一、双目标定
#include "opencv2/calib3d.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <vector>
#include <string>
#include <algorithm>
#include <iostream>
#include <iterator>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
using namespace cv;
using namespace std;
static void StereoCalib(const vector<string>& imagelist, Size boardSize, float squareSize)
{
bool displayCorners = false; // 是否展示角点
bool showRectified = true; // 释放展示修正图
if( imagelist.size() % 2 != 0 )
{
cout << "Error: the image list contains odd (non-even) number of elements\n";
return;
}
vector<vector<Point2f> > imagePoints[2]; // 角点，像素坐标
vector<vector<Point3f> > objectPoints; // 角点，物理3D坐标
Size imageSize;
int i, j, k, nimages = (int)imagelist.size()/2;
imagePoints[0].resize(nimages);
imagePoints[1].resize(nimages);
vector<string> goodImageList;
for( i = j = 0; i < nimages; i++ )
{
for( k = 0; k < 2; k++ )
{
const string& filename = imagelist[i*2+k];
Mat img = imread(filename, 0);
if(img.empty())
break;
if( imageSize == Size() )
imageSize = img.size();
else if( img.size() != imageSize )
{
cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
break;
}
bool found = false;
vector<Point2f>& corners = imagePoints[k][j];
for( int scale = 1; scale <= 2; scale++ )
{
Mat timg;
if( scale == 1 )
timg = img;
else
resize(img, timg, Size(), scale, scale, INTER_LINEAR_EXACT);
found = findChessboardCorners(timg, boardSize, corners,
CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE);
if( found )
{
if( scale > 1 )
{
Mat cornersMat(corners);
cornersMat *= 1./scale;
}
break;
}
}
if( displayCorners )
{
cout << filename << endl;
Mat cimg, cimg1;
cvtColor(img, cimg, COLOR_GRAY2BGR);
drawChessboardCorners(cimg, boardSize, corners, found);
double sf = 640./MAX(img.rows, img.cols);
resize(cimg, cimg1, Size(), sf, sf, INTER_LINEAR_EXACT);
imshow("corners", cimg1);
char c = (char)waitKey(500);
if( c == 27 || c == 'q' || c == 'Q' )
exit(-1);
}
if( !found )
break;
cornerSubPix(img, corners, Size(11,11), Size(-1,-1),
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01));
}
if( k == 2 )
{
goodImageList.push_back(imagelist[i*2]);
goodImageList.push_back(imagelist[i*2+1]);
j++;
}
}
cout << j << " pairs have been successfully detected.\n";
nimages = j;
if( nimages < 2 )
{
cout << "Error: too little pairs to run the calibration\n";
return;
}
imagePoints[0].resize(nimages);
imagePoints[1].resize(nimages);
objectPoints.resize(nimages);
for( i = 0; i < nimages; i++ )
for (j = 0; j < boardSize.height; j++)
for (k = 0; k < boardSize.width; k++)
objectPoints[i].push_back(Point3f(k * squareSize, j * squareSize, 0));
cout << "Running stereo calibration ...\n";
Mat cameraMatrix[2], distCoeffs[2];
cameraMatrix[0] = initCameraMatrix2D(objectPoints, imagePoints[0], imageSize, 0);
cameraMatrix[1] = initCameraMatrix2D(objectPoints, imagePoints[1], imageSize, 0);
cout << "initial cameraMatrix[0] = " << endl << cameraMatrix[0] << endl; // 初始内参矩阵3*3
cout << "initial cameraMatrix[1] = " << endl << cameraMatrix[1] << endl; // 初始内参矩阵3*3
