yolov8_nano.cpp

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#include <chrono>
#include <iostream>
 
// Includes common necessary includes for development using depthai library
#include "depthai/depthai.hpp"
 
/*
The code is the same as for Tiny-yolo-V3 and V4, the only difference is the blob file and anchors that are not needed for V8. 
The blob was compiled following this tutorial: https://github.com/TNTWEN/OpenVINO-YOLOV4
*/
 
static const std::vector<std::string> labelMap = {
    "person",        "bicycle",      "car",           "motorbike",     "aeroplane",   "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",         "sofa",          "pottedplant", "bed",         "diningtable", "toilet",       "tvmonitor",
    "laptop",        "mouse",        "remote",        "keyboard",      "cell phone",  "microwave",   "oven",        "toaster",      "sink",
    "refrigerator",  "book",         "clock",         "vase",          "scissors",    "teddy bear",  "hair drier",  "toothbrush"};
 
static std::atomic<bool> syncNN{true};
 
int main(int argc, char** argv) {
    using namespace std;
    using namespace std::chrono;
    std::string nnPath(BLOB_PATH);
 
    // If path to blob specified, use that
    if(argc > 1) {
        nnPath = std::string(argv[1]);
    }
 
    // Print which blob we are using
    printf("Using blob at path: %s\n", nnPath.c_str());
 
    // Create pipeline
    dai::Pipeline pipeline;
 
    // Define sources and outputs
    auto camRgb = pipeline.create<dai::node::ColorCamera>();
    auto detectionNetwork = pipeline.create<dai::node::YoloDetectionNetwork>();
    auto xoutRgb = pipeline.create<dai::node::XLinkOut>();
    auto nnOut = pipeline.create<dai::node::XLinkOut>();
 
    xoutRgb->setStreamName("rgb");
    nnOut->setStreamName("detections");
 
    // Properties
    camRgb->setPreviewSize(640, 352);
    camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P);
    camRgb->setInterleaved(false);
    camRgb->setColorOrder(dai::ColorCameraProperties::ColorOrder::BGR);
    camRgb->setFps(40);
 
    // Network specific settings
    detectionNetwork->setConfidenceThreshold(0.5f);
    detectionNetwork->setNumClasses(80);
    detectionNetwork->setCoordinateSize(4);
    detectionNetwork->setIouThreshold(0.5f);
    detectionNetwork->setBlobPath(nnPath);
    detectionNetwork->setNumInferenceThreads(2);
    detectionNetwork->input.setBlocking(false);
 
    // Linking
    camRgb->preview.link(detectionNetwork->input);
    if(syncNN) {
        detectionNetwork->passthrough.link(xoutRgb->input);
    } else {
        camRgb->preview.link(xoutRgb->input);
    }
 
    detectionNetwork->out.link(nnOut->input);
 
    // Connect to device and start pipeline
    dai::Device device(pipeline);
 
    // Output queues will be used to get the rgb frames and nn data from the outputs defined above
    auto qRgb = device.getOutputQueue("rgb", 4, false);
    auto qDet = device.getOutputQueue("detections", 4, false);
 
    cv::Mat frame;
    std::vector<dai::ImgDetection> detections;
    auto startTime = steady_clock::now();
    int counter = 0;
    float fps = 0;
    auto color2 = cv::Scalar(255, 255, 255);
 
    // Add bounding boxes and text to the frame and show it to the user
    auto displayFrame = [](std::string name, cv::Mat frame, std::vector<dai::ImgDetection>& detections) {
        auto color = cv::Scalar(255, 0, 0);
        // nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
        for(auto& detection : detections) {
            int x1 = detection.xmin * frame.cols;
            int y1 = detection.ymin * frame.rows;
            int x2 = detection.xmax * frame.cols;
            int y2 = detection.ymax * frame.rows;
 
            uint32_t labelIndex = detection.label;
            std::string labelStr = to_string(labelIndex);
            if(labelIndex < labelMap.size()) {
                labelStr = labelMap[labelIndex];
            }
            cv::putText(frame, labelStr, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255);
            std::stringstream confStr;
            confStr << std::fixed << std::setprecision(2) << detection.confidence * 100;
            cv::putText(frame, confStr.str(), cv::Point(x1 + 10, y1 + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, 255);
            cv::rectangle(frame, cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)), color, cv::FONT_HERSHEY_SIMPLEX);
        }
        // Show the frame
        cv::imshow(name, frame);
    };
 
    while(true) {
        std::shared_ptr<dai::ImgFrame> inRgb;
        std::shared_ptr<dai::ImgDetections> inDet;
 
        if(syncNN) {
            inRgb = qRgb->get<dai::ImgFrame>();
            inDet = qDet->get<dai::ImgDetections>();
        } else {
            inRgb = qRgb->tryGet<dai::ImgFrame>();
            inDet = qDet->tryGet<dai::ImgDetections>();
        }
 
        counter++;
        auto currentTime = steady_clock::now();
        auto elapsed = duration_cast<duration<float>>(currentTime - startTime);
        if(elapsed > seconds(1)) {
            fps = counter / elapsed.count();
            counter = 0;
            startTime = currentTime;
        }
 
        if(inRgb) {
            frame = inRgb->getCvFrame();
            std::stringstream fpsStr;
            fpsStr << "NN fps: " << std::fixed << std::setprecision(2) << fps;
            cv::putText(frame, fpsStr.str(), cv::Point(2, inRgb->getHeight() - 4), cv::FONT_HERSHEY_TRIPLEX, 0.4, color2);
        }
 
        if(inDet) {
            detections = inDet->detections;
        }
 
        if(!frame.empty()) {
            displayFrame("rgb", frame, detections);
        }
 
        int key = cv::waitKey(1);
        if(key == 'q' || key == 'Q') {
            return 0;
        }
    }
    return 0;
}