Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleOCR into fixocr

README.md

@@ -3,10 +3,8 @@ English | [简体中文](README_cn.md)
 ## Introduction
 PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
 
-**Live stream on coming day**:  July 21, 2020 at 8 pm BiliBili station live stream
-
 **Recent updates**
-
+- 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
 - 2020.7.15, Add mobile App demo , support both iOS and  Android  ( based on easyedge and Paddle Lite)
 - 2020.7.15, Improve the  deployment ability, add the C + +  inference , serving deployment. In addtion, the benchmarks of the ultra-lightweight OCR model are provided.
 - 2020.7.15, Add several related datasets, data annotation and synthesis tools.
@@ -26,6 +24,8 @@ PaddleOCR aims to create rich, leading, and practical OCR tools that help users
 
 ![](doc/imgs_results/11.jpg)
 
+![](doc/imgs_results/img_10.jpg)
+
 [More visualization](./doc/doc_en/visualization_en.md)
 
 You can also quickly experience the ultra-lightweight OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)

README_cn.md

@@ -3,9 +3,8 @@
 ## 简介
 PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
 
-**直播预告：2020年7月21日晚8点B站直播，PaddleOCR开源大礼包全面解读，直播地址当天更新**
-
 **近期更新**
+- 2020.7.23 发布7月21日B站直播课回放和PPT，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
 - 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
 - 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
 - 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具

deploy/android_demo/app/src/main/AndroidManifest.xml

@@ -25,6 +25,15 @@
                 android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
                 android:label="Settings">
         </activity>
+        <provider
+            android:name="android.support.v4.content.FileProvider"
+            android:authorities="com.baidu.paddle.lite.demo.ocr.fileprovider"
+            android:exported="false"
+            android:grantUriPermissions="true">
+            <meta-data
+                android:name="android.support.FILE_PROVIDER_PATHS"
+                android:resource="@xml/file_paths"></meta-data>
+        </provider>
     </application>
 
 </manifest>
\ No newline at end of file

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java

@@ -3,14 +3,17 @@ package com.baidu.paddle.lite.demo.ocr;
 import android.Manifest;
 import android.app.ProgressDialog;
 import android.content.ContentResolver;
+import android.content.Context;
 import android.content.Intent;
 import android.content.SharedPreferences;
 import android.content.pm.PackageManager;
 import android.database.Cursor;
 import android.graphics.Bitmap;
 import android.graphics.BitmapFactory;
+import android.media.ExifInterface;
 import android.net.Uri;
 import android.os.Bundle;
+import android.os.Environment;
 import android.os.Handler;
 import android.os.HandlerThread;
 import android.os.Message;
@@ -19,6 +22,7 @@ import android.provider.MediaStore;
 import android.support.annotation.NonNull;
 import android.support.v4.app.ActivityCompat;
 import android.support.v4.content.ContextCompat;
+import android.support.v4.content.FileProvider;
 import android.support.v7.app.AppCompatActivity;
 import android.text.method.ScrollingMovementMethod;
 import android.util.Log;
@@ -32,6 +36,8 @@ import android.widget.Toast;
 import java.io.File;
 import java.io.IOException;
 import java.io.InputStream;
+import java.text.SimpleDateFormat;
+import java.util.Date;
 
 public class MainActivity extends AppCompatActivity {
     private static final String TAG = MainActivity.class.getSimpleName();
@@ -69,6 +75,7 @@ public class MainActivity extends AppCompatActivity {
     protected float[] inputMean = new float[]{};
     protected float[] inputStd = new float[]{};
     protected float scoreThreshold = 0.1f;
+    private String currentPhotoPath;
 
     protected Predictor predictor = new Predictor();
 
@@ -368,18 +375,56 @@ public class MainActivity extends AppCompatActivity {
     }
 
     private void takePhoto() {
-        Intent takePhotoIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
-        if (takePhotoIntent.resolveActivity(getPackageManager()) != null) {
-            startActivityForResult(takePhotoIntent, TAKE_PHOTO_REQUEST_CODE);
+        Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
+        // Ensure that there's a camera activity to handle the intent
+        if (takePictureIntent.resolveActivity(getPackageManager()) != null) {
+            // Create the File where the photo should go
+            File photoFile = null;
+            try {
+                photoFile = createImageFile();
+            } catch (IOException ex) {
+                Log.e("MainActitity", ex.getMessage(), ex);
+                Toast.makeText(MainActivity.this,
+                        "Create Camera temp file failed: " + ex.getMessage(), Toast.LENGTH_SHORT).show();
+            }
+            // Continue only if the File was successfully created
+            if (photoFile != null) {
+                Log.i(TAG, "FILEPATH " + getExternalFilesDir("Pictures").getAbsolutePath());
+                Uri photoURI = FileProvider.getUriForFile(this,
+                        "com.baidu.paddle.lite.demo.ocr.fileprovider",
+                        photoFile);
+                currentPhotoPath = photoFile.getAbsolutePath();
+                takePictureIntent.putExtra(MediaStore.EXTRA_OUTPUT, photoURI);
+                startActivityForResult(takePictureIntent, TAKE_PHOTO_REQUEST_CODE);
+                Log.i(TAG, "startActivityForResult finished");
+            }
         }
+
+    }
+
+    private File createImageFile() throws IOException {
+        // Create an image file name
+        String timeStamp = new SimpleDateFormat("yyyyMMdd_HHmmss").format(new Date());
+        String imageFileName = "JPEG_" + timeStamp + "_";
+        File storageDir = getExternalFilesDir(Environment.DIRECTORY_PICTURES);
+        File image = File.createTempFile(
+                imageFileName,  /* prefix */
+                ".bmp",         /* suffix */
+                storageDir      /* directory */
+        );
+
+        return image;
     }
 
