Open CV——相机校准和立体匹配
函数有变化时，一定要记得去示例里查看相关示例
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include vector tempPointSet;
for (int i = 0; ifor (int j = 0; j/* 假设定标板放在世界坐标系中z=0的平面上 */
Point3f tempPoint;
tempPoint.x = i*square_size.width;
tempPoint.y = j*square_size.height;
tempPoint.z = 0;
tempPointSet.push_back(tempPoint);
}
}
object_Points.push_back(tempPointSet);
}
/* 初始化每幅图像中的角点数，这里我们假设每幅图像中都可以看到完整的定标板 */
for (int i = 0; i< image_count; i++)
{
point_counts.push_back(board_size.width*board_size.height);
}
/* 开始定标 */
calibrateCamera(object_Points, corners_Seq, image_size, intrinsic_matrix, distortion_coeffs, rotation_vectors, translation_vectors);
std::cout << "定标完成！\n";
/************************************************************************
对定标结果进行评价
*************************************************************************/
std::cout << "开始评价定标结果………………" << endl;
double total_err = 0.0; /* 所有图像的平均误差的总和 */
double err = 0.0; /* 每幅图像的平均误差 */
vectorvector tempPointSet = object_Points[i];
/**** 通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点 ****/
projectPoints(tempPointSet, rotation_vectors[i], translation_vectors[i], intrinsic_matrix, distortion_coeffs, image_points2);
/* 计算新的投影点和旧的投影点之间的误差*/
vector(0, j) = Vec2f(image_points2[j].x, image_points2[j].y);
tempImagePointMat.at(0, j) = Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
}
err = norm(image_points2Mat, tempImagePointMat, NORM_L2);
total_err += err /= point_counts[i];
std::cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
}
std::cout << "总体平均误差：" << total_err / image_count << "像素" << endl;
fout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl;
std::cout << "评价完成！" << endl;
/************************************************************************
保存定标结果
*************************************************************************/
std::cout << "开始保存定标结果………………" << endl;
Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */
fout << "相机内参数矩阵：" << endl;
fout << intrinsic_matrix << endl << endl;
fout << "畸变系数：\n";
fout << distortion_coeffs << endl << endl << endl;
for (int i = 0; i"第" << i + 1 << "幅图像的旋转向量：" << endl;
fout << rotation_vectors[i] << endl;
/* 将旋转向量转换为相对应的旋转矩阵 */
Rodrigues(rotation_vectors[i], rotation_matrix);
fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;
fout << rotation_matrix << endl;
fout << "第" << i + 1 << "幅图像的平移向量：" << endl;
fout << translation_vectors[i] << endl << endl;
}
std::cout << "完成保存" << endl;
fout << endl;
/************************************************************************
显示定标结果
*************************************************************************/
// Mat mapx = Mat(image_size,CV_32FC1);
// Mat mapy = Mat(image_size,CV_32FC1);
// Mat R = Mat::eye(3,3,CV_32F);
// std::cout<<"保存矫正图像"<
// string imageFileName;
// std::stringstream StrStm;
// for (int i = 0 ; i != image_count ; i++)
// {
// std::cout<<"Frame #"<
// Mat newCameraMatrix = Mat(3,3,CV_32FC1,Scalar::all(0));
// initUndistortRectifyMap(intrinsic_matrix,distortion_coeffs,R,intrinsic_matrix,image_size,CV_32FC1,mapx,mapy);
// StrStm.clear();
// imageFileName.clear();
// StrStm<
// StrStm>>imageFileName;
// imageFileName += ".jpg";
// Mat t = imread(imageFileName);
// Mat newimage = t.clone();
// cv::remap(t,newimage,mapx, mapy, INTER_LINEAR);
// StrStm.clear();
// imageFileName.clear();
// StrStm<
// StrStm>>imageFileName;
// imageFileName += "_d.jpg";
// imwrite(imageFileName,newimage);
// }
// std::cout<<"保存结束"<
return 0;
} - 1
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#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include int count = 0,n=0;
