标定图片（本科时拍摄
标定代码
#include <cv.h> #include <highgui.h> #include <stdio.h> #include <stdlib.h> #include <iostream> #include <opencv2/opencv.hpp> using namespace std; using namespace cv; class CCalibration { public: CCalibration(CvSize _board_sz, double _board_dt, int _n_boards = 15); ~CCalibration(); public: bool doCalibrate(const CvMat* const image_points, const CvMat* const object_points,const CvMat* const point_counts, CvSize size); bool calibrateFromCamera(); bool calibrateFromFile(); void display(); protected: private: CvSize board_sz; //标定板信息 int n_boards; //视场总数 double board_dt; //相邻视场间的获取时间间隔 private: CvMat* intrinsic_matrix;//内参数矩阵 CvMat* distortion_coeffs;//畸变矩阵 }; CCalibration::CCalibration(CvSize _board_sz, double _board_dt, int _n_boards) { //标定板的信息 board_sz = _board_sz; board_dt = _board_dt; n_boards = _n_boards; //为标定参数分配内存 intrinsic_matrix = cvCreateMat(3,3,CV_32FC1); distortion_coeffs = cvCreateMat(4,1,CV_32FC1); } CCalibration::~CCalibration() { cvReleaseMat(&intrinsic_matrix); cvReleaseMat(&distortion_coeffs); } /* * 函数名称：calibrateFromCamera * 函数功能：直接从相机实时获取标定板图像，用于标定 * 函数入口： * 输入参数：五 * 输出参数：无 * 返 回 值：是否标定成功，true表示成功，false表示失败 * 其它说明： */ bool CCalibration::calibrateFromCamera() { cvNamedWindow("Calibration",CV_WINDOW_AUTOSIZE); cvNamedWindow("Live",CV_WINDOW_AUTOSIZE); CvCapture* capture = cvCreateCameraCapture( 0 );//将要标定的摄像头 assert( capture ); int board_n = board_sz.width * board_sz.height;//角点总数 CvMat* image_points = cvCreateMat(n_boards*board_n,2,CV_32FC1);// cvMat* cvCreateMat ( int rows, int cols, int type ) CvMat* object_points = cvCreateMat(n_boards*board_n,3,CV_32FC1);//cvCreateMat预定义类型的结构如下：CV_<bit_depth> (S|U|F)C<number_of_channels> CvMat* point_counts = cvCreateMat(n_boards,1,CV_32SC1);//cvCreateMat矩阵的元素可以是32位浮点型数据(CV_32FC1)，或者是无符号的8位三元组的整型数据(CV_8UC3) CvPoint2D32f* corners = new CvPoint2D32f[ board_n ]; IplImage *image = cvQueryFrame( capture ); //imgSize = cvGetSize(image); IplImage *gray_image = cvCreateImage(cvGetSize(image),8,1);//subpixel 创建单通道灰度图像 int corner_count; int successes = 0;//图像系列index int step, frame = 0; //忽略开始前2s时间的图片 for (int i = 0; i < 33*2; i++) { image = cvQueryFrame(capture); cvShowImage("Live",image); cvWaitKey(30); } //获取足够多视场图片用于标定 while (successes < n_boards) { image = cvQueryFrame(capture); cvShowImage("Live", image); cvWaitKey(33);//一帧的时间间隔 //每隔board_dt秒取一张图像 if ( (frame++ % ((int)(33 * board_dt)) ) == 0 ) { //Find chessboard corners: int found = cvFindChessboardCorners(image, board_sz, corners, &corner_count, CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS); if(found == 0) continue;//未正确找到角点，继续下一次 //Get Subpixel accuracy on those corners cvCvtColor(image, gray_image, CV_BGR2GRAY); //转换为灰度图像 cvFindCornerSubPix(gray_image, corners, corner_count, //cvFindChessboardCorners找到的角点仅仅是近似值，必须调用此函数达到亚像素精度，如果第一次定位... cvSize(11,11),cvSize(-1,-1), cvTermCriteria( //角点时忽略调用此函数，那么会导致标定的实际错误 CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 30, 0.1 )); // 如果该视场获得了好的结果，保存它 // 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 用来访问矩阵每个元素的宏，这个宏只对单通道矩阵有效，多通道会报错... CV_MAT_ELEM(*image_points, float,i,1) = corners[j].y; //CV_MAT_ELEM( matrix, elemtype, row, col ) CV_MAT_ELEM(*object_points,float,i,0) = j/board_sz.width; //matrix：要访问的矩阵,elemtype：矩阵元素的类型,row：所要访问元素的行数,col：所要访问元素的列数 CV_MAT_ELEM(*object_points,float,i,1) = j%board_sz.width; CV_MAT_ELEM(*object_points,float,i,2) = 0.0f; } CV_MAT_ELEM(*point_counts, int,successes,0) = board_n; successes++; } //Draw corners cvDrawChessboardCorners(image, board_sz, corners, corner_count, found);//found为cvFindChessboardCorners的返回值 char text[10]; sprintf(text,"%d/%d", successes,n_boards); CvFont font = cvFont(2,2); cvPutText(image,text,cvPoint(40,40),&font,cvScalar(0,0,255)); cvShowImage( "Calibration", image ); } } //获取了足够多视场，结束获取 cvDestroyWindow("Calibration"); cvDestroyWindow("Live"); //计算 doCalibrate(image_points, object_points, point_counts, cvGetSize(image)); //结束 delete []corners; cvReleaseMat(&image_points); cvReleaseMat(&object_points); cvReleaseMat(&point_counts); cvReleaseImage(&gray_image); cvReleaseCapture(&capture); return true; }/* calibrateFromCamera() */ /* * 函数名称：calibrateFromCamera * 函数功能：根据已获取的图像文件（.bmp格式），标定相机 * 函数入口： * 输入参数：无 * 输出参数：无 * 返 回 值：是否标定成功，true表示成功，false表示失败 * 其它说明： 只接受.bmp格式的图片，且图片尺寸要相同，若要标定其他格式图片，请将本函数内的.bmp替换成.jpg * 文件统一命名格式为 calib_N.bmp,其中N必须从0开始 */ bool CCalibration::calibrateFromFile() { cvNamedWindow("Calibration", CV_WINDOW_AUTOSIZE); //cvNamedWindow("FileImage", CV_WINDOW_AUTOSIZE); int board_n = board_sz.width * board_sz.height;//每张图角点总数 CvMat* image_points = cvCreateMat(n_boards*board_n,2,CV_32FC1);// cvMat* cvCreateMat ( int rows, int cols, int type ) CvMat* object_points = cvCreateMat(n_boards*board_n,3,CV_32FC1);//cvCreateMat预定义类型的结构如下：CV_<bit_depth> (S|U|F)C<number_of_channels> CvMat* point_counts = cvCreateMat(n_boards,1,CV_32SC1);//cvCreateMat矩阵的元素可以是32位浮点型数据(CV_32FC1)，或者是无符号的8位三元组的整型数据(CV_8UC3) CvPoint2D32f* corners = new CvPoint2D32f[ board_n ]; char imgName[20] = "1.jpg";//加载图 IplImage *image = cvLoadImage(imgName,1); IplImage *gray_image = cvCreateImage(cvGetSize(image),8,1);//subpixel 创建单通道灰度图像 int corner_count; int successes = 0, index = 1;//图像系列index int step; //获取足够多视场图片用于标定 while (successes < n_boards) { sprintf(imgName, "%d.jpg",index++); //一次加载n_boards张图片 image = cvLoadImage(imgName,1); if ( !image ) break; //无此图片，则停止 cvWaitKey(1000*board_dt);//一帧的时间间隔 //Find chessboard corners: int found = cvFindChessboardCorners(image, board_sz, corners, &corner_count, CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS); if(found == 0) continue;//未正确找到角点，继续下一次 //Get Subpixel accuracy on those corners cvCvtColor(image, gray_image, CV_BGR2GRAY); //转换为灰度图像 cvFindCornerSubPix(gray_image, corners, corner_count, //cvFindChessboardCorners找到的角点仅仅是近似值，必须调用此函数达到亚像素精度，如果第一次定位... cvSize(11,11),cvSize(-1,-1), cvTermCriteria( //角点时忽略调用此函数，那么会导致标定的实际错误 CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 30, 0.1 )); // 如果该视场获得了好的结果，保存它 // 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 用来访问矩阵每个元素的宏，这个宏只对单通道矩阵有效，多通道会报错... CV_MAT_ELEM(*image_points, float,i,1) = corners[j].y; //CV_MAT_ELEM( matrix, elemtype, row, col ) CV_MAT_ELEM(*object_points,float,i,0) = j/board_sz.width; //matrix：要访问的矩阵,elemtype：矩阵元素的类型,row：所要访问元素的行数,col：所要访问元素的列数 CV_MAT_ELEM(*object_points,float,i,1) = j%board_sz.width; CV_MAT_ELEM(*object_points,float,i,2) = 