双目 机器视觉-- 测距
【摘要】 目录
1.双目图片--测距：
流程说明：
2. 实时采集数据，进行测距
首先进行双目定标，获取双目摄像头内部的参数后，进行测距。
注意：双目定标的效果会影响测距的精准度，建议大家在做双目定标时，做好一些（尽量让误差小）
本次的双目视觉测距，基于BM算法。
1.双目图片--测距：
效果：
本人通过测试，误差是1cm.
其中参数：B...
目录
首先进行双目定标，获取双目摄像头内部的参数后，进行测距。
注意：双目定标的效果会影响测距的精准度，建议大家在做双目定标时，做好一些（尽量让误差小）
本次的双目视觉测距，基于BM算法。
1.双目图片--测距：
效果：
本人通过测试，误差是1cm.
其中参数：BlockSize、UniquenessRatio、NumDisparities 根据实际情况来调整；
源代码：
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/* 双目测距 */
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#include <opencv2/opencv.hpp>
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#include <iostream>
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#include <math.h>
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using namespace std;
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using namespace cv;
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const int imageWidth = 640; //摄像头的分辨率
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const int imageHeight = 360;
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Vec3f point3;
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float d;
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Size imageSize = Size(imageWidth, imageHeight);
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Mat rgbImageL, grayImageL;
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Mat rgbImageR, grayImageR;
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Mat rectifyImageL, rectifyImageR;
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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
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Rect validROIR;
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Mat mapLx, mapLy, mapRx, mapRy; //映射表
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Mat Rl, Rr, Pl, Pr, Q; //校正旋转矩阵R，投影矩阵P 重投影矩阵Q
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Mat xyz; //三维坐标
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Point origin; //鼠标按下的起始点
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Rect selection; //定义矩形选框
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bool selectObject = false; //是否选择对象
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int blockSize = 0, uniquenessRatio = 0, numDisparities = 0;
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Ptr<StereoBM> bm = StereoBM::create(16, 9);
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/*事先标定好的左相机的内参矩阵
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fx 0 cx
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0 fy cy
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0 0 1
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*/
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Mat cameraMatrixL = (Mat_<double>(3, 3) << 418.523322187048, -1.26842201390676, 343.908870120890,
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0, 421.222568242056, 235.466208987968,
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0, 0, 1);
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//获得的畸变参数
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/*418.523322187048 0 0
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-1.26842201390676 421.222568242056 0
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344.758267538961 243.318992284899 1 */ //2
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Mat distCoeffL = (Mat_<double>(5, 1) << 0.006636837611004, 0.050240447649195, 0.006681263320267, 0.003130367429418, 0);
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//[0.006636837611004,0.050240447649195] [0.006681263320267,0.003130367429418]
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/*事先标定好的右相机的内参矩阵
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fx 0 cx
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0 fy cy
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0 0 1
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*/
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Mat cameraMatrixR = (Mat_<double>(3, 3) << 417.417985082506, 0.498638151824367, 309.903372309072,
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0, 419.795432389420, 230.6,
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0, 0, 1);
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/*
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417.417985082506 0 0
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0.498638151824367 419.795432389420 0
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309.903372309072 236.256106972796 1
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*/ //2
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Mat distCoeffR = (Mat_<double>(5, 1) << -0.038407383078874, 0.236392800301615, 0.004121779274885, 0.002296129959664, 0);
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//[-0.038407383078874,0.236392800301615] [0.004121779274885,0.002296129959664]
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Mat T = (Mat_<double>(3, 1) << -1.210187345641146e+02, 0.519235426836325, -0.425535566316217);//T平移向量
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//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
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//对应Matlab所得T参数
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//Mat rec = (Mat_<double>(3, 1) << -0.00306, -0.03207, 0.00206);//rec旋转向量，对应matlab om参数 我
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Mat rec = (Mat_<double>(3, 3) << 0.999341122700880, -0.00206388651740061, 0.0362361815232777,
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0.000660748031451783, 0.999250989651683, 0.0386913826603732,
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-0.0362888948713456, -0.0386419468010579, 0.998593969567432); //rec旋转向量，对应matlab om参数 我
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/* 0.999341122700880 0.000660748031451783 -0.0362888948713456
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-0.00206388651740061 0.999250989651683 -0.0386419468010579
