Merge branch 'develop' into develop

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.
@@ -214,3 +212,4 @@ We welcome all the contributions to PaddleOCR and appreciate for your feedback v
 - Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
 - 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.

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
@@ -206,8 +205,9 @@ PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训
 ## 贡献代码
 我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
 
-- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 贡献了英文文档。
+- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 贡献了英文文档
 - 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
 - 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
 - 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。

deploy/android_demo/README.md

@@ -1,6 +1,6 @@
 # 如何快速测试
 ### 1. 安装最新版本的Android Studio
-可以从https://developer.android.com/studio下载。本Demo使用是4.0版本Android Studio编写。
+可以从https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
 
 ### 2. 按照NDK 20 以上版本 
 Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。

deploy/android_demo/app/build.gradle

@@ -3,11 +3,11 @@ import java.security.MessageDigest
 apply plugin: 'com.android.application'
 
 android {
-    compileSdkVersion 28
+    compileSdkVersion 29
     defaultConfig {
         applicationId "com.baidu.paddle.lite.demo.ocr"
-        minSdkVersion 15
+        minSdkVersion 23
-        targetSdkVersion 28
+        targetSdkVersion 29
         versionCode 1
         versionName "1.0"
         testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
@@ -39,9 +39,8 @@ android {
 
 dependencies {
     implementation fileTree(include: ['*.jar'], dir: 'libs')
-    implementation 'com.android.support:appcompat-v7:28.0.0'
+    implementation 'androidx.appcompat:appcompat:1.1.0'
-    implementation 'com.android.support.constraint:constraint-layout:1.1.3'
+    implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
-    implementation 'com.android.support:design:28.0.0'
     testImplementation 'junit:junit:4.12'
     androidTestImplementation 'com.android.support.test:runner:1.0.2'
     androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'

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

@@ -14,10 +14,10 @@
             android:roundIcon="@mipmap/ic_launcher_round"
             android:supportsRtl="true"
             android:theme="@style/AppTheme">
+        <!-- to test MiniActivity, change this to com.baidu.paddle.lite.demo.ocr.MiniActivity -->
         <activity android:name="com.baidu.paddle.lite.demo.ocr.MainActivity">
             <intent-filter>
                 <action android:name="android.intent.action.MAIN"/>
-
                 <category android:name="android.intent.category.LAUNCHER"/>
             </intent-filter>
         </activity>
@@ -25,6 +25,15 @@
                 android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
                 android:label="Settings">
         </activity>
+        <provider
+            android:name="androidx.core.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>

deploy/android_demo/app/src/main/cpp/native.cpp

@@ -30,7 +30,7 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(JNIEnv *env, jobject
 }
 
 /**
- * "LITE_POWER_HIGH" 转为 paddle::lite_api::LITE_POWER_HIGH
+ * "LITE_POWER_HIGH" convert to paddle::lite_api::LITE_POWER_HIGH
  * @param cpu_mode
  * @return
  */

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.cpp

@@ -37,7 +37,7 @@ int OCR_PPredictor::init_from_file(const std::string &det_model_path, const std:
     return RETURN_OK;
 }
 /**
- * 调试用，保存第一步的框选结果
+ * for debug use, show result of First Step
  * @param filter_boxes
  * @param boxes
  * @param srcimg

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h

@@ -12,26 +12,26 @@
 namespace ppredictor {
 
 /**
- * 配置
+ * Config
  */
 struct OCR_Config {
-    int thread_num = 4; // 线程数
+    int thread_num = 4; // Thread num
     paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
 };
 
 /**
- * 一个四边形内图片的推理结果,
+ * PolyGone Result
  */
 struct OCRPredictResult {
-    std::vector<int> word_index; //
+    std::vector<int> word_index;
     std::vector<std::vector<int>> points;
     float score;
 };
 
 /**
- * OCR 一共有2个模型进行推理，
+ * OCR there are 2 models
- * 1. 使用第一个模型（det），框选出多个四边形
+ * 1. First model（det），select polygones to show where are the texts
- * 2. 从原图从抠出这些多边形，使用第二个模型（rec），获取文本
+ * 2. crop from the origin images, use these polygones to infer
  */
 class OCR_PPredictor : public PPredictor_Interface {
 public:
@@ -50,7 +50,7 @@ public:
     int init(const std::string &det_model_content, const std::string &rec_model_content);
     int init_from_file(const std::string &det_model_path, const std::string &rec_model_path);
     /**
-     * 返回OCR结果
+     * Return OCR result
      * @param dims
      * @param input_data
      * @param input_len
@@ -69,7 +69,7 @@ public:
 private:
 
     /**
-     * 从第一个模型的结果中计算有文字的四边形
+     * calcul Polygone from the result image of first model
      * @param pred
      * @param output_height
      * @param output_width
@@ -81,7 +81,7 @@ private:
                         const cv::Mat &origin);
 
     /**
-     * 第二个模型的推理
+     * infer for second model
      *
      * @param boxes
      * @param origin
@@ -91,14 +91,14 @@ private:
     infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes, const cv::Mat &origin);
 
     /**
-     * 第二个模型提取文字的后处理
+     * Postprocess or sencod model to extract text
      * @param res
      * @return
      */
     std::vector<int> postprocess_rec_word_index(const PredictorOutput &res);
 
     /**
-     * 计算第二个模型的文字的置信度
+     * calculate confidence of second model text result
      * @param res
      * @return
      */

deploy/android_demo/app/src/main/cpp/ppredictor.h

@@ -7,7 +7,7 @@
 namespace ppredictor {
 
 /**
- * PaddleLite Preditor 通用接口
+ * PaddleLite Preditor Common Interface
  */
 class PPredictor_Interface {
 public:
@@ -21,7 +21,7 @@ public:
 };
 
 /**
- * 通用推理
+ * Common Predictor
  */
 class PPredictor : public PPredictor_Interface {
 public:
@@ -33,9 +33,9 @@ public:
     }
 
     /**
-     * 初始化paddlitelite的opt模型，nb格式，与init_paddle二选一
+     * init paddlitelite opt model，nb format ，or use ini_paddle
      * @param model_content
-     * @return 0 目前是固定值0， 之后其他值表示失败
+     * @return 0
      */
     virtual int init_nb(const std::string &model_content);
 

deploy/android_demo/app/src/main/cpp/predictor_output.h

@@ -21,10 +21,10 @@ public:
     const std::vector<std::vector<uint64_t>> get_lod() const;
     const  std::vector<int64_t> get_shape() const;
 
-    std::vector<float> data; // 通常是float返回，与下面的data_int二选一
+    std::vector<float> data; // return float, or use data_int
-    std::vector<int> data_int; // 少数层是int返回，与 data二选一
+    std::vector<int> data_int; // several layers return int ，or use data
-    std::vector<int64_t> shape; // PaddleLite输出层的shape
+    std::vector<int64_t> shape; // PaddleLite output shape
-    std::vector<std::vector<uint64_t>> lod; // PaddleLite输出层的lod
+    std::vector<std::vector<uint64_t>> lod; // PaddleLite output lod
 
 private:
     std::unique_ptr<const paddle::lite_api::Tensor> _tensor;

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

@@ -19,15 +19,16 @@ package com.baidu.paddle.lite.demo.ocr;
 import android.content.res.Configuration;
 import android.os.Bundle;
 import android.preference.PreferenceActivity;
-import android.support.annotation.LayoutRes;
-import android.support.annotation.Nullable;
-import android.support.v7.app.ActionBar;
-import android.support.v7.app.AppCompatDelegate;
-import android.support.v7.widget.Toolbar;
 import android.view.MenuInflater;
 import android.view.View;
 import android.view.ViewGroup;
 
+import androidx.annotation.LayoutRes;
+import androidx.annotation.Nullable;
+import androidx.appcompat.app.ActionBar;
+import androidx.appcompat.app.AppCompatDelegate;
+import androidx.appcompat.widget.Toolbar;
+
 /**
  * A {@link PreferenceActivity} which implements and proxies the necessary calls
  * to be used with AppCompat.

