opencv對影象進行標定
阿新 • • 發佈：2019-02-13
簡介
本篇是使用opencv函式：cvFindChessboardCorners、cvFindCornerSubPix、cvDrawChessboardCorners，來找到、優化並顯示出來標定棋盤 圖片的角點。 關於這三個函式得講解看，可以參考：http://www.360doc.cn/article/10724725_367761079.html
角點檢測
具體程式碼
具體程式碼如下：
#include <opencv2/opencv.hpp> #include <stdio.h> using namespace cv; using namespace std; int boardWidth, boardHeight; int boardTotal; CvSize boardSize; CvPoint2D32f * image_points_buf; Mat srcColor, srcGray; IplImage srcIp; CvMat cvmatSrc; int nowNumber, found; char* picName; void initFindCorner(){ boardSize = cvSize(10, 7); boardWidth = boardSize.width; boardHeight = boardSize.height; boardTotal = boardWidth*boardHeight; image_points_buf = new CvPoint2D32f[boardTotal]; srcColor = imread(picName); imshow("源影象", srcColor); cvtColor(srcColor, srcGray, COLOR_BGR2GRAY); imshow("灰階圖", srcGray); srcIp = srcGray; cvmatSrc = srcColor; } void findCornersWork(){ found=cvFindChessboardCorners(&srcIp, boardSize, image_points_buf, \ &nowNumber, CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS); printf("捕獲角點數量:%d\n", nowNumber); cvFindCornerSubPix(&srcIp, image_points_buf, nowNumber, cvSize(11,11), cvSize(-1,-1), cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,0.1)); cvDrawChessboardCorners(&cvmatSrc, boardSize, image_points_buf, nowNumber, found); imshow("角點標識圖", srcColor); } int main(int argc, char* argv[]){ if(argc < 2){ cout << "Please input Picture name !!\n" << endl; return -1; } picName = argv[1]; initFindCorner(); findCornersWork(); waitKey(); return 0; }
程式碼講解
1、初始化
void initFindCorner(){ boardSize = cvSize(10, 7); boardWidth = boardSize.width; boardHeight = boardSize.height; boardTotal = boardWidth*boardHeight; image_points_buf = new CvPoint2D32f[boardTotal]; srcColor = imread(picName); imshow("源影象", srcColor); cvtColor(srcColor, srcGray, COLOR_BGR2GRAY); imshow("灰階圖", srcGray); srcIp = srcGray; cvmatSrc = srcColor; }
首先設定預先設定圖片的角點個數，本例使用的棋盤圖片角點個數為：10X7，建立儲存角點的結構:image_points_buf，接著匯入棋盤圖片，
並轉為灰階影象。
2、角點檢測和顯示
void findCornersWork(){ found=cvFindChessboardCorners(&srcIp, boardSize, image_points_buf, \ &nowNumber, CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS); printf("捕獲角點數量:%d\n", nowNumber); cvFindCornerSubPix(&srcIp, image_points_buf, nowNumber, cvSize(11,11), cvSize(-1,-1), cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,0.1)); cvDrawChessboardCorners(&cvmatSrc, boardSize, image_points_buf, nowNumber, found); imshow("角點標識圖", srcColor); }
使用cvFindChessboardCorners進行角點檢測，檢測到的角點儲存在image_points_buf，檢測到的角點數量儲存在nowNumber，如果nowNumber的值，和
實際圖片上的角點數量相等，就表示角點檢測成功。 接著cvFindCornerSubPix、cvDrawChessboardCorners將這些檢測到的角點位置在圖片：cvmatSrc上，顯示輸出。
結果顯示
顯示的結果如下：
畸變校正