     @Override
     protected void onActivityResult(int requestCode, int resultCode, Intent data) {
         super.onActivityResult(requestCode, resultCode, data);
-        if (resultCode == RESULT_OK && data != null) {
+        if (resultCode == RESULT_OK) {
             switch (requestCode) {
                 case OPEN_GALLERY_REQUEST_CODE:
+                    if (data == null) {
+                        break;
+                    }
                     try {
                         ContentResolver resolver = getContentResolver();
                         Uri uri = data.getData();
@@ -393,9 +438,22 @@ public class MainActivity extends AppCompatActivity {
                     }
                     break;
                 case TAKE_PHOTO_REQUEST_CODE:
-                    Bundle extras = data.getExtras();
-                    Bitmap image = (Bitmap) extras.get("data");
-                    onImageChanged(image);
+                    if (currentPhotoPath != null) {
+                        ExifInterface exif = null;
+                        try {
+                            exif = new ExifInterface(currentPhotoPath);
+                        } catch (IOException e) {
+                            e.printStackTrace();
+                        }
+                        int orientation = exif.getAttributeInt(ExifInterface.TAG_ORIENTATION,
+                                ExifInterface.ORIENTATION_UNDEFINED);
+                        Log.i(TAG, "rotation " + orientation);
+                        Bitmap image = BitmapFactory.decodeFile(currentPhotoPath);
+                        image = Utils.rotateBitmap(image, orientation);
+                        onImageChanged(image);
+                    } else {
+                        Log.e(TAG, "currentPhotoPath is null");
+                    }
                     break;
                 default:
                     break;

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java

@@ -35,8 +35,8 @@ public class OCRPredictorNative {
 
     }
 
-    public void release(){
-        if (nativePointer != 0){
+    public void release() {
+        if (nativePointer != 0) {
             nativePointer = 0;
             destory(nativePointer);
         }

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java

@@ -127,12 +127,12 @@ public class Predictor {
     }
 
     public void releaseModel() {
-        if (paddlePredictor != null){
+        if (paddlePredictor != null) {
             paddlePredictor.release();
             paddlePredictor = null;
         }
         isLoaded = false;
-        cpuThreadNum = 4;
+        cpuThreadNum = 1;
         cpuPowerMode = "LITE_POWER_HIGH";
         modelPath = "";
         modelName = "";
@@ -287,9 +287,7 @@ public class Predictor {
         if (image == null) {
             return;
         }
-        // Scale image to the size of input tensor
-        Bitmap rgbaImage = image.copy(Bitmap.Config.ARGB_8888, true);
-        this.inputImage = rgbaImage;
+        this.inputImage = image.copy(Bitmap.Config.ARGB_8888, true);
     }
 
     private ArrayList<OcrResultModel> postprocess(ArrayList<OcrResultModel> results) {
@@ -310,7 +308,7 @@ public class Predictor {
 
     private void drawResults(ArrayList<OcrResultModel> results) {
         StringBuffer outputResultSb = new StringBuffer("");
-        for (int i=0;i<results.size();i++) {
+        for (int i = 0; i < results.size(); i++) {
             OcrResultModel result = results.get(i);
             StringBuilder sb = new StringBuilder("");
             sb.append(result.getLabel());
@@ -320,7 +318,7 @@ public class Predictor {
                 sb.append("(").append(p.x).append(",").append(p.y).append(") ");
             }
             Log.i(TAG, sb.toString());
-            outputResultSb.append(i+1).append(": ").append(result.getLabel()).append("\n");
+            outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
         }
         outputResult = outputResultSb.toString();
         outputImage = inputImage;