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

@@ -3,23 +3,22 @@ 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;
 import android.preference.PreferenceManager;
 import android.provider.MediaStore;
-import android.support.annotation.NonNull;
-import android.support.v4.app.ActivityCompat;
-import android.support.v4.content.ContextCompat;
-import android.support.v7.app.AppCompatActivity;
 import android.text.method.ScrollingMovementMethod;
 import android.util.Log;
 import android.view.Menu;
@@ -29,9 +28,17 @@ import android.widget.ImageView;
 import android.widget.TextView;
 import android.widget.Toast;
 
+import androidx.annotation.NonNull;
+import androidx.appcompat.app.AppCompatActivity;
+import androidx.core.app.ActivityCompat;
+import androidx.core.content.ContextCompat;
+import androidx.core.content.FileProvider;
+
 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 +76,7 @@ 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 +376,56 @@ public class MainActivity extends AppCompatActivity {
     }
 
     private void takePhoto() {
-        Intent takePhotoIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
+        Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
-        if (takePhotoIntent.resolveActivity(getPackageManager()) != null) {
+        // Ensure that there's a camera activity to handle the intent
-            startActivityForResult(takePhotoIntent, TAKE_PHOTO_REQUEST_CODE);
+        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 +439,22 @@ public class MainActivity extends AppCompatActivity {
                     }
                     break;
                 case TAKE_PHOTO_REQUEST_CODE:
-                    Bundle extras = data.getExtras();
+                    if (currentPhotoPath != null) {
-                    Bitmap image = (Bitmap) extras.get("data");
+                        ExifInterface exif = null;
-                    onImageChanged(image);
+                        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/MiniActivity.java

@@ -0,0 +1,157 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.os.Build;
+import android.os.Bundle;
+import android.os.Handler;
+import android.os.HandlerThread;
+import android.os.Message;
+import android.util.Log;
+import android.view.View;
+import android.widget.Button;
+import android.widget.ImageView;
+import android.widget.TextView;
+import android.widget.Toast;
+
+import androidx.appcompat.app.AppCompatActivity;
+
+import java.io.IOException;
+import java.io.InputStream;
+
+public class MiniActivity extends AppCompatActivity {
+
+
+    public static final int REQUEST_LOAD_MODEL = 0;
+    public static final int REQUEST_RUN_MODEL = 1;
+    public static final int REQUEST_UNLOAD_MODEL = 2;
+    public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
+    public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
+    public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
+    public static final int RESPONSE_RUN_MODEL_FAILED = 3;
+
+    private static final String TAG = "MiniActivity";
+
+    protected Handler receiver = null; // Receive messages from worker thread
+    protected Handler sender = null; // Send command to worker thread
+    protected HandlerThread worker = null; // Worker thread to load&run model
+    protected volatile Predictor predictor = null;
+
+    private String assetModelDirPath = "models/ocr_v1_for_cpu";
+    private String assetlabelFilePath = "labels/ppocr_keys_v1.txt";
+
+    private Button button;
+    private ImageView imageView; // image result
+    private TextView textView; // text result
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_mini);
+
+        Log.i(TAG, "SHOW in Logcat");
+
+        // Prepare the worker thread for mode loading and inference
+        worker = new HandlerThread("Predictor Worker");
+        worker.start();
+        sender = new Handler(worker.getLooper()) {
+            public void handleMessage(Message msg) {
+                switch (msg.what) {
+                    case REQUEST_LOAD_MODEL:
+                        // Load model and reload test image
+                        if (!onLoadModel()) {
+                            runOnUiThread(new Runnable() {
+                                @Override
+                                public void run() {
+                                    Toast.makeText(MiniActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
+                                }
+                            });
+                        }
+                        break;
+                    case REQUEST_RUN_MODEL:
+                        // Run model if model is loaded
+                        final boolean isSuccessed = onRunModel();
+                        runOnUiThread(new Runnable() {
+                            @Override
+                            public void run() {
+                                if (isSuccessed){
+                                    onRunModelSuccessed();
+                                }else{
+                                    Toast.makeText(MiniActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
+                                }
+                            }
+                        });
+                        break;
+                }
+            }
+        };
+        sender.sendEmptyMessage(REQUEST_LOAD_MODEL); // corresponding to REQUEST_LOAD_MODEL， to call onLoadModel()
+
+        imageView = findViewById(R.id.imageView);
+        textView = findViewById(R.id.sample_text);
+        button = findViewById(R.id.button);
+        button.setOnClickListener(new View.OnClickListener() {
+            @Override
+            public void onClick(View v) {
+                sender.sendEmptyMessage(REQUEST_RUN_MODEL);
+            }
+        });
+
+
+    }
+
+    @Override
+    protected void onDestroy() {
+        onUnloadModel();
+        if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.JELLY_BEAN_MR2) {
+            worker.quitSafely();
+        } else {
+            worker.quit();
+        }
+        super.onDestroy();
+    }
+
+    /**
+     * call in onCreate, model init
+     *
+     * @return
+     */
+    private boolean onLoadModel() {
+        if (predictor == null) {
+            predictor = new Predictor();
+        }
+        return predictor.init(this, assetModelDirPath, assetlabelFilePath);
+    }
+
+    /**
+     * init engine
+     * call in onCreate
+     *
+     * @return
+     */
+    private boolean onRunModel() {
+        try {
+            String assetImagePath = "images/5.jpg";
+            InputStream imageStream = getAssets().open(assetImagePath);
+            Bitmap image = BitmapFactory.decodeStream(imageStream);
+            // Input is Bitmap
+            predictor.setInputImage(image);
+            return predictor.isLoaded() && predictor.runModel();
+        } catch (IOException e) {
+            e.printStackTrace();
+            return false;
+        }
+    }
+
+    private void onRunModelSuccessed() {
+        Log.i(TAG, "onRunModelSuccessed");
+        textView.setText(predictor.outputResult);
+        imageView.setImageBitmap(predictor.outputImage);
+    }
+
+    private void onUnloadModel() {
+        if (predictor != null) {
+            predictor.releaseModel();
+        }
+    }
+}

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(){
+    public void release() {
-        if (nativePointer != 0){
+        if (nativePointer != 0) {
             nativePointer = 0;
             destory(nativePointer);
         }

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

@@ -38,7 +38,7 @@ public class Predictor {
     protected float scoreThreshold = 0.1f;
     protected Bitmap inputImage = null;
     protected Bitmap outputImage = null;
-    protected String outputResult = "";
+    protected volatile String outputResult = "";
     protected float preprocessTime = 0;
     protected float postprocessTime = 0;
 
