Made input image const to be in align with the ed sensor integration application

Author: IasonTheodorou

inc/yolo_inference.h

@@ -66,15 +66,15 @@ class YOLO_V8
 public:
     const char* CreateSession(DL_INIT_PARAM& iParams);
 
-    const char* RunSession(cv::Mat& iImg, std::vector<DL_RESULT>& oResult);
+    const char* RunSession(const cv::Mat& iImg, std::vector<DL_RESULT>& oResult);
 
     char* WarmUpSession();
 
     template<typename N>
-    char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims,
+    char* TensorProcess(clock_t& starttime_1, const cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims,
         std::vector<DL_RESULT>& oResult);
 
-    char* PreProcess(cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg);
+    char* PreProcess(const cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg);
 
     std::vector<std::string> classes{};
 

src/yolo_inference.cpp

@@ -24,7 +24,7 @@ namespace Ort
 // Definition: Flattened image to blob (and normalizaed) for deep learning inference. Also reorganize from HWC to CHW.
 // Note: Not in the header file since it is not used outside of this file.
 template<typename T>
-char* BlobFromImage(cv::Mat& iImg, T& iBlob) {
+char* BlobFromImage(const cv::Mat& iImg, T& iBlob) {
     int channels = iImg.channels();
     int imgHeight = iImg.rows;
     int imgWidth = iImg.cols;
@@ -44,7 +44,7 @@ char* BlobFromImage(cv::Mat& iImg, T& iBlob) {
 }
 
 
-char* YOLO_V8::PreProcess(cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg)
+char* YOLO_V8::PreProcess(const cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg)
 {
     if (iImg.channels() == 3)
     {
@@ -169,7 +169,7 @@ const char* YOLO_V8::CreateSession(DL_INIT_PARAM& iParams) {
 }
 
 
-const char* YOLO_V8::RunSession(cv::Mat& iImg, std::vector<DL_RESULT>& oResult) {
+const char* YOLO_V8::RunSession(const cv::Mat& iImg, std::vector<DL_RESULT>& oResult) {
 #ifdef benchmark
     clock_t starttime_1 = clock();
 #endif // benchmark
@@ -199,7 +199,7 @@ const char* YOLO_V8::RunSession(cv::Mat& iImg, std::vector<DL_RESULT>& oResult)
 
 
 template<typename N>
-char* YOLO_V8::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims,
+char* YOLO_V8::TensorProcess(clock_t& starttime_1, const cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims,
     std::vector<DL_RESULT>& oResult) {
     Ort::Value inputTensor = Ort::Value::CreateTensor<typename std::remove_pointer<N>::type>(
         Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1),