OpenCV4.8+YOLO11C++部署推理

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就会发现，跟YOLOv8输入与输出结果完全一致，没有什么改变。

推理演示截图：

代码已经全部测试过了，可以直接调用：

#include 
#include 
#include 
std::string label_map = "D:/python/yolov5-7.0/classes.txt";
int main(int argc, char** argv) {
    std::vector<std::string> classNames;
    std::ifstream fp(label_map);
    std::string name;
    while (!fp.eof()) {
        getline(fp, name);
        if (name.length()) {
            classNames.push_back(name);
        }
    }
    fp.close();
    std::vector<cv::Scalar> colors;
    colors.push_back(cv::Scalar(0, 255, 0));
    colors.push_back(cv::Scalar(0, 255, 255));
    colors.push_back(cv::Scalar(255, 255, 0));
    colors.push_back(cv::Scalar(255, 0, 0));
    colors.push_back(cv::Scalar(0, 0, 255));
    std::string onnxpath = "D:/projects/yolov8n.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, 1x84x8400
        cv::Mat outs(preds.size[1], preds.size[2], CV_32F, preds.ptr());
        cv::Mat det_output = outs.t();
        float confidence_threshold = 0.5;
        std::vector<cv::Rect> boxes;
        std::vector classIds;
        std::vector confidences;
        for (int i = 0; i < det_output.rows; i++) {
            cv::Mat classes_scores = det_output.row(i).colRange(4, preds.size[1]);
            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(i, 0);
                float cy = det_output.at(i, 1);
                float ow = det_output.at(i, 2);
                float oh = det_output.at(i, 3);
                int x = static_cast((cx - 0.5 * ow) * x_factor);
                int y = static_cast((cy - 0.5 * oh) * y_factor);
                int width = static_cast(ow * x_factor);
                int height = static_cast(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 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(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 + YOLOv8", frame);
    }
    cv::waitKey(0);
    cv::destroyAllWindows();
    return 0;
}