opencv使用convexityDefects計算輪廓凸缺陷

weixin_34321977發表於2016-03-29

一概念：

Convexity hull, Convexity defects

如上圖所示，黑色的輪廓線為convexity hull, 而convexity hull與手掌之間的部分為convexity defects. 每個convexity defect區域有四個特徵量：起始點（startPoint），結束點(endPoint)，距離convexity hull最遠點(farPoint)，最遠點到convexity hull的距離(depth)。

二．OpenCV中的相關函式

void convexityDefects(InputArray contour, InputArray convexhull, OutputArrayconvexityDefects)

引數：

coutour: 輸入引數，檢測到的輪廓，可以呼叫findContours函式得到；

convexhull: 輸入引數，檢測到的凸包，可以呼叫convexHull函式得到。注意，convexHull函式可以得到vector<vector<Point>>和vector<vector<int>>兩種型別結果，這裡的convexhull應該為vector<vector<int>>型別，否則通不過ASSERT檢查；

convexityDefects：輸出引數，檢測到的最終結果，應為vector<vector<Vec4i>>型別，Vec4i儲存了起始點（startPoint），結束點(endPoint)，距離convexity hull最遠點(farPoint)以及最遠點到convexity hull的距離(depth)

三．程式碼

//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);
 }

　　另一個版本的說法

首先介紹今天主角：void convexityDefects(InputArray contour, InputArray、convexhull, OutputArray convexityDefects)

使用時注意，最後一個引數 convexityDefects 是儲存 Vec4i 的向量(vector<varname>)，函式計算成功後向量的大小是輪廓凸缺陷的數量，向量每個元素Vec4i儲存了4個整型資料，因為Vec4i對[]實現了過載，所以可以使用 _vectername[i][0] 來訪問向量 _vactername的第i個元素的第一個分量。再說 Vec4i 中儲存的四個整形資料，

Opencv 使用這四個元素表示凸缺陷，

第一個名字叫做 start_index,表示缺陷在輪廓上的開始處，他的值是開始點在函式第一個引數 contour 中的下標索引；

Vec4i 第二個元素的名字叫
end_index，顧名思義其對應的值就是缺陷結束處在 contour 中的下標索引；

Vec4i 第三個元素 farthest_pt_index 是缺陷上距離輪廓凸包(convexhull)最遠的點；

Vec4i最後的元素叫
fixpt_depth，fixpt_depth/256 表示了
輪廓上以 farthest_pt_index 為下標的點到輪廓凸包的(convexhull)的距離,以畫素為單位。

All is so easy!下面就是簡單的程式碼示例（首先計算兩個輪廓的凸包，然後計算兩個輪廓的凸缺陷）:

// 計算凸缺陷 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);
	// 輪廓點組成的陣列
	vector<Point> points_01,points_02;


	// 給輪廓組賦值
	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;
	// 計算凸包
	convexHull(points_01, hull_01, true);
	convexHull(points_02, hull_02, true);

	// 繪製輪廓
	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);
	}
	// 繪製凸包輪廓
	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畫出來
	if( isContourConvex(points_01) )
	{
		cout<<"img_01的輪廓是凸包"<<endl;
	}else{
		cout<<"img_01的輪廓不是凸包"<<endl;
		convexityDefects(
			points_01,
			Mat(hull_01),
			defects
			);
		// 繪製缺陷
		cout<<"共"<<defects.size()<<"處缺陷"<<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<<">到輪廓的距離為："<<defects[i][3]/256<<"px"<<endl;
		}
		defects.clear();
	}
	if( isContourConvex( points_02 ) )
	{
		cout<<"img_02的輪廓是凸包"<<endl;
	}else{
		cout<<"img_02的輪廓不是凸包"<<endl;
		vector<Vec4i> defects;
		convexityDefects(
			points_01,
			Mat(hull_01),
			defects
			);
		// 繪製出缺陷的輪廓
		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);
			// 因為 img_02 沒有缺陷所以就懶的寫那些輸出程式碼了
		}
		defects.clear();
	}

