引自:http://www.xuebuyuan.com/1684976.html
http://blog.csdn.net/lichengyu/article/details/38392473
http://www.cnblogs.com/yemeishu/archive/2013/01/19/2867286.html谈谈NITE 2与OpenCV结合提取指尖坐标
一 概念:
Convexity hull, Convexity defects
如上图所示,黑色的轮廓线为convexity hull, 而convexity hull与手掌之间的部分为convexity defects. 每个convexity defect区域有四个特征量:起始点(startPoint),结束点(endPoint),距离convexity hull最远点(farPoint),最远点到convexity hull的距离(depth)。
二.OpenCV中的相关函数
void convexityDefects(InputArray contour, InputArray convexhull, OutputArrayconvexityDefects)
参数:
coutour: 输入参数,检测到的轮廓,可以调用findContours函数得到;
convexhull: 输入参数,检测到的凸包,可以调用convexHull函数得到。注意,convexHull函数可以得到vector<vector<Point>>和vector<vector<int>>两种类型结果,这里的convexhull应该为vector<vector<int>>类型,否则通不过ASSERT检查;
convexityDefects:输出参数,检测到的最终结果,应为vector<vector<Vec4i>>类型,Vec4i存储了起始点(startPoint),结束点(endPoint),距离convexity hull最远点(farPoint)以及最远点到convexity hull的距离(depth)
三.代码
//http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/hull/hull.html //http://www.codeproject.com/Articles/782602/Beginners-guide-to-understand-Fingertips-counting #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <iostream> #include <stdio.h> #include <stdlib.h> using namespace cv; using namespace std; Mat src; Mat src_gray; int thresh = 100; int max_thresh = 255; RNG rng(12345); /// Function header void thresh_callback(int, void* ); /** @function main */ int main( int argc, char** argv ) { /// Load source image and convert it to gray src = imread( argv[1], 1 ); /// Convert image to gray and blur it cvtColor( src, src_gray, CV_BGR2GRAY ); blur( src_gray, src_gray, Size(3,3) ); /// Create Window char* source_window = "Source"; namedWindow( source_window, CV_WINDOW_AUTOSIZE ); imshow( source_window, src ); createTrackbar( " Threshold:", "Source", &thresh, max_thresh, thresh_callback ); thresh_callback( 0, 0 ); waitKey(0); return(0); } /** @function thresh_callback */ void thresh_callback(int, void* ) { Mat src_copy = src.clone(); Mat threshold_output; vector<vector<Point> > contours; vector<Vec4i> hierarchy; /// Detect edges using Threshold threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY ); /// Find contours findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) ); /// Find the convex hull object for each contour vector<vector<Point> >hull( contours.size() ); // Int type hull vector<vector<int>> hullsI( contours.size() ); // Convexity defects vector<vector<Vec4i>> defects( contours.size() ); for( size_t i = 0; i < contours.size(); i++ ) { convexHull( Mat(contours[i]), hull[i], false ); // find int type hull convexHull( Mat(contours[i]), hullsI[i], false ); // get convexity defects convexityDefects(Mat(contours[i]),hullsI[i], defects[i]); } /// Draw contours + hull results Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 ); for( size_t i = 0; i< contours.size(); i++ ) { Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) ); drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() ); drawContours( drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point() ); // draw defects size_t count = contours[i].size(); std::cout<<"Count : "<<count<<std::endl; if( count < 300 ) continue; vector<Vec4i>::iterator d =defects[i].begin(); while( d!=defects[i].end() ) { Vec4i& v=(*d); //if(IndexOfBiggestContour == i) { int startidx=v[0]; Point ptStart( contours[i][startidx] ); // point of the contour where the defect begins int endidx=v[1]; Point ptEnd( contours[i][endidx] ); // point of the contour where the defect ends int faridx=v[2]; Point ptFar( contours[i][faridx] );// the farthest from the convex hull point within the defect int depth = v[3] / 256; // distance between the farthest point and the convex hull if(depth > 20 && depth < 80) { line( drawing, ptStart, ptFar, CV_RGB(0,255,0), 2 ); line( drawing, ptEnd, ptFar, CV_RGB(0,255,0), 2 ); circle( drawing, ptStart, 4, Scalar(255,0,100), 2 ); circle( drawing, ptEnd, 4, Scalar(255,0,100), 2 ); circle( drawing, ptFar, 4, Scalar(100,0,255), 2 ); } /*printf("start(%d,%d) end(%d,%d), far(%d,%d)\n", ptStart.x, ptStart.y, ptEnd.x, ptEnd.y, ptFar.x, ptFar.y);*/ } d++; } } /// Show in a window namedWindow( "Hull demo", CV_WINDOW_AUTOSIZE ); imshow( "Hull demo", drawing ); //imwrite("convexity_defects.jpg", drawing); }
