在OpenCV3中，StereoBM算法发生了比较大的变化，StereoBM被定义为纯虚类，因此不能直接实例化，只能用智能指针的形式实例化，也不用StereoBMState类来设置了，而是改成用bm->set...的形式。（转载请注明出处）
详细参数代码请查看链接
另外，双目标定，立体匹配和测距的原理网上的已经很全啦，就不多啰嗦啦。使用的matlab自带的标定工具箱进行的标定。下面就上代码吧，废话少说
。。。。
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
const int imageWidth = 1024; //摄像头的分辨率
const int imageHeight = 768;
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<StereoBM> 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_<double>(3, 3) << 1324.4092, 0, 521.2833,
0, 1328.0859, 392.6668,
0, 0, 1);
//对应matlab里的左相机标定矩阵
Mat distCoeffL = (Mat_<double>(1, 5) << -0.58627, 0.16595, 0, 0.00000, 0.00000);
//对应Matlab所得左i相机畸变参数
Mat cameraMatrixR = (Mat_<double>(3, 3) << 1323.9, 0, 523.453,
0, 1326.88, 397.6582,
0, 0, 1);
//对应matlab里的右相机标定矩阵
Mat distCoeffR = (Mat_<double>(1, 5) << -0.63278, 0.47043, 0.00000, 0.00000, 0.00000);
//对应Matlab所得右相机畸变参数
Mat T = (Mat_<double>(3, 1) << -120.9319, 0.3701, 0.1176);//T平移向量
//对应Matlab所得T参数
Mat rec = (Mat_<double>(3, 1) << 0.0022, 0.0113, -0.0001);//rec旋转向量，对应matlab om参数
Mat R;//R 旋转矩阵
/*****伪彩色函数*****/
void F_Gray2Color(CvMat* gray_mat, CvMat* color_mat)
{
if (color_mat)
cvZero(color_mat);
int stype = CV_MAT_TYPE(gray_mat->type), dtype = CV_MAT_TYPE(color_mat->type);
int rows = gray_mat->rows, cols = gray_mat->cols;
// 判断输入的灰度图和输出的伪彩色图是否大小相同、格式是否符合要求
if (CV_ARE_SIZES_EQ(gray_mat, color_mat) && stype == CV_8UC1 && dtype == CV_8UC3)
{
CvMat* red = cvCreateMat(rows, cols, CV_8U); // 红色分量
CvMat* green = cvCreateMat(rows, cols, CV_8U); // 绿色分量
CvMat* blue = cvCreateMat(rows, cols, CV_8U); // 蓝色分量
CvMat* mask = cvCreateMat(rows, cols, CV_8U);
// 计算各彩色通道的像素值
cvSubRS(gray_mat, cvScalar(255), blue); // blue(I) = 255 - gray(I)
cvCopy(gray_mat, red); // red(I) = gray(I)
cvCopy(gray_mat, green); // green(I) = gray(I),if gray(I) < 128
cvCmpS(green, 128, mask, CV_CMP_GE); // green(I) = 255 - gray(I), if gray(I) >= 128
cvSubRS(green, cvScalar(255), green, mask);
cvConvertScale(green, green, 2.0, 0.0);
// 合成伪彩色图
cvMerge(blue, green, red, NULL, color_mat);
cvReleaseMat(&red);
cvReleaseMat(&green);
cvReleaseMat(&blue);
cvReleaseMat(&mask);
}
else cout << "大小格式不符合要求" << endl;
}
/*****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;
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;
imshow("disparity", disp8);
/*Mat im=imread("left11.png",0);
imshow("im", im);*/
CvMat disp8cv = disp8;
int m = disp8.rows;
int n = disp8.cols;
CvMat* vdispRGB= cvCreateMat(m, n, CV_8UC3);
//disp.convertTo(disp8, CV_8U, 255 / (numDisparities *16.));
F_Gray2Color(&disp8cv, vdispRGB);
//CvMat* b = &disp8cv;
CvMat* b = vdispRGB;
Mat gray2= cvarrToMat(b);
imshow("伪彩色深度图", gray2);
}
/*****描述：鼠标操作回调*****/
static void onMouse(int event, int x, int y, int, void*)
{
if (selectObject)
{
selection.x = MIN(x, origin.x);
selection.y = MIN(y, origin.y);
selection.width = std::abs(x - origin.x);
selection.height = std::abs(y - origin.y);
}
switch (event)
{
case EVENT_LBUTTONDOWN: //鼠标左按钮按下的事件
origin = Point(x, y);
selection = Rect(x, y, 0, 0);
selectObject = true;
cout << origin << "in world coordinate is: " << xyz.at<Vec3f>(origin) << endl;
break;
case EVENT_LBUTTONUP: //鼠标左按钮释放的事件
selectObject = false;
if (selection.width > 0 && selection.height > 0)
break;
}
}
/*****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("left11.png", CV_LOAD_IMAGE_COLOR);
cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
rgbImageR = imread("right11.png", 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);
namedWindow("ImageL After Rectify", WINDOW_NORMAL);
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);
//鼠标响应函数setMouseCallback(窗口名称, 鼠标回调函数, 传给回调函数的参数，一般取0)
setMouseCallback("disparity", onMouse, 0);
namedWindow("伪彩色深度图", CV_WINDOW_AUTOSIZE);
// 创建SAD窗口 Trackbar
createTrackbar("BlockSize:\n", "伪彩色深度图", &blockSize, 8, stereo_match);
// 创建视差唯一性百分比窗口 Trackbar
createTrackbar("UniquenessRatio:\n", "伪彩色深度图", &uniquenessRatio, 50, stereo_match);
// 创建视差窗口 Trackbar
createTrackbar("NumDisparities:\n", "伪彩色深度图", &numDisparities, 16, stereo_match);
//鼠标响应函数setMouseCallback(窗口名称, 鼠标回调函数, 传给回调函数的参数，一般取0)
setMouseCallback("伪彩色深度图", onMouse, 0);
stereo_match(0, 0);
waitKey(0);
return 0;
}本人菜鸟一只，代码也是参考的一篇博客上的， 。。。。代码有简单的修改，增加了伪彩色深度图显示。直接使用的话需要改动图片大小，标定数据和读图地址，另外算法的参数也得微调，不然出来的图会乱七八糟。。。
下面放一些效果图。。。。（深度图的效果一般，bm算法速度快，效果确实差些，参数调的也有点着急）
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