stringstream tempname;
string filename;
int key;
string msg;
int baseLine;
Size textSize;
while(n < image_count )
{
frame.setTo(0);
cap>>frame;
if(mode == DETECTION)
{
key = 0xff & waitKey(30);
if( (key & 255) == 27 )
break;
if( cap.isOpened() && key == 'g' )
{
mode = CAPTURING;
}
}
if(mode == CAPTURING)
{
key = 0xff & waitKey(30);
if( (key & 255) == 32 )
{
image_size = frame.size();
/* 提取角点 */
Mat imageGray;
cvtColor(frame, imageGray , CV_RGB2GRAY);
bool patternfound = findChessboardCorners(frame, board_size, corners,CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + CALIB_CB_FAST_CHECK );
if (patternfound)
{
n++;
tempname<>filename;
filename+=".jpg";
/* 亚像素精确化 */
cornerSubPix(imageGray, corners, Size(11, 11), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
count += corners.size();
corners_Seq.push_back(corners);
imwrite(filename,frame);
tempname.clear();
filename.clear();
}
else
{
std::cout<<"Detect Failed.\n";
}
}
}
msg = mode == CAPTURING ? "100/100/s" : mode == CALIBRATED ? "Calibrated" : "Press 'g' to start";
baseLine = 0;
textSize = getTextSize(msg, 1, 1, 1, &baseLine);
Point textOrigin(frame.cols - 2*textSize.width - 10, frame.rows - 2*baseLine - 10);
if( mode == CAPTURING )
{
msg = format( "%d/%d",n,image_count);
}
putText( frame, msg, textOrigin, 1, 1,mode != CALIBRATED ? Scalar(0,0,255) : Scalar(0,255,0));
imshow("Calibration",frame);
key = 0xff & waitKey(1);
if( (key & 255) == 27 )
break;
}
std::cout<<"角点提取完成！\n";
/************************************************************************
摄像机定标
*************************************************************************/
std::cout<<"开始定标………………"<25,25); /**** 实际测量得到的定标板上每个棋盘格的大小 ****/
vector<vectorvector tempPointSet;
for (int i=0;ifor (int j=0;j/* 假设定标板放在世界坐标系中z=0的平面上 */
Point3f tempPoint;
tempPoint.x = i*square_size.width;
tempPoint.y = j*square_size.height;
tempPoint.z = 0;
tempPointSet.push_back(tempPoint);
}
}
object_Points.push_back(tempPointSet);
}
/* 初始化每幅图像中的角点数，这里我们假设每幅图像中都可以看到完整的定标板 */
for (int i=0; i< image_count; i++)
{
point_counts.push_back(board_size.width*board_size.height);
}
/* 开始定标 */
calibrateCamera(object_Points, corners_Seq, image_size, intrinsic_matrix ,distortion_coeffs, rotation_vectors, translation_vectors);
std::cout<<"定标完成！\n";
/************************************************************************
对定标结果进行评价
*************************************************************************/
std::cout<<"开始评价定标结果………………"<double total_err = 0.0; /* 所有图像的平均误差的总和 */
double err = 0.0; /* 每幅图像的平均误差 */
vector"每幅图像的定标误差："<for (int i=0; ivector tempPointSet = object_Points[i];
/**** 通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点 ****/
projectPoints(tempPointSet, rotation_vectors[i], translation_vectors[i], intrinsic_matrix, distortion_coeffs, image_points2);
/* 计算新的投影点和旧的投影点之间的误差*/
vector(0,j) = Vec2f(image_points2[j].x, image_points2[j].y);
tempImagePointMat.at(0,j) = Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
}
err = norm(image_points2Mat, tempImagePointMat, NORM_L2);
total_err += err/= point_counts[i];
std::cout<<"第"<1<<"幅图像的平均误差："<"像素"<"第"<1<<"幅图像的平均误差："<"像素"<std::cout<<"总体平均误差："<"像素"<"总体平均误差："<"像素"<std::cout<<"评价完成！"</************************************************************************
保存定标结果
*************************************************************************/
std::cout<<"开始保存定标结果………………"<3,3,CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */
fout<<"相机内参数矩阵："<"畸变系数：\n";
fout<for (int i=0; i"第"<1<<"幅图像的旋转向量："</* 将旋转向量转换为相对应的旋转矩阵 */
Rodrigues(rotation_vectors[i],rotation_matrix);
