c++ - OpenCV : How to find the center of mass/centroid for motion information

Question

问题是我无法用现有代码实现质心，在检测到的对象被矩形包围后使用哪个图像对象等，这样我就可以获得路径的轨迹。 我正在使用 Opencv2.3 。我发现有两种方法 - Link1和 Link2谈谈moments的用法。另一种方法是使用边界框的信息Link3 .矩量法需要图像阈值化。然而，当使用 SURF 时，图像是灰度的。因此，通过灰色图像进行阈值处理时会显示白色图像!现在，我很难理解我应该如何使用下面的代码计算质心(特别是我应该使用什么而不是 points[i].x 因为我正在使用

obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt )

在我的例子中 numPoints=good_matches.size()，表示特征点的数量)如文档中所述。如果任何人都可以提出如何将 SURF 与质心一起使用的实现，那将会很有帮助。

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"

using namespace cv;

int main()
{
    Mat object = imread( "object.png", CV_LOAD_IMAGE_GRAYSCALE );

    if( !object.data )
    {
        std::cout<< "Error reading object " << std::endl;
        return -1;
    }

    //Detect the keypoints using SURF Detector
    int minHessian = 500;

    SurfFeatureDetector detector( minHessian );
    std::vector<KeyPoint> kp_object;

    detector.detect( object, kp_object );

    //Calculate descriptors (feature vectors)
    SurfDescriptorExtractor extractor;
    Mat des_object;

    extractor.compute( object, kp_object, des_object );

    FlannBasedMatcher matcher;

    VideoCapture cap(0);

    namedWindow("Good Matches");

    std::vector<Point2f> obj_corners(4);

    //Get the corners from the object
    obj_corners[0] = cvPoint(0,0);
    obj_corners[1] = cvPoint( object.cols, 0 );
    obj_corners[2] = cvPoint( object.cols, object.rows );
    obj_corners[3] = cvPoint( 0, object.rows );

    char key = 'a';
    int framecount = 0;
    while (key != 27)
    {
        Mat frame;
        cap >> frame;

        if (framecount < 5)
        {
            framecount++;
            continue;
        }

        Mat des_image, img_matches;
        std::vector<KeyPoint> kp_image;
        std::vector<vector<DMatch > > matches;
        std::vector<DMatch > good_matches;
        std::vector<Point2f> obj;
        std::vector<Point2f> scene;
        std::vector<Point2f> scene_corners(4);
        Mat H;
        Mat image;

        cvtColor(frame, image, CV_RGB2GRAY);

        detector.detect( image, kp_image );
        extractor.compute( image, kp_image, des_image );

        matcher.knnMatch(des_object, des_image, matches, 2);

        for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
        {
            if((matches[i][0].distance < 0.6*(matches[i][4].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
            {
                good_matches.push_back(matches[i][0]);
            }
        }

        //Draw only "good" matches
        drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

        if (good_matches.size() >= 4)
        {
            for( int i = 0; i < good_matches.size(); i++ )
            {
                //Get the keypoints from the good matches
                obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
                scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
            }

            H = findHomography( obj, scene, CV_RANSAC );

            perspectiveTransform( obj_corners, scene_corners, H);

            //Draw lines between the corners (the mapped object in the scene image )
            line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
            line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
            line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
            line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
        }

        //Show detected matches
        imshow( "Good Matches", img_matches );

        key = waitKey(1);
    }
    return 0;
}

Answer 1

所以，你已经得到了你的点列表，

obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );

我认为，基于此计算质心是完全有效的，不需要进一步的图像处理。

有两种方法，“质心”方式，就是所有点的平均位置，如下所示:

Point2f cen(0,0);
for ( size_t i=0; i<scene.size(); i++ )
{
    cen.x += scene[i].x;
    cen.y += scene[i].y;
}
cen.x /= scene.size(); 
cen.y /= scene.size(); 

和“bbox 中心”方式

Point2f pmin(1000000,1000000);
Point2f pmax(0,0);
for ( size_t i=0; i<scene.size(); i++ )
{
    if ( scene[i].x < pmin.x ) pmin.x = scene[i].x;
    if ( scene[i].y < pmin.y ) pmin.y = scene[i].y;
    if ( scene[i].x > pmax.x ) pmax.x = scene[i].x;
    if ( scene[i].y > pmax.y ) pmax.y = scene[i].y;
}
Point2f cen( (pmax.x-pmin.x)/2,  (pmax.y-pmin.y)/2);

请注意，结果会有所不同!它们仅对圆形和正方形、点对称对象相同

// now draw a circle around the centroid:
cv::circle( img, cen, 10, Scalar(0,0,255), 2 );

// and a line connecting the query and train points:
cv::line( img, scene[i], obj[i], Scalar(255,0,0), 2 );