working yolo default model with opencv

misc/rtsp_ai_player/src-onnx-runtime/aiengineinferenceonnxruntime.cpp

@@ -90,6 +90,9 @@ void AiEngineInferencevOnnxRuntime::performInferenceSlot(cv::Mat frame)
                 qDebug() << "performInferenceSlot() invalid classId =" << detection.classId;
                 continue;
             }
+            else {
+                cv::imwrite("scaledImage.png", scaledImage);
+            }
 
             // Add detected objects to the results
             AiEngineObject object;

misc/rtsp_ai_player/src-opencv-onnx/inference.cpp

@@ -28,11 +28,13 @@ std::vector<Detection> Inference::runInference(const cv::Mat &input)
     int rows = outputs[0].size[1];
     int dimensions = outputs[0].size[2];
 
-    bool yolov8 = false;
+    bool yolov8 = true;
     // yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
     // yolov8 has an output of shape (batchSize, 84,  8400) (Num classes + box[x,y,w,h])
+    /*
     if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
     {
+        std::cout << "yolov8 = " << yolov8 << std::endl;
         yolov8 = true;
         rows = outputs[0].size[2];
         dimensions = outputs[0].size[1];
@@ -40,6 +42,8 @@ std::vector<Detection> Inference::runInference(const cv::Mat &input)
         outputs[0] = outputs[0].reshape(1, dimensions);
         cv::transpose(outputs[0], outputs[0]);
     }
+    */
+
     float *data = (float *)outputs[0].data;
 
     float x_factor = modelInput.cols / modelShape.width;