@@ -46,6 +46,16 @@ public class Predictor {
     public Predictor() {
     }
 
+    public boolean init(Context appCtx, String modelPath, String labelPath) {
+        isLoaded = loadModel(appCtx, modelPath, cpuThreadNum, cpuPowerMode);
+        if (!isLoaded) {
+            return false;
+        }
+        isLoaded = loadLabel(appCtx, labelPath);
+        return isLoaded;
+    }
+
+
     public boolean init(Context appCtx, String modelPath, String labelPath, int cpuThreadNum, String cpuPowerMode,
                         String inputColorFormat,
                         long[] inputShape, float[] inputMean,
@@ -76,11 +86,7 @@ public class Predictor {
             Log.e(TAG, "Only  BGR color format is supported.");
             return false;
         }
-        isLoaded = loadModel(appCtx, modelPath, cpuThreadNum, cpuPowerMode);
+        boolean isLoaded = init(appCtx, modelPath, labelPath);
-        if (!isLoaded) {
-            return false;
-        }
-        isLoaded = loadLabel(appCtx, labelPath);
         if (!isLoaded) {
             return false;
         }
@@ -127,12 +133,12 @@ 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 = "";
@@ -222,7 +228,7 @@ public class Predictor {
         for (int i = 0; i < warmupIterNum; i++) {
             paddlePredictor.runImage(inputData, width, height, channels, inputImage);
         }
-        warmupIterNum = 0; // 之后不要再warm了
+        warmupIterNum = 0; // do not need warm
         // Run inference
         start = new Date();
         ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
@@ -287,9 +293,7 @@ public class Predictor {
         if (image == null) {
             return;
         }
-        // Scale image to the size of input tensor
+        this.inputImage = image.copy(Bitmap.Config.ARGB_8888, true);
-        Bitmap rgbaImage = image.copy(Bitmap.Config.ARGB_8888, true);
-        this.inputImage = rgbaImage;
     }
 
     private ArrayList<OcrResultModel> postprocess(ArrayList<OcrResultModel> results) {
@@ -310,7 +314,7 @@ 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());
@@ -319,8 +323,8 @@ public class Predictor {
             for (Point p : result.getPoints()) {
                 sb.append("(").append(p.x).append(",").append(p.y).append(") ");
             }
-            Log.i(TAG, sb.toString());
+            Log.i(TAG, sb.toString()); // show LOG in Logcat panel
-            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/SettingsActivity.java

@@ -5,7 +5,8 @@ import android.os.Bundle;
 import android.preference.CheckBoxPreference;
 import android.preference.EditTextPreference;
 import android.preference.ListPreference;
-import android.support.v7.app.ActionBar;
+
+import androidx.appcompat.app.ActionBar;
 
 import java.util.ArrayList;
 import java.util.List;

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/layout/activity_main.xml

@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="utf-8"?>
-<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
                                              xmlns:app="http://schemas.android.com/apk/res-auto"
                                              xmlns:tools="http://schemas.android.com/tools"
                                              android:layout_width="match_parent"
@@ -96,4 +96,4 @@
 
     </RelativeLayout>
 
-</android.support.constraint.ConstraintLayout>
+</androidx.constraintlayout.widget.ConstraintLayout>

deploy/android_demo/app/src/main/res/layout/activity_mini.xml

@@ -0,0 +1,46 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- for MiniActivity Use Only -->
+<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:app="http://schemas.android.com/apk/res-auto"
+    xmlns:tools="http://schemas.android.com/tools"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    app:layout_constraintLeft_toLeftOf="parent"
+    app:layout_constraintLeft_toRightOf="parent"
+    tools:context=".MainActivity">
+
+    <TextView
+        android:id="@+id/sample_text"
+        android:layout_width="0dp"
+        android:layout_height="wrap_content"
+        android:text="Hello World!"
+        app:layout_constraintLeft_toLeftOf="parent"
+        app:layout_constraintRight_toRightOf="parent"
+        app:layout_constraintTop_toBottomOf="@id/imageView"
+        android:scrollbars="vertical"
+        />
+
+    <ImageView
+        android:id="@+id/imageView"
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:paddingTop="20dp"
+        android:paddingBottom="20dp"
+        app:layout_constraintBottom_toTopOf="@id/imageView"
+        app:layout_constraintLeft_toLeftOf="parent"
+        app:layout_constraintRight_toRightOf="parent"
+        app:layout_constraintTop_toTopOf="parent"
+        tools:srcCompat="@tools:sample/avatars" />
+
+    <Button
+        android:id="@+id/button"
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:layout_marginBottom="4dp"
+        android:text="Button"
+        app:layout_constraintBottom_toBottomOf="parent"
+        app:layout_constraintLeft_toLeftOf="parent"
+        app:layout_constraintRight_toRightOf="parent"
+        tools:layout_editor_absoluteX="161dp" />
+
+</androidx.constraintlayout.widget.ConstraintLayout>

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

@@ -0,0 +1,4 @@
+<?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>

deploy/cpp_infer/CMakeLists.txt

@@ -1,8 +1,17 @@
 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")
+if (WITH_MKL)
-set(CMAKE_STATIC_LIBRARY_PREFIX "")
+    ADD_DEFINITIONS(-DUSE_MKL)
-message("flags" ${CMAKE_CXX_FLAGS})
+endif()
-set(CMAKE_CXX_FLAGS_RELEASE "-O3")
 
 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})
+if (WIN32)
-find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+  include_directories("${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)
+if (NOT WIN32)
-      include_directories("${TENSORRT_ROOT}/include")
+  if (WITH_TENSORRT AND WITH_GPU)
-      link_directories("${TENSORRT_ROOT}/lib")
+     include_directories("${TENSORRT_DIR}/include")
-endif()
+     link_directories("${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}
+  if (WIN32)
-               ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+    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)
+  if(NOT WIN32)
-    set(DEPS ${DEPS}
+    if (WITH_TENSORRT)
-        ${TENSORRT_ROOT}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
-    set(DEPS ${DEPS}
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
-        ${TENSORRT_ROOT}/lib/libnvinfer_plugin${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()

deploy/cpp_infer/docs/windows_vs2019_build.md

@@ -0,0 +1,95 @@
+# 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

@@ -1,8 +1,7 @@
-
 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/lite/readme.md

@@ -18,7 +18,7 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#docker)
 2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#android)
 3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#id13)
-4. [Windows](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/x86.html#windows)
+4. [Windows](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/x86.html#id4)
 
 ### 1.2 准备预测库
 
@@ -84,7 +84,7 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
 
 |模型简介|检测模型|识别模型|Paddle-Lite版本|
 |-|-|-|-|
-|超轻量级中文OCR opt优化模型|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|2.6.1|
+|超轻量级中文OCR opt优化模型|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|develop|
 
 如果直接使用上述表格中的模型进行部署，可略过下述步骤，直接阅读 [2.2节](#2.2与手机联调)。
 

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.
@@ -17,7 +17,7 @@ deployment solutions for end-side deployment issues.
 [build for Docker](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#docker)
 [build for Linux](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#android)
 [build for MAC OS](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#id13)
-[build for windows](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/x86.html#windows)
+[build for windows](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/x86.html#id4)
 
