OpenCV轮廓检测，计算物体旋转角度

// FindRotation-angle.cpp : 定义控制台应用程序的入口点。
//

// findContours.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"



#include <iostream>
#include <vector>
#include <opencv2/opencv.hpp> 
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>


#pragma comment(lib,"opencv_core2410d.lib")      
#pragma comment(lib,"opencv_highgui2410d.lib")      
#pragma comment(lib,"opencv_imgproc2410d.lib") 

#define PI 3.1415926

using namespace std;
using namespace cv;



int hough_line(Mat src)
{
	//【1】载入原始图和Mat变量定义   
	Mat srcImage = src;//imread("1.jpg");  //工程目录下应该有一张名为1.jpg的素材图
	Mat midImage,dstImage;//临时变量和目标图的定义

	//【2】进行边缘检测和转化为灰度图
	Canny(srcImage, midImage, 50, 200, 3);//进行一此canny边缘检测
	cvtColor(midImage,dstImage, CV_GRAY2BGR);//转化边缘检测后的图为灰度图

	//【3】进行霍夫线变换
	vector<Vec4i> lines;//定义一个矢量结构lines用于存放得到的线段矢量集合
	HoughLinesP(midImage, lines, 1, CV_PI/180, 80, 50, 10 );

	//【4】依次在图中绘制出每条线段
	for( size_t i = 0; i < lines.size(); i++ )
	{
		Vec4i l = lines[i];
		line( dstImage, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(186,88,255), 1, CV_AA);
	}

	//【5】显示原始图  
	imshow("【原始图】", srcImage);  

	//【6】边缘检测后的图 
	imshow("【边缘检测后的图】", midImage);  

	//【7】显示效果图  
	imshow("【效果图】", dstImage);  

	//waitKey(0);  

	return 0;  
}

int main()
{
	// Read input binary image

	char *image_name = "test.jpg";
	cv::Mat image = cv::imread(image_name,0);
	if (!image.data)
		return 0; 

	cv::namedWindow("Binary Image");
	cv::imshow("Binary Image",image);


	
	// 从文件中加载原图  
	   IplImage *pSrcImage = cvLoadImage(image_name, CV_LOAD_IMAGE_UNCHANGED);  
	  
		   // 转为2值图
		
	 cvThreshold(pSrcImage,pSrcImage,200,255,cv::THRESH_BINARY_INV);
		   
	
	   image = cv::Mat(pSrcImage,true);

	   cv::imwrite("binary.jpg",image);

	// Get the contours of the connected components
	std::vector<std::vector<cv::Point>> contours;
	cv::findContours(image, 
		contours, // a vector of contours 
		CV_RETR_EXTERNAL, // retrieve the external contours
		CV_CHAIN_APPROX_NONE); // retrieve all pixels of each contours

	// Print contours' length
	std::cout << "Contours: " << contours.size() << std::endl;
	std::vector<std::vector<cv::Point>>::const_iterator itContours= contours.begin();
	for ( ; itContours!=contours.end(); ++itContours) 
	{

		std::cout << "Size: " << itContours->size() << std::endl;
	}

	// draw black contours on white image
	cv::Mat result(image.size(),CV_8U,cv::Scalar(255));
	cv::drawContours(result,contours,
		-1, // draw all contours
		cv::Scalar(0), // in black
		2); // with a thickness of 2

	cv::namedWindow("Contours");
	cv::imshow("Contours",result);






	// Eliminate too short or too long contours
	int cmin= 100;  // minimum contour length
	int cmax= 1000; // maximum contour length
	std::vector<std::vector<cv::Point>>::const_iterator itc= contours.begin();
	while (itc!=contours.end()) {

		if (itc->size() < cmin || itc->size() > cmax)
			itc= contours.erase(itc);
		else 
			++itc;
	}

