Enables use of multiple TPUs in OPI5

misc/rtsp_ai_player/README.md

@@ -2,14 +2,32 @@
 
 `rtsp_ai_player` is an application that listens to an RTSP stream, analyzes images with an AI model, and shows the results visually. It also controls a gimbal camera to zoom in on the recognized objects. Application uses YOLOv8 AI models converted to the ONNX format.
 
-### How to convert PT file to ONNX format
+### 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
 ```
 
 ## How to use application locally on a Linux PC.
 
-### Install ONNX runtime
+# Speed up compilations
+```bash
+echo "export MAKEFLAGS=\"-j8\"" >> ~/.bashrc
+echo "export PATH=/usr/lib/ccache:\$PATH" >> ~/.bashrc
+```
+
+### Install OpenCV 4.10.0
+```bash
+sudo apt update
+sudo apt install libgtk-3-dev libpng-dev cmake ffmpeg libavcodec-dev libavformat-dev libavfilter-dev
+wget https://github.com/opencv/opencv/archive/refs/tags/4.10.0.zip
+unzip 4.10.0.zip
+cd opencv-4.10.0
+mkdir build && cd build
+cmake -DCMAKE_INSTALL_PREFIX=/opt/opencv-4.10.0 -DBUILD_opencv_world=ON -DOPENCV_GENERATE_PKGCONFIG=ON -DBUILD_PERF_TESTS=OFF -DBUILD_TESTS=OFF ..
+make -j8 && sudo make install
+```
+
+### Install ONNX runtime 1.18.0
 ```bash
 wget https://github.com/microsoft/onnxruntime/releases/download/v1.18.0/onnxruntime-linux-x64-1.18.0.tgz
 sudo tar xf onnxruntime-linux-x64-1.18.0.tgz -C /opt
@@ -24,15 +42,20 @@ mkdir mediamtx
 tar xf mediamtx_v1.8.4_linux_amd64.tar.gz -C mediamtx
 ```
 
-### Launch mediamtx RTSP server:
+### If you use video file from the local RTSP server:
 ```bash
 cd mediamtx
 ./mediamtx
 ```
 
-### Play mp4 video file from RTSP server:
+### Play Azaion mp4 video file from RTSP server ... :
 ```bash
-ffmpeg -re -stream_loop -1 -i $HOME/azaion/models/videos/for_ai_short.mp4 -c copy -f rtsp rtsp://localhost:8554/live.stream
+ffmpeg -re -stream_loop -1 -i $HOME/azaion/videos/for_ai_cut.mp4 -c copy -f rtsp rtsp://localhost:8554/live.stream
+```
+
+### ... or play simple video file from RTSP server:
+```bash
+ffmpeg -re -stream_loop -1 -i $HOME/azaion/videos/table.mp4 -c copy -f rtsp rtsp://localhost:8554/live.stream
 ```
 
 ### Test RTSP streaming with ffplay:
@@ -40,7 +63,14 @@ ffmpeg -re -stream_loop -1 -i $HOME/azaion/models/videos/for_ai_short.mp4 -c cop
 ffplay -rtsp_transport tcp rtsp://localhost:8554/live.stream
 ```
 
-### Compile and run rtsp_ai_player:
+### Compile and run rtsp_ai_player with YOLOv8 medium AI mode:
+```bash
+cd autopilot/misc/rtsp_ai_player
+qmake6 && make
+./rtsp_ai_player ~/azaion/models/onnx/yolov8m.onnx
+```
+
+### Compile and run rtsp_ai_player with Azaion AI mode:
 ```bash
 cd autopilot/misc/rtsp_ai_player
 qmake6 && make

misc/rtsp_ai_player/aiengine.cpp

@@ -19,6 +19,11 @@ AiEngine::AiEngine(QString modelPath, QObject *parent)
 
 #if defined(OPI5_BUILD)
     mInference  = new AiEngineInferenceOpi5(modelPath);
+    mInference->initialize(0);
+    mInference2 = new AiEngineInferenceOpi5(modelPath);
+    mInference2->initialize(1);
+    mInference3 = new AiEngineInferenceOpi5(modelPath);
+    mInference3->initialize(2);
 #elif defined(OPENCV_BUILD)
     mInference = new AiEngineInferenceOpencvOnnx(modelPath);
 #else
@@ -31,6 +36,20 @@ AiEngine::AiEngine(QString modelPath, QObject *parent)
     connect(this, &AiEngine::inferenceFrame, mInference, &AiEngineInference::performInferenceSlot, Qt::QueuedConnection);
     inferenceThread->start();
 