 ## 3. Download prebuild library for android and ios
 
@@ -155,7 +155,7 @@ demo/cxx/ocr/
 |-- debug/  
 |   |--ch_det_mv3_db_opt.nb             Detection model
 |   |--ch_rec_mv3_crnn_opt.nb           Recognition model
-|   |--11.jpg                           image for OCR
+|   |--11.jpg                           Image for OCR
 |   |--ppocr_keys_v1.txt                Dictionary file
 |   |--libpaddle_light_api_shared.so    C++ .so file
 |   |--config.txt                       Config file

deploy/pdserving/det_local_server.py

@@ -0,0 +1,77 @@
+# 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
+if sys.argv[1] == 'gpu':
+    from paddle_serving_server_gpu.web_service import WebService
+elif sys.argv[1] == 'cpu'
+    from paddle_serving_server.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")
+if sys.argv[1] == 'gpu':
+    ocr_service.set_gpus("0")
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292)
+ocr_service.init_det()
+ocr_service.run_debugger_service()
+ocr_service.run_web_service()

deploy/pdserving/det_web_server.py

@@ -0,0 +1,78 @@
+# 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
+if sys.argv[1] == 'gpu':
+    from paddle_serving_server_gpu.web_service import WebService
+elif sys.argv[1] == 'cpu':
+    from paddle_serving_server.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")
+if sys.argv[1] == 'gpu':
+    ocr_service.set_gpus("0")
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+ocr_service.init_det()
+ocr_service.run_rpc_service()
+ocr_service.run_web_service()

deploy/pdserving/ocr_local_server.py

@@ -0,0 +1,113 @@
+# 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
+if sys.argv[1] == 'gpu':
+    from paddle_serving_server_gpu.web_service import WebService
+elif sys.argv[1] == 'cpu':
+    from paddle_serving_server.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()
+        if sys.argv[1] == 'gpu':
+            self.det_client.load_model_config(
+                det_model_config, gpu=True, profile=False)
+        elif sys.argv[1] == 'cpu':
+            self.det_client.load_model_config(
+                det_model_config, gpu=False, 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")
+if sys.argv[1] == 'gpu':
+    ocr_service.run_debugger_service(gpu=True)
+elif sys.argv[1] == 'cpu':
+    ocr_service.run_debugger_service()
+ocr_service.run_web_service()

deploy/pdserving/ocr_web_client.py

@@ -0,0 +1,37 @@
+# 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

@@ -0,0 +1,105 @@
+# 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
+if sys.argv[1] == 'gpu':
+    from paddle_serving_server_gpu.web_service import WebService
+elif sys.argv[1] == 'cpu':
+    from paddle_serving_server.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")
+if sys.argv[1] == 'gpu':
+    ocr_service.set_gpus("0")
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292)
+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

@@ -1,28 +1,115 @@
-# Paddle Serving 服务部署
+# Paddle Serving 服务部署(Beta)
 
-本教程将介绍基于[Paddle Serving](https://github.com/PaddlePaddle/Serving)部署在线预测服务的详细步骤。
+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**
+
+**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8月中正式上线
+#GPU用户下载server包使用这个链接
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_server_gpu-0.3.2-py3-none-any.whl
+python -m pip install paddle_serving_server_gpu-0.3.2-py3-none-any.whl
+#CPU版本使用这个链接
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_server-0.3.2-py3-none-any.whl
+python -m pip install paddle_serving_server-0.3.2-py3-none-any.whl
+#客户端和App包使用以下链接（CPU，GPU通用）
+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_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8月中正式上线
+#GPU用户下载server包使用这个链接
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_server_gpu-0.3.2-py2-none-any.whl
+python -m pip install paddle_serving_server_gpu-0.3.2-py2-none-any.whl 
+#CPU版本使用这个链接
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/others/paddle_serving_server-0.3.2-py2-none-any.whl
+python -m pip install paddle_serving_server-0.3.2-py2-none-any.whl
+
+#客户端和App包使用以下链接（CPU，GPU通用）
+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_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|||
+|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|||
+|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
 
+```
+# cpu，gpu启动二选一，以下是cpu启动
+python -m paddle_serving_server.serve --model ocr_det_model --port 9293 
+python ocr_web_server.py cpu
+# gpu启动
+python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
+python ocr_web_server.py gpu
+```
+
 #### 方式2. 启动快速版服务
 
+```
+# cpu，gpu启动二选一，以下是cpu启动
+python ocr_local_server.py cpu
+# gpu启动
+python ocr_local_server.py gpu
+```
 
 ## 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，并且在命令行参数注明用的CPU或是GPU来提供服务。
+
+| 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

@@ -0,0 +1,72 @@
+# 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

@@ -0,0 +1,77 @@
+# 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
+if sys.argv[1] == 'gpu':
+    from paddle_serving_server_gpu.web_service import WebService
+elif sys.argv[1] == 'cpu':
+    from paddle_serving_server.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.init_rec()
+if sys.argv[1] == 'gpu':
+    ocr_service.set_gpus("0")
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+ocr_service.run_rpc_service()
+ocr_service.run_web_service()

doc/doc_ch/android_demo.md

@@ -0,0 +1,57 @@
+# Android Demo 快速测试
+
+
+### 1. 安装最新版本的Android Studio
+
+可以从 https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
+
+### 2. 创建新项目
+
+Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。
+
+如果您是初学者，可以用以下方式安装和测试NDK编译环境。
+点击 File -> New ->New Project，  新建  "Native C++" project
+
+
+1. Start a new Android Studio project
+    在项目模版中选择 Native C++ 选择PaddleOCR/depoly/android_demo 路径
+    进入项目后会自动编译，第一次编译会花费较长的时间，建议添加代理加速下载。
+
+**代理添加：**
+
+选择 Android Studio -> Perferences -> Appearance & Behavior -> System Settings -> HTTP Proxy -> Manual proxy configuration
+
+![](../demo/proxy.png)
+
+2. 开始编译
+
+点击编译按钮，连接手机，跟着Android Studio的引导完成操作。
+
+在 Android Studio 里看到下图，表示编译完成：
+
+![](../demo/build.png)
+
+**提示:** 此时如果出现下列找不到OpenCV的报错信息，请重新点击编译，编译完成后退出项目，再次进入。
+
+![](../demo/error.png)
+
+### 3. 发送到手机端
+
+完成编译，点击运行，在手机端查看效果。
+
+### 4. 如何自定义demo图片
+
+1. 图片存放路径:android_demo/app/src/main/assets/images
+
+   将自定义图片放置在该路径下
+
+2. 配置文件: android_demo/app/src/main/res/values/strings.xml
+
+   修改 IMAGE_PATH_DEFAULT 为自定义图片名即可
+
+
+# 获得更多支持
+前往[端计算模型生成平台EasyEdge](https://ai.baidu.com/easyedge/app/open_source_demo?referrerUrl=paddlelite)，获得更多开发支持：
+
+- Demo APP：可使用手机扫码安装，方便手机端快速体验文字识别
+- SDK：模型被封装为适配不同芯片硬件和操作系统SDK，包括完善的接口，方便进行二次开发

doc/doc_ch/config.md

@@ -60,6 +60,8 @@
 |         beta1           |    设置一阶矩估计的指数衰减率  |       0.9         |               \             |
 |         beta2           |    设置二阶矩估计的指数衰减率  |     0.999         |               \             |
 |         decay           |         是否使用decay       |    \              |               \             |
-|      function(decay)    |         设置decay方式       |   cosine_decay    |       目前只支持cosin_decay  |
+|      function(decay)    |         设置decay方式       |   -    |       目前支持cosine_decay与piecewise_decay  |
-|      step_each_epoch    |      每个epoch包含多少次迭代  |         20       | 计算方式：total_image_num / (batch_size_per_card * card_size) |
+|      step_each_epoch    |      每个epoch包含多少次迭代, cosine_decay时有效   |         20       | 计算方式：total_image_num / (batch_size_per_card * card_size) |
-|        total_epoch      |    总共迭代多少个epoch        |       1000      | 与Global.epoch_num 一致        |
+|        total_epoch      |    总共迭代多少个epoch, cosine_decay时有效        |       1000      | 与Global.epoch_num 一致        |
+|        boundaries      |    学习率下降时的迭代次数间隔, piecewise_decay时有效       |       -      | 参数为列表形式        |
+|        decay_rate      |    学习率衰减系数, piecewise_decay时有效       |       -      |  \        |

