OpenCV编程->双目标定（一）

static void
StereoCalib(const vector& imagelist, Size boardSize, bool useCalibrated = true, bool showRectified = true)
{
	//SingleCameraCalibR(); //右相机内参及焦距测试函数
	//SingleCameraCalibL(); //左相机内参及焦距测试函数
	if (imagelist.size() % 2 != 0)
	{
		cout << "Error: the image list contains odd (non-even) number of elements\n";
		return;
	}

	//bool displayCorners = false;//true;
	bool displayCorners = true;//true;
	const int maxScale = 2;
	//const float squareSize = 1.f;  // Set this to your actual square size
	const float squareSize = 20.f;  // Set this to your actual square size
	// ARRAY AND VECTOR STORAGE:

	vector > imagePoints[2];
	vector > objectPoints;
	Size imageSize;

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

	imagePoints[0].resize(nimages);
	imagePoints[1].resize(nimages);
	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)
			{
				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,
					CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE);
				//角点绘制   测试通过
			//	cv::drawChessboardCorners(img, cv::Size(3, 7), corners, found);
				cv::imshow("chessboard corners", img);
			//	waitKey(100);
				if (found)
				{
					if (scale > 1)
					{
						Mat cornersMat(corners);
						cornersMat *= 1. / scale;
					}
					break;
				}
			}
			//角点检测 测试成功
			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;
			}
			else
			{
				cout << "found success !" << endl;
			}
				
			cornerSubPix(img, corners, Size(11, 11), Size(-1, -1),
				TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS,
				30, 0.01));
		}
		cout << k << endl;
		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;
	}
	//开始标定
	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(j*squareSize, k*squareSize, 0));
	}

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

	Mat cameraMatrix[2], distCoeffs[2];
	cameraMatrix[0] = Mat::eye(3, 3, CV_64F);
	cameraMatrix[1] = Mat::eye(3, 3, CV_64F);
	Mat R, T, E, F;
	//目标匹配
	//测试图像的匹配性


	/*
	标定摄像头
	由于左右摄像机分别都经过了单目标定
	所以在此处选择flag = CALIB_USE_INTRINSIC_GUESS 
	objectPoints,世界坐标系中的点
	imagePoints[0], 左相机中的角点
	imagePoints[1],右相机中的角点
	cameraMatrix[0],左相机的内参矩阵
	distCoeffs[0],左相机的畸变矩阵
	cameraMatrix[1],右相机的内参矩阵
	distCoeffs[1],右相机的畸变矩阵
	imageSize,图像输入大小
	R, T, E, F, 旋转向量  位移向量
	CV_CALIB_USE_INTRINSIC_GUESS：使用该参数时，在cameraMatrix矩阵中应该有fx,fy,cx,cy的估计值。
	否则的话，将初始化(cx,cy）图像的中心点，使用最小二乘估算出fx，fy。如果内参数矩阵和畸变居中
	已知的时候，应该标定模块中的solvePnP()函数计算外参数矩阵。

	CV_CALIB_FIX_PRINCIPAL_POINT：在进行优化时会固定光轴点。当CV_CALIB_USE_INTRINSIC_GUESS参数
	被设置，光轴点将保持在中心或者某个输入的值。

	CV_CALIB_FIX_ASPECT_RATIO：固定fx/fy的比值，只将fy作为可变量，进行优化计算。当CV_CALIB_USE_
	INTRINSIC_GUESS没有被设置，fx和fy将会被忽略。只有fx/fy的比值在计算中会被用到。

	CV_CALIB_ZERO_TANGENT_DIST：设定切向畸变参数（p1,p2）为零。

	CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6：对应的径向畸变在优化中保持不变。如果设置了CV_CALIB_USE_
	INTRINSIC_GUESS参数，

	CV_CALIB_RATIONAL_MODEL：计算k4，k5，k6三个畸变参数。如果没有设置，则只计算其它5个畸变参数。
	*/
	
	double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
		cameraMatrix[0], distCoeffs[0],
		cameraMatrix[1], distCoeffs[1],
		imageSize, R, T, E, F,
		TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 100, 1e-5),
		CV_CALIB_FIX_ASPECT_RATIO +
		CV_CALIB_ZERO_TANGENT_DIST +
		CV_CALIB_SAME_FOCAL_LENGTH +
		CV_CALIB_RATIONAL_MODEL +
		CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5);
	
	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]);
			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 reprojection err = " << err / npoints << endl;