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0.0362361815232777 0.0386913826603732 0.998593969567432 */
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//Mat T = (Mat_<double>(3, 1) << -48.4, 0.241, -0.0344);//T平移向量
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//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
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//对应Matlab所得T参数
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Mat R;//R 旋转矩阵
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/*****立体匹配*****/
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void stereo_match(int, void*)
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{
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bm->setBlockSize(2 * blockSize + 5); //SAD窗口大小，5~21之间为宜
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bm->setROI1(validROIL);
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bm->setROI2(validROIR);
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bm->setPreFilterCap(31);
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bm->setMinDisparity(0); //最小视差，默认值为0, 可以是负值，int型
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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
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bm->setTextureThreshold(10);
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bm->setUniquenessRatio(uniquenessRatio);//uniquenessRatio主要可以防止误匹配
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bm->setSpeckleWindowSize(100);
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bm->setSpeckleRange(32);
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bm->setDisp12MaxDiff(-1);
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Mat disp, disp8;
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bm->compute(rectifyImageL, rectifyImageR, disp);//输入图像必须为灰度图
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disp.convertTo(disp8, CV_8U, 255 / ((numDisparities * 16 + 16)*16.));//计算出的视差是CV_16S格式
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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。
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xyz = xyz * 16;
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imshow("disparity", disp8);
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}
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/*****描述：鼠标操作回调*****/
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static void onMouse(int event, int x, int y, int, void*)
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{
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if (selectObject)
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{
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selection.x = MIN(x, origin.x);
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selection.y = MIN(y, origin.y);
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selection.width = std::abs(x - origin.x);
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selection.height = std::abs(y - origin.y);
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}
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switch (event)
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{
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case EVENT_LBUTTONDOWN: //鼠标左按钮按下的事件
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origin = Point(x, y);
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selection = Rect(x, y, 0, 0);
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selectObject = true;
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//cout << origin << "in world coordinate is: " << xyz.at<Vec3f>(origin) << endl;
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point3 = xyz.at<Vec3f>(origin);
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point3[0];
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//cout << "point3[0]:" << point3[0] << "point3[1]:" << point3[1] << "point3[2]:" << point3[2]<<endl;
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cout << "世界坐标：" << endl;
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cout << "x: " << point3[0] << " y: " << point3[1] << " z: " << point3[2] << endl;
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d = point3[0] * point3[0]+ point3[1] * point3[1]+ point3[2] * point3[2];
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d = sqrt(d); //mm
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// cout << "距离是:" << d << "mm" << endl;
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d = d / 10.0; //cm
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cout << "距离是:" << d << "cm" << endl;
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// d = d/1000.0; //m
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// cout << "距离是:" << d << "m" << endl;
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break;
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case EVENT_LBUTTONUP: //鼠标左按钮释放的事件
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selectObject = false;
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if (selection.width > 0 && selection.height > 0)
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break;
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}
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}
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/*****主函数*****/
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int main()
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{
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/*
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立体校正
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*/
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Rodrigues(rec, R); //Rodrigues变换
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stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q, CALIB_ZERO_DISPARITY,
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0, imageSize, &validROIL, &validROIR);
