OpenCV轮廓检测，计算物体旋转角度

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 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;}

这个是原来实现的代码的博客文章：http://www.linuxidc.com/Linux/2015-02/114135.htm

这个是原来实现的代码的博客文章：http://www.linuxidc.com/Linux/2015-02/114135.htm