另一个版本的说法
首先介绍今天主角:void convexityDefects(InputArray contour, InputArray、convexhull, OutputArray convexityDefects)
使用时注意,最后一个参数 convexityDefects 是存储 Vec4i 的向量(vector<varname>),函数计算成功后向量的大小是轮廓凸缺陷的数量,向量每个元素Vec4i存储了4个整型数据,因为Vec4i对[]实现了重载,所以可以使用 _vectername[i][0] 来访问向量 _vactername的第i个元素的第一个分量。再说 Vec4i 中存储的四个整形数据,
Opencv 使用这四个元素表示凸缺陷,
第一个名字叫做
start_index,表示缺陷在轮廓上的开始处,他的值是开始点在函数第一个参数 contour 中的下标索引;
Vec4i 第二个元素的名字叫
end_index, 顾名思义其对应的值就是缺陷结束处在 contour 中的下标索引;
Vec4i 第三个元素
farthest_pt_index 是缺陷上距离 轮廓凸包(convexhull)最远的点;
Vec4i最后的元素叫
fixpt_depth,fixpt_depth/256 表示了
轮廓上以 farthest_pt_index 为下标的点到 轮廓凸包的(convexhull)的距离,以像素为单位。
All is so easy!下面就是简单的代码示例(首先计算两个轮廓的凸包,然后计算两个轮廓的凸缺陷):
// 计算凸缺陷 convexityDefect // #include "stdafx.h" #include <opencv.hpp> #include <iostream> using namespace std; using namespace cv; int _tmain(int argc, _TCHAR* argv[]) { Mat *img_01 = new Mat(400, 400, CV_8UC3); Mat *img_02 = new Mat(400, 400, CV_8UC3); *img_01 = Scalar::all(0); *img_02 = Scalar::all(0); // 轮廓点组成的数组 vector<Point> points_01,points_02; // 给轮廓组赋值 points_01.push_back(Point(10, 10));points_01.push_back(Point(10,390)); points_01.push_back(Point(390, 390));points_01.push_back(Point(150, 250)); points_02.push_back(Point(10, 10));points_02.push_back(Point(10,390)); points_02.push_back(Point(390, 390));points_02.push_back(Point(250, 150)); vector<int> hull_01,hull_02; // 计算凸包 convexHull(points_01, hull_01, true); convexHull(points_02, hull_02, true); // 绘制轮廓 for(int i=0;i < 4;++i) { circle(*img_01, points_01[i], 3, Scalar(0,255,255), CV_FILLED, CV_AA); circle(*img_02, points_02[i], 3, Scalar(0,255,255), CV_FILLED, CV_AA); } // 绘制凸包轮廓 CvPoint poi_01 = points_01[hull_01[hull_01.size()-1]]; for(int i=0;i < hull_01.size();++i) { line(*img_01, poi_01, points_01[i], Scalar(255,255,0), 1, CV_AA); poi_01 = points_01[i]; } CvPoint poi_02 = points_02[hull_02[hull_02.size()-1]]; for(int i=0;i < hull_02.size();++i) { line(*img_02, poi_02, points_02[i], Scalar(255,255,0), 1, CV_AA); poi_02 = points_02[i]; } vector<Vec4i> defects; // 如果有凸缺陷就把它画出来 if( isContourConvex(points_01) ) { cout<<"img_01的轮廓是凸包"<<endl; }else{ cout<<"img_01的轮廓不是凸包"<<endl; convexityDefects( points_01, Mat(hull_01), defects ); // 绘制缺陷 cout<<"共"<<defects.size()<<"处缺陷"<<endl; for(int i=0;i < defects.size();++i) { circle(*img_01, points_01[defects[i][0]], 6, Scalar(255,0,0), 2, CV_AA); circle(*img_01, points_01[defects[i][1]], 6, Scalar(255,0,0), 2, CV_AA); circle(*img_01, points_01[defects[i][2]], 6, Scalar(255,0,0), 2, CV_AA); line(*img_01, points_01[defects[i][0]], points_01[defects[i][1]], Scalar(255,0,0), 1, CV_AA); line(*img_01, points_01[defects[i][1]], points_01[defects[i][2]], Scalar(255,0,0), 1, CV_AA); line(*img_01, points_01[defects[i][2]], points_01[defects[i][0]], Scalar(255,0,0), 1, CV_AA); cout<<"第"<<i<<"缺陷<"<<points_01[defects[i][0]].x<<","<<points_01[defects[i][0]].y <<">,<"<<points_01[defects[i][1]].x<<","<<points_01[defects[i][1]].y <<">,<"<<points_01[defects[i][2]].x<<","<<points_01[defects[i][2]].y<<">到轮廓的距离为:"<<defects[i][3]/256<<"px"<<endl; } defects.clear(); } if( isContourConvex( points_02 ) ) { cout<<"img_02的轮廓是凸包"<<endl; }else{ cout<<"img_02的轮廓不是凸包"<<endl; vector<Vec4i> defects; convexityDefects( points_01, Mat(hull_01), defects ); // 绘制出缺陷的轮廓 for(int i=0;i < defects.size();++i) { circle(*img_02, points_01[defects[i][0]], 6, Scalar(255,0,0), 2, CV_AA); circle(*img_02, points_01[defects[i][1]], 6, Scalar(255,0,0), 2, CV_AA); circle(*img_02, points_01[defects[i][2]], 6, Scalar(255,0,0), 2, CV_AA); line(*img_02, points_01[defects[i][0]], points_01[defects[i][1]], Scalar(255,0,0), 1, CV_AA); line(*img_02, points_01[defects[i][1]], points_01[defects[i][2]], Scalar(255,0,0), 1, CV_AA); line(*img_02, points_01[defects[i][2]], points_01[defects[i][0]], Scalar(255,0,0), 1, CV_AA); // 因为 img_02 没有缺陷所以就懒的写那些输出代码了 } defects.clear(); } imshow("img_01 的轮廓和凸包:", *img_01); imshow("img_02 的轮廓和凸包:", *img_02); cvWaitKey(); return 0; }