	// save intrinsic parameters
	FileStorage fs("intrinsics.yml", CV_STORAGE_WRITE);
	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";
	/*
	立体校正的时候需要两幅图像共面并且行对准 以使得立体匹配更加的可靠
	使得两幅图像共面的方法就是把两个摄像头的图像投影到一个公共成像面上，这样每幅图像从本图像平面投影到公共图像平面都需要一个旋转矩阵R
	stereoRectify 这个函数计算的就是从图像平面投影都公共成像平面的旋转矩阵Rl,Rr。 Rl,Rr即为左右相机平面行对准的校正旋转矩阵。
	左相机经过Rl旋转，右相机经过Rr旋转之后，两幅图像就已经共面并且行对准了。
	其中Pl,Pr为两个相机的投影矩阵，其作用是将3D点的坐标转换到图像的2D点的坐标:P*[X Y Z 1]' =[x y w]
	Q矩阵为重投影矩阵，即矩阵Q可以把2维平面(图像平面)上的点投影到3维空间的点:Q*[x y d 1] = [X Y Z W]。其中d为左右两幅图像的时差
	*/
	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]);

	//保存数据
	fs.open("extrinsics.yml", CV_STORAGE_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 intrinsic 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)
	{
		cout << "use Calibrated " << endl;
		// we already computed everything
	}
	// 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;
		// 计算图像矫正的单应变换矩阵
		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];
	}
	/*
	根据stereoRectify 计算出来的R 和 P 来计算图像的映射表 mapx,mapy
	mapx,mapy这两个映射表接下来可以给remap()函数调用，来校正图像，使得两幅图像共面并且行对准
	ininUndistortRectifyMap()的参数newCameraMatrix就是校正后的摄像机矩阵。在openCV里面，校正后的计算机矩阵Mrect是跟投影矩阵P一起返回的。
	所以我们在这里传入投影矩阵P，此函数可以从投影矩阵P中读出校正后的摄像机矩阵
	cameraMatrix——输入的摄像机内参数矩阵
	distCoeffs——输入的摄像机畸变系数矩阵
	R——输入的第一和第二相机坐标系之间的旋转矩阵
	newCameraMatrix——输入的校正后的3X3摄像机矩阵（也可用cvStereoRectify()得出的3X4的左或右投影矩阵，其实系统会自动提取该矩阵前三列的有用部分作为输入参数）
	size——摄像机采集的无失真图像尺寸
	m1type——map1的数据类型，可以是CV_32FC1或CV_16SC2
	map1——输出的X坐标重映射参数 
	map2——输出的Y坐标重映射参数
	*/
	//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 rgbImageL, grayImageL;
	Mat rgbImageR, grayImageR;
	char filename1[100];
	//读取左边的图像
	sprintf_s(filename1, "ImageL1.jpg");
	rgbImageL = imread(filename1, CV_LOAD_IMAGE_COLOR);
	cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);

	//读取右边的图像
	sprintf_s(filename1, "ImageRGB1.jpg");
	rgbImageR = imread(filename1, CV_LOAD_IMAGE_COLOR);
	cvtColor(rgbImageR, grayImageR, CV_BGR2GRAY);

	Mat rectifyImageL, rectifyImageR;
	cvtColor(grayImageL, rectifyImageL, CV_GRAY2BGR);
	cvtColor(grayImageR, rectifyImageR, CV_GRAY2BGR);

	imshow("Rectify Before L", rectifyImageL);
	waitKey(500);
	imshow("Rectify Before R", rectifyImageR);
	waitKey(500);
	
	//经过remap之后，左右相机的图像已经共面并且行对准了
	
	remap(rectifyImageL, rectifyImageL, rmap[0][0], rmap[0][1], INTER_LINEAR);
	remap(rectifyImageR, rectifyImageR, rmap[1][0], rmap[1][1], INTER_LINEAR);

	//imshow("ImageL", rectifyImageL);
	//waitKey(500);
	//imshow("ImageR", rectifyImageR);
	//waitKey(500);
	//system("pause");
	

	/*
	把校正结果显示出来
	把左右两幅图像显示到同一个画面上
	这里只显示了最后一副图像的校正结果。并没有把所有的图像都显示出来
	*/
	
	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);
	}
	
	//显示每一对标定图像
	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], CV_INTER_LINEAR);
			imshow("单目相机校正",rimg);
			waitKey(500);
			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, CV_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);//双目校正
		waitKey(500);
		imwrite("re.jpg",canvas);
		char c = (char)waitKey();
		if (c == 27 || c == 'q' || c == 'Q')
			break;
	}
}