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initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pr, imageSize, CV_32FC1, mapLx, mapLy);
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initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);
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/*
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读取图片
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*/
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rgbImageL = imread("image_left_1.jpg", CV_LOAD_IMAGE_COLOR);
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cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
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rgbImageR = imread("image_right_1.jpg", CV_LOAD_IMAGE_COLOR);
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cvtColor(rgbImageR, grayImageR, CV_BGR2GRAY);
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imshow("ImageL Before Rectify", grayImageL);
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imshow("ImageR Before Rectify", grayImageR);
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/*
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经过remap之后，左右相机的图像已经共面并且行对准了
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*/
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remap(grayImageL, rectifyImageL, mapLx, mapLy, INTER_LINEAR);
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remap(grayImageR, rectifyImageR, mapRx, mapRy, INTER_LINEAR);
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/*
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把校正结果显示出来
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*/
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Mat rgbRectifyImageL, rgbRectifyImageR;
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cvtColor(rectifyImageL, rgbRectifyImageL, CV_GRAY2BGR); //伪彩色图
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cvtColor(rectifyImageR, rgbRectifyImageR, CV_GRAY2BGR);
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//单独显示
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//rectangle(rgbRectifyImageL, validROIL, Scalar(0, 0, 255), 3, 8);
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//rectangle(rgbRectifyImageR, validROIR, Scalar(0, 0, 255), 3, 8);
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imshow("ImageL After Rectify", rgbRectifyImageL);
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imshow("ImageR After Rectify", rgbRectifyImageR);
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//显示在同一张图上
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Mat canvas;
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double sf;
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int w, h;
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sf = 600. / MAX(imageSize.width, imageSize.height);
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w = cvRound(imageSize.width * sf);
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h = cvRound(imageSize.height * sf);
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canvas.create(h, w * 2, CV_8UC3); //注意通道
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//左图像画到画布上
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Mat canvasPart = canvas(Rect(w * 0, 0, w, h)); //得到画布的一部分
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resize(rgbRectifyImageL, canvasPart, canvasPart.size(), 0, 0, INTER_AREA); //把图像缩放到跟canvasPart一样大小
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Rect vroiL(cvRound(validROIL.x*sf), cvRound(validROIL.y*sf), //获得被截取的区域
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cvRound(validROIL.width*sf), cvRound(validROIL.height*sf));
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//rectangle(canvasPart, vroiL, Scalar(0, 0, 255), 3, 8); //画上一个矩形
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cout << "Painted ImageL" << endl;
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//右图像画到画布上
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canvasPart = canvas(Rect(w, 0, w, h)); //获得画布的另一部分
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resize(rgbRectifyImageR, canvasPart, canvasPart.size(), 0, 0, INTER_LINEAR);
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Rect vroiR(cvRound(validROIR.x * sf), cvRound(validROIR.y*sf),
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cvRound(validROIR.width * sf), cvRound(validROIR.height * sf));
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//rectangle(canvasPart, vroiR, Scalar(0, 0, 255), 3, 8);
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cout << "Painted ImageR" << endl;
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//画上对应的线条
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for (int i = 0; i < canvas.rows; i += 16)
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line(canvas, Point(0, i), Point(canvas.cols, i), Scalar(0, 255, 0), 1, 8);
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imshow("rectified", canvas);
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/*
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立体匹配
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*/
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namedWindow("disparity", CV_WINDOW_AUTOSIZE);
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// 创建SAD窗口 Trackbar
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createTrackbar("BlockSize:\n", "disparity", &blockSize, 8, stereo_match);
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// 创建视差唯一性百分比窗口 Trackbar
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createTrackbar("UniquenessRatio:\n", "disparity", &uniquenessRatio, 50, stereo_match);
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// 创建视差窗口 Trackbar
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createTrackbar("NumDisparities:\n", "disparity", &numDisparities, 16, stereo_match);
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//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)