Mat R, T, E, F;
double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
cameraMatrix[0], distCoeffs[0],
cameraMatrix[1], distCoeffs[1],
imageSize, R, T, E, F,
CALIB_FIX_ASPECT_RATIO +
CALIB_ZERO_TANGENT_DIST +
CALIB_USE_INTRINSIC_GUESS +
CALIB_SAME_FOCAL_LENGTH +
CALIB_RATIONAL_MODEL +
CALIB_FIX_K3 + CALIB_FIX_K4 + CALIB_FIX_K5,
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-5) );
cout << "done with RMS error = " << rms << endl;
cout << "cameraMatrix[0] = " << endl << cameraMatrix[0] << endl; // 标定后内参矩阵3*3
cout << "cameraMatrix[1] = " << endl << cameraMatrix[1] << endl; // 标定后内参矩阵3*3
cout << "distCoeffs[0] = " << endl << distCoeffs[0] << endl; // 标定后畸变系数向量1*14
cout << "distCoeffs[1] = " << endl << distCoeffs[1] << endl; // 标定后畸变系数向量1*14
cout << "R = " << endl << R << endl; // 两相机之间的旋转矩阵3*3
cout << "T = " << endl << T << endl; // 两相机之间的平移向量3*1
double err = 0;
int npoints = 0;
vector<Vec3f> lines[2];
for( i = 0; i < nimages; i++ )
{
int npt = (int)imagePoints[0][i].size();
Mat imgpt[2];
for( k = 0; k < 2; k++ )
{
imgpt[k] = Mat(imagePoints[k][i]);
undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
computeCorrespondEpilines(imgpt[k], k+1, F, lines[k]);
}
for( j = 0; j < npt; j++ )
{
double errij = fabs(imagePoints[0][i][j].x*lines[1][j][0] +
imagePoints[0][i][j].y*lines[1][j][1] + lines[1][j][2]) +
fabs(imagePoints[1][i][j].x*lines[0][j][0] +
imagePoints[1][i][j].y*lines[0][j][1] + lines[0][j][2]);
err += errij;
}
npoints += npt;
}
cout << "average epipolar err = " << err / npoints << endl;
// save intrinsic parameters
FileStorage fs("intrinsics.yml", FileStorage::WRITE);
if( fs.isOpened() )
{
fs << "M1" << cameraMatrix[0] << "D1" << distCoeffs[0] <<
"M2" << cameraMatrix[1] << "D2" << distCoeffs[1];
fs.release();
}
Mat R1, R2, P1, P2, Q;
Rect validRoi[2];
stereoRectify(cameraMatrix[0], distCoeffs[0], cameraMatrix[1], distCoeffs[1], imageSize, R, T,
R1, R2, P1, P2, Q, CALIB_ZERO_DISPARITY, 1, imageSize, &validRoi[0], &validRoi[1]);
cout << "P1 = " << endl << P1 << endl; // 相机1在修正坐标系下的投影矩阵3*4
cout << "P2 = " << endl << P2 << endl; // 相机2在修正坐标系下的投影矩阵3*4
cout << "roi1 = " << validRoi[0].x << " " << validRoi[0].y << " " << validRoi[0].width << " " << validRoi[0].height << endl;
cout << "roi2 = " << validRoi[1].x << " " << validRoi[1].y << " " << validRoi[1].width << " " << validRoi[1].height << endl;
fs.open("extrinsics.yml", FileStorage::WRITE);
if( fs.isOpened() )
{
fs << "R" << R << "T" << T << "R1" << R1 << "R2" << R2 << "P1" << P1 << "P2" << P2 << "Q" << Q;
fs.release();
}
bool isVerticalStereo = fabs(P2.at<double>(1, 3)) > fabs(P2.at<double>(0, 3)); // 判断双目相机是否垂直放置
Mat rmap[2][2];
initUndistortRectifyMap(cameraMatrix[0], distCoeffs[0], R1, P1, imageSize, CV_16SC2, rmap[0][0], rmap[0][1]);
initUndistortRectifyMap(cameraMatrix[1], distCoeffs[1], R2, P2, imageSize, CV_16SC2, rmap[1][0], rmap[1][1]);
Mat canvas;
double sf;
int w, h;
if( !isVerticalStereo )
{
sf = 600./MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width*sf);
h = cvRound(imageSize.height*sf);
canvas.create(h, w*2, CV_8UC3); // 双目水平放置
}
else
{
sf = 300./MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width*sf);
h = cvRound(imageSize.height*sf);
canvas.create(h*2, w, CV_8UC3); // 双目垂直放置
}
for( i = 0; i < nimages; i++ )
{
for( k = 0; k < 2; k++ )
{
Mat img = imread(goodImageList[i*2+k], 0), rimg, cimg;
remap(img, rimg, rmap[k][0], rmap[k][1], INTER_LINEAR);
cvtColor(rimg, cimg, COLOR_GRAY2BGR);
Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));
resize(cimg, canvasPart, canvasPart.size(), 0, 0, INTER_AREA);
Rect vroi(cvRound(validRoi[k].x* sf), cvRound(validRoi[k].y* sf),
cvRound(validRoi[k].width* sf), cvRound(validRoi[k].height* sf));
rectangle(canvasPart, vroi, Scalar(0, 0, 255), 3, 8);
}
if( !isVerticalStereo )
for( j = 0; j < canvas.rows; j += 16 )
line(canvas, Point(0, j), Point(canvas.cols, j), Scalar(0, 255, 0), 1, 8);
else
for( j = 0; j < canvas.cols; j += 16 )
line(canvas, Point(j, 0), Point(j, canvas.rows), Scalar(0, 255, 0), 1, 8);
imshow("rectified", canvas);
char c = (char)waitKey();
if( c == 27 || c == 'q' || c == 'Q' )
break;
}
}
static bool readStringList( const string& filename, vector<string>& l )
{
l.resize(0);
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
FileNode n = fs.getFirstTopLevelNode();
if( n.type() != FileNode::SEQ )
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
for( ; it != it_end; ++it )
l.push_back((string)*it);
return true;
}
int main()
{
Size boardSize = Size(9, 6); // 9*6标定板
float squareSize = 1.0; // 棋盘格尺寸
vector<string> imagelist; // 图像名称列表
bool ok = readStringList("stereo_calib.xml", imagelist); // 读取xml内容
if (!ok || imagelist.empty())
{
cout << "can not open file or the string list is empty" << endl;
return -1;
}
StereoCalib(imagelist, boardSize, squareSize);
return 0;
}
二、计算视差
#define _CRT_SECURE_NO_WARNINGS
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/utility.hpp"
#include <stdio.h>
#include <iostream>
using namespace cv;
using namespace std;
static void saveXYZ(const char* filename, const Mat& mat)
{
const double max_z = 1.0e4;
FILE* fp = fopen(filename, "wt");
for(int y = 0; y < mat.rows; y++)
{
for(int x = 0; x < mat.cols; x++)
{
Vec3f point = mat.at<Vec3f>(y, x);
if(fabs(point[2] - max_z) < FLT_EPSILON || fabs(point[2]) > max_z) continue;
fprintf(fp, "%f %f %f\n", point[0], point[1], point[2]);
}
}
fclose(fp);
}
int main()
{
string img1_filename = "left01.jpg";
string img2_filename = "right01.jpg";
string intrinsic_filename = "intrinsics.yml";
string extrinsic_filename = "extrinsics.yml";
string disparity_filename = "disparity.jpg";
string point_cloud_filename = "point.xyz";
Mat img1 = imread(img1_filename, IMREAD_UNCHANGED);
Mat img2 = imread(img2_filename, IMREAD_UNCHANGED);
if (img1.empty())
{
printf("Command-line parameter error: could not load the first input image file\n");
return -1;
}
if (img2.empty())
{
printf("Command-line parameter error: could not load the second input image file\n");
return -1;