前面已經講解了如果找到棋盤標點圖片的角點，這裡在此基礎上，繼續進行後續的校正處理。 首先是找到多張圖片的角點，接著將這些角點匯入到函式cvCalibrateCamera2，進行camera內參數矩陣和畸變係數向量的生成。通過cvInitUndistortMap， 利用之前生成的內參數矩陣和畸變向量，計算出畸變對映到mapx和mapy中。最後cvRemap利用mapx、mapy對輸入影象進行畸變校正。 可以參考文件：http://blog.csdn.net/guvcolie/article/details/7454632
具體程式碼
#include <opencv2/opencv.hpp>
#include <stdio.h>
using namespace cv;
using namespace std;
int main(int argc, char* argv[]){
int cube_length=10;
int cam_Dx = 100; //橫軸方向長度
int cam_Dy = 100; //縱軸方向長度
int number_image = 7;
int a=1;
int number_image_copy= 7;
CvSize board_size=cvSize(10,7);
int board_width=board_size.width;
int board_height=board_size.height;
int total_per_image=board_width*board_height;
CvPoint2D32f * image_points_buf = new CvPoint2D32f[total_per_image];
CvMat * image_points=cvCreateMat(number_image*total_per_image,2,CV_32FC1);//影象座標系
CvMat * object_points=cvCreateMat(number_image*total_per_image,3,CV_32FC1);//世界座標系
CvMat * point_counts=cvCreateMat(number_image,1,CV_32SC1); //角點存放位置
CvMat * intrinsic_matrix=cvCreateMat(3,3,CV_32FC1); //內參數矩陣
CvMat * distortion_coeffs=cvCreateMat(4,1,CV_32FC1); //畸變係數向量
char picName[7][10] = {"1.jpg", "2.jpg", "3.jpg", "4.jpg", "5.jpg", "6.jpg", "7.jpg"};
IplImage * show;
int count;
int found;
int step;
int successes=0;
while(a<=number_image_copy){
show=cvLoadImage(picName[a-1],-1);
found=cvFindChessboardCorners(show,board_size,image_points_buf,&count,
CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS);
if(found==0){
cout<<"第"<<a<<"幀圖片無法找到棋盤格所有角點!\n\n";
cvNamedWindow("RePlay",1);
cvShowImage("RePlay",show);
cvWaitKey(0);
}else{
cout<<"第"<<a<<"幀影象成功獲得"<<count<<"個角點...\n";
IplImage * gray_image= cvCreateImage(cvGetSize(show),8,1);
cvCvtColor(show,gray_image,CV_BGR2GRAY);
cout<<"獲取源影象灰度圖過程完成...\n";
cvFindCornerSubPix(gray_image,image_points_buf,count,cvSize(11,11),cvSize(-1,-1),
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,0.1));
cout<<"灰度圖亞畫素化過程完成...\n";
cvDrawChessboardCorners(show,board_size,image_points_buf,count,found);
cout<<"在源影象上繪製角點過程完成...\n\n";
}
if(total_per_image==count){
step=successes*total_per_image;
for(int i=step,j=0;j<total_per_image;++i,++j){
CV_MAT_ELEM(*image_points,float,i,0)=image_points_buf[j].x;
CV_MAT_ELEM(*image_points,float,i,1)=image_points_buf[j].y;// 求完每個角點橫縱座標值都存在image_point_buf裡
CV_MAT_ELEM(*object_points,float,i,0)=(float)((j/cube_length) * cam_Dx);
CV_MAT_ELEM(*object_points,float,i,1)=(float)((j%cube_length) * cam_Dy);
CV_MAT_ELEM(*object_points,float,i,2)=0.0f;
}
CV_MAT_ELEM(*point_counts,int,successes,0)=total_per_image;
successes++;
}
a++;
}
cout<<"*********************************************\n";
cout<<number_image<<"幀圖片中，標定成功的圖片為"<<successes<<"幀...\n";
cout<<number_image<<"幀圖片中，標定失敗的圖片為"<<number_image-successes<<"幀...\n\n";
cout<<"*********************************************\n\n";
IplImage * show_colie;
show_colie = show;
CvMat * object_points2=cvCreateMat(successes*total_per_image,3,CV_32FC1);
