OpenCV輪廓檢測，計算物體旋轉角度

OpenCV輪廓檢測，計算物體旋轉角度

效果還是有點問題的，希望大家共同探討一下

 

 

// FindRotation-angle.cpp : 定義控制檯應用程序的入口點。
//

// findContours.cpp : 定義控制檯應用程序的入口點。
//

#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】載入原始圖和Mat變量定義  
 Mat srcImage = src;//imread("1.jpg");  //工程目錄下應該有一張名爲1.jpg的素材圖
 Mat midImage,dstImage;//臨時變量和目標圖的定義

 //【2】進行邊緣檢測和轉化爲灰度圖
 Canny(srcImage, midImage, 50, 200, 3);//進行一此canny邊緣檢測
 cvtColor(midImage,dstImage, CV_GRAY2BGR);//轉化邊緣檢測後的圖爲灰度圖

 //【3】進行霍夫線變換
 vector<Vec4i> lines;//定義一個矢量結構lines用於存放得到的線段矢量集合
 HoughLinesP(midImage, lines, 1, CV_PI/180, 80, 50, 10 );

 //【4】依次在圖中繪製出每條線段
 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】顯示原始圖  
 imshow("【原始圖】", srcImage); 

 //【6】邊緣檢測後的圖 
 imshow("【邊緣檢測後的圖】", midImage); 

 //【7】顯示效果圖  
 imshow("【效果圖】", 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);


 
 // 從文件中加載原圖  
    IplImage *pSrcImage = cvLoadImage(image_name, CV_LOAD_IMAGE_UNCHANGED);  
  
    // 轉爲2值圖
  
  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 
 //////////////////////////////////////////////////////////////////////////////
 //霍夫變換進行直線檢測，此處使用的是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】進行邊緣檢測和轉化爲灰度圖
 //Canny(result_line, tempimage, 50, 200, 3);//進行一此canny邊緣檢測
 //imshow("canny",tempimage);
 //waitKey(0);

 //cvtColor(tempimage,result_line, CV_GRAY2BGR);//轉化邊緣檢測後的圖爲灰度圖
 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);  //開闢內存空間


 CvSeq*      contour = NULL;    //CvSeq類型 存放檢測到的圖像輪廓邊緣所有的像素值，座標值特徵的結構體以鏈表形式

 cvFindContours( pSrcImage, storage, &contour, sizeof(CvContour),CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);//這函數可選參數還有不少

 

 for(; contour; contour = contour->h_next)  //如果contour不爲空，表示找到一個以上輪廓，這樣寫法只顯示一個輪廓
  //如改爲for(; contour; contour = contour->h_next) 就可以同時顯示多個輪廓
 { 

  End_Rage2D = cvMinAreaRect2(contour);  
  //代入cvMinAreaRect2這個函數得到最小包圍矩形  這裏已得出被測物體的角度，寬度,高度，和中點座標點存放在CvBox2D類型的結構體中，
  //主要工作基本結束。
  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;      //被測物體旋轉角度 
 
 }
 cv::imshow("lalalal",result);
 cv::waitKey();
 return 0;


}

這個是原來實現的代碼的博客文章：http://www.linuxidc.com/Linux/2015-02/114135.htm

這個是原來實現的代碼的博客文章：http://www.linuxidc.com/Linux/2015-02/114135.htm