OpenCV轮廓检测 物体旋转角度

原文地址：http://blog.csdn.net/wangyaninglm/article/details/43959947

 

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
#include
#include
#include
#include
#include


#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 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> 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>::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>::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 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>::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