}
Size img_size = img1.size();
Rect roi1, roi2;
Mat Q;
if( !intrinsic_filename.empty() )
{
FileStorage fs(intrinsic_filename, FileStorage::READ); // 读取内参矩阵
if(!fs.isOpened())
{
printf("Failed to open file %s\n", intrinsic_filename.c_str());
return -1;
}
Mat M1, D1, M2, D2;
fs["M1"] >> M1;
fs["D1"] >> D1;
fs["M2"] >> M2;
fs["D2"] >> D2;
fs.open(extrinsic_filename, FileStorage::READ); // 读取外参矩阵
if(!fs.isOpened())
{
printf("Failed to open file %s\n", extrinsic_filename.c_str());
return -1;
}
Mat R, T, R1, P1, R2, P2;
fs["R"] >> R;
fs["T"] >> T;
stereoRectify( M1, D1, M2, D2, img_size, R, T,
R1, R2, P1, P2, Q, CALIB_ZERO_DISPARITY, -1, img_size, &roi1, &roi2 );
cout << "roi1 = " << roi1.x << " " << roi1.y << " " << roi1.width << " " << roi1.height << endl;
cout << "roi2 = " << roi2.x << " " << roi2.y << " " << roi2.width << " " << roi2.height << endl;
cout << "R1 = " << endl << R1 << endl;
cout << "P1 = " << endl << P1 << endl;
cout << "R2 = " << endl << R2 << endl;
cout << "P2 = " << endl << P2 << endl;
Mat map11, map12, map21, map22;
initUndistortRectifyMap(M1, D1, R1, P1, img_size, CV_16SC2, map11, map12);
initUndistortRectifyMap(M2, D2, R2, P2, img_size, CV_16SC2, map21, map22);
Mat img1r, img2r;
remap(img1, img1r, map11, map12, INTER_LINEAR);
remap(img2, img2r, map21, map22, INTER_LINEAR);
img1 = img1r;
img2 = img2r;
imwrite("remap_left.jpg", img1);
imwrite("remap_right.jpg", img2);
}
int numberOfDisparities = ((img_size.width/8) + 15) & -16;
cout << "numberOfDisparities = " << numberOfDisparities << endl; // 80 = (640/8+15) & -16
Ptr<StereoSGBM> sgbm = StereoSGBM::create(0, 16, 3); // SGBM算法
sgbm->setPreFilterCap(63); // 水平sobel预处理后，映射滤波器大小
int cn = img1.channels();
int sgbmWinSize = 7;
sgbm->setBlockSize(sgbmWinSize);
sgbm->setP1(8 * cn * sgbmWinSize * sgbmWinSize); // 越大视差越平滑
sgbm->setP2(32 * cn * sgbmWinSize * sgbmWinSize); // 越大视差越平滑
sgbm->setMinDisparity(0); // 最小视差
sgbm->setNumDisparities(numberOfDisparities); // 视差搜索范围
sgbm->setUniquenessRatio(10);
sgbm->setSpeckleWindowSize(100); // 视差连通区域像素点个数的大小
sgbm->setSpeckleRange(32); // 视差连通条件
sgbm->setDisp12MaxDiff(1); // 左右一致性检测最大容许误差阈值
sgbm->setMode(StereoSGBM::MODE_SGBM);
Mat disp, disp8; // 视差图
sgbm->compute(img1, img2, disp); // CV_16SC1
disp.convertTo(disp8, CV_8U, 255/(numberOfDisparities*16.)); // 16S--->8U
if( true )
{
namedWindow("left", 1);
imshow("left", img1);
namedWindow("right", 1);
imshow("right", img2);
namedWindow("disparity", 0);
imshow("disparity", disp8);
printf("press any key to continue...");
fflush(stdout);
waitKey();
printf("\n");
}
if (!disparity_filename.empty())
{
printf("save disparity file...");
fflush(stdout);
imwrite(disparity_filename, disp8);
printf("\n");
}
if(!point_cloud_filename.empty())
{
printf("storing the point cloud...");
fflush(stdout);
Mat xyz;
reprojectImageTo3D(disp, xyz, Q, true);
saveXYZ(point_cloud_filename.c_str(), xyz);
printf("\n");
}
return 0;
}
三、函数介绍
initCameraMatrix2D()
Finds an initial camera matrix from 3D-2D point correspondences.