fout<<"第"<1<<"幅图像的旋转矩阵："<"第"<1<<"幅图像的平移向量："<std::cout<<"完成保存"</************************************************************************
显示定标结果
*************************************************************************/
// Mat mapx = Mat(image_size,CV_32FC1);
// Mat mapy = Mat(image_size,CV_32FC1);
// Mat R = Mat::eye(3,3,CV_32F);
// std::cout<<"保存矫正图像"<
// string imageFileName;
// std::stringstream StrStm;
// for (int i = 0 ; i != image_count ; i++)
// {
// std::cout<<"Frame #"<
// Mat newCameraMatrix = Mat(3,3,CV_32FC1,Scalar::all(0));
// initUndistortRectifyMap(intrinsic_matrix,distortion_coeffs,R,intrinsic_matrix,image_size,CV_32FC1,mapx,mapy);
// StrStm.clear();
// imageFileName.clear();
// StrStm<
// StrStm>>imageFileName;
// imageFileName += ".jpg";
// Mat t = imread(imageFileName);
// Mat newimage = t.clone();
// cv::remap(t,newimage,mapx, mapy, INTER_LINEAR);
// StrStm.clear();
// imageFileName.clear();
// StrStm<
// StrStm>>imageFileName;
// imageFileName += "_d.jpg";
// imwrite(imageFileName,newimage);
// }
// std::cout<<"保存结束"<
return 0;
} - 1
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由于Open CV3的stereocalibration变化了，重新修改以后才能正常运行。成功运行，但是效果很差。可能是参数设置有问题
#include
#include (y, x); //三通道浮点型
if (fabs(point[2] - max_z) < FLT_EPSILON || fabs(point[2]) > max_z)
continue;
fp << point[0] << " " << point[1] << " " << point[2] << endl;
}
}
fp.close();
}
// 存储视差数据
//---opencv只支持xml、yml的数据文件读写功能，matlab无法读取，可以保存为txt再导入matlab
void saveDisp(const string filename, const Mat& mat)
{
ofstream fp(filename, ios::out);
fp << mat.rows << endl;
fp << mat.cols << endl;
for (int y = 0; y < mat.rows; y++)
{
for (int x = 0; x < mat.cols; x++)
{
double disp = mat.at<short>(y, x); // 这里视差矩阵是CV_16S 格式的，故用 short 类型读取
fp << disp << endl; // 若视差矩阵是 CV_32F 格式，则用 float 类型读取
}
}
fp.close();
}
//视差图的伪彩色图显示
/*
使灰度图中亮度越高的像素点，在伪彩色图中对应的点越趋向于红色；亮度越低，则对应的伪彩色越趋向于蓝色；
总体上按照灰度值高低，由红渐变至蓝，中间色为绿色
*/
void F_Gray2Color(Mat gray_mat, Mat& color_mat)
{
color_mat = Mat::zeros(gray_mat.size(), CV_8UC3);
int rows = color_mat.rows, cols = color_mat.cols;
Mat red = Mat(gray_mat.rows, gray_mat.cols, CV_8U);
Mat green = Mat(gray_mat.rows, gray_mat.cols, CV_8U);
Mat blue = Mat(gray_mat.rows, gray_mat.cols, CV_8U);
Mat mask = Mat(gray_mat.rows, gray_mat.cols, CV_8U);
subtract(gray_mat, Scalar(255), blue); // blue(I) = 255 - gray(I)
red = gray_mat.clone(); // red(I) = gray(I)
green = gray_mat.clone(); // green(I) = gray(I),if gray(I) < 128
compare(green, 128, mask, CMP_GE); // green(I) = 255 - gray(I), if gray(I) >= 128
subtract(green, Scalar(255), green, mask);
convertScaleAbs(green, green, 2.0, 2.0);//dst(I)=abs(src(I)*scale + shift)
vector> leftPtsList(fileList.size());
//vector> rightPtsList(fileList.size());
for (uint i = 0; i < fileList.size(); ++i)
{
vector
//std::cout<<"右侧图像： 宽度"<
Mat leftImg, rightImg, leftSimg, rightSimg, leftCimg, rightCimg, leftMask, rightMask;
// BGT -> GRAY
if (leftRawImg.type() == CV_8UC3)
cvtColor(leftRawImg, leftImg, CV_BGR2GRAY); //转为灰度图
else
leftImg = leftRawImg.clone();
if (rightRawImg.type() == CV_8UC3)
cvtColor(rightRawImg, rightImg, CV_BGR2GRAY);
else
rightImg = rightRawImg.clone();
imgSize = leftImg.size();
//图像滤波预处理
//---依次对图像进行平滑处理和滤波处理，使得白色棋盘格及边缘在图像上更突出
resize(leftImg, leftMask, Size(200, 200)); //resize对原图像img重新调整大小生成mask图像大小为200*200
resize(rightImg, rightMask, Size(200, 200));
GaussianBlur(leftMask, leftMask, Size(13, 13), 7);
GaussianBlur(rightMask, rightMask, Size(13, 13), 7);
resize(leftMask, leftMask, imgSize);
resize(rightMask, rightMask, imgSize);
medianBlur(leftMask, leftMask, 9); //中值滤波
medianBlur(rightMask, rightMask, 9);
//???