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Utils.java

@@ -2,6 +2,8 @@ package com.baidu.paddle.lite.demo.ocr;
 
 import android.content.Context;
 import android.graphics.Bitmap;
+import android.graphics.Matrix;
+import android.media.ExifInterface;
 import android.os.Environment;
 
 import java.io.*;
@@ -110,4 +112,48 @@ public class Utils {
         }
         return Bitmap.createScaledBitmap(bitmap, newWidth, newHeight, true);
     }
+
+    public static Bitmap rotateBitmap(Bitmap bitmap, int orientation) {
+
+        Matrix matrix = new Matrix();
+        switch (orientation) {
+            case ExifInterface.ORIENTATION_NORMAL:
+                return bitmap;
+            case ExifInterface.ORIENTATION_FLIP_HORIZONTAL:
+                matrix.setScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_180:
+                matrix.setRotate(180);
+                break;
+            case ExifInterface.ORIENTATION_FLIP_VERTICAL:
+                matrix.setRotate(180);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_TRANSPOSE:
+                matrix.setRotate(90);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_90:
+                matrix.setRotate(90);
+                break;
+            case ExifInterface.ORIENTATION_TRANSVERSE:
+                matrix.setRotate(-90);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_270:
+                matrix.setRotate(-90);
+                break;
+            default:
+                return bitmap;
+        }
+        try {
+            Bitmap bmRotated = Bitmap.createBitmap(bitmap, 0, 0, bitmap.getWidth(), bitmap.getHeight(), matrix, true);
+            bitmap.recycle();
+            return bmRotated;
+        }
+        catch (OutOfMemoryError e) {
+            e.printStackTrace();
+            return null;
+        }
+    }
 }

deploy/android_demo/app/src/main/res/xml/file_paths.xml

+<?xml version="1.0" encoding="utf-8"?>
+<paths xmlns:android="http://schemas.android.com/apk/res/android">
+    <external-files-path name="my_images" path="Pictures" />
+</paths>
\ No newline at end of file

deploy/cpp_infer/CMakeLists.txt

 project(ocr_system CXX C)
+
 option(WITH_MKL        "Compile demo with MKL/OpenBlas support, default use MKL."       ON)
 option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    OFF)
 option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static."   ON)
-option(USE_TENSORRT "Compile demo with TensorRT."   OFF)
+option(WITH_TENSORRT "Compile demo with TensorRT."   OFF)
+
+SET(PADDLE_LIB "" CACHE PATH "Location of libraries")
+SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
+SET(CUDA_LIB "" CACHE PATH "Location of libraries")
+SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
+SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
+
+set(DEMO_NAME "ocr_system")
 
 
 macro(safe_set_static_flag)
@@ -15,24 +24,60 @@ macro(safe_set_static_flag)
     endforeach(flag_var)
 endmacro()
 
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -g -fpermissive")
-set(CMAKE_STATIC_LIBRARY_PREFIX "")
-message("flags" ${CMAKE_CXX_FLAGS})
-set(CMAKE_CXX_FLAGS_RELEASE "-O3")
+if (WITH_MKL)
+    ADD_DEFINITIONS(-DUSE_MKL)
+endif()
 
 if(NOT DEFINED PADDLE_LIB)
   message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib")
 endif()
-if(NOT DEFINED DEMO_NAME)
-  message(FATAL_ERROR "please set DEMO_NAME with -DDEMO_NAME=demo_name")
+
+if(NOT DEFINED OPENCV_DIR)
+    message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
 endif()
 
 
-set(OPENCV_DIR ${OPENCV_DIR})
-find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+if (WIN32)
+  include_directories("${PADDLE_LIB}/paddle/fluid/inference")
+  include_directories("${PADDLE_LIB}/paddle/include")
+  link_directories("${PADDLE_LIB}/paddle/fluid/inference")
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
+
+else ()
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+  include_directories("${PADDLE_LIB}/paddle/include")
+  link_directories("${PADDLE_LIB}/paddle/lib")
+endif ()
 include_directories(${OpenCV_INCLUDE_DIRS})
 
-include_directories("${PADDLE_LIB}/paddle/include")
+if (WIN32)
+    add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
+    set(CMAKE_C_FLAGS_DEBUG   "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_C_FLAGS_RELEASE  "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
+    set(CMAKE_CXX_FLAGS_DEBUG  "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_CXX_FLAGS_RELEASE   "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
+    if (WITH_STATIC_LIB)
+        safe_set_static_flag()
+        add_definitions(-DSTATIC_LIB)
+    endif()
+else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o3 -std=c++11")
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+endif()
+message("flags" ${CMAKE_CXX_FLAGS})
+
+
+if (WITH_GPU)
+    if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
+        message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
+    endif()
+    if (NOT WIN32)
+        if (NOT DEFINED CUDNN_LIB)
+            message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
+        endif()
+    endif(NOT WIN32)
+endif()
+
 include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
 include_directories("${PADDLE_LIB}/third_party/install/glog/include")
 include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
@@ -43,10 +88,12 @@ include_directories("${PADDLE_LIB}/third_party/eigen3")
 
 include_directories("${CMAKE_SOURCE_DIR}/")
 
