攝像頭校正 camera calibration - part3 OpenCV programming

攝像頭校正 camera calibration - part3 OpenCV programming

在前兩篇文章  camera calibration -part1 camera model 以及 camera calibration -part2 camera calibration 中介紹了 camera 內部參數(intrinsic parameter) , 形變參數 (distortion parameter) 以及校正的方法和原理, 本文說明如何使用 OpenCV 實作camera校正 程序.
校正的程序分為兩部分;




          (1)影像的擷取,
          (2)讀取步驟 (1) 擷取的影像序列, 進行校正.
第 (1) 部分的功能是影像擷取以及存檔, 儲存的影像序列供步驟 (2) 使用, 這部分的程式代碼比較簡單:

01 #include <stdio.h>
02 #include <iostream>
03 #include <vector>
04 #include "StdAfx.h"
05 #include "CameraDS.h"
06 
07 #include <cv.h>
08 #include <highgui.h>
09 #include "opencv2/opencv.hpp"
10 
11 
12 using namespace cv;
13 using namespace std;
14 
15 
16 #define CAP_WIDTH  640
17 #define CAP_HEIGHT 480
18 int main()
19 {
20     cv::Mat img;
21     IplImage* capimg;
22     int  DeviceCamCount = 0;
23     CCameraDS *pCamDS   = NULL;
24 
25     int        CamCount = 0;
26     int        CamIndex[10];
27 
28     pCamDS  = new CCameraDS();
29     DeviceCamCount = pCamDS->CameraCount();
30     if ( DeviceCamCount == 0 )
31     {
32         printf( "No Camera detected....\n" );
33         exit(0);
34     }
35 
36     for (int i = 0, j = 0; i < DeviceCamCount; i++)
37     {
38         char szCamName[1024];
39 
40         int retval = pCamDS->CameraName(i, szCamName, sizeof(szCamName));
41 
42         if (retval >0)
43         {
44             printf("Camera #%d's Name is '%s'.\n", i, szCamName);
45             if ( !strcmp( szCamName, "USB µø°T¸Ë¸m" ) )
46             {
47                 CamIndex[ j++ ] = i;
48                 CamCount++;
49             }
50         }
51         else
52         {
53             printf("Can not get Camera #%d's name.\n", i);
54         }
55     }
56 
57     if ( pCamDS->OpenCamera( CamIndex[0], false,
58                              CAP_WIDTH, CAP_HEIGHT ) == false )
59     {
60         printf( "USB Camera 0 do not support w=%d:h=%d\n",
61                 CAP_WIDTH, CAP_HEIGHT );
62         return (-1);
63     }
64 
65     int loop  = 1;
66     int key;
67     int file_idx = 0;
68     char filename[256];
69     char fileidx[10];
70 
71     while ( loop )
72     {
73         //camera >> img;
74         capimg=pCamDS->QueryFrame();
75         img = capimg;
76         if (img.empty())
77         {
78             std::cerr << "ERROR: Could not capture image" << std::endl;
79         }
80         imshow("image", img);
81         key = cvWaitKey( 5 );
82         if ( key == 27 )
83         {
84             loop = 0;
85         }
86         else if (key =='s' || key=='S' )
87         {
88             sprintf( fileidx, "%03d", file_idx++ );
89             strcpy( filename, "SAVE_IMG" );
90             strcat( filename, fileidx );
91             strcat( filename, ".bmp" );
92 
93             imwrite( filename, img );
94             printf("Image file %s saved.\n", filename);
95         }
96         else if (key =='r' || key=='R' )
97         {
98             file_idx = 0;
99         }
100     }
101     //camera.close();
102 }
103 

代碼 86~95, 當擺放好 校正板後, 按下 S 鍵, 就可以存檔. 檔名固定為 SAVE_IMGXXX.bmp
如圖一所式 為筆者測試用的影像序列:

圖一 校正用影像序列

擺放的訣竅在於儘量放置在畫面的邊緣, 並且包含不同的深度. 基本上需要 15~20 張有效的影像.

第二部分的程式代碼如下:

01 #include <stdio.h>
02 #include <iostream>
03 #include <vector>
04 #include "StdAfx.h"
05 #include "CameraDS.h"
06 
07 #include <cv.h>
08 #include <highgui.h>
09 #include "opencv2/opencv.hpp"
10 
11 
12 using namespace cv;
13 using namespace std;
14 
15 
16 #define CAP_WIDTH  640
17 #define CAP_HEIGHT 480
18 
19 std::vector<std::vector<cv::Point3f>> objectPoints;
20 std::vector<std::vector<cv::Point2f>> imagePoints;
21 
22 cv::Mat cameraMatrix;
23 cv::Mat distCoeffs;
24 
25 cv::Mat map1,map2;
26 
27 int main()
28 {
29     cv::Mat img;
30     cv::Mat img2;
31     IplImage* capimg;
32     int  DeviceCamCount = 0;
33     CCameraDS *pCamDS   = NULL;
34 
35     int        CamCount = 0;
36     int        CamIndex[10];
37 
38     pCamDS  = new CCameraDS();
39     DeviceCamCount = pCamDS->CameraCount();
40 
41     int loop  = 1;
42     int key;
43     int file_idx = 0;
44     char filename[256];
45     char fileidx[10];
46 
47     int i;
48     cv::Size boardSize( 8, 6 );
49     cv::Size imageSize(CAP_WIDTH, CAP_HEIGHT);
50     std::vector<cv::Point2f> imageCorners;
51     std::vector<cv::Point3f> objectCorners;
52 
53     // The corners are at 3D location (X,Y,Z)= (i,j,0)
54     for (int i=0; i<boardSize.height; i++)
55     {
56         for (int j=0; j<boardSize.width; j++)
57         {
58             objectCorners.push_back(cv::Point3f(i, j, 0.0f));
59         }
60     }
61 
62     while ( loop )
63     {
64         // find file:
65         sprintf( fileidx, "%03d", file_idx++ );
66         strcpy( filename, "SAVE_IMG" );
67         strcat( filename, fileidx );
68         strcat( filename, ".bmp" );
69 
70         printf("IMG = %s\n", filename);
71 
72         img = imread( filename, 0 );
73         if ( img.empty())
74             loop = 0;
75         else
76         {
77             bool found = cv::findChessboardCorners(
78                              img, boardSize, imageCorners);
79 
80             cornerSubPix(img, imageCorners,
81                          cv::Size(5,5),
82                          cv::Size(-1,-1),
83                          cv::TermCriteria(cv::TermCriteria::MAX_ITER +
84                                           cv::TermCriteria::EPS,
85                                           30,      // max number of iterations
86                                           0.1));   // min accuracy
87             if (imageCorners.size() == boardSize.area())
88             {
89                 imagePoints.push_back(imageCorners);
90                 objectPoints.push_back(objectCorners);
91             }
92             img.copyTo( img2 );
93             drawChessboardCorners(img2, boardSize, imageCorners, found);
94 
95             imshow( "image", img2 );
96 
97             key = cvWaitKey(400);
98             //key = cvWaitKey( 5 );
99             if ( key == 27 )
100             {
101                 loop = 0;
102             }
103         }
104     }
105 
106     std::vector<cv::Mat> rvecs, tvecs;
107     // start calibration
108     calibrateCamera(objectPoints, // the 3D points
109                     imagePoints, // the image points
110                     imageSize,   // image size
111                     cameraMatrix,// output camera matrix
112                     distCoeffs,  // output distortion matrix
113                     rvecs, tvecs,// Rs, Ts
114                     0);       // set options
115 
116     FileStorage fs("test.yml", FileStorage::WRITE);
117     fs << "intrinsic" << cameraMatrix;
118     fs << "distcoeff" << distCoeffs;
119 }
120 

筆者使用的校正 pattern 長寬各有 8 以及 6 個角點. 因此代碼 48 行 設定為 boardSize(8, 6)
54~60 行定義校正 pattern 角點的 3D coordinate.
62 行的 while loop 將影像序列 一張一張的讀入, 每讀入一張, 就以 77 以及 80 行的代碼進行角點提取. 87 行判斷是否正常提取到校正 pattern  上的所有48 個角點, 若是, 則將這些角點在影像上的座標儲存起來. 代碼 93 行則是將其取到的角點加以標示. 當所有的影像都完成角點提取, 則 108 行使用 calibrationCamera 進行攝像頭校正.
在這裡, 我們感興趣的是 camera 內部參數以及型變參數,  calibrationCamera 會為我們計算這些參數並分別儲存在 cameraMatrix 以及 distCoeffs.
116~118 行, 我將 cameraMatrix以及 distCoeffs 的內容儲存到 test.yml 檔案.
圖二是其中一張影像的角點提取結果:

圖二 角點提取

校正出的內參以及型變參數如下:

%YAML:1.0
intrinsic: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 7.2042901435965223e+002, 0., 3.2967552840264563e+002, 0.,
             7.1972210146332782e+002, 2.6487944722387311e+002, 0., 0., 1. ]
distcoeff: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [ 1.0477467445014127e-001, -1.1727926119997072e+000,
             3.4074683728567670e-003, 4.3480547323742199e-003,
             2.2097821427154618e+000 ]


fx = 7.2042901435965223e+002
fy = 7.1972210146332782e+002
cx = 3.2967552840264563e+002
cy = 2.6487944722387311e+002

k1 = 1.0477467445014127e-001
k2 = -1.1727926119997072e+000
p1 = 3.4074683728567670e-003
p2 = 4.3480547323742199e-003
k3 = 2.2097821427154618e+000