for (int v = 0; v < imgSize.height; v++) {
for (int u = 0; u < imgSize.width; u++) {
int leftX = ((int)leftImg.at(v, u) - (int)leftMask.at(v, u)) * 2 + 128;
int rightX = ((int)rightImg.at(v, u) - (int)rightMask.at(v, u)) * 2 + 128;
leftImg.at(v, u) = max(min(leftX, 255), 0);
rightImg.at(v, u) = max(min(rightX, 255), 0);
}
}
//?
//寻找角点， 图像缩放
resize(leftImg, leftSimg, Size(), imgScale, imgScale); //图像以0.5的比例缩放
resize(rightImg, rightSimg, Size(), imgScale, imgScale);
cvtColor(leftSimg, leftCimg, CV_GRAY2BGR); //转为BGR图像，cimg和simg是800*600的图像
cvtColor(rightSimg, rightCimg, CV_GRAY2BGR);
//寻找棋盘角点
bool leftFound = findChessboardCorners(leftCimg, patSize, leftPts, CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS);
bool rightFound = findChessboardCorners(rightCimg, patSize, rightPts, CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS);
if (leftFound)
cornerSubPix(leftSimg, leftPts, Size(11, 11), Size(-1, -1),
TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 300, 0.01));//进行亚像素角点优化
if (rightFound)
cornerSubPix(rightSimg, rightPts, Size(11, 11), Size(-1, -1),
TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 300, 0.01)); //亚像素
//放大为原来的尺度
for (uint j = 0; j < leftPts.size(); j++) //该幅图像共132个角点，坐标乘以2，还原角点位置
leftPts[j] *= 1. / imgScale;
for (uint j = 0; j < rightPts.size(); j++)
rightPts[j] *= 1. / imgScale;
//显示
string leftWindowName = "Left Corner Pic", rightWindowName = "Right Corner Pic";
Mat leftPtsTmp = Mat(leftPts) * imgScale; //再次乘以 imgScale
Mat rightPtsTmp = Mat(rightPts) * imgScale;
drawChessboardCorners(leftCimg, patSize, leftPtsTmp, leftFound); //绘制角点坐标并显示
imshow(leftWindowName, leftCimg);
imwrite("输出/DrawChessBoard/" + to_string(i) + "_left.jpg", leftCimg);
waitKey(200);
drawChessboardCorners(rightCimg, patSize, rightPtsTmp, rightFound); //绘制角点坐标并显示
imshow(rightWindowName, rightCimg);
imwrite("输出/DrawChessBoard/" + to_string(i) + "_right.jpg", rightCimg);
waitKey(200);
cv::destroyAllWindows();
//保存角点坐标
if (leftFound && rightFound)
{
imagePoints[0].push_back(leftPts);
imagePoints[1].push_back(rightPts); //保存角点坐标
std::cout << "图片 " << i << " 处理成功！" << endl;
idx.push_back(i);
}
}
cv::destroyAllWindows();
imagePoints[0].resize(idx.size());
imagePoints[1].resize(idx.size());
std::cout << "成功标定的标定板个数为" << idx.size() << " 序号分别为: ";
for (unsigned int i = 0; i < idx.size(); ++i)
std::cout << idx[i] << " ";
//生成物点坐标
//---物点坐标Z坐标为零，横纵坐标均从0开始，然后按照设定的patSize依次设定。每张棋盘格的物点坐标是相同的
vector<vector bm = StereoBM::create(16, 9);
bm->setPreFilterType(CV_STEREO_BM_NORMALIZED_RESPONSE);
bm->setPreFilterSize(9);
bm->setPreFilterCap(31);
bm->setBlockSize(21);
bm->setMinDisparity(-16);
bm->setNumDisparities(64);
bm->setTextureThreshold(10);
bm->setUniquenessRatio(5);
bm->setSpeckleWindowSize(100);
bm->setSpeckleRange(32);
bm->setROI1(roi1);