-if (USE_TENSORRT AND WITH_GPU)
-      include_directories("${TENSORRT_ROOT}/include")
-      link_directories("${TENSORRT_ROOT}/lib")
-endif()
+if (NOT WIN32)
+  if (WITH_TENSORRT AND WITH_GPU)
+     include_directories("${TENSORRT_DIR}/include")
+     link_directories("${TENSORRT_DIR}/lib")
+  endif()
+endif(NOT WIN32)
 
 link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
 
@@ -57,17 +104,24 @@ link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
 link_directories("${PADDLE_LIB}/paddle/lib")
 
 
-AUX_SOURCE_DIRECTORY(./src SRCS)
-add_executable(${DEMO_NAME} ${SRCS})
-
 if(WITH_MKL)
   include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
-  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
-               ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+  if (WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.lib
+            ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.lib)
+  else ()
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
+            ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+    execute_process(COMMAND cp -r ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib)
+  endif ()
   set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
   if(EXISTS ${MKLDNN_PATH})
     include_directories("${MKLDNN_PATH}/include")
-    set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+    if (WIN32)
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
+    else ()
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+    endif ()
   endif()
 else()
   set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
@@ -82,24 +136,66 @@ else()
       ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
 endif()
 
-set(EXTERNAL_LIB "-lrt -ldl -lpthread -lm")
+if (NOT WIN32)
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB} 
+        glog gflags protobuf z xxhash
+        )
+    if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+    if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+else()
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags_static libprotobuf xxhash)
+    set(DEPS ${DEPS} libcmt shlwapi)
+    if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+    if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+endif(NOT WIN32)
 
-set(DEPS ${DEPS}
-    ${MATH_LIB} ${MKLDNN_LIB}
-    glog gflags protobuf z xxhash
-    ${EXTERNAL_LIB} ${OpenCV_LIBS})
 
 if(WITH_GPU)
-  if (USE_TENSORRT)
-    set(DEPS ${DEPS}
-        ${TENSORRT_ROOT}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
-    set(DEPS ${DEPS}
-        ${TENSORRT_ROOT}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+  if(NOT WIN32)
+    if (WITH_TENSORRT)
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+    endif()
+    set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
+  else()
+    set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDNN_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
   endif()
-  set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
-  set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX} )
-  set(DEPS ${DEPS} ${CUDA_LIB}/libcublas${CMAKE_SHARED_LIBRARY_SUFFIX} )
-  set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX} )
 endif()
 
+
+if (NOT WIN32)
+    set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
+    set(DEPS ${DEPS} ${EXTERNAL_LIB})
+endif()
+
+set(DEPS ${DEPS} ${OpenCV_LIBS})
+
+AUX_SOURCE_DIRECTORY(./src SRCS)
+add_executable(${DEMO_NAME} ${SRCS})
+
 target_link_libraries(${DEMO_NAME} ${DEPS})
+
+if (WIN32 AND WITH_MKL)
+    add_custom_command(TARGET ${DEMO_NAME} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
+    )
+endif()
\ No newline at end of file