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setMouseCallback("disparity", onMouse, 0);
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stereo_match(0, 0);
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waitKey(0);
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return 0;
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}
流程说明：
先采集左右摄像头的图片，然后，修改一下指定的图片，可以进行测距。
里面有双目摄像头的参数，具体需要自己定标和矫正后，然后，填入。
双目定标可以参考：双目视觉 定标+矫正 （基于MATLAB）
双目数据转化可以参考：双目视觉 三维重建、测距 ---准备工作（数据转化）
详细讲解摄像头参数：
1）Mat cameraMatrixL 左相机的内参矩阵
2）Mat distCoeffL = (Mat_<double>(5, 1) ....... 左相机 畸变参数 即K1，K2，P1，P2，K3。
3） Mat cameraMatrixR 右相机的内参矩阵
4）Mat distCoeffR = (Mat_<double>(5, 1) ....... 右相机畸变参数 即K1，K2，P1，P2，K3。
5) Mat T = (Mat_<double>(3, 1) << -1.210187345641146e+02, 0.519235426836325, -0.425535566316217);// 相机的 平移向量
6) Mat rec = (Mat_<double>(3, 3) << 0.99934112270088................... 相机的旋转向量
一共6个相机参数，1、2是 左相机的参数； 3、4是 右相机的参数； 5、6是相机（相对）整体的参数。
2. 实时采集数据，进行测距
效果：
源代码：
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/******************************/
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/* 立体匹配和测距 */
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/******************************/
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#include <opencv2/opencv.hpp>
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#include <iostream>
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#include <math.h>
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using namespace std;
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using namespace cv;
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const int imageWidth = 640; //摄像头的分辨率
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const int imageHeight = 360;
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Vec3f point3;
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float d;
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Size imageSize = Size(imageWidth, imageHeight);
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Mat rgbImageL, grayImageL;
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Mat rgbImageR, grayImageR;
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Mat rectifyImageL, rectifyImageR;
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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
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Rect validROIR;
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Mat mapLx, mapLy, mapRx, mapRy; //映射表
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Mat Rl, Rr, Pl, Pr, Q; //校正旋转矩阵R，投影矩阵P 重投影矩阵Q
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Mat xyz; //三维坐标
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Point origin; //鼠标按下的起始点
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Rect selection; //定义矩形选框
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bool selectObject = false; //是否选择对象
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int blockSize = 0, uniquenessRatio = 0, numDisparities = 0;
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Ptr<StereoBM> bm = StereoBM::create(16, 9);
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/*事先标定好的左相机的内参矩阵
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fx 0 cx
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0 fy cy
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0 0 1
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*/
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Mat cameraMatrixL = (Mat_<double>(3, 3) << 418.523322187048, -1.26842201390676, 343.908870120890,
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0, 421.222568242056, 235.466208987968,
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0, 0, 1);
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//获得的畸变参数
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/*418.523322187048 0 0
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-1.26842201390676 421.222568242056 0
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344.758267538961 243.318992284899 1 */ //2
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Mat distCoeffL = (Mat_<double>(5, 1) << 0.006636837611004, 0.050240447649195, 0.006681263320267, 0.003130367429418, 0);
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//[0.006636837611004,0.050240447649195] [0.006681263320267,0.003130367429418]
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/*事先标定好的右相机的内参矩阵
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fx 0 cx
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0 fy cy
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0 0 1
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*/
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Mat cameraMatrixR = (Mat_<double>(3, 3) << 417.417985082506, 0.498638151824367, 309.903372309072,
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0, 419.795432389420, 230.6,
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0, 0, 1);
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/*
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417.417985082506 0 0
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0.498638151824367 419.795432389420 0
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309.903372309072 236.256106972796 1
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*/ //2
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Mat distCoeffR = (Mat_<double>(5, 1) << -0.038407383078874, 0.236392800301615, 0.004121779274885, 0.002296129959664, 0);
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//[-0.038407383078874,0.236392800301615] [0.004121779274885,0.002296129959664]
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Mat T = (Mat_<double>(3, 1) << -1.210187345641146e+02, 0.519235426836325, -0.425535566316217);//T平移向量
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//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
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//对应Matlab所得T参数