0.0f; } CV_MAT_ELEM(*point_counts, int,successes,0) = board_n; successes++; } //Draw corners cvDrawChessboardCorners(image, board_sz, corners, corner_count, found);//found为cvFindChessboardCorners的返回值 char text[10]; sprintf(text,"%d/%d", successes,n_boards); CvFont font = cvFont(2,2); cvPutText(image,text,cvPoint(40,40),&font,cvScalar(0,0,255)); cvShowImage( "Calibration", image ); } //获取了足够多视场，结束获取 cvDestroyWindow("Calibration"); //cvDestroyWindow("FileImage"); doCalibrate(image_points, object_points, point_counts, cvGetSize(image)); delete []corners; cvReleaseMat(&image_points); cvReleaseMat(&object_points); cvReleaseMat(&point_counts); cvReleaseImage(&image); cvReleaseImage(&gray_image); return true; } /* calibrateFromFile() */ /* * 函数名称：doCalibrate * 函数功能：计算相机内参数和畸变参数 * 函数入口： * 输入参数：存储图像角点坐标（成像仪坐标）信息的矩阵指针image_points，存储有标定板角点坐标（世界坐标）信息的矩阵指针object_points * 存储有各图像寻找到的角点个数信息的矩阵指针point_counts，图像尺寸size * 输出参数：无 * 返 回 值： 是否成功，true成功，false失败 * 其它说明： 标定结果同时存储到当前目录Intrinsics.xml，Distortion.xml文件中 */ bool CCalibration::doCalibrate(const CvMat* const image_points, const CvMat* const object_points,const CvMat* const point_counts, CvSize size) { //****************************开始标定*************************// // 初始化内参数矩阵的fx和fy为1.0f CV_MAT_ELEM( *intrinsic_matrix, float, 0, 0 ) = 1.0f; CV_MAT_ELEM( *intrinsic_matrix, float, 1, 1 ) = 1.0f; //**************计算标定参数*************// //CALIBRATE THE CAMERA! cvCalibrateCamera2( object_points, image_points, point_counts, size, intrinsic_matrix, distortion_coeffs, NULL, NULL,0 //CV_CALIB_FIX_ASPECT_RATIO ); //SAVE THE INTRINSICS AND DISTORTIONS cvSave("Intrinsics.xml",intrinsic_matrix);//保存摄像头内参数 cvSave("Distortion.xml",distortion_coeffs);//保存摄像头外参数 return true; }/* doCalibrate() */ /* * 函数名称：display * 函数功能：根据标定参数，显示修正后的视频图像 * 函数入口： * 输入参数：无 * 输出参数：无 * 返 回 值： * 其它说明： */ void CCalibration::display() { cvNamedWindow("Undistort", CV_WINDOW_AUTOSIZE);//显示修正后图像 CvCapture *capture = cvCreateCameraCapture(0); IplImage *frame = cvQueryFrame(capture); IplImage *imgUndistort = cvCreateImage(cvGetSize(frame),frame->depth,frame->nChannels); // EXAMPLE OF LOADING THESE MATRICES BACK IN: CvMat *intrinsic = (CvMat*)cvLoad("Intrinsics.xml");//加载摄像头内参数 CvMat *distortion = (CvMat*)cvLoad("Distortion.xml");//加载摄像头外参数 // Build the undistort map which we will use for all subsequent frames. IplImage* mapx = cvCreateImage( cvGetSize(frame), IPL_DEPTH_32F, 1 ); IplImage* mapy = cvCreateImage( cvGetSize(frame), IPL_DEPTH_32F, 1 ); //计算畸变映射 即根据摄像头内、外参数，计算出如果没有这些畸变的话，摄像头获得的理想图像 cvInitUndistortMap(intrinsic, distortion, mapx, mapy); while(cvWaitKey(33) != 27) //ESC { frame = cvQueryFrame(capture); cvRemap( frame, imgUndistort, mapx, mapy); cvShowImage("Undistort", imgUndistort); } cvReleaseCapture(&capture); cvReleaseImage(&imgUndistort); cvDestroyWindow("Undistort"); }/* display() */ void main() { //CCalibration calib(cvSize(7,8),1,10); CCalibration calib(cvSize(6,8),1,10); //从相机中获取图像标定 calib.calibrateFromCamera(); //从已有图像中标定 //calib.calibrateFromFile(); //运用标定结果显示修正后图像 //calib.display(); //system("pause"); }
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标定载物台的比例系数后即可测量零件尺寸