deploy/cpp_infer/docs/windows_vs2019_build.md

+# Visual Studio 2019 Community CMake 编译指南
+
+PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测试。微软从`Visual Studio 2017`开始即支持直接管理`CMake`跨平台编译项目，但是直到`2019`才提供了稳定和完全的支持，所以如果你想使用CMake管理项目编译构建，我们推荐你使用`Visual Studio 2019`环境下构建。
+
+
+## 前置条件
+* Visual Studio 2019
+* CUDA 9.0 / CUDA 10.0，cudnn 7+ （仅在使用GPU版本的预测库时需要）
+* CMake 3.0+
+
+请确保系统已经安装好上述基本软件，我们使用的是`VS2019`的社区版。
+
+**下面所有示例以工作目录为 `D:\projects`演示**。
+
+### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference
+
+PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载:  [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/windows_cpp_inference.html)
+
+解压后`D:\projects\fluid_inference`目录包含内容为：
+```
+fluid_inference
+├── paddle # paddle核心库和头文件
+|
+├── third_party # 第三方依赖库和头文件
+|
+└── version.txt # 版本和编译信息
+```
+
+### Step2: 安装配置OpenCV
+
+1. 在OpenCV官网下载适用于Windows平台的3.4.6版本， [下载地址](https://sourceforge.net/projects/opencvlibrary/files/3.4.6/opencv-3.4.6-vc14_vc15.exe/download)  
+2. 运行下载的可执行文件，将OpenCV解压至指定目录，如`D:\projects\opencv`
+3. 配置环境变量，如下流程所示  
+    - 我的电脑->属性->高级系统设置->环境变量
+    - 在系统变量中找到Path（如没有，自行创建），并双击编辑
+    - 新建，将opencv路径填入并保存，如`D:\projects\opencv\build\x64\vc14\bin`
+
+### Step3: 使用Visual Studio 2019直接编译CMake
+
+1. 打开Visual Studio 2019 Community，点击`继续但无需代码`
+![step2](https://paddleseg.bj.bcebos.com/inference/vs2019_step1.png)
+2. 点击： `文件`->`打开`->`CMake`
+![step2.1](https://paddleseg.bj.bcebos.com/inference/vs2019_step2.png)
+
+选择项目代码所在路径，并打开`CMakeList.txt`：
+
+![step2.2](https://paddleseg.bj.bcebos.com/inference/vs2019_step3.png)
+
+3. 点击：`项目`->`cpp_inference_demo的CMake设置`
+
+![step3](https://paddleseg.bj.bcebos.com/inference/vs2019_step4.png)
+
+4. 点击`浏览`，分别设置编译选项指定`CUDA`、`CUDNN_LIB`、`OpenCV`、`Paddle预测库`的路径
+
+三个编译参数的含义说明如下（带`*`表示仅在使用**GPU版本**预测库时指定, 其中CUDA库版本尽量对齐，**使用9.0、10.0版本，不使用9.2、10.1等版本CUDA库**）：
+
+|  参数名   | 含义  |
+|  ----  | ----  |
+| *CUDA_LIB  | CUDA的库路径 |
+| *CUDNN_LIB | CUDNN的库路径 |
+| OPENCV_DIR  | OpenCV的安装路径 |
+| PADDLE_LIB | Paddle预测库的路径 |
+
+**注意：**
+  1. 使用`CPU`版预测库，请把`WITH_GPU`的勾去掉
+  2. 如果使用的是`openblas`版本，请把`WITH_MKL`勾去掉
+
+![step4](https://paddleseg.bj.bcebos.com/inference/vs2019_step5.png)
+
+**设置完成后**, 点击上图中`保存并生成CMake缓存以加载变量`。
+
+5. 点击`生成`->`全部生成`
+
+![step6](https://paddleseg.bj.bcebos.com/inference/vs2019_step6.png)
+
+
+### Step4: 预测及可视化
+
+上述`Visual Studio 2019`编译产出的可执行文件在`out\build\x64-Release`目录下，打开`cmd`，并切换到该目录：
+
+```
+cd D:\projects\PaddleOCR\deploy\cpp_infer\out\build\x64-Release
+```
+可执行文件`ocr_system.exe`即为样例的预测程序，其主要使用方法如下
+
+```shell
+#预测图片 `D:\projects\PaddleOCR\doc\imgs\10.jpg`  
+.\ocr_system.exe D:\projects\PaddleOCR\deploy\cpp_infer\tools\config.txt D:\projects\PaddleOCR\doc\imgs\10.jpg
+```
+
+第一个参数为配置文件路径，第二个参数为需要预测的图片路径。
+
+
+### 注意
+* 在Windows下的终端中执行文件exe时，可能会发生乱码的现象，此时需要在终端中输入`CHCP 65001`，将终端的编码方式由GBK编码(默认)改为UTF-8编码，更加具体的解释可以参考这篇博客：[https://blog.csdn.net/qq_35038153/article/details/78430359](https://blog.csdn.net/qq_35038153/article/details/78430359)。

deploy/cpp_infer/readme.md

@@ -7,6 +7,9 @@
 
 ### 运行准备
 - Linux环境，推荐使用docker。
+- Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译。
+
+* 该文档主要介绍基于Linux环境的PaddleOCR C++预测流程，如果需要在Windows下基于预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)
 
 ### 1.1 编译opencv库
 

deploy/cpp_infer/src/config.cpp

@@ -44,7 +44,7 @@ Config::LoadConfig(const std::string &config_path) {
   std::map<std::string, std::string> dict;
   for (int i = 0; i < config.size(); i++) {
     // pass for empty line or comment
-    if (config[i].size() <= 1 or config[i][0] == '#') {
+    if (config[i].size() <= 1 || config[i][0] == '#') {
       continue;
     }
     std::vector<std::string> res = split(config[i], " ");

deploy/cpp_infer/src/utility.cpp

@@ -39,22 +39,21 @@ std::vector<std::string> Utility::ReadDict(const std::string &path) {
 void Utility::VisualizeBboxes(
     const cv::Mat &srcimg,
     const std::vector<std::vector<std::vector<int>>> &boxes) {
-  cv::Point rook_points[boxes.size()][4];
-  for (int n = 0; n < boxes.size(); n++) {
-    for (int m = 0; m < boxes[0].size(); m++) {
-      rook_points[n][m] = cv::Point(int(boxes[n][m][0]), int(boxes[n][m][1]));
-    }
-  }
   cv::Mat img_vis;
   srcimg.copyTo(img_vis);
   for (int n = 0; n < boxes.size(); n++) {
-    const cv::Point *ppt[1] = {rook_points[n]};
+    cv::Point rook_points[4];
+    for (int m = 0; m < boxes[n].size(); m++) {
+      rook_points[m] = cv::Point(int(boxes[n][m][0]), int(boxes[n][m][1]));
+    }
+
+    const cv::Point *ppt[1] = {rook_points};
     int npt[] = {4};
     cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
   }
 
