Opencv C++ 调用 tensorflow 模型、caffe模型

• 新建源文件 main.cpp，内容如下：

#include
#include

#include
#include
#include

#include

using namespace cv;
using namespace dnn;

std::vector<std::string> classes;
std::vector<Vec3b> colors;

void showLegend();

void colorizeSegmentation(const Mat &score, Mat &segm);

int main(int argc, char** argv) try {

    float confThreshold, nmsThreshold, scale;
    cv::Scalar mean;
    bool swapRB;
    int inpWidth, inpHeight;

    String modelPath, configPath, classesFile;

    int modelType = 0;

    if (modelType == 0){
        confThreshold = 0.5;
        nmsThreshold = 0.4;
        scale = 1.0;
        mean = Scalar{ 0,0,0 };
        swapRB = false;
        inpWidth = 500;
        inpHeight = 500;

        modelPath = "models/fcn8s-heavy-pascal.caffemodel";
        configPath = "models/fcn8s-heavy-pascal.prototxt";
        classesFile = "models/object_detection_classes_pascal_voc.txt";
    }
    else if (modelType == 1){

        confThreshold = 0.5;
        nmsThreshold = 0.4;
        scale = 0.00392;
        mean = Scalar{ 0,0,0 };
        swapRB = false;
        inpWidth = 512;
        inpHeight = 256;

        modelPath = "models/Enet-model-best.net";
        configPath = "";
        classesFile = "models/enet-classes.txt";
    }

    String colorFile = "";

    String framework = "";

    int backendId = cv::dnn::DNN_BACKEND_OPENCV;
    int targetId = cv::dnn::DNN_TARGET_CPU;

    if (!classesFile.empty()) {
        const std::string file = classesFile;
        std::ifstream ifs(file.c_str());
        if (!ifs.is_open())
            CV_Error(Error::StsError, "File " + file + " not found");
        std::string line;

        if (modelType == 0)
            classes.push_back("background");

        while (std::getline(ifs, line)) {
            classes.push_back(line);
        }
    }

    if (!colorFile.empty()) {
        const std::string file = colorFile;
        std::ifstream ifs(file.c_str());
        if (!ifs.is_open())
            CV_Error(Error::StsError, "File " + file + " not found");
        std::string line;
        while (std::getline(ifs, line)) {
            std::istringstream colorStr(line.c_str());
            Vec3b color;
            for (int i = 0; i < 3 && !colorStr.eof(); ++i)
                colorStr >> color[i];
            colors.push_back(color);
        }
    }

    CV_Assert(!modelPath.empty());

    Net net = readNet(modelPath, configPath, framework);
    net.setPreferableBackend(backendId);
    net.setPreferableTarget(targetId);

    static const std::string kWinName = "Deep learning semantic segmentation in OpenCV";
    namedWindow(kWinName, WINDOW_AUTOSIZE);

    VideoCapture cap;

    cap.open("models/car.jpg");

    if (!cap.isOpened()) {
        std::cout << "VideoCapture open failed." << std::endl;
        return 0;
    }

    Mat frame, blob;
    while (waitKey(1) < 0) {
        cap >> frame;
        if (frame.empty()) {
            waitKey();
            break;
        }

        blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);

        net.setInput(blob);

        Mat score = net.forward();

        Mat segm;
        colorizeSegmentation(score, segm);

        resize(segm, segm, frame.size(), 0, 0, INTER_NEAREST);
        addWeighted(frame, 0.5, segm, 0.5, 0.0, frame);

        std::vector<double> layersTimes;
        double freq = getTickFrequency() / 1000;
        double t = net.getPerfProfile(layersTimes) / freq;
        std::string label = format("Inference time: %.2f ms", t);
        putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));

        imshow(kWinName, frame);

        if (!classes.empty())
            showLegend();

    }
    return 0;
}
catch (std::exception & e) {
    std::cerr << e.what() << std::endl;
}

void colorizeSegmentation(const Mat &score, Mat &segm)
{
    const int rows = score.size[2];
    const int cols = score.size[3];
    const int chns = score.size[1];

    if (colors.empty()) {

        colors.push_back(Vec3b());
        for (int i = 1; i < chns; ++i) {
            Vec3b color;
            for (int j = 0; j < 3; ++j)
                color[j] = (colors[i - 1][j] + rand() % 256) / 2;
            colors.push_back(color);
        }
    }
    else if (chns != (int)colors.size()) {
        CV_Error(Error::StsError, format("Number of output classes does not match "
            "number of colors (%d != %zu)", chns, colors.size()));
    }

    Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
    Mat maxVal(rows, cols, CV_32FC1, score.data);
    for (int ch = 1; ch < chns; ch++) {
        for (int row = 0; row < rows; row++) {
            const float *ptrScore = score.ptr<float>(0, ch, row);
            uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
            float *ptrMaxVal = maxVal.ptr<float>(row);
            for (int col = 0; col < cols; col++) {
                if (ptrScore[col] > ptrMaxVal[col]) {
                    ptrMaxVal[col] = ptrScore[col];
                    ptrMaxCl[col] = (uchar)ch;
                }
            }
        }
    }

    segm.create(rows, cols, CV_8UC3);
    for (int row = 0; row < rows; row++) {
        const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
        Vec3b *ptrSegm = segm.ptr<Vec3b>(row);
        for (int col = 0; col < cols; col++) {
            ptrSegm[col] = colors[ptrMaxCl[col]];
        }
    }
}

void showLegend()
{
    static const int kBlockHeight = 30;
    static Mat legend;
    if (legend.empty()) {
        const int numClasses = (int)classes.size();
        if ((int)colors.size() != numClasses) {
            CV_Error(Error::StsError, format("Number of output classes does not match "
                "number of labels (%zu != %zu)", colors.size(), classes.size()));
        }
        legend.create(kBlockHeight * numClasses, 200, CV_8UC3);
        for (int i = 0; i < numClasses; i++) {
            Mat block = legend.rowRange(i * kBlockHeight, (i + 1) * kBlockHeight);
            block.setTo(colors[i]);
            putText(block, classes[i], Point(0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX, 0.5, Vec3b(255, 255, 255));
        }
        namedWindow("Legend", WINDOW_AUTOSIZE);
        imshow("Legend", legend);
    }
}

Original: https://blog.csdn.net/woha1yo/article/details/117907025
Author: woha1yo
Title: Opencv C++ 调用 tensorflow 模型、caffe模型