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;
}

使用匹配形状比较两个轮廓

0 个答案: