使用opencv标定双目摄像头

0.前言

       接触双目相机标定有一段时间了，这里将自己的学习进行一下小结。双目标定的目的是用于后面的双目测距，不过有关双目测距的内容这里先只介绍双目测距的原理，不涉及双目测距算法。

1.三角测量

      这一部分内容《学习opencv》中文版的p452-p454有了详细的论述

      注意得到最后的公式是存在很多前提的，包括：

（1）两台摄像机光轴平行、焦距相等，且各自的主点具有相同的像素坐标

（2）两台摄像机成像平面共面且像素行对齐

      不过我们拿到的双摄设备往往不能达到这种要求，所以需要标定这一步骤来进行相关的校正

2.双目标定

      opencv已经提供了一个demo（samples\cpp\stereo_calib.cpp）实现了标定流程。在此之前按照《学习opencv》相关内容进行不同角度棋盘格图像的采集，选取包含较多位置、角度变化的10-20对图像作为待处理数据。       自己使用vs2015+opencv310，调用stereo_calib.cpp封装好的函数进行了标定，目测效果还可以。这里将附有注释的代码附上来

stereo_calib.h 

#ifndef _STEREO_CALIB_H_
#define _STEREO_CALIB_H_
#include 
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

using namespace cv;
using namespace std;

// 获取某一文件夹下所有文件的绝对路径信息
void getFiles(std::string path, std::vector& files);

/*
 * 功能：标定双目相机并保存标定参数
 * 参数：
 *    imagelist: 标定图片的路径信息
 *    boardSize: 棋盘格宽高方向的角点个数
 *    squareSize: 棋盘格的方块物理边长
 *    displayCorners: 是否展示检测的角点
 *    useCalibrated: 是否利用标定中计算得到的已知参数
 *    showRectified: 是否输出对齐图像结果
 */
void StereoCalib(const vector& imagelist, Size boardSize, float squareSize, bool displayCorners, bool useCalibrated, bool showRectified);


#endif

stereo_calib.cpp

#include "stereoCalib.h"



void getFiles(std::string path, std::vector& files)
{
	long hFile = 0;
	struct _finddata_t fileinfo;
	std::string p;
	if ((hFile = _findfirst(p.assign(path).append("\\*").c_str(), &fileinfo)) != -1)
	{
		do
		{
			if ((fileinfo.attrib & _A_SUBDIR))
			{
				if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)
					getFiles(p.assign(path).append("\\").append(fileinfo.name), files);
			}
			else
			{
				files.push_back(p.assign(path).append("\\").append(fileinfo.name));
			}
		} while (_findnext(hFile, &fileinfo) == 0);
		_findclose(hFile);
	}

}

void
StereoCalib(const vector& imagelist, Size boardSize, float squareSize, bool displayCorners, bool useCalibrated, bool showRectified)
{
	if (imagelist.size() % 2 != 0)
	{
		cout << "Error: the image list contains odd (non-even) number of elements\n";
		return;
	}

	const int maxScale = 2;

	vector > imagePoints[2];// 保存检测到的角点像素坐标
	vector > objectPoints;// 保存角点的世界坐标
	Size imageSize;

	int i, j, k, nimages = (int)imagelist.size() / 2;

	imagePoints[0].resize(nimages);
	imagePoints[1].resize(nimages);// 分配空间
	vector 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)// 左右两幅图像大小需要相同
			{
				cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
				break;
			}
			bool found = false;
			vector& corners = imagePoints[k][j];
			for (int scale = 1; scale <= maxScale; scale++)
			{
				Mat timg;
				if (scale == 1)
					timg = img;
				else
					resize(img, timg, Size(), scale, scale);
				found = findChessboardCorners(timg, boardSize, corners,
					CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE);// 计算角点
				if (found)
				{
					// 未能找到角点时，放大原图重新寻找一次
					if (scale > 1)
					{
						Mat cornersMat(corners);
						cornersMat *= 1. / scale;
					}
					break;
				}
			}
			
			// 显示角点
			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);
				imshow("corners", cimg1);
				char c = (char)waitKey(500);
				if (c == 27 || c == 'q' || c == 'Q') //Allow ESC to quit
					exit(-1);
			}
			else
				putchar('.');
			if (!found)
				break;
			// 计算亚像素级别角点信息
			cornerSubPix(img, corners, Size(11, 11), Size(-1, -1),
				TermCriteria(TermCriteria::COUNT + TermCriteria::EPS,
					30, 0.01));
		}
		if (k == 2)
		{
			// 保存成功找到角点的图像对文件名
			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));// 保存角点的世界坐标
	}

	cout << "Running stereo calibration ...\n";