inputOutput:
objectPoints---vector<vector<Point3f>>---n个图片对，p*q个特征点的3D坐标
imagePoints1---vector<vector<Point2f>>---相机1的n个图，p*q个特征点像素坐标
imageSize---Size---图片尺寸
cameraMatrix1---Mat---相机1的内参矩阵3*3---OutputArray
stereoCalibrate()
inputOutput:
objectPoints---vector<vector<Point3f>>---n个图片对，p*q个特征点的3D坐标
imagePoints1---vector<vector<Point2f>>---相机1的n个图，p*q个特征点像素坐标
imagePoints2---vector<vector<Point2f>>---相机2的n个图，p*q个特征点像素坐标
cameraMatrix1---Mat---相机1的内参矩阵3*3---InputOutputArray
distCoeffs1---Mat---相机1的畸变矩阵1*14---InputOutputArray
cameraMatrix2---Mat---相机2的内参矩阵3*3---InputOutputArray
distCoeffs2---Mat---相机2的畸变矩阵1*14---InputOutputArray
imageSize---Size---图片尺寸
R---Mat---相机1和相机2坐标系之间的旋转矩阵3*3 ---OutputArray
T---Mat---相机1和相机2坐标系之间的平移向量3*1 ---OutputArray
E---Mat---本质矩阵essential matrix 3*3 ---OutputArray
F---Mat---基本矩阵fundamental matrix 3*3 ---OutputArray
undistortPoints()
Computes the ideal point coordinates from the observed point coordinates.
stereoRectify()
Computes rectification transforms for each head of a calibrated stereo camera.
inputOutput:
cameraMatrix1---Mat---相机1的内参矩阵3*3
distCoeffs1---Mat---相机1的畸变矩阵1*14
cameraMatrix2---Mat---相机2的内参矩阵3*3
distCoeffs2---Mat---相机2的畸变矩阵1*14
imageSize---Size---图片尺寸
R---Mat---相机1和相机2坐标系之间的旋转矩阵3*3
T---Mat---相机1和相机2坐标系之间的平移向量3*1
R1---Mat---相机1的修正变换(旋转矩阵)3*3 --- OutputArray
R2---Mat---相机2的修正变换(旋转矩阵)3*3 --- OutputArray
P1---Mat---相机1在修正坐标系下的投影矩阵3*4 --- OutputArray
P2---Mat---相机2在修正坐标系下的投影矩阵3*4 --- OutputArray
Q---Mat---视差图到深度图的映射矩阵4*4 --- OutputArray
If alpha=-1 or absent, the function performs the default scaling. (size=img size)
If alpha=0 means rectified images are zoomed and shifted only valid pixels visible.
If alpha=1 means rectified image is decimated and shifted so that all the
pixels from the original images from the cameras are retained in the rectified images.
initUndistortRectifyMap()
Computes the undistortion and rectification transformation map.
inputOutput:
cameraMatrix1---Mat---相机1的内参矩阵3*3
distCoeffs1---Mat---相机1的畸变矩阵1*14
R1---Mat---相机1的修正变换(旋转矩阵)3*3
P1---Mat---相机1在修正坐标系下的投影矩阵3*4
imageSize---Size---图片尺寸
map1---Mat---第一个输出map OutputArray
map2---Mat---第二个输出map OutputArray
remap()
Applies a generic geometrical transformation to an image.
reprojectImageTo3D()
Reprojects a disparity image to 3D space.
重投影矩阵(透视变换矩阵)Q中cx和cy为左相机在图像中的坐标，f为焦距，Tx为两台相机投影中心间的平移值(负值)，即基线baseline, cx’为右相机主点在图像中的坐标。
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