CvMat * image_points2=cvCreateMat(successes*total_per_image,2,CV_32FC1);
CvMat * point_counts2=cvCreateMat(successes,1,CV_32SC1);
for(int i=0;i<successes*total_per_image;++i){
CV_MAT_ELEM(*image_points2,float,i,0)=CV_MAT_ELEM(*image_points,float,i,0);//用來儲存角點提取成功的影象的角點
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){
CV_MAT_ELEM(*point_counts2,int,i,0)=CV_MAT_ELEM(*point_counts,int,i,0);
}
cvReleaseMat(&object_points);
cvReleaseMat(&image_points);
cvReleaseMat(&point_counts);
CV_MAT_ELEM(*intrinsic_matrix,float,0,0)=1.0f;
CV_MAT_ELEM(*intrinsic_matrix,float,1,1)=1.0f;
cvCalibrateCamera2(object_points2,image_points2,point_counts2,cvGetSize(show_colie),
intrinsic_matrix,distortion_coeffs,NULL,NULL,0);
cvSave("Intrinsics.xml",intrinsic_matrix);
cvSave("Distortion.xml",distortion_coeffs);
cout<<"攝像機矩陣、畸變係數向量已經分別儲存在名為Intrinsics.xml、Distortion.xml文件中\n\n";
CvMat * intrinsic=(CvMat *)cvLoad("Intrinsics.xml");
CvMat * distortion=(CvMat *)cvLoad("Distortion.xml");
IplImage * mapx=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
IplImage * mapy=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
cvInitUndistortMap(intrinsic,distortion,mapx,mapy);
cvNamedWindow("原始影象",1);
cvNamedWindow("非畸變影象",1);
show_colie = cvLoadImage(argv[1],-1);
IplImage * clone=cvCloneImage(show_colie);
cvShowImage("原始影象",show_colie);
cvRemap(clone,show_colie,mapx,mapy);
cvReleaseImage(&clone);
cvShowImage("非畸變影象",show_colie);
cvWaitKey(0);
return 0;
}程式碼講解
1、首先這裡是使用了了7張棋盤圖片用來標定，所以cvFindChessboardCorners函式，會依次尋找7次角點。如果找到角點成功，則將對應結果儲存到 image_points、object_points中，注意儲存到object_points的時候需要做計算：(float)((j/cube_length) * cam_Dx);
while(a<=number_image_copy){
show=cvLoadImage(picName[a-1],-1);
found=cvFindChessboardCorners(show,board_size,image_points_buf,&count,
CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS);
if(found==0){
cout<<"第"<<a<<"幀圖片無法找到棋盤格所有角點!\n\n";
cvNamedWindow("RePlay",1);
cvShowImage("RePlay",show);
cvWaitKey(0);
}else{
cout<<"第"<<a<<"幀影象成功獲得"<<count<<"個角點...\n";
IplImage * gray_image= cvCreateImage(cvGetSize(show),8,1);
cvCvtColor(show,gray_image,CV_BGR2GRAY);
cout<<"獲取源影象灰度圖過程完成...\n";
cvFindCornerSubPix(gray_image,image_points_buf,count,cvSize(11,11),cvSize(-1,-1),
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,0.1));
cout<<"灰度圖亞畫素化過程完成...\n";
cvDrawChessboardCorners(show,board_size,image_points_buf,count,found);
cout<<"在源影象上繪製角點過程完成...\n\n";
}
if(total_per_image==count){
step=successes*total_per_image;
for(int i=step,j=0;j<total_per_image;++i,++j){
CV_MAT_ELEM(*image_points,float,i,0)=image_points_buf[j].x;
CV_MAT_ELEM(*image_points,float,i,1)=image_points_buf[j].y;// 求完每個角點橫縱座標值都存在image_point_buf裡
CV_MAT_ELEM(*object_points,float,i,0)=(float)((j/cube_length) * cam_Dx);
CV_MAT_ELEM(*object_points,float,i,1)=(float)((j%cube_length) * cam_Dy);
