检测边缘（连接边缘）并找到边缘长度和连接分量回转半径

Posted 2023-02-22

【中文标题】检测边缘（连接边缘）并找到边缘长度和连接分量回转半径

【英文标题】：detecting edges(connected edges) and finding edge length and connected component Radius of gyration

【发布时间】：2016-12-16 18:31:19

【问题描述】：

original image 我正在尝试检测此图像中连接边界的集群。我需要找到这些边缘的长度以及各个集群的回转半径。 我正在使用 opencv 2.4.13。 我使用以下代码使用轮廓检测​​质量簇。

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;

Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);

/// Function header
void thresh_callback(int, void* );

/** @function main */
int main( int argc, char** argv )

  /// Load source image and convert it to gray
  src = imread( argv[1], 1 );

  /// Convert image to gray and blur it
  cvtColor( src, src_gray, CV_BGR2GRAY );
  blur( src_gray, src_gray, Size(3,3) );

  /// Create Window
  char* source_window = "Source";
  namedWindow( source_window, CV_WINDOW_AUTOSIZE );
  imshow( source_window, src );

  createTrackbar( " Canny thresh:", "Source", &thresh, max_thresh, thresh_callback );
  thresh_callback( 0, 0 );

  waitKey(0);
  return(0);


/** @function thresh_callback */
void thresh_callback(int, void* )

  Mat canny_output;
  vector<vector<Point> > contours;
  vector<Vec4i> hierarchy;

  /// Detect edges using canny
  Canny( src_gray, canny_output, thresh, thresh*2, 3 );
  /// Find contours
  findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );

  /// Get the moments
  vector<Moments> mu(contours.size() );
  for( int i = 0; i < contours.size(); i++ )
      mu[i] = moments( contours[i], false ); 

  ///  Get the mass centers:
  vector<Point2f> mc( contours.size() );
  for( int i = 0; i < contours.size(); i++ )
      mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); 

  /// Draw contours
  Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
  Mat drawing2 = Mat::zeros( canny_output.size(), CV_8UC3 );
  for( int i = 0; i< contours.size(); i++ )
     if(arcLength( contours[i], true )>900)
       Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
       drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
       circle( drawing, mc[i], 4, color, -1, 8, 0 );
     
     int length=0;
     int j=0;
   for( int i = 0; i< contours.size(); i++ )
   
    if(arcLength( contours[i], true )>length)
    
        length=arcLength( contours[i], true );
        j=i;
    
    
   Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
       drawContours( drawing2, contours, j, color, 2, 8, hierarchy, 0, Point() );
       circle( drawing2, mc[j], 4, color, -1, 8, 0 ); 

  /// Show in a window
  namedWindow( "Contours", CV_WINDOW_AUTOSIZE );
  imshow( "Contours", drawing );
  namedWindow( "Contours2", CV_WINDOW_AUTOSIZE );
  imshow( "Contours_max", drawing2 );

  /// Calculate the area with the moments 00 and compare with the result of the OpenCV function
  printf("\t Info: Area and Contour Length \n");
  for( int i = 0; i< contours.size(); i++ )
     

        if(arcLength( contours[i], true )>900)
       printf(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f \n", i, mu[i].m00, contourArea(contours[i]), arcLength( contours[i], true ) );
       Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
       drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
       circle( drawing, mc[i], 4, color, -1, 8, 0 );
     

问题是公共共享边的轮廓变得不同，从逻辑上讲，它们应该属于同一个集群。我给出的以下轮廓图像。 contour extracted above a certain length

我们可以看到许多具有相同共享边的轮廓被分别视为不同的轮廓。我希望它们成为相同边界集群的一部分。还建议我如何检测边界的长度和回转半径。 请帮忙。

【参考方案1】：

我对你的问题感到非常困惑（希望在评论中澄清，但我太菜鸟无法评论）

根据我的所见所闻，我唯一的建议是您可能不想使用精明的过滤器。需要明确的是，您的原始图像已经有边缘......运行一个精明的过滤器会给你“双边缘”，我认为你不想要，但同样，我什至不确定你想要实现什么。