	imshow("img_01 的輪廓和凸包：", *img_01);
	imshow("img_02 的輪廓和凸包：", *img_02);
	cvWaitKey();
	
	return 0;
}

OpenCV計算機視覺學習（8）——影像輪廓處理（輪廓繪製，輪廓檢索，輪廓填充，輪廓近似）
2020-10-26
OpenCV計算機視覺
OpenCV 形狀分析（上）：計算輪廓中心
2016-07-07
OpenCV
OpenCV 查詢輪廓
2021-06-07
OpenCV
opencv——輪廓發現與輪廓(二值影像)分析
2021-05-22
OpenCV
【OpenCV教程】輪廓檢測過程
2024-08-20
OpenCV
Python 影像處理 OpenCV （15）：影像輪廓
2020-07-28
PythonOpenCV
opencv查詢輪廓---cvFindContours && cvDrawCountours 用法及例子
2015-11-13
OpenCV
OpenCv查詢輪廓-cv2.findContours()函式
2021-03-01
OpenCV函式
OpenCV7影像金字塔與輪廓檢測
2020-11-18
OpenCV
Halcon缺陷檢測例項轉OpenCV實現(三) 物體凸缺陷檢測
2020-12-14
OpenCV
影像輪廓處理
2022-12-23
CSS outline輪廓
2018-07-28
CSS
Python-OpenCV 處理影象（五）：影象中邊界和輪廓檢測
2016-05-30
PythonOpenCV
OpenCV使用ParallelLoopBody進行平行計算
2020-12-09
OpenCVParallelOOP
answerOpenCV輪廓類問題解析
2018-07-20
OpenCV
【Python】【OpenCV】關於cv2.findContours()輪廓索引（編號）解析（RETR_TREE）
2023-12-26
PythonOpenCV索引
二值影象分析之輪廓分析
2017-05-02
AsyncDisplayKit原始碼分析（一）輪廓梳理
2017-12-13
原始碼
怎樣用好CorelDRAW中輪廓圖工具
2016-09-07
OpenCV-Python教程（9）（10）（11）：使用霍夫變換檢測直線直方圖均衡化輪廓檢測
2016-05-05
OpenCVPython直方圖
CSS 不規則的輪廓-outline
2022-05-13
CSS
python-opencv 影像捕捉多個不規則輪廓，與輪廓內接區域(圓/矩形)思路-持續更新編輯中(會附上詳細的思路解釋和圖片)
2020-09-08
PythonOpenCV
opencv計算曲線長度
2024-04-30
OpenCV
邊框(Border) 和輪廓(Outline) 屬性
2022-04-17
python如何獲取最優輪廓係數
2021-09-11
Python
地圖小區景點邊界輪廓實現
2018-09-27
地圖
利用marching square algorithm獲取圖形輪廓
2013-12-12
Go
刪除按鈕點選後的虛線輪廓
2020-03-12
CorelDRAW輪廓描摹點陣圖結合案例詳解
2017-07-11
計算幾何（一）：凸包問題（Convex Hull）
2020-09-22
Halcon缺陷檢測例項轉OpenCV實現(二) PCB印刷缺陷檢測
2020-12-14
OpenCV
WPF 筆跡演算法從點集轉筆跡輪廓
2023-10-12
演算法
使用Java8的Nashorn彌補Node.js密集計算的缺陷
2014-03-26
JavaNode.js
opencv 開運算、閉運算
2020-06-07
OpenCV
“鑽石的缺陷”為量子計算機提供完美介面VG
2022-03-14
計算機
OpenCV矩陣運算
2016-12-28
OpenCV矩陣
WPS演示教程：妙用自定義動畫輕鬆繪製城市輪廓圖
2016-09-27
動畫
自定義 View 梳理：用貝塞爾曲線繪製酷炫輪廓背景
2017-09-01
View

opencv使用convexityDefects計算輪廓凸缺陷

相關文章