OpenCV-CameraClabration 相机校准

 

由于针孔成像的原理,所以会使得出现了摄入的图片的扭曲和偏移,所以我们要通过相机校准来恢复图片...
同样相机校准可以用到在真实3d世界中,相关相机测量.
因此在相机的天然单元(像素点)和物理世界之间单元的关联对于3D重建是相当重要的.
在相机校准的过程中,同时给了我们相机的几何模型同 视网膜的扭曲模型.这两种信息的模型定义了相机的内部参数.
相机校准一共会需要有4个内部的参数[针对于相机本身] 和 6个外部参数[针对于于每一张图片]

有个Tool可以从这个链接下载到: http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/example.html

其实也就是20几张棋盘的图片而已:

代码如下:

// HW3CameraCalibration.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include <cv.h>
#include <highgui.h>
#include <cvaux.h>
#include <cxcore.h>
#include <iostream>
#include <string.h>
#include <stdio.h>
#include <Windows.h>
#include <fstream>
#include <cmath>
using namespace std;

#pragma comment(lib,"cv200d")
#pragma comment(lib,"cvaux200d")
#pragma comment(lib,"cxcore200d")
#pragma comment(lib,"highgui200d")
int n_boards = 0;
const int board_dt = 20;
int board_w;
int board_h;
char pic_dir[255] = "Date/img_1.bmp";

string cvt2str( int x )
{
int d = x;
string ans = "";
while( x > 0 )
{
   d = x%10;
   ans = char(d+'0')+ans;
   x /= 10;
}
return ans;
}
void inc( int k )
{
string hd= "Date/img_";
string mid = cvt2str( k );
string last = ".bmp";
int j = 0, i;
for( i = 0, j = 0; i < hd.length(); i ++, j ++) pic_dir[j] = hd[i];
for( i = 0; i < mid.length(); i ++, j ++) pic_dir[j] = mid[i];
for( i = 0; i < last.length(); i ++, j ++) pic_dir[j] = last[i];
cout<<"pic: "<< pic_dir << endl;
}
int _tmain(int argc, char* argv[]) {

board_w = 7;
board_h = 7;
n_boards = 30;
int board_n = board_w * board_h;
CvSize board_sz = cvSize( board_w, board_h );
cvNamedWindow( "Calibration" );
//ALLOCATE STORAGE
CvMat* image_points      = cvCreateMat(n_boards*board_n,2,CV_32FC1);
CvMat* object_points     = cvCreateMat(n_boards*board_n,3,CV_32FC1);
CvMat* point_counts      = cvCreateMat(n_boards,1,CV_32SC1);
CvMat* intrinsic_matrix = cvCreateMat(3,3,CV_32FC1);
CvMat* distortion_coeffs = cvCreateMat(5,1,CV_32FC1);
CvPoint2D32f* corners = new CvPoint2D32f[ board_n ];
int corner_count;
int successes = 0;
int step, frame = 0;

IplImage *image = cvLoadImage(pic_dir);
IplImage *gray_image = cvCreateImage(cvGetSize(image),8,1); //subpixel
// CAPTURE CORNER VIEWS LOOP UNTIL WE’VE GOT n_boards
// SUCCESSFUL CAPTURES (ALL CORNERS ON THE BOARD ARE FOUND)
//
while(successes < n_boards) {
  
   if( successes == n_boards ) break;
   //Skip every board_dt frames to allow user to move chessboard
   if( 1 ) {
    //Find chessboard corners:
    int found = cvFindChessboardCorners(
     image, board_sz, corners, &corner_count,
     CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS
     );
    //Get Subpixel accuracy on those corners
    cvCvtColor(image, gray_image, CV_BGR2GRAY);
    cvFindCornerSubPix(gray_image, corners, corner_count,
     cvSize(11,11),cvSize(-1,-1), cvTermCriteria(
     CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 30, 0.1 ));
    //Draw it
    cvDrawChessboardCorners(image, board_sz, corners,
     corner_count, found);
    cvShowImage( "Calibration", image );
    // If we got a good board, add it to our data
    if( corner_count == board_n ) {
     step = successes*board_n;
     for( int i=step, j=0; j<board_n; ++i,++j ) {
      CV_MAT_ELEM(*image_points, float,i,0) = corners[j].x;
      CV_MAT_ELEM(*image_points, float,i,1) = corners[j].y;
      CV_MAT_ELEM(*object_points,float,i,0) = j/board_w;
      CV_MAT_ELEM(*object_points,float,i,1) = j%board_w;
      CV_MAT_ELEM(*object_points,float,i,2) = 0.0f;
     }
     CV_MAT_ELEM(*point_counts, int,successes,0) = board_n;
     successes++;
    