   cv::imwrite("./ocr_vis.png", img_vis);
-  std::cout << "The detection visualized image saved in ./ocr_vis.png.pn"
+  std::cout << "The detection visualized image saved in ./ocr_vis.png"
             << std::endl;
 }
 

deploy/cpp_infer/tools/build.sh

-
 OPENCV_DIR=your_opencv_dir
 LIB_DIR=your_paddle_inference_dir
 CUDA_LIB_DIR=your_cuda_lib_dir
-CUDNN_LIB_DIR=/your_cudnn_lib_dir
+CUDNN_LIB_DIR=your_cudnn_lib_dir
 
 BUILD_DIR=build
 rm -rf ${BUILD_DIR}
@@ -11,7 +10,6 @@ cd ${BUILD_DIR}
 cmake .. \
     -DPADDLE_LIB=${LIB_DIR} \
     -DWITH_MKL=ON \
-    -DDEMO_NAME=ocr_system \
     -DWITH_GPU=OFF \
     -DWITH_STATIC_LIB=OFF \
     -DUSE_TENSORRT=OFF \

deploy/cpp_infer/tools/config.txt

@@ -15,8 +15,7 @@ det_model_dir  ./inference/det_db
 # rec config
 rec_model_dir  ./inference/rec_crnn
 char_list_file ../../ppocr/utils/ppocr_keys_v1.txt
-img_path  ../../doc/imgs/11.jpg
 
 # show the detection results
-visualize 0
+visualize 1
 

deploy/pdserving/det_local_server.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 paddle_serving_client import Client
+import cv2
+import sys
+import numpy as np
+import os
+from paddle_serving_client import Client
+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
+from paddle_serving_server_gpu.web_service import WebService
+import time
+import re
+import base64
+
+
+class OCRService(WebService):
+    def init_det(self):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), 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, feed=[], fetch=[]):
+        data = base64.b64decode(feed[0]["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.ori_h, self.ori_w, _ = im.shape
+        det_img = self.det_preprocess(im)
+        _, self.new_h, self.new_w = det_img.shape
+        return {"image": det_img[np.newaxis, :].copy()}, ["concat_1.tmp_0"]
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        det_out = fetch_map["concat_1.tmp_0"]
+        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])
+        return {"dt_boxes": dt_boxes.tolist()}
+
+
+ocr_service = OCRService(name="ocr")
+ocr_service.load_model_config("ocr_det_model")
+ocr_service.set_gpus("0")
+ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+ocr_service.init_det()
+ocr_service.run_debugger_service()
+ocr_service.run_web_service()

deploy/pdserving/det_web_server.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 paddle_serving_client import Client
+import cv2
+import sys
+import numpy as np
+import os
+from paddle_serving_client import Client
+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
+from paddle_serving_server_gpu.web_service import WebService
+import time
+import re
+import base64
+
+
+class OCRService(WebService):
+    def init_det(self):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), 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, feed=[], fetch=[]):
+        data = base64.b64decode(feed[0]["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.ori_h, self.ori_w, _ = im.shape
+        det_img = self.det_preprocess(im)
+        _, self.new_h, self.new_w = det_img.shape
+        print(det_img)
+        return {"image": det_img}, ["concat_1.tmp_0"]
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        det_out = fetch_map["concat_1.tmp_0"]
+        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])
+        return {"dt_boxes": dt_boxes.tolist()}
+
+
+ocr_service = OCRService(name="ocr")
+ocr_service.load_model_config("ocr_det_model")
+ocr_service.set_gpus("0")
+ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+ocr_service.init_det()
+ocr_service.run_rpc_service()
+ocr_service.run_web_service()

deploy/pdserving/ocr_local_server.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 paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+import sys
+import numpy as np
+import os
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+from paddle_serving_server_gpu.web_service import WebService
+from paddle_serving_app.local_predict import Debugger
+import time
+import re
+import base64
+
+
+class OCRService(WebService):
+    def init_det_debugger(self, det_model_config):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.det_client = Debugger()
+        self.det_client.load_model_config(
+            det_model_config, gpu=True, profile=False)
+        self.ocr_reader = OCRReader()
+
+    def preprocess(self, feed=[], fetch=[]):
+        data = base64.b64decode(feed[0]["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        ori_h, ori_w, _ = im.shape
+        det_img = self.det_preprocess(im)
+        _, new_h, new_w = det_img.shape
+        det_img = det_img[np.newaxis, :]
+        det_img = det_img.copy()
+        det_out = self.det_client.predict(
+            feed={"image": det_img}, fetch=["concat_1.tmp_0"])
+        filter_func = FilterBoxes(10, 10)
+        post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+        sorted_boxes = SortedBoxes()
+        ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
+        dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
+        dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
+        dt_boxes = sorted_boxes(dt_boxes)
+        get_rotate_crop_image = GetRotateCropImage()
+        img_list = []
+        max_wh_ratio = 0
+        for i, dtbox in enumerate(dt_boxes):
+            boximg = 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)
+        if len(img_list) == 0:
+            return [], []
+        _, 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
+        feed = {"image": imgs.copy()}
+        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+        return feed, fetch
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": res_lst}
+        return res
+
+
+ocr_service = OCRService(name="ocr")
+ocr_service.load_model_config("ocr_rec_model")
+ocr_service.prepare_server(workdir="workdir", port=9292)
+ocr_service.init_det_debugger(det_model_config="ocr_det_model")
+ocr_service.run_debugger_service(gpu=True)
+ocr_service.run_web_service()