doc/doc_ch/detection.md

@@ -26,7 +26,7 @@ wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_la
 提供的标注文件格式为，其中中间是"\t"分隔：
 ```
 " 图像文件名                    json.dumps编码的图像标注信息"
-ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
+ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
 ```
 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，在文本检测任务中并不需要这个信息。

doc/doc_ch/installation.md

@@ -3,15 +3,15 @@
 经测试PaddleOCR可在glibc 2.23上运行，您也可以测试其他glibc版本或安装glic 2.23
 PaddleOCR 工作环境
 - PaddlePaddle 1.7+
-- python3
+- python3.7
 - glibc 2.23
 - cuDNN 7.6+ (GPU)
 
-建议使用我们提供的docker运行PaddleOCR，有关docker使用请参考[链接](https://docs.docker.com/get-started/)。
+建议使用我们提供的docker运行PaddleOCR，有关docker、nvidia-docker使用请参考[链接](https://docs.docker.com/get-started/)。
 
 *如您希望使用 mac 或 windows直接运行预测代码，可以从第2步开始执行。*
 
-1. （建议）准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。
+**1. （建议）准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。**
 ```
 # 切换到工作目录下
 cd /home/Projects
@@ -21,10 +21,10 @@ cd /home/Projects
 如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
 sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
 
-如果您的机器安装的是CUDA9，请运行以下命令创建容器
+如果使用CUDA9，请运行以下命令创建容器
 sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
 
-如果您的机器安装的是CUDA10，请运行以下命令创建容器
+如果使用CUDA10，请运行以下命令创建容器
 sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev /bin/bash
 
 您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
@@ -47,7 +47,7 @@ docker images
 hub.baidubce.com/paddlepaddle/paddle   latest-gpu-cuda9.0-cudnn7-dev    f56310dcc829
 ```
 
-2. 安装PaddlePaddle Fluid v1.7
+**2. 安装PaddlePaddle Fluid v1.7**
 ```
 pip3 install --upgrade pip
 
@@ -64,7 +64,7 @@ python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/
 更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
 ```
 
-3. 克隆PaddleOCR repo代码
+**3. 克隆PaddleOCR repo代码**
 ```
 【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
 
@@ -75,8 +75,11 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
 ```
 
-4. 安装第三方库
+**4. 安装第三方库**
 ```
 cd PaddleOCR
 pip3 install -r requirments.txt
 ```
+
+注意，windows环境下，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载shapely安装包完成安装，
+直接通过pip安装的shapely库可能出现`[winRrror 126] 找不到指定模块的问题`。

doc/doc_ch/recognition.md

@@ -21,12 +21,11 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 * 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。
-
 - 训练集
 
 首先请将训练图片放入同一个文件夹（train_images），并用一个txt文件（rec_gt_train.txt）记录图片路径和标签。
 
-* 注意： 默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错
+**注意：** 默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错
 
 ```
 " 图像文件名                 图像标注信息 "
@@ -41,12 +40,9 @@ PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通
 wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
 # 测试集标签
 wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
-
-
 ```
 
 最终训练集应有如下文件结构：
-
 ```
 |-train_data
     |-ic15_data
@@ -150,7 +146,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
 
 如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
 
-* 提示： 可通过 -c 参数选择 `configs/rec/` 路径下的多种模型配置进行训练，PaddleOCR支持的识别算法有：
+**提示：** 可通过 -c 参数选择 `configs/rec/` 路径下的多种模型配置进行训练，PaddleOCR支持的识别算法有：
 
 
 | 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |

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：  
+* 在Linux环境下，安装示例如下：
-```hub install deploy/hubserving/ocr_det/```  
+```shell
+# 安装检测服务模块：  
+hub install deploy/hubserving/ocr_det/
 
-或，安装识别服务模块：    
+# 或，安装识别服务模块：  
-```hub install deploy/hubserving/ocr_rec/```  
+hub install deploy/hubserving/ocr_rec/
 
-或，安装检测+识别串联服务模块：  
+# 或，安装检测+识别串联服务模块：  
-```hub install deploy/hubserving/ocr_system/```  
+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）
@@ -69,7 +86,7 @@ $ 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
@@ -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_ch/update.md

@@ -1,4 +1,5 @@
 # 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/android_demo_en.md

@@ -0,0 +1,60 @@
+# Android Demo quick start
+
+### 1. Install the latest version of Android Studio
+
+It can be downloaded from https://developer.android.com/studio . This Demo is written by Android Studio version 4.0.
+
+### 2. Create a new project
+
+The NDK version 20b is used in the demo test, and the compilation can be successfully supported for version 20 and above.
+
+If you are a beginner, you can install and test the NDK compilation environment in the following ways.
+
+File -> New ->New Project  to create  "Native C++" project
+
+1. Start a new Android Studio project
+
+   Select Native C++ in the project template, select Paddle OCR/deploy/android_demo path
+   After entering the project, it will be automatically compiled. The first compilation
+   will take a long time. It is recommended to add an agent to speed up the download.
+
+**Agent add:**
+
+  Android Studio -> Perferences -> Appearance & Behavior -> System Settings -> HTTP Proxy -> Manual proxy configuration
+
+![](../demo/proxy.png)
+
+2. Start compilation
+
+Click the compile button, connect the phone, and follow the instructions of Android Studio to complete the operation.
+
+When you see the following picture in Android Studio, the compilation is complete:
+
+![](../demo/build.png)
+
+**Tip:** At this time, if the following error message that OpenCV cannot be found appears, please re-click compile,
+exit the project after compiling, and enter again.
+
+![](../demo/error.png)
+
+### 3. Send to mobile
+
+Complete the compilation, click Run, and check the effect on the mobile phone.
+
+### 4. How to customize the demo picture
+
+1. Image storage path: android_demo/app/src/main/assets/images
+
+   Place the custom picture under this path
+
+2. Configuration file: android_demo/app/src/main/res/values/strings.xml
+
+   Modify IMAGE_PATH_DEFAULT to a custom picture name
+
+# Get more support
+
+Go to [EasyEdge](https://ai.baidu.com/easyedge/app/open_source_demo?referrerUrl=paddlelite) to get more development support:
+
+- Demo APP: You can use your mobile phone to scan the code to install, which is convenient for the mobile terminal to quickly experience text recognition
+
+- SDK: The model is packaged to adapt to different chip hardware and operating system SDKs, including a complete interface to facilitate secondary development

doc/doc_en/config_en.md

@@ -60,6 +60,8 @@ Take `rec_icdar15_train.yml` as an example:
 |         beta1           |    Set the exponential decay rate for the 1st moment estimates  |       0.9         |               \             |
 |         beta2           |    Set the exponential decay rate for the 2nd moment estimates  |     0.999         |               \             |
 |         decay           |         Whether to use decay       |    \              |               \             |
-|      function(decay)    |         Set the decay function       |   cosine_decay    |           Only support cosine_decay            |
+|      function(decay)    |         Set the decay function       |   cosine_decay    |         Support cosine_decay and piecewise_decay            |
-|      step_each_epoch    |      The number of steps in an epoch.  |         20       | Calculation ：total_image_num / (batch_size_per_card * card_size) |
+|      step_each_epoch    |      The number of steps in an epoch. Used in cosine_decay  |         20       | Calculation ：total_image_num / (batch_size_per_card * card_size) |
-|        total_epoch      |    The number of epochs      |       1000      | Consistent with Global.epoch_num      |
+|        total_epoch      |    The number of epochs. Used in cosine_decay      |       1000      | Consistent with Global.epoch_num      |
+|        boundaries      |    The step intervals to reduce learning rate. Used in piecewise_decay       |       -      |  The format is list        |
+|        decay_rate      |    Learning rate decay rate. Used in piecewise_decay       |       -      |  \        |

doc/doc_en/detection_en.md

@@ -25,7 +25,7 @@ After decompressing the data set and downloading the annotation file, PaddleOCR/
 The provided annotation file format is as follow, seperated by "\t":
 ```
 " Image file name             Image annotation information encoded by json.dumps"
-ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
+ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
 ```
 The image annotation after json.dumps() encoding is a list containing multiple dictionaries. The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
 

doc/doc_en/installation_en.md

@@ -4,28 +4,28 @@ After testing, paddleocr can run on glibc 2.23. You can also test other glibc ve
 
 PaddleOCR working environment:
 - PaddlePaddle1.7
-- python3
+- python3.7
 - glibc 2.23
 
 It is recommended to use the docker provided by us to run PaddleOCR, please refer to the use of docker [link](https://docs.docker.com/get-started/).
 
 *If you want to directly run the prediction code on mac or windows, you can start from step 2.*
 
-1. (Recommended) Prepare a docker environment. The first time you use this image, it will be downloaded automatically. Please be patient.
+**1. (Recommended) Prepare a docker environment. The first time you use this image, it will be downloaded automatically. Please be patient.**
 ```
 # Switch to the working directory
 cd /home/Projects
 # You need to create a docker container for the first run, and do not need to run the current command when you run it again
 # Create a docker container named ppocr and map the current directory to the /paddle directory of the container
 