CV_MAT_ELEM(*object_points,float,i,2)=0.0f;
}
CV_MAT_ELEM(*point_counts,int,successes,0)=total_per_image;
successes++;
}
a++;
} 其中successes用來儲存，需找角點成功的次數，如果7次都成功，則successes為7。
2、將找到的角點資訊，重新儲存到image_points2和object_points2中，利用cvCalibrateCamera2來計算矩陣、向量係數到intrinsic_matrix、distortion_coeffs，本儲存到本地檔案。
IplImage * show_colie;
show_colie = show;
CvMat * object_points2=cvCreateMat(successes*total_per_image,3,CV_32FC1);
CvMat * image_points2=cvCreateMat(successes*total_per_image,2,CV_32FC1);
CvMat * point_counts2=cvCreateMat(successes,1,CV_32SC1);
for(int i=0;i<successes*total_per_image;++i){
CV_MAT_ELEM(*image_points2,float,i,0)=CV_MAT_ELEM(*image_points,float,i,0);//用來儲存角點提取成功的影象的角點
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){
CV_MAT_ELEM(*point_counts2,int,i,0)=CV_MAT_ELEM(*point_counts,int,i,0);
}
cvReleaseMat(&object_points);
cvReleaseMat(&image_points);
cvReleaseMat(&point_counts);
CV_MAT_ELEM(*intrinsic_matrix,float,0,0)=1.0f;
CV_MAT_ELEM(*intrinsic_matrix,float,1,1)=1.0f;
cvCalibrateCamera2(object_points2,image_points2,point_counts2,cvGetSize(show_colie),
intrinsic_matrix,distortion_coeffs,NULL,NULL,0);
cvSave("Intrinsics.xml",intrinsic_matrix);
cvSave("Distortion.xml",distortion_coeffs); 3、函式cvInitUndistortMap和cvRemap，通過之前計算的矩陣、向量係數，對需要校正的影象：show_colie進行處理，並分別顯示出來。
IplImage * mapx=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
IplImage * mapy=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
cvInitUndistortMap(intrinsic,distortion,mapx,mapy);
cvNamedWindow("原始影象",1);
cvNamedWindow("非畸變影象",1);
show_colie = cvLoadImage(argv[1],-1);
IplImage * clone=cvCloneImage(show_colie);
cvShowImage("原始影象",show_colie);
cvRemap(clone,show_colie,mapx,mapy);
cvReleaseImage(&clone);
cvShowImage("非畸變影象",show_colie);video畸變校正
在之前的基礎上，修改被校正的輸入即可，簡單的話，在前一個例子中，將如下程式碼：
IplImage * mapx=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
IplImage * mapy=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
cvInitUndistortMap(intrinsic,distortion,mapx,mapy);
cvNamedWindow("原始影象",1);
cvNamedWindow("非畸變影象",1);
show_colie = cvLoadImage(argv[1],-1);
IplImage * clone=cvCloneImage(show_colie);
cvShowImage("原始影象",show_colie);
cvRemap(clone,show_colie,mapx,mapy);
cvReleaseImage(&clone);
cvShowImage("非畸變影象",show_colie);
</source lang>
替換為：
<source lang="cpp" line>
VideoCapture capture(argv[1]);
if (!capture.isOpened()){
return 0;
}
while(1){
if (!capture.read(frame)){
break;
}
ipFrame = frame;
IplImage * clone=cvCloneImage(&ipFrame);
cvShowImage("原始影象", &ipFrame);
cvRemap(clone,show_colie,mapx,mapy);
cvReleaseImage(&clone);
cvShowImage("非畸變影象", show_colie);
if (waitKey(5) == 'q'){
break;
}
} 就是將被校正的影象修改為，從video中獲取，迴圈校正顯示。
效果演示
對應的圖片畸變校正效果如下：
原影象 校正後影象
程式碼下載：http://download.csdn.net/detail/u011630458/9268829