+#ifdef OPI5_BUILD
+    QThread *inferenceThread2 = new QThread(this);
+    mInference2->moveToThread(inferenceThread2);
+    connect(mInference2, &AiEngineInference::resultsReady, this, &AiEngine::inferenceResultsReceivedSlot, Qt::QueuedConnection);
+    connect(this, &AiEngine::inferenceFrame2, mInference2, &AiEngineInference::performInferenceSlot, Qt::QueuedConnection);
+    inferenceThread2->start();
+
+    QThread *inferenceThread3 = new QThread(this);
+    mInference3->moveToThread(inferenceThread3);
+    connect(mInference3, &AiEngineInference::resultsReady, this, &AiEngine::inferenceResultsReceivedSlot, Qt::QueuedConnection);
+    connect(this, &AiEngine::inferenceFrame3, mInference3, &AiEngineInference::performInferenceSlot, Qt::QueuedConnection);
+    inferenceThread3->start();
+#endif
+
     mGimbalClient = new AiEngineGimbalClient(this);
 }
 
@@ -38,6 +57,7 @@ AiEngine::AiEngine(QString modelPath, QObject *parent)
 void AiEngine::start(void)
 {
     mRtspListener->startListening();
+    mElapsedTimer.start();
 }
 
 
@@ -49,6 +69,9 @@ void AiEngine::stop(void)
 
 void AiEngine::inferenceResultsReceivedSlot(AiEngineInferenceResult result)
 {
+    mFrameCounter++;
+    qDebug() << "FPS = " << (mFrameCounter / (mElapsedTimer.elapsed()/1000.0f));
+
     //qDebug() << "AiEngine got inference results in thread: " << QThread::currentThreadId();
     mGimbalClient->inferenceResultSlot(result);
     cv::imshow("Received Frame", result.frame);
@@ -64,4 +87,14 @@ void AiEngine::frameReceivedSlot(cv::Mat frame)
         //qDebug() << "AiEngine. Inference thread is free. Sending frame to it.";
         emit inferenceFrame(frame.clone());
     }
+#ifdef OPI5_BUILD
+    else if (mInference2->isActive() == false) {
+        //qDebug() << "AiEngine. Inference thread is free. Sending frame to it.";
+        emit inferenceFrame2(frame.clone());
+    }
+    else if (mInference3->isActive() == false) {
+        //qDebug() << "AiEngine. Inference thread is free. Sending frame to it.";
+        emit inferenceFrame3(frame.clone());
+    }
+#endif
 }

misc/rtsp_ai_player/aiengine.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include <QObject>
+#include <QElapsedTimer>
 #include <opencv2/core.hpp>
 #include <opencv2/videoio.hpp>
 #include "aienginertsplistener.h"
@@ -21,9 +22,15 @@ public slots:
 
 signals:
     void inferenceFrame(cv::Mat frame);
+    void inferenceFrame2(cv::Mat frame);
+    void inferenceFrame3(cv::Mat frame);
 
 private:
+    QElapsedTimer mElapsedTimer;
+    uint32_t mFrameCounter = 0;
     AiEngineRtspListener *mRtspListener;
     AiEngineInference *mInference;
+    AiEngineInference *mInference2;
+    AiEngineInference *mInference3;
     AiEngineGimbalClient *mGimbalClient;
 };

misc/rtsp_ai_player/aiengineconfig.h

@@ -0,0 +1,12 @@
+#pragma once
+
+#include <QString>
+
+#ifdef OPI5_BUILD
+QString rtspVideoUrl = "rtsp://192.168.0.1:8554/live.stream";
+#else
+// Video file from the local MTX RTSP server or gimbal camera.
+QString rtspVideoUrl = "rtsp://localhost:8554/live.stream";
+//QString rtspVideoUrl = "rtsp://192.168.0.25:8554/main.264";
+#endif
+

misc/rtsp_ai_player/aienginegimbalclient.cpp

@@ -10,7 +10,7 @@ AiEngineGimbalClient::AiEngineGimbalClient(QObject *parent)
     // Create server and run it in the new thread.
     // Connect all signal and slots here. No need to do the same in AiEngineGimbalServer class.
