在opencv c ++函数中找到凸缺陷点

Question

我正在尝试恢复凸性缺陷点，但该函数只返回整数，你能告诉我如何找到这些点吗？

    vector<vector<Point> >hull2( contours.size() );
          vector<vector<int>> hull(contours.size());
std::vector<cv::Vec4i> convexityDefectsSet;    

   for( int i = 0; i < contours.size(); i++ )   {
      convexHull( Mat(contours[i]), hull[i], false );
        convexHull(Mat(contours[i]), hull2[i], false);
      if (contours[i].size() > 3) {
      cv::convexityDefects(Mat(contours[i]), hull[i], convexityDefectsSet);

      for (int cDefIt = 0; cDefIt < convexityDefectsSet.size(); cDefIt++) {

            int startIdx = convexityDefectsSet[cDefIt].val[0];

            int endIdx = convexityDefectsSet[cDefIt].val[1];

            int defectPtIdx = convexityDefectsSet[cDefIt].val[2];

            double depth = static_cast<double>(convexityDefectsSet[cDefIt].val[3]) / 256.0;

            std::cout << startIdx << ' ' << endIdx << ' ' << defectPtIdx << ' ' << depth << '\n' << '\n' << std::endl;

            Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
            Point2f p(defectPtIdx, defectPtIdx);
            circle(frame, p , 10, color, 2, 8, 0 );
        }
      }}

Answer 1

我认为这是我的一段代码（应该检测手（需要调整颜色检测器）并搜索转换缺陷）。但是您可以将其用作代码的基础：

#include <stdlib.h>
#include <stdio.h>
#include <iostream>
#include <ctype.h>
#include <time.h> 

#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\video\tracking.hpp>
#include <opencv2\highgui\highgui.hpp>

using namespace cv;
using namespace std;

// Detect Skin from YCrCb
Mat DetectYCrCb(Mat img, Scalar min, Scalar max) {
    Mat skin;
    cvtColor(img, skin, cv::COLOR_BGR2YCrCb);
    inRange(skin, min, max, skin);
    Mat rect_12 = getStructuringElement(cv::MORPH_RECT, Size(12,12) , Point(6,6));
    erode(skin, skin, rect_12,Point(),1);
    Mat rect_6 = getStructuringElement(cv::MORPH_RECT, Size(6,6) , Point(3,3));
    dilate(skin,skin,rect_6,Point(),2);
    return skin;    
}

void DetectContour(Mat img){
    Mat drawing = Mat::zeros( img.size(), CV_8UC3 );
    vector<vector<Point> > contours;
    vector<vector<Point> > bigContours;
    vector<Vec4i> hierarchy;

    findContours(img,contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE, Point());

    if(contours.size()>0)
    {
        vector<std::vector<int> >hull( contours.size() );
        vector<vector<Vec4i>> convDef(contours.size() );
        vector<vector<Point>> hull_points(contours.size());
        vector<vector<Point>> defect_points(contours.size());


        for( int i = 0; i < contours.size(); i++ )
        {
            if(contourArea(contours[i])>5000)
            {
                convexHull( contours[i], hull[i], false );
                convexityDefects( contours[i],hull[i], convDef[i]);

                for(int k=0;k<hull[i].size();k++)
                {           
                    int ind=hull[i][k];
                    hull_points[i].push_back(contours[i][ind]);
                }

                for(int k=0;k<convDef[i].size();k++)
                {           
                    if(convDef[i][k][3]>20*256) // filter defects by depth
                    {
                    int ind_0=convDef[i][k][0];
                    int ind_1=convDef[i][k][1];
                    int ind_2=convDef[i][k][2];
                    defect_points[i].push_back(contours[i][ind_2]);
                    cv::circle(drawing,contours[i][ind_0],5,Scalar(0,255,0),-1);
                    cv::circle(drawing,contours[i][ind_1],5,Scalar(0,255,0),-1);
                    cv::circle(drawing,contours[i][ind_2],5,Scalar(0,0,255),-1);
                    cv::line(drawing,contours[i][ind_2],contours[i][ind_0],Scalar(0,0,255),1);
                    cv::line(drawing,contours[i][ind_2],contours[i][ind_1],Scalar(0,0,255),1);
                    }
                }

                drawContours( drawing, contours, i, Scalar(0,255,0), 1, 8, vector<Vec4i>(), 0, Point() );
                drawContours( drawing, hull_points, i, Scalar(255,0,0), 1, 8, vector<Vec4i>(), 0, Point() );
            }
        }
    }
    imshow( "Hull demo", drawing );
}


int main( int argc, char** argv )
{
    Mat frame,copyFrame;
    VideoCapture capture(0);
    namedWindow( "Hull demo", cv::WINDOW_AUTOSIZE );
    namedWindow( "Video", cv::WINDOW_AUTOSIZE );
    if (capture.isOpened()){
        while(true)
        {
            capture >> frame;
            imshow( "Video", frame);

            Mat skinYCrCb = DetectYCrCb(frame,Scalar(0, 100, 80), Scalar(255, 185, 135));

            DetectContour(skinYCrCb);

            int c = waitKey(10);
            if( (char)c == 27 ) 
            { 
                break; 
            } 
        }
    }
    cv::destroyAllWindows();
    return 0;
}

Answer 2

根据documentation：

In C++ and the new Python/Java interface each convexity defect is represented 
as 4-element integer vector [...]: (start_index, end_index, farthest_pt_index, 
fixpt_depth), where indices are 0-based indices in the original contour of 
the convexity defect...

它们对应于用于生成凸包的原始轮廓中的索引，即您的 contours[i] 变量。 例如，第一个点的坐标通过以下方式获得：

cv::Point start = contours.at(startIdx);