	Mat cameraMatrix[2], distCoeffs[2];// 左右相机的内参矩阵和畸变矩阵
	//cameraMatrix[0] = initCameraMatrix2D(objectPoints, imagePoints[0], imageSize, 0);
	//cameraMatrix[1] = initCameraMatrix2D(objectPoints, imagePoints[1], imageSize, 0);
	cameraMatrix[0] = Mat::eye(3, 3, CV_64F);
	cameraMatrix[1] = Mat::eye(3, 3, CV_64F);

	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;

	// CALIBRATION QUALITY CHECK
	// because the output fundamental matrix implicitly
	// includes all the output information,
	// we can check the quality of calibration using the
	// epipolar geometry constraint: m2^t*F*m1=0
	double err = 0;
	int npoints = 0;
	vector 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]);
			// 利用已经求得的内参矩阵和畸变矩阵计算点的矫正坐标
			undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
			// 利用基础矩阵求得图像中点在另一图像中对应的极线
			computeCorrespondEpilines(imgpt[k], k + 1, F, lines[k]);
		}
		for (j = 0; j < npt; j++)
		{
			// 理论上应满足点位于极线之上，这里使用偏差来估算矫正误差
			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);
	// 保存相机内参数
	if (fs.isOpened())
	{
		fs << "M1" << cameraMatrix[0] << "D1" << distCoeffs[0] <<
			"M2" << cameraMatrix[1] << "D2" << distCoeffs[1];
		fs.release();
	}
	else
		cout << "Error: can not save the intrinsic parameters\n";

	Mat R1, R2, P1, P2, Q;
	Rect validRoi[2];
	
	// 计算用于后续对其操作用到的矩阵
	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]);
	
	// 保存外参数信息
	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();
	}
	else
		cout << "Error: can not save the extrinsic parameters\n";

	// OpenCV can handle left-right
	// or up-down camera arrangements
	bool isVerticalStereo = fabs(P2.at(1, 3)) > fabs(P2.at(0, 3));

	// COMPUTE AND DISPLAY RECTIFICATION
	if (!showRectified)
		return;

	Mat rmap[2][2];
	// IF BY CALIBRATED (BOUGUET'S METHOD)
	if (useCalibrated)
	{
		// we already computed everything
		// 到这里已经计算得到所有信息，所以不会执行下面的分支
	}
	// OR ELSE HARTLEY'S METHOD
	else
		// use intrinsic parameters of each camera, but
		// compute the rectification transformation directly
		// from the fundamental matrix
	{
		vector allimgpt[2];
		for (k = 0; k < 2; k++)
		{
			for (i = 0; i < nimages; i++)
				std::copy(imagePoints[k][i].begin(), imagePoints[k][i].end(), back_inserter(allimgpt[k]));
		}
		F = findFundamentalMat(Mat(allimgpt[0]), Mat(allimgpt[1]), FM_8POINT, 0, 0);
		Mat H1, H2;
		stereoRectifyUncalibrated(Mat(allimgpt[0]), Mat(allimgpt[1]), F, imageSize, H1, H2, 3);

		R1 = cameraMatrix[0].inv()*H1*cameraMatrix[0];
		R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
		P1 = cameraMatrix[0];
		P2 = cameraMatrix[1];
	}

	//Precompute maps for cv::remap()
	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);
	}
	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);
	}

	for (i = 0; i < nimages; i++)
	{
		for (k = 0; k < 2; k++)
		{
			Mat img = imread(goodImageList[i * 2 + k], 0), rimg, cimg;
			// 对齐操作
			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);
			if (useCalibrated)
			{
				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;
	}
}

demo.cpp 提供标定示例，在指定路径下放入标定用图像文件后执行

#include "stereoCalib.h"

//保存标定图像的绝对路径
string filepath = "C:\\Users\\wiley.li\\Documents\\Visual Studio 2015\\Projects\\stereoCalibrate\\Images";

int main()
{
	vector files;
	getFiles(filepath, files);
	StereoCalib(files, Size(6, 4), 41.5, false, true, true);

	waitKey();
	return 0;
}

3.运行结果

      如果只是仿真，可以直接使用opencv提供的样本进行标定（\samples\data目录下的棋盘格标定图片，需要左右视角成对使用），左右图像对齐结果类似下方

4.其他

（1）自己在进行标定时，发现用于计算的标定图像质量会影响到标定的好坏，所以采集标定文件时尽量让彼此的图像包含尽可能多的棋盘格的角度、位置信息

（2）标定结束后就可以利用保存了的摄像头参数信息进行后续的双目测距，过一阵子再总结这方面的学习