OpenCV4.8+YOLOv8对象检测C++推理演示

std::string onnxpath = "D:/python/yolov5-7.0/yolov5s.onnx";
auto net = cv::dnn::readNetFromONNX(onnxpath);

net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);

cv::VideoCapture capture("D:/images/video/sample.mp4");
cv::Mat frame;
while (true) {
    bool ret = capture.read(frame);
    if (frame.empty()) {
        break;
    }
    int64 start = cv::getTickCount();
    // 图象预处理 - 格式化操作
    int w = frame.cols;
    int h = frame.rows;
    int _max = std::max(h, w);
    cv::Mat image = cv::Mat::zeros(cv::Size(_max, _max), CV_8UC3);
    cv::Rect roi(0, 0, w, h);
    frame.copyTo(image(roi));

    float x_factor = image.cols / 640.0f;
    float y_factor = image.rows / 640.0f;

    // 推理
    cv::Mat blob = cv::dnn::blobFromImage(image, 1 / 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true, false);
    net.setInput(blob);
    cv::Mat preds = net.forward();

    // 后处理, 1x25200x85
    cv::Mat det_output(preds.size[1], preds.size[2], CV_32F, preds.ptr<float>());
    float confidence_threshold = 0.5;
    std::vector<cv::Rect> boxes;
    std::vector<int> classIds;
    std::vector<float> confidences;
    for (int i = 0; i < det_output.rows; i++) {
        float confidence = det_output.at<float>(i, 4);
        if (confidence < 0.25) {
            continue;
        }
        cv::Mat classes_scores = det_output.row(i).colRange(5, preds.size[2]);
        cv::Point classIdPoint;
        double score;
        minMaxLoc(classes_scores, 0, &score, 0, &classIdPoint);

        // 置信度 0～1之间
        if (score > 0.25)
        {
            float cx = det_output.at<float>(i, 0);
            float cy = det_output.at<float>(i, 1);
            float ow = det_output.at<float>(i, 2);
            float oh = det_output.at<float>(i, 3);
            int x = static_cast<int>((cx - 0.5 * ow) * x_factor);
            int y = static_cast<int>((cy - 0.5 * oh) * y_factor);
            int width = static_cast<int>(ow * x_factor);
            int height = static_cast<int>(oh * y_factor);
            cv::Rect box;
            box.x = x;
            box.y = y;
            box.width = width;
            box.height = height;

            boxes.push_back(box);
            classIds.push_back(classIdPoint.x);
            confidences.push_back(score);
        }
    }

    // NMS
    std::vector<int> indexes;
    cv::dnn::NMSBoxes(boxes, confidences, 0.25, 0.50, indexes);
    for (size_t i = 0; i < indexes.size(); i++) {
        int index = indexes[i];
        int idx = classIds[index];
        cv::rectangle(frame, boxes[index], colors[idx % 5], 2, 8);
        cv::rectangle(frame, cv::Point(boxes[index].tl().x, boxes[index].tl().y - 20),
            cv::Point(boxes[index].br().x, boxes[index].tl().y), cv::Scalar(255, 255, 255), -1);
        cv::putText(frame, classNames[idx], cv::Point(boxes[index].tl().x, boxes[index].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, cv::Scalar(0, 0, 0));
    }

    float t = (cv::getTickCount() - start) / static_cast<float>(cv::getTickFrequency());
    putText(frame, cv::format("FPS: %.2f", 1.0 / t), cv::Point(20, 40), cv::FONT_HERSHEY_PLAIN, 2.0, cv::Scalar(255, 0, 0), 2, 8);

    char c = cv::waitKey(1);
    if (c == 27) {
        break;
    }
    cv::imshow("OpenCV4.8 + YOLOv5", frame);
}