@ -3,7 +3,7 @@
 
 This tutorial will introduce how to use paddle-lite to deploy paddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
 
-addle Lite is a lightweight inference engine for PaddlePaddle.
+paddle-lite is a lightweight inference engine for PaddlePaddle.
 It provides efficient inference capabilities for mobile phones and IOTs,
 and extensively integrates cross-platform hardware to provide lightweight
 deployment solutions for end-side deployment issues.

deploy/pdserving/readme.md

@@ -9,9 +9,21 @@
 我们推荐用户使用GPU来做Paddle Serving的OCR服务部署 
 
 **CUDA版本：9.0**
+
 **CUDNN版本：7.0**
+
 **操作系统版本：CentOS 6以上**
 
+**Python3操作指南：**
+```
+#以下提供beta版本的paddle serving whl包，欢迎试用，正式版会在7月底正式上线
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_server_gpu-0.3.2-py3-none-any.whl
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_client-0.3.2-cp36-none-any.whl
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_app-0.1.2-py3-none-any.whl
+python -m pip install paddle_serving_app-0.1.2-py3-none-any.whl paddle_serving_server_gpu-0.3.2-py3-none-any.whl paddle_serving_client-0.3.2-cp36-none-any.whl
+```
+
+**Python2操作指南：**
 ```
 #以下提供beta版本的paddle serving whl包，欢迎试用，正式版会在7月底正式上线
 wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_server_gpu-0.3.2-py2-none-any.whl

doc/doc_ch/serving.md

@@ -28,21 +28,38 @@ deploy/hubserving/ocr_system/
 # 安装paddlehub  
 pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 
-# 设置环境变量  
+# 在Linux下设置环境变量
 export PYTHONPATH=.
+# 在Windows下设置环境变量
+SET PYTHONPATH=.
 ```
 
 ### 2. 安装服务模块
-PaddleOCR提供3种服务模块，根据需要安装所需模块。如： 
+PaddleOCR提供3种服务模块，根据需要安装所需模块。
 
-安装检测服务模块：  
-```hub install deploy/hubserving/ocr_det/```  
+* 在Linux环境下，安装示例如下：
+```shell
+# 安装检测服务模块：  
+hub install deploy/hubserving/ocr_det/
 
-或，安装识别服务模块：    
-```hub install deploy/hubserving/ocr_rec/```  
+# 或，安装识别服务模块：  
+hub install deploy/hubserving/ocr_rec/
 
-或，安装检测+识别串联服务模块：  
-```hub install deploy/hubserving/ocr_system/```  
+# 或，安装检测+识别串联服务模块：  
+hub install deploy/hubserving/ocr_system/
+```
+
+* 在Windows环境下(文件夹的分隔符为`\`)，安装示例如下：
+```shell
+# 安装检测服务模块：  
+hub install deploy\hubserving\ocr_det\
+
+# 或，安装识别服务模块：  
+hub install deploy\hubserving\ocr_rec\
+
+# 或，安装检测+识别串联服务模块：
+hub install deploy\hubserving\ocr_system\
+```
 
 ### 3. 启动服务
 #### 方式1. 命令行命令启动（仅支持CPU）
@@ -157,4 +174,3 @@ hub serving start -c deploy/hubserving/ocr_system/config.json
 
 - 5、重新启动服务  
 ```hub serving start -m ocr_system```  
-

doc/doc_en/serving_en.md

@@ -29,26 +29,39 @@ The following steps take the 2-stage series service as an example. If only the d
 # Install paddlehub  
 pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 
-# Set environment variables  
+# Set environment variables on Linux
 export PYTHONPATH=.
+# Set environment variables on Windows
+SET PYTHONPATH=.
 ```
 
 ### 2. Install Service Module
-PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs. Such as:  
+PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs.
 
-Install the detection service module:  
+* On Linux platform, the examples are as follows.
 ```shell
+# Install the detection service module:
 hub install deploy/hubserving/ocr_det/
-```  
-Or, install the recognition service module:  
-```shell
+
+# Or, install the recognition service module:
 hub install deploy/hubserving/ocr_rec/
-```  
-Or, install the 2-stage series service module:  
-```shell
+
+# Or, install the 2-stage series service module:
 hub install deploy/hubserving/ocr_system/
 ```
 
+* On Windows platform, the examples are as follows.
+```shell
+# Install the detection service module:
+hub install deploy\hubserving\ocr_det\
+
+# Or, install the recognition service module:
+hub install deploy\hubserving\ocr_rec\
+
+# Or, install the 2-stage series service module:
+hub install deploy\hubserving\ocr_system\
+```
+
 ### 3. Start service
 #### Way 1. Start with command line parameters (CPU only)
 

tools/infer/predict_det.py

@@ -135,6 +135,9 @@ if __name__ == "__main__":
     text_detector = TextDetector(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 = cv2.imread(image_file)
         if img is None:
@@ -147,6 +150,7 @@ if __name__ == "__main__":
         print("Predict time of %s:" % image_file, elapse)
         src_im = utility.draw_text_det_res(dt_boxes, image_file)
         img_name_pure = image_file.split("/")[-1]
-        cv2.imwrite("./inference_results/det_res_%s" % img_name_pure, src_im)
+        cv2.imwrite(
+            os.path.join(draw_img_save, "det_res_%s" % img_name_pure), src_im)
     if count > 1:
         print("Avg Time:", total_time / (count - 1))