注释改为英语的

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，
- * 1. 使用第一个模型（det），框选出多个四边形
- * 2. 从原图从抠出这些多边形，使用第二个模型（rec），获取文本
+ * OCR there are 2 models
+ * 1. First model（det），select polygones to show where are the texts
+ * 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<int> data_int; // 少数层是int返回，与 data二选一
-    std::vector<int64_t> shape; // PaddleLite输出层的shape
-    std::vector<std::vector<uint64_t>> lod; // PaddleLite输出层的lod
+    std::vector<float> data; // return float, or use data_int
+    std::vector<int> data_int; // several layers return int ，or use data
+    std::vector<int64_t> shape; // PaddleLite output shape
+    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/MiniActivity.java

@@ -41,8 +41,8 @@ public class MiniActivity extends AppCompatActivity {
     private String assetlabelFilePath = "labels/ppocr_keys_v1.txt";
 
     private Button button;
-    private ImageView imageView; // 显示图像
-    private TextView textView; // 显示结果
+    private ImageView imageView; // image result
+    private TextView textView; // text result
 
     @Override
     protected void onCreate(Bundle savedInstanceState) {
@@ -85,7 +85,7 @@ public class MiniActivity extends AppCompatActivity {
                 }
             }
         };
-        sender.sendEmptyMessage(REQUEST_LOAD_MODEL); // 对应上面的REQUEST_LOAD_MODEL， 调用onLoadModel()
+        sender.sendEmptyMessage(REQUEST_LOAD_MODEL); // corresponding to REQUEST_LOAD_MODEL， to call onLoadModel()
 
         imageView = findViewById(R.id.imageView);
         textView = findViewById(R.id.sample_text);
@@ -112,7 +112,7 @@ public class MiniActivity extends AppCompatActivity {
     }
 
     /**
-     * onCreate的时候调用， 模型初始化
+     * call in onCreate, model init
      *
      * @return
      */
@@ -124,7 +124,8 @@ public class MiniActivity extends AppCompatActivity {
     }
 
     /**
-     * on
+     * init engine
+     * call in onCreate
      *
      * @return
      */
@@ -133,7 +134,7 @@ public class MiniActivity extends AppCompatActivity {
             String assetImagePath = "images/5.jpg";
             InputStream imageStream = getAssets().open(assetImagePath);
             Bitmap image = BitmapFactory.decodeStream(imageStream);
-            // 这里输入是Bitmap
+            // Input is Bitmap
             predictor.setInputImage(image);
             return predictor.isLoaded() && predictor.runModel();
         } catch (IOException e) {

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

@@ -228,7 +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);
@@ -323,7 +323,7 @@ public class Predictor {
             for (Point p : result.getPoints()) {
                 sb.append("(").append(p.x).append(",").append(p.y).append(") ");
             }
-            Log.i(TAG, sb.toString()); // 这里在logcat里打印结果
+            Log.i(TAG, sb.toString()); // show LOG in Logcat panel
             outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
         }
         outputResult = outputResultSb.toString();