-#If you want to use docker in a CPU environment, use docker instead of nvidia-docker to create docker
+#If using CPU, use docker instead of nvidia-docker to create docker
 sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
 ```
-If you have cuda9 installed on your machine, please run the following command to create a container:
+If using CUDA9, please run the following command to create a container:
 ```
 sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
 ```
-If you have cuda10 installed on your machine, please run the following command to create a container:
+If using CUDA10, please run the following command to create a container:
 ```
 sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev /bin/bash
 ```
@@ -49,7 +49,7 @@ docker images
 hub.baidubce.com/paddlepaddle/paddle   latest-gpu-cuda9.0-cudnn7-dev    f56310dcc829
 ```
 
-2. Install PaddlePaddle Fluid v1.7 (the higher version is not supported yet, the adaptation work is in progress)
+**2. Install PaddlePaddle Fluid v1.7 (the higher version is not supported yet, the adaptation work is in progress)**
 ```
 pip3 install --upgrade pip
 
@@ -65,7 +65,7 @@ python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/
 For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
 
 
-3. Clone PaddleOCR repo
+**3. Clone PaddleOCR repo**
 ```
 # Recommend
 git clone https://github.com/PaddlePaddle/PaddleOCR
@@ -77,8 +77,14 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 # Note: The cloud-hosting code may not be able to synchronize the update with this GitHub project in real time. There might be a delay of 3-5 days. Please give priority to the recommended method.
 ```
 
-4. Install third-party libraries
+**4. Install third-party libraries**
 ```
 cd PaddleOCR
 pip3 install -r requirments.txt
 ```
+
+If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows.
+
+Please try to download Shapely whl file using [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
+
+Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)

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:  
+# Or, install the recognition service module:
-```shell
 hub install deploy/hubserving/ocr_rec/
-```  
+
-Or, install the 2-stage series service module:  
+# Or, install the 2-stage series service module:
-```shell
 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)
 

doc/doc_en/update_en.md

@@ -1,5 +1,5 @@
 # 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.

ppocr/data/det/db_process.py

@@ -17,7 +17,7 @@ import cv2
 import numpy as np
 import json
 import sys
-from ppocr.utils.utility import initial_logger
+from ppocr.utils.utility import initial_logger, check_and_read_gif
 logger = initial_logger()
 
 from .data_augment import AugmentData
@@ -100,7 +100,9 @@ class DBProcessTrain(object):
 
     def __call__(self, label_infor):
         img_path, gt_label = self.convert_label_infor(label_infor)
-        imgvalue = cv2.imread(img_path)
+        imgvalue, flag = check_and_read_gif(img_path)
+        if not flag:
+            imgvalue = cv2.imread(img_path)
         if imgvalue is None:
             logger.info("{} does not exist!".format(img_path))
             return None

ppocr/data/det/east_process.py

@@ -17,6 +17,7 @@ import cv2
 import numpy as np
 import json
 import sys
+import os
 
 class EASTProcessTrain(object):
     def __init__(self, params):
@@ -52,7 +53,7 @@ class EASTProcessTrain(object):
         label_infor = label_infor.decode()
         label_infor = label_infor.encode('utf-8').decode('utf-8-sig')
         substr = label_infor.strip("\n").split("\t")
-        img_path = self.img_set_dir + substr[0]
+        img_path = os.path.join(self.img_set_dir, substr[0])
         label = json.loads(substr[1])
         nBox = len(label)
         wordBBs, txts, txt_tags = [], [], []

ppocr/data/rec/dataset_traversal.py

@@ -185,6 +185,7 @@ class SimpleReader(object):
         if params['mode'] != 'test':
             self.img_set_dir = params['img_set_dir']
             self.label_file_path = params['label_file_path']
+        self.use_gpu = params['use_gpu']
         self.char_ops = params['char_ops']
         self.image_shape = params['image_shape']
         self.loss_type = params['loss_type']
@@ -213,6 +214,15 @@ class SimpleReader(object):
         if self.mode != 'train':
             process_id = 0
 
+        def get_device_num():
+            if self.use_gpu:
+                gpus = os.environ.get("CUDA_VISIBLE_DEVICES", 1)
+                gpu_num = len(gpus.split(','))
+                return gpu_num
+            else:
+                cpu_num = os.environ.get("CPU_NUM", 1)
+                return int(cpu_num)
+
         def sample_iter_reader():
             if self.mode != 'train' and self.infer_img is not None:
                 image_file_list = get_image_file_list(self.infer_img)
@@ -233,10 +243,16 @@ class SimpleReader(object):
                 img_num = len(label_infor_list)
                 img_id_list = list(range(img_num))
                 random.shuffle(img_id_list)
-                if sys.platform == "win32":
+                if sys.platform == "win32" and self.num_workers != 1:
                     print("multiprocess is not fully compatible with Windows."
                           "num_workers will be 1.")
                     self.num_workers = 1
+                if self.batch_size * get_device_num(
+                ) * self.num_workers > img_num:
+                    raise Exception(
+                        "The number of the whole data ({}) is smaller than the batch_size * devices_num * num_workers ({})".
+                        format(img_num, self.batch_size * get_device_num() *
+                               self.num_workers))
                 for img_id in range(process_id, img_num, self.num_workers):
                     label_infor = label_infor_list[img_id_list[img_id]]
                     substr = label_infor.decode('utf-8').strip("\n").split("\t")

ppocr/data/rec/img_tools.py

@@ -360,7 +360,7 @@ def process_image(img,
         text = char_ops.encode(label)
         if len(text) == 0 or len(text) > max_text_length:
             logger.info(
-                "Warning in ppocr/data/rec/img_tools.py:line362: Wrong data type."
+                "Warning in ppocr/data/rec/img_tools.py: Wrong data type."
                 "Excepted string with length between 1 and {}, but "
                 "got '{}'. Label is '{}'".format(max_text_length,
                                                  len(text), label))

ppocr/modeling/backbones/rec_mobilenet_v3.py

@@ -31,16 +31,28 @@ __all__ = [
 
 class MobileNetV3():
     def __init__(self, params):
-        self.scale = params['scale']
+        self.scale = params.get("scale", 0.5)
-        model_name = params['model_name']
+        model_name = params.get("model_name", "small")
+        large_stride = params.get("large_stride", [1, 2, 2, 2])
+        small_stride = params.get("small_stride", [2, 2, 2, 2])
+
+        assert isinstance(large_stride, list), "large_stride type must " \
+            "be list but got {}".format(type(large_stride))
+        assert isinstance(small_stride, list), "small_stride type must " \