bm->setROI2(roi2);
// getter
int pfs = bm->getPreFilterSize();
int pfc = bm->getPreFilterCap();
int bs = bm->getBlockSize();
int md = bm->getMinDisparity();
int nd = bm->getNumDisparities();
int tt = bm->getTextureThreshold();
int ur = bm->getUniquenessRatio();
int sw = bm->getSpeckleWindowSize();
int sr = bm->getSpeckleRange();
/*
// 初始化 stereoBMstate 结构体
StereoBM bm;
int unitDisparity = 15;//40
int numberOfDisparities = unitDisparity * 16;//视差窗口，16的整数倍
bm.state->roi1 = roi1;//视图中的有效像素区域
bm.state->roi2 = roi2;
bm.state->preFilterCap = 13;
bm.state->SADWindowSize = 19;// 窗口大小
bm.state->minDisparity = 0; //最小视差 // 确定匹配搜索从哪里开始 默认值是0
bm.state->numberOfDisparities = numberOfDisparities; // 在该数值确定的视差范围内进行搜索
bm.state->textureThreshold = 1000;//10 低纹理区域的判断阈值 // 保证有足够的纹理以克服噪声
bm.state->uniquenessRatio = 1; //10 视差唯一性百分比 / !!使用匹配功能模式
bm.state->speckleWindowSize = 200; //13 检查视差连通区域变化度的窗口大小 // 检查视差连通区域变化度的窗口大小, 值为 0 时取消 speckle 检查
bm.state->speckleRange = 32; //32 // 视差变化阈值，当窗口内视差变化大于阈值时，该窗口内的视差清零，int 型
bm.state->disp12MaxDiff = -1;//左视差图和右视差图之间的最大容许差异
*/
// 计算
Mat disp, disp8;
int64 t = getTickCount();
// Compute disparity
bm->compute(img1, img2, disp);
//bm(img1, img2, disp);
t = getTickCount() - t;
printf("立体匹配耗时: %fms\n", t * 1000 / getTickFrequency());
// 将16位符号整形的视差矩阵转换为8位无符号整形矩阵
//disp.convertTo(disp8, CV_8U, 255 / (numberOfDisparities*16.));
disp.convertTo(disp8, CV_8U, 255 / (64*16.));
// 视差图转为彩色图
Mat vdispRGB = disp8;
F_Gray2Color(disp8, vdispRGB);
// 将左侧矫正图像与视差图融合
Mat merge_mat = F_mergeImg(img1, disp8);
saveDisp("输出/视差数据.txt", disp);
//显示
if (!no_display) {
imshow("左侧矫正图像", img1);
imwrite("输出/left_undistortRectify.jpg", img1);
imshow("右侧矫正图像", img2);
imwrite("输出/right_undistortRectify.jpg", img2);
imshow("视差图", disp8);
imwrite("输出/视差图.jpg", disp8);
imshow("视差图_彩色.jpg", vdispRGB);
imwrite("输出/视差图_彩色.jpg", vdispRGB);
imshow("左矫正图像与视差图合并图像", merge_mat);
imwrite("输出/左矫正图像与视差图合并图像.jpg", merge_mat);
cv::waitKey();
std::cout << endl;
}
cv::destroyAllWindows();
// 视差图转为深度图
cout << endl << "计算深度映射... " << endl;
Mat xyz;
reprojectImageTo3D(disp, xyz, Q, true); //获得深度图 disp: 720*1280
cv::destroyAllWindows();
cout << endl << "保存点云坐标... " << endl;
saveXYZ(point_cloud_filename, xyz);
cout << endl << endl << "结束" << endl << "Press any key to end... ";
getchar();
return 0;
}
int main()
{
string intrinsic_filename = "intrinsics.yml";
string extrinsic_filename = "extrinsics.yml";
string point_cloud_filename = "输出/point3D.txt";
/* 立体标定 运行一次即可 */
/*initFileList("calib_pic", 1, 26);
stereoCalibrate(intrinsic_filename, extrinsic_filename);*/
/* 立体匹配 */
initFileList("test_pic", 1, 2);
stereoMatch(0, intrinsic_filename, extrinsic_filename, false, point_cloud_filename);
return 0;
}
原文地址：https://blog.csdn.net/u014413083/article/details/53205560#comments