	// draw contours on the original image
	cv::Mat original= cv::imread(image_name);
	cv::drawContours(original,contours,
		-1, // draw all contours
		cv::Scalar(255,255,0), // in white
		2); // with a thickness of 2

	cv::namedWindow("Contours on original");
	cv::imshow("Contours on original",original);

	

	// Let's now draw black contours on white image
	result.setTo(cv::Scalar(255));
	cv::drawContours(result,contours,
		-1, // draw all contours
		cv::Scalar(0), // in black
		1); // with a thickness of 1
	image= cv::imread("binary.jpg",0);

	//imshow("lll",result);
	//waitKey(0);

	// testing the bounding box 
	//////////////////////////////////////////////////////////////////////////////
	//霍夫变换进行直线检测，此处使用的是probabilistic Hough transform（cv::HoughLinesP）而不是standard Hough transform（cv::HoughLines）

	cv::Mat result_line(image.size(),CV_8U,cv::Scalar(255));
	result_line = result.clone();

	hough_line(result_line);

	//Mat tempimage;

	//【2】进行边缘检测和转化为灰度图
	//Canny(result_line, tempimage, 50, 200, 3);//进行一此canny边缘检测
	//imshow("canny",tempimage);
	//waitKey(0);

	//cvtColor(tempimage,result_line, CV_GRAY2BGR);//转化边缘检测后的图为灰度图
	vector<Vec4i> lines;

	cv::HoughLinesP(result_line,lines,1,CV_PI/180,80,50,10);

	for(int i = 0; i < lines.size(); i++)
	{
		line(result_line,cv::Point(lines[i][0],lines[i][1]),cv::Point(lines[i][2],lines[i][3]),Scalar(0,0,0),2,8,0);
	}
	cv::namedWindow("line");
	cv::imshow("line",result_line);
	//waitKey(0);

	/////////////////////////////////////////////////////////////////////////////////////////////
	//

	//std::vector<std::vector<cv::Point>>::const_iterator itc_rec= contours.begin();
	//while (itc_rec!=contours.end())
	//{
	//	cv::Rect r0= cv::boundingRect(cv::Mat(*(itc_rec)));
	//	cv::rectangle(result,r0,cv::Scalar(0),2);
	//		++itc_rec;
	//}

	

	//cv::namedWindow("Some Shape descriptors");
	//cv::imshow("Some Shape descriptors",result);


	CvBox2D     End_Rage2D;
	CvPoint2D32f rectpoint[4];
	CvMemStorage *storage = cvCreateMemStorage(0);  //开辟内存空间


	CvSeq*      contour = NULL;     //CvSeq类型 存放检测到的图像轮廓边缘所有的像素值，坐标值特征的结构体以链表形式

	cvFindContours( pSrcImage, storage, &contour, sizeof(CvContour),CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);//这函数可选参数还有不少



	for(; contour; contour = contour->h_next)   //如果contour不为空，表示找到一个以上轮廓，这样写法只显示一个轮廓
		//如改为for(; contour; contour = contour->h_next) 就可以同时显示多个轮廓
	{  

		End_Rage2D = cvMinAreaRect2(contour);  
		//代入cvMinAreaRect2这个函数得到最小包围矩形  这里已得出被测物体的角度，宽度,高度，和中点坐标点存放在CvBox2D类型的结构体中，
		//主要工作基本结束。
		for(int i = 0;i< 4;i++)
		{
			  //CvArr* s=(CvArr*)&result;
			//cvLine(s,cvPointFrom32f(rectpoint[i]),cvPointFrom32f(rectpoint[(i+1)%4]),CV_G(0,0,255),2);
			line(result,cvPointFrom32f(rectpoint[i]),cvPointFrom32f(rectpoint[(i+1)%4]),Scalar(125),2);
		} 
		cvBoxPoints(End_Rage2D,rectpoint);
	
	std::cout <<" angle:\n"<<(float)End_Rage2D.angle << std::endl;      //被测物体旋转角度 
	
	}
	cv::imshow("lalalal",result);
	cv::waitKey();
	return 0;


}