+            "be list but got {}".format(type(small_stride))
+        assert len(large_stride) == 4, "large_stride length must be " \
+            "4 but got {}".format(len(large_stride))
+        assert len(small_stride) == 4, "small_stride length must be " \
+            "4 but got {}".format(len(small_stride))
+
         self.inplanes = 16
         if model_name == "large":
             self.cfg = [
                 # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, False, 'relu', 1],
+                [3, 16, 16, False, 'relu', large_stride[0]],
-                [3, 64, 24, False, 'relu', (2, 1)],
+                [3, 64, 24, False, 'relu', (large_stride[1], 1)],
                 [3, 72, 24, False, 'relu', 1],
-                [5, 72, 40, True, 'relu', (2, 1)],
+                [5, 72, 40, True, 'relu', (large_stride[2], 1)],
                 [5, 120, 40, True, 'relu', 1],
                 [5, 120, 40, True, 'relu', 1],
                 [3, 240, 80, False, 'hard_swish', 1],
@@ -49,7 +61,7 @@ class MobileNetV3():
                 [3, 184, 80, False, 'hard_swish', 1],
                 [3, 480, 112, True, 'hard_swish', 1],
                 [3, 672, 112, True, 'hard_swish', 1],
-                [5, 672, 160, True, 'hard_swish', (2, 1)],
+                [5, 672, 160, True, 'hard_swish', (large_stride[3], 1)],
                 [5, 960, 160, True, 'hard_swish', 1],
                 [5, 960, 160, True, 'hard_swish', 1],
             ]
@@ -58,15 +70,15 @@ class MobileNetV3():
         elif model_name == "small":
             self.cfg = [
                 # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, True, 'relu', (2, 1)],
+                [3, 16, 16, True, 'relu', (small_stride[0], 1)],
-                [3, 72, 24, False, 'relu', (2, 1)],
+                [3, 72, 24, False, 'relu', (small_stride[1], 1)],
                 [3, 88, 24, False, 'relu', 1],
-                [5, 96, 40, True, 'hard_swish', (2, 1)],
+                [5, 96, 40, True, 'hard_swish', (small_stride[2], 1)],
                 [5, 240, 40, True, 'hard_swish', 1],
                 [5, 240, 40, True, 'hard_swish', 1],
                 [5, 120, 48, True, 'hard_swish', 1],
                 [5, 144, 48, True, 'hard_swish', 1],
-                [5, 288, 96, True, 'hard_swish', (2, 1)],
+                [5, 288, 96, True, 'hard_swish', (small_stride[3], 1)],
                 [5, 576, 96, True, 'hard_swish', 1],
                 [5, 576, 96, True, 'hard_swish', 1],
             ]
@@ -78,7 +90,7 @@ class MobileNetV3():
 
         supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
         assert self.scale in supported_scale, \
-            "supported scale are {} but input scale is {}".format(supported_scale, scale)
+            "supported scales are {} but input scale is {}".format(supported_scale, self.scale)
 
     def __call__(self, input):
         scale = self.scale

ppocr/modeling/heads/rec_ctc_head.py

@@ -32,6 +32,7 @@ class CTCPredict(object):
         self.char_num = params['char_num']
         self.encoder = SequenceEncoder(params)
         self.encoder_type = params['encoder_type']
+        self.fc_decay = params.get("fc_decay", 0.0004)
 
     def __call__(self, inputs, labels=None, mode=None):
         encoder_features = self.encoder(inputs)
@@ -39,7 +40,7 @@ class CTCPredict(object):
             encoder_features = fluid.layers.concat(encoder_features, axis=1)
         name = "ctc_fc"
         para_attr, bias_attr = get_para_bias_attr(
-            l2_decay=0.0004, k=encoder_features.shape[1], name=name)
+            l2_decay=self.fc_decay, k=encoder_features.shape[1], name=name)
         predict = fluid.layers.fc(input=encoder_features,
                                   size=self.char_num + 1,
                                   param_attr=para_attr,

ppocr/utils/utility.py

@@ -14,6 +14,9 @@
 
 import logging
 import os
+import imghdr
+import cv2
+from paddle import fluid
 
 
 def initial_logger():
@@ -61,19 +64,31 @@ def get_image_file_list(img_file):
     if img_file is None or not os.path.exists(img_file):
         raise Exception("not found any img file in {}".format(img_file))
 
-    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
+    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
-    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
+    if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
         imgs_lists.append(img_file)
     elif os.path.isdir(img_file):
         for single_file in os.listdir(img_file):
-            if single_file.split('.')[-1] in img_end:
+            file_path = os.path.join(img_file, single_file)
-                imgs_lists.append(os.path.join(img_file, single_file))
+            if imghdr.what(file_path) in img_end:
+                imgs_lists.append(file_path)
     if len(imgs_lists) == 0:
         raise Exception("not found any img file in {}".format(img_file))
     return imgs_lists
 
 
-from paddle import fluid
+def check_and_read_gif(img_path):
+    if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
+        gif = cv2.VideoCapture(img_path)
+        ret, frame = gif.read()
+        if not ret:
+            logging.info("Cannot read {}. This gif image maybe corrupted.")
+            return None, False
+        if len(frame.shape) == 2 or frame.shape[-1] == 1:
+            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+        imgvalue = frame[:, :, ::-1]
+        return imgvalue, True
+    return None, False
 
 
 def create_multi_devices_program(program, loss_var_name):

tools/export_model.py

@@ -41,27 +41,11 @@ from paddle import fluid
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 from ppocr.utils.save_load import init_model
-from ppocr.utils.character import CharacterOps
+
-from ppocr.utils.utility import create_module
 
 
 def main():
-    config = program.load_config(FLAGS.config)
+    startup_prog, eval_program, place, config, _ = program.preprocess()
-    program.merge_config(FLAGS.opt)
-    logger.info(config)
-
-    # check if set use_gpu=True in paddlepaddle cpu version
-    use_gpu = config['Global']['use_gpu']
-    program.check_gpu(use_gpu)
-
-    alg = config['Global']['algorithm']
-    assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
-    if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
-        config['Global']['char_ops'] = CharacterOps(config['Global'])
-
-    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
-    startup_prog = fluid.Program()
-    eval_program = fluid.Program()
 
     feeded_var_names, target_vars, fetches_var_name = program.build_export(
         config, eval_program, startup_prog)
@@ -88,6 +72,4 @@ def main():
 
 
 if __name__ == '__main__':
-    parser = program.ArgsParser()
-    FLAGS = parser.parse_args()
     main()

tools/infer/predict_det.py

@@ -20,7 +20,7 @@ sys.path.append(os.path.join(__dir__, '../..'))
 import tools.infer.utility as utility
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
-from ppocr.utils.utility import get_image_file_list
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 import cv2
 from ppocr.data.det.east_process import EASTProcessTest
 from ppocr.data.det.db_process import DBProcessTest
