如何可视化提取的特征

Question

我使用opencv249和Visual Studio2013提取了一张图片中四个区域的特征，代码如下：

vector<double> calculateFeatures(Mat src, Mat mask, Rect Rect){
    vector<double> sonuc;
    double Feature1, Feature2, Feature3, Feature4;
    Mat bolum(src, Rect);

    Scalar mean_number = mean(src, mask);
    double num = mean_number.val[0];


    double minVal;
    double maxVal = 0;
    Point minLoc;
    Point maxLoc = 0;
    minMaxLoc(bolum, &minVal, &maxVal, &minLoc, &maxLoc);
    double fark = num - minVal;
    Feature1 = fark;


    double thresh = num*0.95;
    int sayi = countNonZero(bolum < thresh);
    int alan = countNonZero(mask);
    double pixelSayisi = sayi / alan;
    Feature2 = pixelSayisi;



    Mat dst, smoothed;
    GaussianBlur(bolum, smoothed, Size(25, 25), 4, 4);
    Laplacian(smoothed, dst, CV_8UC1, 25, 1, 0, BORDER_DEFAULT);
    cv::Scalar s = cv::sum(dst);
    double toplam = s.val[0];
    Feature3 = toplam;

    cout << "s: " << s << endl;



    Mat dst2;
    cornerHarris(bolum, dst2, 2, 25, 0.04, BORDER_DEFAULT);
    Scalar top = sum(dst2);
    double top2 = top.val[0];
    Feature4 = top2 / alan;

    cout << "Feature4: " << Feature4 << endl;

    sonuc.push_back(Feature1);
    sonuc.push_back(Feature2);
    sonuc.push_back(Feature3);
    sonuc.push_back(Feature4);
    return sonuc;
}

我希望看到该区域的代表性特征图像，以便评估特征是否有用。我该如何实施？

Answer 1

我在此图像上计算了 32x32 块的特征：

您可以为特征的每个维度创建一个图像，像素值由该位置的特征值给出。

如果您最多有 4 个特征，就像在本例中，您可以将所有图像组合成一个 4 通道图像：

完整代码供参考：

#include <opencv2/opencv.hpp>
#include <vector>
using namespace cv;
using namespace std;

vector<double> calculateFeatures(Mat src, Mat mask, Rect Rect){
    vector<double> sonuc;
    double Feature1, Feature2, Feature3, Feature4;
    Mat bolum(src, Rect);

    Scalar mean_number = mean(src, mask);
    double num = mean_number.val[0];


    double minVal;
    double maxVal = 0;
    Point minLoc;
    Point maxLoc = 0;
    minMaxLoc(bolum, &minVal, &maxVal, &minLoc, &maxLoc);
    double fark = num - minVal;
    Feature1 = fark;


    double thresh = num*0.95;
    int sayi = countNonZero(bolum < thresh);
    int alan = countNonZero(mask);
    double pixelSayisi = sayi / alan;
    Feature2 = pixelSayisi;



    Mat dst, smoothed;
    GaussianBlur(bolum, smoothed, Size(25, 25), 4, 4);
    Laplacian(smoothed, dst, CV_8UC1, 25, 1, 0, BORDER_DEFAULT);
    cv::Scalar s = cv::sum(dst);
    double toplam = s.val[0];
    Feature3 = toplam;

    cout << "s: " << s << endl;



    Mat dst2;
    cornerHarris(bolum, dst2, 2, 25, 0.04, BORDER_DEFAULT);
    Scalar top = sum(dst2);
    double top2 = top.val[0];
    Feature4 = top2 / alan;

    cout << "Feature4: " << Feature4 << endl;

    sonuc.push_back(Feature1);
    sonuc.push_back(Feature2);
    sonuc.push_back(Feature3);
    sonuc.push_back(Feature4);
    return sonuc;
}

int main() 
{
    Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);

    int featuresSize = 4;


    int patch_size = 32;
    int right = patch_size - (img.cols % patch_size);
    int bottom = patch_size - (img.rows % patch_size);
    copyMakeBorder(img, img, 0, bottom, 0, right, BORDER_REFLECT101);

    vector<Mat1d> visual;
    for (int i = 0; i < featuresSize; ++i)
    {
        Mat1d v(img.rows, img.cols);
        v.setTo(0);
        visual.push_back(v.clone());
    }

    for (int r = 0; r < img.rows; r += patch_size)
    {
        for (int c = 0; c < img.cols; c += patch_size)
        {
            Rect roi(c, r, patch_size, patch_size);
            Mat1b mask(img.rows, img.cols, uchar(0));
            mask(roi) = uchar(255);

            vector<double> features = calculateFeatures(img, mask, roi);
            for (int i = 0; i < featuresSize; ++i)
            {
                visual[i](roi) = features[i];
            }
        }
    }

    for (int i = 0; i < featuresSize; ++i)
    {
        normalize(visual[i], visual[i], 0.0, 1.0, NORM_MINMAX);

        Mat1b b;
        visual[i].convertTo(b, CV_8U, 255.0);

        imshow("Feature " + to_string(i), b);
    }

    // Makes sense only if nFeatures <= 4
    Mat all;
    merge(visual, all);

    Mat allb;
    all.convertTo(allb, CV_8U, 255.0);

    imshow("All", all);

    waitKey(0);



    return 0;
}

如何可视化提取的特征

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