use rgb

Signed-off-by: zhangdanfeng <[email protected]>

Author: zhangdanfeng

Makefile-rv

@@ -62,8 +62,8 @@ LIBS += $(TFLITE_RUY_LIB_DIR)/libruy_ctx.a \
 	$(TFLITE_RUY_LIB_DIR)/libruy_prepare_packed_matrices.a \
 	$(TFLITE_RUY_LIB_DIR)/libruy_denormal.a
 
-# LIBS += /home/craft/workspace/DNN/XNNPACK/build/linux/JDSK/libXNNPACK.a
-LIBS += $(TFLITE_XNNPACK_DIR)/libXNNPACK.a
+LIBS += /home/craft/workspace/DNN/XNNPACK/build/linux/JDSK/libXNNPACK.a
+# LIBS += $(TFLITE_XNNPACK_DIR)/libXNNPACK.a
 LIBS += $(TFLITE_PTHREADPOOL_DIR)/libpthreadpool.a
 LIBS += $(TFLITE_FLATBUF_LIB_DIR)/libflatbuffers.a
 LIBS += $(TFLITE_FARMHASH_LIB_DIR)/libfarmhash.a

detection.cc

@@ -160,6 +160,8 @@ int main(int argc, char **argv) {
 
   auto start = std::chrono::high_resolution_clock::now();
   // Predict on the input image
+  cv::Mat show_image;
+  input_image.copyTo(show_image);
   model.run(input_image, out_pred);
   auto stop = std::chrono::high_resolution_clock::now();
   auto duration =
@@ -176,13 +178,13 @@ int main(int argc, char **argv) {
     auto box = boxes[i];
     auto score = scores[i];
     auto label = labels[i];
-    cv::rectangle(input_image, box, cv::Scalar(255, 0, 0), 2);
-    cv::putText(input_image, labelNames[label], cv::Point(box.x, box.y),
+    cv::rectangle(show_image, box, cv::Scalar(255, 0, 0), 2);
+    cv::putText(show_image, labelNames[label], cv::Point(box.x, box.y),
                 cv::FONT_HERSHEY_COMPLEX, 1.0, cv::Scalar(255, 255, 255), 1,
                 cv::LINE_AA);
   }
 
-  cv::imwrite("out.png", input_image);
+  cv::imwrite("out.png", show_image);
 
   std::cout << "detection completes! " << std::endl;
 

detection/image.jpg

@@ -63,7 +63,7 @@ void YOLOV5::loadModel(const std::string path) {
 }
 
 void YOLOV5::preprocess(cv::Mat &image) {
-  // cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
+  cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
   // cv::resize(image, image, cv::Size(_in_height, _in_width), cv::INTER_CUBIC);
   cv::resize(image, image, cv::Size(_in_height, _in_width));
   cv::imwrite("image.jpg", image);
@@ -149,7 +149,7 @@ void YOLOV5::nonMaximumSupprition(std::vector<std::vector<float>> &predV,
                     indices);
 }
 
-void YOLOV5::run(cv::Mat frame, Prediction &out_pred) {
+void YOLOV5::run(cv::Mat &frame, Prediction &out_pred) {
   _img_height = frame.rows;
   _img_width = frame.cols;
 

detection/out.png

@@ -43,7 +43,7 @@ class YOLOV5 {
   // Take a model path as string
   void loadModel(const std::string path);
   // Take an image and return a prediction
-  void run(cv::Mat image, Prediction &out_pred);
+  void run(cv::Mat &image, Prediction &out_pred);
 
   void getLabelsName(std::string path, std::vector<std::string> &labelNames);