Add compile option to use YOLOv8 ONNX models for testing.

- added qmake option yolo_onnx to use normal YOLOv8 ONNX models. This makes possible to test
  gimbals camera inside without real model.
- reduced confidence threshold requirement in AiEngineInferencevOnnxRuntime from 0.5 to 0.2
- make printing prettier with ONNX Runtime
- removed unnecessary cv::Mat::clone()

Type: Improvement
Issue: https://denyspopov.atlassian.net/browse/AZ-39

misc/rtsp_ai_player/README.md

@@ -4,7 +4,7 @@
 
 ### How to convert Azaion AI model file to ONNX format
 ```bash
-yolo export model=azaion-2024-06-28.pt dynamic=False format=onnx imgsz=640,640
+yolo export model=azaion-2024-08-13.pt dynamic=False format=onnx imgsz=640,640
 ```
 
 ## How to use application locally on a Linux PC.
@@ -74,7 +74,7 @@ qmake6 && make
 ```bash
 cd autopilot/misc/rtsp_ai_player
 qmake6 && make
-./rtsp_ai_player ~/azaion/models/azaion/azaion-2024-06-28.onnx
+./rtsp_ai_player ~/azaion/models/azaion/azaion-2024-08-13.onnx
 ```
 ### Compile and run rtsp_ai_player with YOLOv8 medium model and gimbal camera:
 ```bash

misc/rtsp_ai_player/aiengine.cpp

@@ -92,16 +92,16 @@ void AiEngine::frameReceivedSlot(cv::Mat frame)
 
     if (mInference->isActive() == false) {
         //qDebug() << "AiEngine. Inference thread is free. Sending frame to it.";
-        emit inferenceFrame(frame.clone());
+        emit inferenceFrame(frame);
     }
 #ifdef OPI5_BUILD
     else if (mInference2->isActive() == false) {
         //qDebug() << "AiEngine. Inference thread is free. Sending frame to it.";
-        emit inferenceFrame2(frame.clone());
+        emit inferenceFrame2(frame);
     }
     else if (mInference3->isActive() == false) {
         //qDebug() << "AiEngine. Inference thread is free. Sending frame to it.";
-        emit inferenceFrame3(frame.clone());
+        emit inferenceFrame3(frame);
     }
 #endif
 }

misc/rtsp_ai_player/rtsp_ai_player.pro

@@ -15,15 +15,21 @@ else {
     message("Not using real gimbal camera.")
 }
 
+yolo_onnx {
+    QMAKE_CXXFLAGS += -DYOLO_ONNX
+}
+
 opi5 {
     message("OPI5 build")
+    CONFIG += link_pkgconfig
     PKGCONFIG += opencv4 librga stb libturbojpeg
     INCLUDEPATH += /usr/include/rga
     QMAKE_CXXFLAGS += -DOPI5_BUILD
     LIBS += /usr/local/lib/librknnrt.so
     SOURCES += $$PWD/src-opi5/*.c $$PWD/src-opi5/*.cpp $$PWD/src-opi5/*.cc
     HEADERS += $$PWD/src-opi5/*.h
-} else:opencv {
+}
+else:opencv {
     message("OpenCV build")
     message("You must use YOLOv8 ONNX files. Azaion model does not work!")
     message("OpenCV must be version 4.10.0 installed to /usr/local/")

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

@@ -4,7 +4,7 @@
 #include "aiengineinferenceonnxruntime.h"
 
 
-static const float confThreshold = 0.4f;
+static const float confThreshold = 0.2f;
 static const float iouThreshold  = 0.4f;
 static const float maskThreshold = 0.5f;
 
@@ -15,21 +15,7 @@ AiEngineInferencevOnnxRuntime::AiEngineInferencevOnnxRuntime(QString modelPath,
 {
     qDebug() << "TUOMAS AiEngineInferencevOnnxRuntime() mModelPath=" << mModelPath;
 
-    /*
-    mClassNames = {
-        "Armoured vehicle",
-        "Truck",
-        "Vehicle",
-        "Artillery",
-        "Shadow artillery",
-        "Trenches",
-        "Military man",
-        "Tyre tracks",
-        "Additional protection tank",
-        "Smoke"
-    };
-    */
-
+#ifdef YOLO_ONNX
     mClassNames = {
         "person",
         "bicycle",
@@ -112,6 +98,20 @@ AiEngineInferencevOnnxRuntime::AiEngineInferencevOnnxRuntime(QString modelPath,
         "hair drier",
         "toothbrush"
     };
+#else
+    mClassNames = {
+        "Armoured vehicle",
+        "Truck",
+        "Vehicle",
+        "Artillery",
+        "Shadow artillery",
+        "Trenches",
+        "Military man",
+        "Tyre tracks",
+        "Additional protection tank",
+        "Smoke"
+    };
+#endif
 }
 
 
@@ -122,28 +122,31 @@ cv::Mat AiEngineInferencevOnnxRuntime::drawLabels(const cv::Mat &image, const st
     for (const auto &detection : detections)
     {
         cv::rectangle(result, detection.box, cv::Scalar(0, 255, 0), 2);
-        std::string label = mClassNames[detection.classId].toStdString() + ": " + std::to_string(detection.conf);
+
+        int confidence = roundf(detection.conf * 100);
+        std::string label = mClassNames[detection.classId].toStdString() + ": " + std::to_string(confidence) + "%";
 
         int baseLine;
-        cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+        cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_COMPLEX, 0.5, 1, &baseLine);
+        cv::Point labelOrigin(detection.box.x, detection.box.y - labelSize.height - baseLine);
 
         cv::rectangle(
             result,
-            cv::Point(detection.box.x, detection.box.y - labelSize.height),
-            cv::Point(detection.box.x + labelSize.width, detection.box.y + baseLine),
+            labelOrigin,
+            cv::Point(detection.box.x + labelSize.width, detection.box.y),
             cv::Scalar(255, 255, 255),
             cv::FILLED);
 
         cv::putText(
             result,
             label,
-            cv::Point(
-                detection.box.x,
-                detection.box.y),
-            cv::FONT_HERSHEY_SIMPLEX,
+            cv::Point(detection.box.x, detection.box.y - baseLine + 2),
+            cv::FONT_HERSHEY_COMPLEX,
             0.5,
             cv::Scalar(0, 0, 0),
-            1);
+            1,
+            cv::LINE_AA);
+
     }
 
     return result;
@@ -152,15 +155,14 @@ cv::Mat AiEngineInferencevOnnxRuntime::drawLabels(const cv::Mat &image, const st
 
 void AiEngineInferencevOnnxRuntime::performInferenceSlot(cv::Mat frame)
 {
-    //qDebug() << __PRETTY_FUNCTION__;
+    qDebug() << __PRETTY_FUNCTION__;
 
     try {
         mActive = true;
         cv::Mat scaledImage                  = resizeAndPad(frame);
-        //cv::imwrite("/tmp/frame.png", scaledImage);
-
         std::vector<Yolov8Result> detections = mPredictor.predict(scaledImage);
 
+#ifdef YOLO_ONNX
         // Only keep following detected objects.
         // car    = 2
         // train  = 6
@@ -174,6 +176,7 @@ void AiEngineInferencevOnnxRuntime::performInferenceSlot(cv::Mat frame)
                                             result.classId != 46;
                                  });
         detections.erase(it, detections.end());
+#endif
 
         AiEngineInferenceResult result;