vs2015 + opencv3 双目摄像头获取深度图（C++实现）

1.准备工作

    要想获取深度图，就必须得做好一些基本工作，首先参考博客：vs2015 + opencv3 双目摄像头标定（C++实现），获得左右摄像头的内参和外参（cameraMatrixL、distCoeffL、cameraMatrixR和distCoeffR），以及R旋转矢量 T平移矢量。

 

2.获取深度图的步骤

    1）stereoRectify执行双目校正，获得校正旋转矩阵R，投影矩阵P 重投影矩阵Q

    2）initUndistortRectifyMap 分别生成两个图像校正所需的像素映射矩阵
    3）remap分别对两个图像进行校正，校正之后，由不共面到共面，可以直观参考下图：

    校正前：

    校正后：

    4）stereoBM生成视差图

 

3.代码实现

/******************************/
/*        立体匹配        */
/******************************/

#include   
#include   

using namespace std;
using namespace cv;

const int imageWidth = 640;                             //摄像头的分辨率  
const int imageHeight = 360;
Size imageSize = Size(imageWidth, imageHeight);

Mat rgbImageL, grayImageL;
Mat rgbImageR, grayImageR;
Mat rectifyImageL, rectifyImageR;

Rect validROIL;//图像校正之后，会对图像进行裁剪，这里的validROI就是指裁剪之后的区域  
Rect validROIR;

Mat mapLx, mapLy, mapRx, mapRy;     //映射表  
Mat Rl, Rr, Pl, Pr, Q;              //校正旋转矩阵R，投影矩阵P 重投影矩阵Q
Mat xyz;              //三维坐标

Point origin;         //鼠标按下的起始点
Rect selection;      //定义矩形选框
bool selectObject = false;    //是否选择对象

int blockSize = 0, uniquenessRatio = 0, numDisparities = 0;
Ptr bm = StereoBM::create(16, 9);

/*
事先标定好的相机的参数
fx 0 cx
0 fy cy
0 0  1
*/
Mat cameraMatrixL = (Mat_(3, 3) << 8.6447560726240727e+02, 0, 3.1396077284379163e+02,
	0, 7.9990966684818886e+02, 4.1785668776720030e+02,
	0, 0, 1);
Mat distCoeffL = (Mat_(5, 1) << -8.3290443472456477e-01, -1.6141488144887639e+00, -2.0021554243964865e-01, 
	-5.0519644795720990e-03, 3.9975929024497163e+00);

Mat cameraMatrixR = (Mat_(3, 3) << 8.3828754655778573e+02, 0, 3.2434901263473841e+02,
	0, 7.8106334038218915e+02, 4.4025865037526029e+02,
	0, 0, 1);
Mat distCoeffR = (Mat_(5, 1) << -6.9576263466271460e-01, -9.7033517758028354e-01, -1.6926908600014581e-01,
	-4.1180802214907403e-03, 1.9769688036876045e+00);


Mat T = (Mat_(3, 1) << -5.4680441729569061e+01, 3.1352955093965731e+00, -1.0681705203000762e+01);//T平移向量
Mat rec = (Mat_(3, 1) << -0.00306, -0.03207, 0.00206);//rec旋转向量
Mat R = (Mat_(3, 3) << 9.9981573574061489e-01, 7.2404315717654781e-03, -1.7778377769287649e-02,
	-7.8525554838869468e-03, 9.9937018554944201e-01, -3.4605918673635071e-02,
	1.7516618903926539e-02, 3.4739147737507171e-02, 9.9924289323299487e-01);//R 旋转矩阵



int getDisparityImage(cv::Mat& disparity, cv::Mat& disparityImage, bool isColor)
{
	cv::Mat disp8u;
	disp8u = disparity;


	// 转换为伪彩色图像 或 灰度图像
	if (isColor)
	{
		if (disparityImage.empty() || disparityImage.type() != CV_8UC3 || disparityImage.size() != disparity.size())
		{
			disparityImage = cv::Mat::zeros(disparity.rows, disparity.cols, CV_8UC3);
		}

		for (int y = 0; y(y, x);
				uchar r, g, b;

				if (val == 0)
					r = g = b = 0;
				else
				{
					r = 255 - val;
					g = val < 128 ? val * 2 : (uchar)((255 - val) * 2);
					b = val;
				}

				disparityImage.at(y, x) = cv::Vec3b(r, g, b);

