Image Processing27（Image Moments )

Goal

In this tutorial you will learn how to:

• Use the OpenCV function cv::moments
• 计算图像的矩
  
• Use the OpenCV function cv::contourArea
• 计算轮廓面积
  
• Use the OpenCV function cv::arcLength
• 计算轮廓或者曲线的长度
  

Theory

Code

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>

using namespace cv;
using namespace std;

Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);
void thresh_callback(int, void* );
int main( int argc, char** argv )

{
  CommandLineParser parser( argc, argv, "{@input | lena.jpg | input image}" );
  src = imread( parser.get<String>( "@input" ), IMREAD_COLOR );
  if( src.empty() )
  {
      cout << "Could not open or find the image!\n" << endl;
      cout << "usage: " << argv[0] << " <Input image>" << endl;
      exit(0);
  }
  cvtColor( src, src_gray, COLOR_BGR2GRAY );
  blur( src_gray, src_gray, Size(3,3) );
  const char* source_window = "Source";
  namedWindow( source_window, WINDOW_AUTOSIZE );
  imshow( source_window, src );
  createTrackbar( " Canny thresh:", "Source", &thresh, max_thresh, thresh_callback );
  thresh_callback( 0, 0 );
  waitKey(0);
  return(0);
}
void thresh_callback(int, void* )
{
  Mat canny_output;
  vector<vector<Point> > contours;
  Canny( src_gray, canny_output, thresh, thresh*2, 3 );
  findContours( canny_output, contours, RETR_TREE, CHAIN_APPROX_SIMPLE );
 
  vector<Moments> mu(contours.size() );
  for( size_t i = 0; i < contours.size(); i++ )
     { mu[i] = moments( contours[i], false ); }
     
  vector<Point2f> mc( contours.size() );
  for( size_t i = 0; i < contours.size(); i++ )
     { mc[i] = Point2f( static_cast<float>(mu[i].m10/mu[i].m00) , static_cast<float>(mu[i].m01/mu[i].m00) ); }
   //m00 m10 m01 m20 m11 m02 m30 m21 m12 m03  
   //计算出来质量中心
     
  Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
  for( size_t i = 0; i< contours.size(); i++ )
     {
       Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
       drawContours( drawing, contours, (int)i, color, 2, LINE_8 );
       circle( drawing, mc[i], 4, color, -1, 8, 0 );
     }
  namedWindow( "Contours", WINDOW_AUTOSIZE );
  imshow( "Contours", drawing );
  printf("\t Info: Area and Contour Length \n");
 
  for( size_t i = 0; i< contours.size(); i++ )
     {
       printf(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f \n", (int)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, (int)i, color, 2, LINE_8 );
       circle( drawing, mc[i], 4, color, -1, 8, 0 );
     }
}

Explanation

struct returned by cv::moments

The spatial moments Moments::mji are computed as:(几何矩的计算方式)

mji=∑x,y(array(x,y)⋅xj⋅yi)

The central moments Moments::muji are computed as:(中心矩的计算方式)

muji=∑x,y(array(x,y)⋅(x−x¯)j⋅(y−y¯)i)

where (x¯,y¯) is the mass center:（质心的计算方式）

x¯=m10m00,y¯=m01m00

The normalized central moments Moments::nuij are computed as:（归一化）

nuji=mujim(i+j)/2+100.

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)

set(CMAKE_CXX_FLAGS "-std=c++11")
project( DisplayImage )
find_package( OpenCV REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )
add_executable( DisplayImage main.cpp )
target_link_libraries( DisplayImage ${OpenCV_LIBS} )

install(TARGETS DisplayImage RUNTIME DESTINATION bin

Results