 
-    mGimbalServer = new AiEngineGimbalServer(this);
+    mGimbalServer = new AiEngineGimbalServer();
     QThread *gimbalServerThread = new QThread(this);
     mGimbalServer->moveToThread(gimbalServerThread);
 
@@ -85,6 +85,33 @@ void AiEngineGimbalClient::inferenceResultSlot(AiEngineInferenceResult result)
     // TODO!! Just increasing number for testing purposes ATM.
     static int index = 0;
 
+    // Find best possible target ...
+    int bestObjectIndex = -1;
+    float bestObjectProb = -1;
+    for (int i = 0; i < result.objects.size(); i++) {
+        const AiEngineObject &object = result.objects[i];
+        if (object.propability > bestObjectProb) {
+            bestObjectIndex = i;
+            bestObjectProb  = object.propability;
+        }
+    }
+
+    // ... if found, then ask camera to zoom to it.
+    if (bestObjectIndex >= 0) {
+        const AiEngineObject &object = result.objects[bestObjectIndex];
+
+        AiEngineCameraTarget target;
+        target.rectangle = object.rectangle;
+        target.index     = index++;
+
+        qDebug() << "Found best target from index" << bestObjectIndex
+                 << "Name:" << object.classStr
+                 << "Probability:" << bestObjectProb;
+
+        emit zoomToAiTarget(target);
+    }
+
+    /*
     // We got list of all recognized objects, but at least for now we will zoom to all objects at
     // once and not for each invidually. Got minimal coordinates which contains the all objects.
     AiEngineRectangle groupRect = getGroupCoordinates(result.objects);
@@ -105,9 +132,5 @@ void AiEngineGimbalClient::inferenceResultSlot(AiEngineInferenceResult result)
     }
 
     qDebug() << "inferenceResultSlot() Zooming to square top=" << groupRect.top << "x" << groupRect.left << "and bottom:" << groupRect.bottom << "x" << groupRect.right;
-
+    */
-    AiEngineCameraTarget target;
-    target.rectangle = groupRect;
-    target.index     = index++;
-    emit zoomToAiTarget(target);
 }

misc/rtsp_ai_player/aiengineinference.h

@@ -17,6 +17,7 @@ public:
     AiEngineRectangle rectangle;
     float propability;
     int classId;
+    QString classStr;
 };
 
 
@@ -38,10 +39,11 @@ protected:
     cv::Mat resizeAndPad(const cv::Mat& src);
     QString mModelPath;
     bool mActive;
+    int mNumber;
 
 public slots:
     virtual void performInferenceSlot(cv::Mat frame) = 0;
-
+    virtual void initialize(int number) = 0;
 signals:
     void resultsReady(AiEngineInferenceResult results);
 };

misc/rtsp_ai_player/aienginertsplistener.cpp

@@ -1,7 +1,7 @@
 #include <QDebug>
 #include <QtConcurrent/QtConcurrent>
 #include "aienginertsplistener.h"
-#include "config.h"
+#include "aiengineconfig.h"
 
 
 AiEngineRtspListener::AiEngineRtspListener(QObject *parent)

misc/rtsp_ai_player/config.h

@@ -1,10 +0,0 @@
-#pragma once
-
-#include <QString>
-
-#ifdef OPI5_BUILD
-//QString rtspVideoUrl = "rtsp://192.168.168.11:8554/live.stream";
-QString rtspVideoUrl = "rtsp://192.168.0.25:8554/main.264";
-#else
-QString rtspVideoUrl = "rtsp://localhost:8554/live.stream";
-#endif

misc/rtsp_ai_player/rtsp_ai_player.pro

@@ -1,7 +1,6 @@
 QT += core network serialport
 QT -= gui
-CONFIG += c++11 link_pkgconfig concurrent console
+CONFIG += c++11 concurrent console
-PKGCONFIG += opencv4
 MOC_DIR = moc
 OBJECTS_DIR = obj
 
@@ -21,8 +20,10 @@ opi5 {
     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/")
+    INCLUDEPATH += /opt/opencv-4.10.0/include/opencv4/
+    LIBS += /opt/opencv-4.10.0/lib/libopencv_world.so
+    QMAKE_LFLAGS += -Wl,-rpath,/opt/opencv-4.10.0/lib
     QMAKE_CXXFLAGS += -DOPENCV_BUILD
-    QMAKE_LFLAGS += -Wl,-rpath,/usr/local/lib
     SOURCES += $$PWD/src-opencv-onnx/*.cpp
     HEADERS += $$PWD/src-opencv-onnx/*.h
 }
@@ -30,10 +31,17 @@ else {
     message("ONNX build")
     message("You must use YOLOv8 ONNX files. Azaion ONNX model also works fine.")