			}
		}
	}
	else
	{
		disp8u.copyTo(disparityImage);
	}

	return 1;
}

																 /*****立体匹配*****/
void stereo_match(int, void*)
{
	bm->setBlockSize(2 * blockSize + 5);     //SAD窗口大小，5~21之间为宜
	bm->setROI1(validROIL);
	bm->setROI2(validROIR);
	bm->setPreFilterCap(31);
	bm->setMinDisparity(0);  //最小视差，默认值为0, 可以是负值，int型
	bm->setNumDisparities(numDisparities * 16 + 16);//视差窗口，即最大视差值与最小视差值之差,窗口大小必须是16的整数倍，int型
	bm->setTextureThreshold(10);
	bm->setUniquenessRatio(uniquenessRatio);//uniquenessRatio主要可以防止误匹配
	bm->setSpeckleWindowSize(100);
	bm->setSpeckleRange(32);
	bm->setDisp12MaxDiff(-1);
	Mat disp, disp8, disparityImage;
	bm->compute(rectifyImageL, rectifyImageR, disp);//输入图像必须为灰度图
	disp.convertTo(disp8, CV_8U, 255 / ((numDisparities * 16 + 16)*16.));//计算出的视差是CV_16S格式
	reprojectImageTo3D(disp, xyz, Q, true); //在实际求距离时，ReprojectTo3D出来的X / W, Y / W, Z / W都要乘以16(也就是W除以16)，才能得到正确的三维坐标信息。
	xyz = xyz * 16;
	getDisparityImage(disp8, disparityImage, true);
	imshow("disparity", disparityImage);
}



/*****主函数*****/
int main()
{
	/*
	立体校正
	*/
	//Rodrigues(rec, R); //Rodrigues变换
	stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q, CALIB_ZERO_DISPARITY,
		0, imageSize, &validROIL, &validROIR);
	initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pr, imageSize, CV_32FC1, mapLx, mapLy);
	initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);

	/*
	读取图片
	*/
	rgbImageL = imread("image_left_5.jpg", CV_LOAD_IMAGE_COLOR);
	cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
	rgbImageR = imread("image_right_5.jpg", CV_LOAD_IMAGE_COLOR);
	cvtColor(rgbImageR, grayImageR, CV_BGR2GRAY);

	imshow("ImageL Before Rectify", grayImageL);
	imshow("ImageR Before Rectify", grayImageR);

	/*
	经过remap之后，左右相机的图像已经共面并且行对准了
	*/
	remap(grayImageL, rectifyImageL, mapLx, mapLy, INTER_LINEAR);
	remap(grayImageR, rectifyImageR, mapRx, mapRy, INTER_LINEAR);

	/*
	把校正结果显示出来
	*/
	Mat rgbRectifyImageL, rgbRectifyImageR;
	cvtColor(rectifyImageL, rgbRectifyImageL, CV_GRAY2BGR);  //伪彩色图
	cvtColor(rectifyImageR, rgbRectifyImageR, CV_GRAY2BGR);

	//单独显示
	//rectangle(rgbRectifyImageL, validROIL, Scalar(0, 0, 255), 3, 8);
	//rectangle(rgbRectifyImageR, validROIR, Scalar(0, 0, 255), 3, 8);
	imshow("ImageL After Rectify", rgbRectifyImageL);
	imshow("ImageR After Rectify", rgbRectifyImageR);

	//显示在同一张图上
	Mat canvas;
	double sf;
	int w, h;
	sf = 600. / MAX(imageSize.width, imageSize.height);
	w = cvRound(imageSize.width * sf);
	h = cvRound(imageSize.height * sf);
	canvas.create(h, w * 2, CV_8UC3);   //注意通道

										//左图像画到画布上
	Mat canvasPart = canvas(Rect(w * 0, 0, w, h));                                //得到画布的一部分  
	resize(rgbRectifyImageL, canvasPart, canvasPart.size(), 0, 0, INTER_AREA);     //把图像缩放到跟canvasPart一样大小  
	Rect vroiL(cvRound(validROIL.x*sf), cvRound(validROIL.y*sf),                //获得被截取的区域    
		cvRound(validROIL.width*sf), cvRound(validROIL.height*sf));
	//rectangle(canvasPart, vroiL, Scalar(0, 0, 255), 3, 8);                      //画上一个矩形  
	cout << "Painted ImageL" << endl;

	//右图像画到画布上
	canvasPart = canvas(Rect(w, 0, w, h));                                      //获得画布的另一部分  
	resize(rgbRectifyImageR, canvasPart, canvasPart.size(), 0, 0, INTER_LINEAR);
	Rect vroiR(cvRound(validROIR.x * sf), cvRound(validROIR.y*sf),
		cvRound(validROIR.width * sf), cvRound(validROIR.height * sf));
	//rectangle(canvasPart, vroiR, Scalar(0, 0, 255), 3, 8);
	cout << "Painted ImageR" << endl;

	//画上对应的线条
	for (int i = 0; i < canvas.rows; i += 16)
		line(canvas, Point(0, i), Point(canvas.cols, i), Scalar(0, 255, 0), 1, 8);
	imshow("rectified", canvas);

	/*
	立体匹配
	*/
	namedWindow("disparity", CV_WINDOW_AUTOSIZE);
	// 创建SAD窗口 Trackbar
	createTrackbar("BlockSize:\n", "disparity", &blockSize, 8, stereo_match);
	// 创建视差唯一性百分比窗口 Trackbar
	createTrackbar("UniquenessRatio:\n", "disparity", &uniquenessRatio, 50, stereo_match);
	// 创建视差窗口 Trackbar
	createTrackbar("NumDisparities:\n", "disparity", &numDisparities, 16, stereo_match);
	stereo_match(0, 0);

	waitKey(0);
	return 0;
}

 

4.参考资料

    https://blog.csdn.net/wangchao7281/article/details/52506691/