opencv::直方图比较

 

直方图比较方法-概述
    对输入的两张图像计算得到直方图H1与H2，归一化到相同的尺度空间然后可以通过计算H1与H2的之间的距离得到两个直方图的相似程度进而比较图像本身的相似程度。
    
    Correlation 相关性比较
    Chi-Square 卡方比较
    Intersection 十字交叉性
    Bhattacharyya distance 巴氏距离

 

直方图比较方法-相关性计算(CV_COMP_CORREL)

 

 

直方图比较方法-卡方计算(CV_COMP_CHISQR)

 

 

直方图比较方法-十字计算(CV_COMP_INTERSECT)

 

 

直方图比较方法-巴氏距离计算(CV_COMP_BHATTACHARYYA )

 

 

首先把图像从RGB色彩空间转换到HSV色彩空间cvtColor
计算图像的直方图，然后归一化到[0~1]之间calcHist和normalize;
使用上述四种比较方法之一进行比较compareHist

 

compareHist(
        InputArray h1,     // 直方图数据，下同
        InputArray H2,
        int method        // 比较方法，上述四种方法之一
)

 

 

string convertToString(double d);
int main(int argc, char** argv) {
    Mat base, test1, test2;
    Mat hsvbase, hsvtest1, hsvtest2;
    base = imread(STRPAHT);
    if (!base.data) {
        printf("could not load image...\n");
        return -1;
    }
    test1 = imread(STRPAHT2);
    test2 = imread(STRPAHT3);

    //从RGB空间转换到HSV空间
    cvtColor(base, hsvbase, CV_BGR2HSV);
    cvtColor(test1, hsvtest1, CV_BGR2HSV);
    cvtColor(test2, hsvtest2, CV_BGR2HSV);

    //计算直方图并归一化
    int h_bins = 50; int s_bins = 60;
    int histSize[] = { h_bins, s_bins };
    // hue varies from 0 to 179, saturation from 0 to 255     
    float h_ranges[] = { 0, 180 };
    float s_ranges[] = { 0, 256 };
    const float* ranges[] = { h_ranges, s_ranges };
    // Use the o-th and 1-st channels     
    int channels[] = { 0, 1 };
    MatND hist_base;
    MatND hist_test1;
    MatND hist_test2;

    calcHist(&hsvbase, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false);
    normalize(hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat());

    calcHist(&hsvtest1, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false);
    normalize(hist_test1, hist_test1, 0, 1, NORM_MINMAX, -1, Mat());

    calcHist(&hsvtest2, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false);
    normalize(hist_test2, hist_test2, 0, 1, NORM_MINMAX, -1, Mat());

    //比较直方图，并返回值
    double basebase = compareHist(hist_base, hist_base, CV_COMP_INTERSECT);
    double basetest1 = compareHist(hist_base, hist_test1, CV_COMP_INTERSECT);
    double basetest2 = compareHist(hist_base, hist_test2, CV_COMP_INTERSECT);
    double tes1test2 = compareHist(hist_test1, hist_test2, CV_COMP_INTERSECT);

    Mat test12;
    test2.copyTo(test12);
    putText(base, convertToString(basebase), Point(50, 50), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0, 0, 255), 2, LINE_AA);
    putText(test1, convertToString(basetest1), Point(50, 50), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0, 0, 255), 2, LINE_AA);
    putText(test2, convertToString(basetest2), Point(50, 50), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0, 0, 255), 2, LINE_AA);
    putText(test12, convertToString(tes1test2), Point(50, 50), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0, 0, 255), 2, LINE_AA);

    namedWindow("base", CV_WINDOW_AUTOSIZE);
    namedWindow("test1", CV_WINDOW_AUTOSIZE);
    namedWindow("test2", CV_WINDOW_AUTOSIZE);

    imshow("base", base);
    imshow("test1", test1);
    imshow("test2", test2);
    imshow("test12", test12);

    waitKey(0);
    return 0;
}

string convertToString(double d) {
    ostringstream os;
    if (os << d)
        return os.str();
    return "invalid conversion";
}

 

转载于:https://www.cnblogs.com/osbreak/p/11496083.html