     QMAKE_CXXFLAGS += -DONNX_BUILD
+
+    # ONNX-runtime
     INCLUDEPATH += /opt/onnxruntime-linux-x64-1.18.0/include
-    LIBS += /opt/onnxruntime-linux-x64-1.18.0/lib/libonnxruntime.so.1.18.0
     QMAKE_LFLAGS += -Wl,-rpath,/opt/onnxruntime-linux-x64-1.18.0/lib
-    QMAKE_LFLAGS += -Wl,-rpath,/usr/local/lib
+    LIBS += /opt/onnxruntime-linux-x64-1.18.0/lib/libonnxruntime.so.1.18.0
+
+    # OpenCV 4.10.0
+    INCLUDEPATH += /opt/opencv-4.10.0/include/opencv4/
+    QMAKE_LFLAGS += -Wl,-rpath,/opt/opencv-4.10.0/lib
+    LIBS += /opt/opencv-4.10.0/lib/libopencv_world.so
+
     SOURCES += $$PWD/src-onnx-runtime/*.cpp
     HEADERS += $$PWD/src-onnx-runtime/*.h
 }

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

@@ -15,6 +15,7 @@ AiEngineInferencevOnnxRuntime::AiEngineInferencevOnnxRuntime(QString modelPath,
 {
     qDebug() << "TUOMAS AiEngineInferencevOnnxRuntime() mModelPath=" << mModelPath;
 
+    /*
     mClassNames = {
         "Armoured vehicle",
         "Truck",
@@ -27,6 +28,90 @@ AiEngineInferencevOnnxRuntime::AiEngineInferencevOnnxRuntime(QString modelPath,
         "Additional protection tank",
         "Smoke"
     };
+    */
+
+    mClassNames = {
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "backpack",
+        "umbrella",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "dining table",
+        "toilet",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "toaster",
+        "sink",
+        "refrigerator",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush"
+    };
 }
 
 
@@ -37,7 +122,7 @@ 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] + ": " + std::to_string(detection.conf);
+        std::string label = mClassNames[detection.classId].toStdString() + ": " + std::to_string(detection.conf);
 
         int baseLine;
         cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
@@ -67,10 +152,29 @@ cv::Mat AiEngineInferencevOnnxRuntime::drawLabels(const cv::Mat &image, const st
 
 void AiEngineInferencevOnnxRuntime::performInferenceSlot(cv::Mat frame)
 {
-    mActive = true;
+    //qDebug() << __PRETTY_FUNCTION__;
 
+    try {
+        mActive = true;
         cv::Mat scaledImage                  = resizeAndPad(frame);
+        //cv::imwrite("/tmp/frame.png", scaledImage);
+
         std::vector<Yolov8Result> detections = mPredictor.predict(scaledImage);
+
+        // Only keep following detected objects.
+        // car    = 2
+        // train  = 6
+        // cup    = 41
+        // banana = 46
+        auto it = std::remove_if(detections.begin(), detections.end(),
+                                 [](const Yolov8Result& result) {
+                                     return result.classId != 2 &&
+                                            result.classId != 6 &&
+                                            result.classId != 41 &&
+                                            result.classId != 46;
+                                 });
+        detections.erase(it, detections.end());
+
         AiEngineInferenceResult result;
 
         for (uint i = 0; i < detections.size(); i++) {
@@ -79,6 +183,7 @@ void AiEngineInferencevOnnxRuntime::performInferenceSlot(cv::Mat frame)
             // Add detected objects to the results
             AiEngineObject object;
             object.classId          = detection.classId;
+            object.classStr         = mClassNames[detection.classId];
             object.propability      = detection.conf;
             object.rectangle.top    = detection.box.y;
             object.rectangle.left   = detection.box.x;
@@ -88,10 +193,25 @@ void AiEngineInferencevOnnxRuntime::performInferenceSlot(cv::Mat frame)
         }
 
         if (result.objects.empty() == false) {
-        qDebug() << __PRETTY_FUNCTION__ << "detections:" << detections.size();
