火焰检测c++opencv

火焰检测是指通过图像处理技术来检测图像中是否存在火焰。在C++中，可以使用OpenCV库来实现火焰检测。可以采用YOLO4TINY的模型来进行检测，该模型具有移植性好、速度快等优点。以下是实现火焰检测的步骤：

// 1. 导入OpenCV库
#include <opencv2/opencv.hpp>

using namespace cv;

int main()
{
    // 2. 读取图像
    Mat image = imread("fire.jpg");

    // 3. 创建YOLO4TINY模型
    dnn::Net net = dnn::readNetFromDarknet("yolov4-tiny.cfg", "yolov4-tiny.weights");
    net.setPreferableBackend(dnn::DNN_BACKEND_OPENCV);
    net.setPreferableTarget(dnn::DNN_TARGET_CPU);

    // 4. 获取输出层名称
    std::vector<String> names;
    std::ifstream ifs("coco.names");
    std::string line;
    while (std::getline(ifs, line)) names.push_back(line);

    // 5. 进行目标检测
    Mat blob = dnn::blobFromImage(image,1 / 255.0, Size(416, 416), Scalar(), true, false);
    net.setInput(blob);
    std::vector<Mat> outs;
    net.forward(outs, net.getUnconnectedOutLayersNames());
    std::vector<int> classIds;
    std::vector<float> confidences;
    std::vector<Rect> boxes;
    for (size_t i = 0; i < outs.size(); ++i)
    {
        // 获取每个输出层的信息
        float* data = (float*)outs[i].data;
        for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
        {
            Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
            Point classIdPoint;
            double confidence;
            minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
            if (confidence > 0.5)
            {
                int centerX = (int)(data[0] * image.cols);
                int centerY = (int)(data[1] * image.rows);
                int width = (int)(data[2] * image.cols);
                int height = (int)(data[3] * image.rows);
                int left = centerX - width / 2;
                int top = centerY - height / 2;                classIds.push_back(classIdPoint.x);
                confidences.push_back((float)confidence);
                boxes.push_back(Rect(left, top, width, height));
            }
        }
    }

    // 6. 绘制检测结果
    std::vector<int> indices;
    dnn::NMSBoxes(boxes, confidences, 0.5, 0.4, indices);
    for (size_t i = 0; i < indices.size(); ++i)
    {
        int idx = indices[i];
        Rect box = boxes[idx];
        int classId = classIds[idx];
        float confidence = confidences[idx];
        Scalar color = Scalar(0, 0, 255);
        rectangle(image, box, color, 2);
        String label = format("%s: %.2f", (const char*)names[classId].c_str(), confidence);
        int baseLine;
        Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
        rectangle(image, Point(box.x, box.y - labelSize.height - baseLine), Point(box.x + labelSize.width, box.y), color, FILLED);
        putText(image, label, Point(box.x, box.y - baseLine), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255, 255, 255));
    }

    // 7. 显示检测结果
    imshow("Fire Detection", image);
    waitKey(0);

    return 0;
}

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