deploy/pdserving/ocr_web_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.
+# -*- coding: utf-8 -*-
+
+import requests
+import json
+import cv2
+import base64
+import os, sys
+import time
+
+def cv2_to_base64(image):
+    #data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(image).decode(
+        'utf8')  #data.tostring()).decode('utf8')
+
+headers = {"Content-type": "application/json"}
+url = "http://127.0.0.1:9292/ocr/prediction"
+test_img_dir = "../../doc/imgs/"
+for img_file in 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)
+    data = {"feed": [{"image": image}], "fetch": ["res"]}
+    r = requests.post(url=url, headers=headers, data=json.dumps(data))
+    print(r.json())

deploy/pdserving/ocr_web_server.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 paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+import sys
+import numpy as np
+import os
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+from paddle_serving_server_gpu.web_service import WebService
+import time
+import re
+import base64
+
+
+class OCRService(WebService):
+    def init_det_client(self, det_port, det_client_config):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.det_client = Client()
+        self.det_client.load_client_config(det_client_config)
+        self.det_client.connect(["127.0.0.1:{}".format(det_port)])
+        self.ocr_reader = OCRReader()
+
+    def preprocess(self, feed=[], fetch=[]):
+        data = base64.b64decode(feed[0]["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        ori_h, ori_w, _ = im.shape
+        det_img = self.det_preprocess(im)
+        det_out = self.det_client.predict(
+            feed={"image": det_img}, fetch=["concat_1.tmp_0"])
+        _, new_h, new_w = det_img.shape
+        filter_func = FilterBoxes(10, 10)
+        post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+        sorted_boxes = SortedBoxes()
+        ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
+        dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
+        dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
+        dt_boxes = sorted_boxes(dt_boxes)
+        get_rotate_crop_image = GetRotateCropImage()
+        feed_list = []
+        img_list = []
+        max_wh_ratio = 0
+        for i, dtbox in enumerate(dt_boxes):
+            boximg = 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)
+        for img in img_list:
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            feed = {"image": norm_img}
+            feed_list.append(feed)
+        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+        return feed_list, fetch
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": res_lst}
+        return res
+
+
+ocr_service = OCRService(name="ocr")
+ocr_service.load_model_config("ocr_rec_model")
+ocr_service.set_gpus("0")
+ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+ocr_service.init_det_client(
+    det_port=9293,
+    det_client_config="ocr_det_client/serving_client_conf.prototxt")
+ocr_service.run_rpc_service()
+ocr_service.run_web_service()