             result.frame = drawLabels(scaledImage, detections);
             emit resultsReady(result);
         }
 
         mActive = false;
+    }
+    catch (const cv::Exception& e) {
+        std::cout << "OpenCV exception caught: " << e.what() << std::endl;
+    }
+    catch (const std::exception& e) {
+        std::cout << "Standard exception caught: " << e.what() << std::endl;
+    }
+    catch (...) {
+        std::cout << "Unknown exception caught" << std::endl;
+    }
+}
+
+
+void AiEngineInferencevOnnxRuntime::initialize(int number)
+{
+    (void)number;
 }

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

@@ -9,6 +9,7 @@ class AiEngineInferencevOnnxRuntime : public AiEngineInference
     Q_OBJECT
 public:
     explicit AiEngineInferencevOnnxRuntime(QString modelPath, QObject *parent = nullptr);
+    void initialize(int number);
 
 public slots:
     void performInferenceSlot(cv::Mat frame) override;
@@ -16,5 +17,5 @@ public slots:
 private:
     cv::Mat drawLabels(const cv::Mat &image, const std::vector<Yolov8Result> &detections);
     YOLOPredictor mPredictor;
-    std::vector<std::string> mClassNames;
+    QVector<QString> mClassNames;
 };

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

@@ -21,10 +21,11 @@ void AiEngineInferenceOpencvOnnx::performInferenceSlot(cv::Mat frame)
         mActive = true;
 
         cv::Mat scaledImage               = resizeAndPad(frame);
+        //cv::imwrite("/tmp/frame.png", scaledImage);
         std::vector<Detection> detections = mInference.runInference(scaledImage);
         AiEngineInferenceResult result;
 
-        //qDebug() << "performInferenceSlot() found " << detections.size() << " objects";
+        qDebug() << "performInferenceSlot() found " << detections.size() << " objects";
 
         for (uint i = 0; i < detections.size(); ++i) {
             const Detection &detection = detections[i];
@@ -38,8 +39,15 @@ void AiEngineInferenceOpencvOnnx::performInferenceSlot(cv::Mat frame)
             object.rectangle.bottom = detection.box.y + detection.box.height;
             object.rectangle.right  = detection.box.x + detection.box.width;
             result.objects.append(object);
+
+            //qDebug() << "performInferenceSlot()" << object.rectangle.top << object.rectangle.left << "and" << object.rectangle.bottom << object.rectangle.right;
         }
 
+        auto end = std::remove_if(detections.begin(), detections.end(), [](const Detection& detection) {
+            return detection.class_id != 0;
+        });
+        detections.erase(end, detections.end());
+
         if (result.objects.empty() == false) {
             result.frame = mInference.drawLabels(scaledImage, detections);
             emit resultsReady(result);
@@ -51,3 +59,9 @@ void AiEngineInferenceOpencvOnnx::performInferenceSlot(cv::Mat frame)
         std::cerr << "performInferenceSlot() Error: " << e.what() << std::endl;
     }
 }
+
+
+void AiEngineInferenceOpencvOnnx::initialize(int number)
+{
+    (void)number;
+}

misc/rtsp_ai_player/src-opencv-onnx/aiengineinferenceopencvonnx.h

@@ -9,6 +9,7 @@ class AiEngineInferenceOpencvOnnx : public AiEngineInference
     Q_OBJECT
 public:
     explicit AiEngineInferenceOpencvOnnx(QString modelPath, QObject *parent = nullptr);
+    void initialize(int number);
 
 public slots:
     void performInferenceSlot(cv::Mat frame) override;

misc/rtsp_ai_player/src-opi5/aiengineinferenceopi5.cpp

@@ -12,13 +12,19 @@ AiEngineInferenceOpi5::AiEngineInferenceOpi5(QString modelPath, QObject *parent)
     : AiEngineInference{modelPath, parent}
 {
     qDebug() << "AiEngineInferenceOpi5() test mModelPath=" << mModelPath;
+}
 
-    memset(&mRrknnAppCtx, 0, sizeof(rknn_app_context_t));
+
+void AiEngineInferenceOpi5::initialize(int number)
+{
+    mNumber = number;
+
