OpenCV轮廓检测，计算物体旋转角度

 

 

效果还是有点问题的，希望大家共同探讨一下

 

 

// 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 3.1415926using 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 imagechar *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 componentsstd::vector<std::vector<cv::Point>> contours;cv::findContours(image, contours, // a vector of contours CV_RETR_EXTERNAL, // retrieve the external contoursCV_CHAIN_APPROX_NONE); // retrieve all pixels of each contours// Print contours' lengthstd::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 imagecv::Mat result(image.size(),CV_8U,cv::Scalar(255));cv::drawContours(result,contours,-1, // draw all contourscv::Scalar(0), // in black2); // with a thickness of 2cv::namedWindow("Contours");cv::imshow("Contours",result);// Eliminate too short or too long contoursint cmin= 100;  // minimum contour lengthint cmax= 1000; // maximum contour lengthstd::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 imagecv::Mat original= cv::imread(image_name);cv::drawContours(original,contours,-1, // draw all contourscv::Scalar(255,255,0), // in white2); // with a thickness of 2cv::namedWindow("Contours on original");cv::imshow("Contours on original",original);// Let's now draw black contours on white imageresult.setTo(cv::Scalar(255));cv::drawContours(result,contours,-1, // draw all contourscv::Scalar(0), // in black1); // with a thickness of 1image= 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;}

 

 

 

 

这个是原来实现的代码的博客文章：

http://blog.csdn.net/wangyaninglm/article/details/41864251

 

 

参考文献：

http://blog.csdn.net/z397164725/article/details/7248096

http://blog.csdn.net/fdl19881/article/details/6730112

http://blog.csdn.net/mine1024/article/details/6044856