@@ -135,8 +135,13 @@ 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)
+        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
@@ -147,6 +152,7 @@ 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))

tools/infer/predict_rec.py

@@ -20,7 +20,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 import tools.infer.utility as utility
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
-from ppocr.utils.utility import get_image_file_list
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 import cv2
 import copy
 import numpy as np
@@ -122,9 +122,9 @@ class TextRecognizer(object):
                     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]])
                     if len(valid_ind) == 0:
                         continue
+                    score = np.mean(probs[valid_ind, ind[valid_ind]])
                     # rec_res.append([preds_text, score])
                     rec_res[indices[beg_img_no + rno]] = [preds_text, score]
             else:
@@ -153,7 +153,9 @@ def main(args):
     valid_image_file_list = []
     img_list = []
     for image_file in image_file_list:
-        img = cv2.imread(image_file, cv2.IMREAD_COLOR)
+        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

tools/infer/predict_system.py

@@ -27,7 +27,7 @@ import copy
 import numpy as np
 import math
 import time
-from ppocr.utils.utility import get_image_file_list
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from PIL import Image
 from tools.infer.utility import draw_ocr
 from tools.infer.utility import draw_ocr_box_txt
@@ -49,18 +49,23 @@ class TextSystem(object):
         points[:, 0] = points[:, 0] - left
         points[:, 1] = points[:, 1] - top
         '''
-        img_crop_width = int(max(np.linalg.norm(points[0] - points[1]),
+        img_crop_width = int(
-                                 np.linalg.norm(points[2] - points[3])))
+            max(
-        img_crop_height = int(max(np.linalg.norm(points[0] - points[3]),
+                np.linalg.norm(points[0] - points[1]),
-                                  np.linalg.norm(points[1] - points[2])))
+                np.linalg.norm(points[2] - points[3])))
-        pts_std = np.float32([[0, 0],
+        img_crop_height = int(
-                              [img_crop_width, 0],
+            max(
+                np.linalg.norm(points[0] - points[3]),
+                np.linalg.norm(points[1] - points[2])))
+        pts_std = np.float32([[0, 0], [img_crop_width, 0],
                               [img_crop_width, img_crop_height],
                               [0, img_crop_height]])
         M = cv2.getPerspectiveTransform(points, pts_std)
-        dst_img = cv2.warpPerspective(img, M, (img_crop_width, img_crop_height),
+        dst_img = cv2.warpPerspective(
-                                      borderMode=cv2.BORDER_REPLICATE,
+            img,
-                                      flags=cv2.INTER_CUBIC)
+            M, (img_crop_width, img_crop_height),
+            borderMode=cv2.BORDER_REPLICATE,
+            flags=cv2.INTER_CUBIC)
         dst_img_height, dst_img_width = dst_img.shape[0:2]
         if dst_img_height * 1.0 / dst_img_width >= 1.5:
             dst_img = np.rot90(dst_img)
@@ -119,7 +124,9 @@ def main(args):
     is_visualize = True
     tackle_img_num = 0
     for image_file in image_file_list:
-        img = cv2.imread(image_file)
+        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
@@ -130,14 +137,14 @@ def main(args):
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
         print("Predict time of %s: %.3fs" % (image_file, elapse))
+
+        drop_score = 0.5
         dt_num = len(dt_boxes)
-        dt_boxes_final = []
         for dno in range(dt_num):
             text, score = rec_res[dno]
-            if score >= 0.5:
+            if score >= drop_score:
                 text_str = "%s, %.3f" % (text, score)
                 print(text_str)
-                dt_boxes_final.append(dt_boxes[dno])
 
         if is_visualize:
             image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@@ -146,7 +153,12 @@ def main(args):
             scores = [rec_res[i][1] for i in range(len(rec_res))]
 
             draw_img = draw_ocr(
-                image, boxes, txts, scores, draw_txt=True, drop_score=0.5)
+                image,
+                boxes,
+                txts,
+                scores,
+                draw_txt=True,
+                drop_score=drop_score)
             draw_img_save = "./inference_results/"
             if not os.path.exists(draw_img_save):
                 os.makedirs(draw_img_save)

tools/infer/utility.py

@@ -169,7 +169,7 @@ def draw_ocr_box_txt(image, boxes, txts):
     img_right = Image.new('RGB', (w, h), (255, 255, 255))
 
     import random
-    # 每次使用相同的随机种子 ，可以保证两次颜色一致
+
     random.seed(0)
     draw_left = ImageDraw.Draw(img_left)
     draw_right = ImageDraw.Draw(img_right)

tools/train.py

@@ -42,27 +42,10 @@ from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 from ppocr.data.reader_main import reader_main
 from ppocr.utils.save_load import init_model
-from ppocr.utils.character import CharacterOps
 from paddle.fluid.contrib.model_stat import summary
 
 
 def main():
-    config = program.load_config(FLAGS.config)
-    program.merge_config(FLAGS.opt)
-    logger.info(config)
-
-    # check if set use_gpu=True in paddlepaddle cpu version
-    use_gpu = config['Global']['use_gpu']
-    program.check_gpu(use_gpu)
-
-    alg = config['Global']['algorithm']
-    assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
-    if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
-        config['Global']['char_ops'] = CharacterOps(config['Global'])
-
-    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
-    startup_program = fluid.Program()
-    train_program = fluid.Program()
     train_build_outputs = program.build(
         config, train_program, startup_program, mode='train')
     train_loader = train_build_outputs[0]
@@ -91,7 +74,7 @@ def main():
 
     # dump mode structure
     if config['Global']['debug']:
-        if 'attention' in config['Global']['loss_type']:
+        if train_alg_type == 'rec' and 'attention' in config['Global']['loss_type']:
             logger.warning('Does not suport dump attention...')
         else:
             summary(train_program)
@@ -109,15 +92,13 @@ def main():
         'fetch_name_list':eval_fetch_name_list,\
         'fetch_varname_list':eval_fetch_varname_list}
 
-    if alg in ['EAST', 'DB']:
+    if train_alg_type == 'det':
         program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
     else:
         program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
 
 
 def test_reader():
-    config = program.load_config(FLAGS.config)
-    program.merge_config(FLAGS.opt)
     logger.info(config)
     train_reader = reader_main(config=config, mode="train")
     import time
@@ -136,7 +117,6 @@ def test_reader():
 
 
 if __name__ == '__main__':
-    parser = program.ArgsParser()
+    startup_program, train_program, place, config, train_alg_type = program.preprocess()
-    FLAGS = parser.parse_args()
     main()
 #     test_reader()