deploy/pdserving/readme.md

+# Paddle Serving 服务部署(Beta)
+
+本教程将介绍基于[Paddle Serving](https://github.com/PaddlePaddle/Serving)部署PaddleOCR在线预测服务的详细步骤。
+
+## 快速启动服务
+
+### 1. 准备环境
+我们先安装Paddle Serving相关组件
+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**
+
+```
+#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
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_app-0.1.2-py2-none-any.whl
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_client-0.3.2-cp27-none-any.whl
+python -m pip install paddle_serving_app-0.1.2-py2-none-any.whl paddle_serving_server_gpu-0.3.2-py2-none-any.whl paddle_serving_client-0.3.2-cp27-none-any.whl
+```
+
+### 2. 模型转换
+可以使用`paddle_serving_app`提供的模型，执行下列命令
+```
+python -m paddle_serving_app.package --get_model ocr_rec
+tar -xzvf ocr_rec.tar.gz
+python -m paddle_serving_app.package --get_model ocr_det
+tar -xzvf ocr_det.tar.gz 
+```
+执行上述命令会下载`db_crnn_mobile`的模型，如果想要下载规模更大的`db_crnn_server`模型，可以在下载预测模型并解压之后。参考[如何从Paddle保存的预测模型转为Paddle Serving格式可部署的模型](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md)。
+
+### 3. 启动服务
+启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表：  
+
+|版本|特点|适用场景|
+|-|-|-|
+|标准版|稳定性高，分布式部署|适用于吞吐量大，需要跨机房部署的情况|
+|快速版|部署方便，预测速度快|适用于对预测速度要求高，迭代速度快的场景|
+
+#### 方式1. 启动标准版服务
+
+```
+python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
+python ocr_web_server.py
+```
+
+#### 方式2. 启动快速版服务
+
+```
+python ocr_local_server.py
+```
+
+## 发送预测请求
+
+```
+python ocr_web_client.py
+```
+
+## 返回结果格式说明
+
+返回结果是json格式
+```
+{u'result': {u'res': [u'\u571f\u5730\u6574\u6cbb\u4e0e\u571f\u58e4\u4fee\u590d\u7814\u7a76\u4e2d\u5fc3', u'\u534e\u5357\u519c\u4e1a\u5927\u5b661\u7d20\u56fe']}}
+```
+我们也可以打印结果json串中`res`字段的每一句话
+```
+土地整治与土壤修复研究中心
+华南农业大学1素图
+```
+
+## 自定义修改服务逻辑
+
+在`ocr_web_server.py`或是`ocr_local_server.py`当中的`preprocess`函数里面做了检测服务和识别服务的前处理，`postprocess`函数里面做了识别的后处理服务，可以在相应的函数中做修改。调用了`paddle_serving_app`库提供的常见CV模型的前处理/后处理库。
+
+如果想要单独启动Paddle Serving的检测服务和识别服务，参见下列表格, 执行对应的脚本即可。
+
+| 模型 | 标准版         | 快速版           |
+| ---- | ----------------- | ------------------- |
+| 检测 | det_web_server.py | det_local_server.py |
+| 识别 | rec_web_server.py | rec_local_server.py |
+
+更多信息参见[Paddle Serving](https://github.com/PaddlePaddle/Serving)

deploy/pdserving/rec_local_server.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 paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+import sys
+import numpy as np
+import os
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+from paddle_serving_server_gpu.web_service import WebService
+import time
+import re
+import base64
+
+
+class OCRService(WebService):
+    def init_rec(self):
+        self.ocr_reader = OCRReader()
+
+    def preprocess(self, feed=[], fetch=[]):
+        img_list = []
+        for feed_data in feed:
+            data = base64.b64decode(feed_data["image"].encode('utf8'))
+            data = np.fromstring(data, np.uint8)
+            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+            img_list.append(im)
+        max_wh_ratio = 0
+        for i, boximg in enumerate(img_list):
+            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 i, img in enumerate(img_list):
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            imgs[i] = norm_img
+        feed = {"image": imgs.copy()}
+        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+        return feed, fetch
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": res_lst}
+        return res
+
+
+ocr_service = OCRService(name="ocr")
+ocr_service.load_model_config("ocr_rec_model")
+ocr_service.set_gpus("0")
+ocr_service.init_rec()
+ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+ocr_service.run_debugger_service()
+ocr_service.run_web_service()

deploy/pdserving/rec_web_server.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 paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+import sys
+import numpy as np
+import os
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+from paddle_serving_server_gpu.web_service import WebService
+import time
+import re
+import base64
+
+
+class OCRService(WebService):
+    def init_rec(self):
+        self.ocr_reader = OCRReader()
+
+    def preprocess(self, feed=[], fetch=[]):
+        # TODO: to handle batch rec images
+        img_list = []
+        for feed_data in feed:
+            data = base64.b64decode(feed_data["image"].encode('utf8'))
+            data = np.fromstring(data, np.uint8)
+            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+            img_list.append(im)
+        feed_list = []
+        max_wh_ratio = 0
+        for i, boximg in enumerate(img_list):
+            h, w = boximg.shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for img in img_list:
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            feed = {"image": norm_img}
+            feed_list.append(feed)
+        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+        return feed_list, fetch
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": res_lst}
+        return res
+
+
+ocr_service = OCRService(name="ocr")
+ocr_service.load_model_config("ocr_rec_model")
+ocr_service.set_gpus("0")
+ocr_service.init_rec()
+ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+ocr_service.run_rpc_service()
+ocr_service.run_web_service()

doc/doc_ch/serving.md

@@ -69,7 +69,7 @@ $ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
 
 #### 方式2. 配置文件启动（支持CPU、GPU）
 **启动命令：**  
-```hub serving start --config/-c config.json```  
+```hub serving start -c config.json```  
 
 其中，`config.json`格式如下： 
 ```python

doc/doc_ch/update.md

 # 更新
+- 2020.7.23 发布7月21日B站直播课回放和PPT，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
 - 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
 - 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
 - 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具

doc/doc_en/update_en.md

 # RECENT UPDATES
-
+- 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
 - 2020.7.15, Add mobile App demo , support both iOS and  Android  ( based on easyedge and Paddle Lite)
 - 2020.7.15, Improve the  deployment ability, add the C + +  inference , serving deployment. In addtion, the benchmarks of the ultra-lightweight Chinese OCR model are provided.
 - 2020.7.15, Add several related datasets, data annotation and synthesis tools.