使用匹配形状比较两个轮廓

Question

我试图在opencv中使用matchshapes（）函数比较两个轮廓 这些是我想要比较的两个图像 问题是matchshapes（）显示这两个图像几乎匹配，即返回一个非常小的值。我不知道我们是否可以使用matchshapes（）来比较不同的轮廓形状 你能否提出一些方法来比较手的不同轮廓形状？
这是我的代码..

#include <opencv2\opencv.hpp>
#include <string>

using namespace cv;
using std::cout;
Mat1b converttogray(Mat3b testcases);
int findmaxcontour(vector<vector<Point>> contours);
RNG rng(12345);
vector<vector<Point>> getcontours(Mat1b image_gray, int i);

/*--------------- SKIN SEGMENTATION ---------------*/
int main() 
{
    Mat1b image_gray;
    int max_contour;
    vector<vector<Point> > contours;
    vector<vector<Point> >  contours2;
    Mat3b first;
    Mat3b frame;
    Mat1b frame_gray;
    String one = "E:/photo/" + std::to_string((0 + 5));
    String a = "a.png";
    String b = "b.png";
    String c = "c.png";
    cout << one + a << "\n";
    cout << one + b << "\n";
    cout << one + c << "\n";
    first = imread(one + c, CV_LOAD_IMAGE_COLOR);

    image_gray = converttogray(first);

    contours = getcontours(image_gray, 1);

    max_contour = findmaxcontour(contours);

    frame = imread("E:/photo/1.png", CV_LOAD_IMAGE_COLOR);

    frame_gray = converttogray(frame);

    contours2 = getcontours(frame_gray, 2);

    int x = findmaxcontour(contours2);

    cout << "\n" << matchShapes(contours[max_contour], contours2[x], CV_CONTOURS_MATCH_I1, 0.0);

    waitKey(0);
}

Mat1b converttogray(Mat3b testcases)
{
    Mat1b image_gray;
    cvtColor(testcases, testcases, CV_BGR2HSV);

    GaussianBlur(testcases, testcases, Size(7, 7), 1, 1);
    medianBlur(testcases, testcases, 15);
    for (int r = 0; r<testcases.rows; ++r)
    {
        for (int c = 0; c<testcases.cols; ++c)
        // 0<H<0.25  -   0.15<S<0.9    -    0.2<V<0.95   
        if ((testcases(r, c)[0]>5) && (testcases(r, c)[0] < 17) && (testcases(r, c)[1]>38) && (testcases(r, c)[1]<250) && (testcases(r, c)[2]>51) && (testcases(r, c)[2]<242)); // do nothing
        else for (int i = 0; i<3; ++i)  testcases(r, c)[i] = 0;
    }

    cvtColor(testcases, testcases, CV_HSV2BGR);
    cvtColor(testcases, image_gray, CV_BGR2GRAY);
    threshold(image_gray, image_gray, 30, 255, CV_THRESH_BINARY);
    morphologyEx(image_gray, image_gray, CV_MOP_ERODE, Mat1b(3, 3, 1), Point(-1, -1), 3);
    morphologyEx(image_gray, image_gray, CV_MOP_OPEN, Mat1b(7, 7, 1), Point(-1, -1), 1);
    morphologyEx(image_gray, image_gray, CV_MOP_CLOSE, Mat1b(9, 9, 1), Point(-1, -1), 1);

    medianBlur(image_gray, image_gray, 15);
    return image_gray;
}

int findmaxcontour(vector<vector<Point>> contours)
{
    int largest_area = 0;
    int largest_contour_index = 0;

    for (int i = 0; i< contours.size(); i++) // iterate through each contour. 
    {
        double a = contourArea(contours[i], false);  //  Find the area of contour
        cout << a << "\n";
        if (a>largest_area)
        {
            largest_area = a;
            largest_contour_index = i;                //Store the index of largest contour
        }
    }

    return largest_contour_index;
}

vector<vector<Point>> getcontours(Mat1b image_gray, int i)
{
    Mat canny_output;
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    Canny(image_gray, canny_output, 50, 150, 3);

    int x=0;

    findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
    x = findmaxcontour(contours);

    Mat drawing = Mat::zeros(canny_output.size(), CV_8UC3);
    for (int i = 0; i< contours.size(); i++)
    {
        Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
        drawContours(drawing, contours, x, color, 2, 8, hierarchy, 0, Point());
    }

    /// Show in a window
    namedWindow(std::to_string(i), CV_WINDOW_AUTOSIZE);
    imshow(std::to_string(i), drawing);
    return contours;
}