+    memset(&mRrknnAppCtx0, 0, sizeof(rknn_app_context_t));
     init_post_process();
 
-    int ret = init_yolov8_model(modelPath.toLocal8Bit(), &mRrknnAppCtx);
+    int ret = init_yolov8_model(mModelPath.toLocal8Bit(), &mRrknnAppCtx0);
     if (ret != 0) {
-        qDebug() << "init_yolov8_model() failure! ret: " << ret << "modelPath = " << modelPath;
+        qDebug() << "init_yolov8_model() failure! ret: " << ret << "modelPath = " << mModelPath << "number:" << number;
         return;
     }
 }
@@ -27,7 +33,7 @@ AiEngineInferenceOpi5::AiEngineInferenceOpi5(QString modelPath, QObject *parent)
 AiEngineInferenceOpi5::~AiEngineInferenceOpi5()
 {
     deinit_post_process();
-    release_yolov8_model(&mRrknnAppCtx);
+    release_yolov8_model(&mRrknnAppCtx0);
 }
 
 
@@ -110,7 +116,7 @@ void AiEngineInferenceOpi5::performInferenceSlot(cv::Mat frame)
     image_buffer_t imgBuffer = convertCV2FrameToImageBuffer(scaledFrame);
 
     object_detect_result_list od_results;
-    int ret = inference_yolov8_model(&mRrknnAppCtx, &imgBuffer, &od_results);
+    int ret = inference_yolov8_model(&mRrknnAppCtx0, &imgBuffer, &od_results, mNumber);
     if (ret != 0) {
         qDebug() << "inference_yolov8_model() failure! ret: " << ret;
         return;

misc/rtsp_ai_player/src-opi5/aiengineinferenceopi5.h

@@ -11,6 +11,7 @@ class AiEngineInferenceOpi5 : public AiEngineInference
 public:
     explicit AiEngineInferenceOpi5(QString modelPath, QObject *parent = nullptr);
     ~AiEngineInferenceOpi5();
+    void initialize(int number) override;
 
 public slots:
     void performInferenceSlot(cv::Mat frame) override;
@@ -21,5 +22,5 @@ private:
     cv::Mat resizeToHalfAndAssigntoTopLeft640x640(const cv::Mat& inputFrame);
     void drawObjects(cv::Mat& image, const object_detect_result_list& result_list);
 
-    rknn_app_context_t mRrknnAppCtx;
+    rknn_app_context_t mRrknnAppCtx0;
 };

misc/rtsp_ai_player/src-opi5/yolov8.cc

@@ -155,7 +155,7 @@ int release_yolov8_model(rknn_app_context_t *app_ctx)
     return 0;
 }
 
-int inference_yolov8_model(rknn_app_context_t *app_ctx, image_buffer_t *img, object_detect_result_list *od_results)
+int inference_yolov8_model(rknn_app_context_t *app_ctx, image_buffer_t *img, object_detect_result_list *od_results, int core)
 {
     int ret;
     image_buffer_t dst_img;
@@ -211,6 +211,29 @@ int inference_yolov8_model(rknn_app_context_t *app_ctx, image_buffer_t *img, obj
         return -1;
     }
 
+    if (core == 1) {
+        ret = rknn_set_core_mask(app_ctx->rknn_ctx, RKNN_NPU_CORE_0);
+        //ret = rknn_set_core_mask(app_ctx->rknn_ctx, RKNN_NPU_CORE_0_1_2);
+        if (ret < 0) {
+            printf("rknn_set_core_mask(RKNN_NPU_CORE_0) fail! ret=%d\n", ret);
+            return -1;
+        }
+    }
+    else if (core == 2) {
+        ret = rknn_set_core_mask(app_ctx->rknn_ctx, RKNN_NPU_CORE_1);
+        if (ret < 0) {
+            printf("rknn_set_core_mask(RKNN_NPU_CORE_1) fail! ret=%d\n", ret);
+            return -1;
+        }
+    }
+    else if (core == 3) {
+        ret = rknn_set_core_mask(app_ctx->rknn_ctx, RKNN_NPU_CORE_2);
+        if (ret < 0) {
+            printf("rknn_set_core_mask(RKNN_NPU_CORE_1) fail! ret=%d\n", ret);
+            return -1;
+        }
+    }
+
     // Run
     printf("rknn_run\n");
     ret = rknn_run(app_ctx->rknn_ctx, nullptr);

misc/rtsp_ai_player/src-opi5/yolov8.h

@@ -37,6 +37,6 @@ int init_yolov8_model(const char* model_path, rknn_app_context_t* app_ctx);
 
 int release_yolov8_model(rknn_app_context_t* app_ctx);
 
-int inference_yolov8_model(rknn_app_context_t* app_ctx, image_buffer_t* img, object_detect_result_list* od_results);
+int inference_yolov8_model(rknn_app_context_t* app_ctx, image_buffer_t* img, object_detect_result_list* od_results, int core);
 
 #endif //_RKNN_DEMO_YOLOV8_H_