[Other] Add const modifier to some OCR interface parameters

examples/vision/ocr/PP-OCRv2/cpp/README.md

@@ -82,6 +82,7 @@ PPOCRv2 的初始化，由检测，识别模型串联构成(无分类器)
 
 > ```  
 > bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
+> bool Predict(const cv::Mat& img, fastdeploy::vision::OCRResult* result);
 > ```
 >
 > 模型预测接口，输入一张图片，返回OCR预测结果

fastdeploy/vision/ocr/ppocr/classifier.cc

@@ -50,7 +50,7 @@ bool Classifier::Initialize() {
   return true;
 }
 
-bool Classifier::Predict(cv::Mat& img, int32_t* cls_label, float* cls_score) {
+bool Classifier::Predict(const cv::Mat& img, int32_t* cls_label, float* cls_score) {
   std::vector<int32_t> cls_labels(1);
   std::vector<float> cls_scores(1);
   bool success = BatchPredict({img}, &cls_labels, &cls_scores);

fastdeploy/vision/ocr/ppocr/classifier.h

@@ -43,7 +43,7 @@ class FASTDEPLOY_DECL Classifier : public FastDeployModel {
              const ModelFormat& model_format = ModelFormat::PADDLE);
   /// Get model's name
   std::string ModelName() const { return "ppocr/ocr_cls"; }
-  virtual bool Predict(cv::Mat& img, int32_t* cls_label, float* cls_score);
+  virtual bool Predict(const cv::Mat& img, int32_t* cls_label, float* cls_score);
   /** \brief BatchPredict the input image and get OCR classification model cls_result.
    *
    * \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.

fastdeploy/vision/ocr/ppocr/ppocr_v2.cc

@@ -74,11 +74,15 @@ bool PPOCRv2::Initialized() const {
   }
   return true; 
 }
-
 bool PPOCRv2::Predict(cv::Mat* img,
                             fastdeploy::vision::OCRResult* result) {
+  return Predict(*img, result);
+}
+
+bool PPOCRv2::Predict(const cv::Mat& img,
+                            fastdeploy::vision::OCRResult* result) {
   std::vector<fastdeploy::vision::OCRResult> batch_result(1);
-  bool success = BatchPredict({*img},&batch_result);
+  bool success = BatchPredict({img},&batch_result);
   if(!success){
     return success;
   }

fastdeploy/vision/ocr/ppocr/ppocr_v2.h

@@ -59,6 +59,7 @@ class FASTDEPLOY_DECL PPOCRv2 : public FastDeployModel {
    * \return true if the prediction successed, otherwise false.
    */
   virtual bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
+  virtual bool Predict(const cv::Mat& img, fastdeploy::vision::OCRResult* result);
   /** \brief BatchPredict the input image and get OCR result.
    *
    * \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.

fastdeploy/vision/ocr/ppocr/recognizer.cc

@@ -53,7 +53,7 @@ bool Recognizer::Initialize() {
   return true;
 }
 
-bool Recognizer::Predict(cv::Mat& img, std::string* text, float* rec_score) {
+bool Recognizer::Predict(const cv::Mat& img, std::string* text, float* rec_score) {
   std::vector<std::string> texts(1);
   std::vector<float> rec_scores(1);
   bool success = BatchPredict({img}, &texts, &rec_scores);

fastdeploy/vision/ocr/ppocr/recognizer.h

@@ -45,7 +45,7 @@ class FASTDEPLOY_DECL Recognizer : public FastDeployModel {
              const ModelFormat& model_format = ModelFormat::PADDLE);
   /// Get model's name
   std::string ModelName() const { return "ppocr/ocr_rec"; }
-  virtual bool Predict(cv::Mat& img, std::string* text, float* rec_score);
+  virtual bool Predict(const cv::Mat& img, std::string* text, float* rec_score);
   /** \brief BatchPredict the input image and get OCR recognition model result.
    *
    * \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.