    }
   } //end skip board_dt between chessboard capture
   //Handle pause/unpause and ESC
    int c = cvWaitKey(15);
   if( c == 'p' ){
    c = 0;
    while(c != 'p' && c != 27){
     c = cvWaitKey(250);
    }
   }
   if(c == 27)
    break;
   inc( successes );
   image = cvLoadImage(pic_dir); //Get next image
}
cout <<" read in done```" << endl;
//END COLLECTION WHILE LOOP.
//ALLOCATE MATRICES ACCORDING TO HOW MANY CHESSBOARDS FOUND
CvMat* object_points2 = cvCreateMat(successes*board_n,3,CV_32FC1);
CvMat* image_points2   = cvCreateMat(successes*board_n,2,CV_32FC1);
CvMat* point_counts2   = cvCreateMat(successes,1,CV_32SC1);

//TRANSFER THE POINTS INTO THE CORRECT SIZE MATRICES
//Below, we write out the details in the next two loops. We could
//instead have written:
//image_points->rows = object_points->rows = \
//successes*board_n; point_counts->rows = successes;
//
for(int i = 0; i<successes*board_n; ++i) {
   CV_MAT_ELEM( *image_points2, float, i, 0) =
    CV_MAT_ELEM( *image_points, float, i, 0);
   CV_MAT_ELEM( *image_points2, float,i,1) =
    CV_MAT_ELEM( *image_points, float, i, 1);

   CV_MAT_ELEM(*object_points2, float, i, 0) =
    CV_MAT_ELEM( *object_points, float, i, 0) ;
   CV_MAT_ELEM( *object_points2, float, i, 1) =
    CV_MAT_ELEM( *object_points, float, i, 1) ;
   CV_MAT_ELEM( *object_points2, float, i, 2) =
    CV_MAT_ELEM( *object_points, float, i, 2) ;
}
for(int i=0; i<successes; ++i){ //These are all the same number
   CV_MAT_ELEM( *point_counts2, int, i, 0) =
    CV_MAT_ELEM( *point_counts, int, i, 0);
}
if(object_points)cvReleaseMat(&object_points);
if(image_points) cvReleaseMat(&image_points);
if(point_counts) cvReleaseMat(&point_counts);
// At this point we have all of the chessboard corners we need.
// Initialize the intrinsic matrix such that the two focal
// lengths have a ratio of 1.0
//
CV_MAT_ELEM( *intrinsic_matrix, float, 0, 0 ) = 1.0f;
CV_MAT_ELEM( *intrinsic_matrix, float, 1, 1 ) = 1.0f;
//CALIBRATE THE CAMERA!
cout << "Calibrate```" << endl;
cvCalibrateCamera2(
   object_points2, image_points2,
   point_counts2, cvGetSize( image ),
   intrinsic_matrix, distortion_coeffs,
   NULL, NULL,0 //CV_CALIB_FIX_ASPECT_RATIO
   );
cout << "Calibrate complete``" << endl;
// SAVE THE INTRINSICS AND DISTORTIONS
cvSave("Intrinsics.xml",intrinsic_matrix);
cvSave("Distortion.xml",distortion_coeffs);
   // EXAMPLE OF LOADING THESE MATRICES BACK IN:
CvMat *intrinsic = (CvMat*)cvLoad("Intrinsics.xml");
CvMat *distortion = (CvMat*)cvLoad("Distortion.xml");
// Build the undistort map that we will use for all
// subsequent frames.
//
IplImage* mapx = cvCreateImage( cvGetSize(image), IPL_DEPTH_32F, 1 );
IplImage* mapy = cvCreateImage( cvGetSize(image), IPL_DEPTH_32F, 1 );
cvInitUndistortMap(
   intrinsic,
   distortion,
   mapx,
   mapy
   );
// Just run the camera to the screen, now showing the raw and
// the undistorted image.
//
cvNamedWindow( "Undistort" );
while(image && successes < 40) {
   IplImage *t = cvCloneImage(image);
   cvShowImage( "Calibration", image ); // Show raw image

   cvRemap( t, image, mapx, mapy );     // Undistort image
   cvReleaseImage(&t);

   cvShowImage( "Undistort", image);     // Show corrected image
   //Handle pause/unpause and ESC
   int c = cvWaitKey(500);
   if(c == 'p' ) {
    c = 0;
    while(c != 'p' && c != 27) {
     c = cvWaitKey(250);
    }
   }
   if(c == 27)
    break;
   successes++;
   inc( successes );
   image = cvLoadImage( pic_dir );
}
return 0;
}

参考了OReilly.Learning.OpenCV.Computer.Vision.with.the.OpenCV.Library.Oct.2008中第十一章的相机校准

一下是图片,可以看到鲜明的改进```:

可以看到未经调整的边缘弯曲很大,而在调整过后的Undistort窗口就直了不少,这便是对相机的参数的一种改进.