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//Mat rec = (Mat_<double>(3, 1) << -0.00306, -0.03207, 0.00206);//rec旋转向量，对应matlab om参数 我
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Mat rec = (Mat_<double>(3, 3) << 0.999341122700880, -0.00206388651740061, 0.0362361815232777,
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0.000660748031451783, 0.999250989651683, 0.0386913826603732,
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-0.0362888948713456, -0.0386419468010579, 0.998593969567432); //rec旋转向量，对应matlab om参数 我
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/* 0.999341122700880 0.000660748031451783 -0.0362888948713456
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-0.00206388651740061 0.999250989651683 -0.0386419468010579
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0.0362361815232777 0.0386913826603732 0.998593969567432 */
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//Mat T = (Mat_<double>(3, 1) << -48.4, 0.241, -0.0344);//T平移向量
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//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
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//对应Matlab所得T参数
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Mat R;//R 旋转矩阵
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/*****立体匹配*****/
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void stereo_match(int, void*)
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{
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bm->setBlockSize(2 * blockSize + 5); //SAD窗口大小，5~21之间为宜
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bm->setROI1(validROIL);
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bm->setROI2(validROIR);
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bm->setPreFilterCap(31);
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bm->setMinDisparity(0); //最小视差，默认值为0, 可以是负值，int型
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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
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bm->setTextureThreshold(10);
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bm->setUniquenessRatio(uniquenessRatio);//uniquenessRatio主要可以防止误匹配
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bm->setSpeckleWindowSize(100);
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bm->setSpeckleRange(32);
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bm->setDisp12MaxDiff(-1);
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Mat disp, disp8;
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bm->compute(rectifyImageL, rectifyImageR, disp);//输入图像必须为灰度图
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disp.convertTo(disp8, CV_8U, 255 / ((numDisparities * 16 + 16)*16.));//计算出的视差是CV_16S格式
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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。
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xyz = xyz * 16;
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imshow("disparity", disp8);
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}
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/*****描述：鼠标操作回调*****/
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static void onMouse(int event, int x, int y, int, void*)
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{
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if (selectObject)
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{
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selection.x = MIN(x, origin.x);
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selection.y = MIN(y, origin.y);
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selection.width = std::abs(x - origin.x);
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selection.height = std::abs(y - origin.y);
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}
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switch (event)
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{
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case EVENT_LBUTTONDOWN: //鼠标左按钮按下的事件
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origin = Point(x, y);
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selection = Rect(x, y, 0, 0);
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selectObject = true;
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//cout << origin << "in world coordinate is: " << xyz.at<Vec3f>(origin) << endl;
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point3 = xyz.at<Vec3f>(origin);
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point3[0];
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//cout << "point3[0]:" << point3[0] << "point3[1]:" << point3[1] << "point3[2]:" << point3[2]<<endl;
-
cout << "世界坐标：" << endl;
-
cout << "x: " << point3[0] << " y: " << point3[1] << " z: " << point3[2] << endl;
-
d = point3[0] * point3[0]+ point3[1] * point3[1]+ point3[2] * point3[2];
-
d = sqrt(d); //mm
-
// cout << "距离是:" << d << "mm" << endl;
-
-
d = d / 10.0; //cm
-
cout << "距离是:" << d << "cm" << endl;
-
-
// d = d/1000.0; //m
-
// cout << "距离是:" << d << "m" << 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, Pl, imageSize, CV_32FC1, mapLx, mapLy);
-
initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);
-
-
/*
-
打开摄像头
-
*/
-
VideoCapture cap;
-
-
cap.open(1); //打开相机，电脑自带摄像头一般编号为0，外接摄像头编号为1，主要是在设备管理器中查看自己摄像头的编号。
-
-
cap.set(CV_CAP_PROP_FRAME_WIDTH, 2560); //设置捕获视频的宽度
-
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 720); //设置捕获视频的高度
-
-
if (!cap.isOpened()) //判断是否成功打开相机
-
-
{
-
-
cout << "摄像头打开失败!" << endl;
-
-
return -1;
-
-
}
-
-
Mat frame, frame_L, frame_R;
-
cap >> frame; //从相机捕获一帧图像
-
-
cout << "Painted ImageL" << endl;
-
cout << "Painted ImageR" << endl;
-
-
while (1) {
-
-
double fScale = 0.5; //定义缩放系数，对2560*720图像进行缩放显示（2560*720图像过大，液晶屏分辨率较小时，需要缩放才可完整显示在屏幕）
-
-
Size dsize = Size(frame.cols*fScale, frame.rows*fScale);
-
Mat imagedst = Mat(dsize, CV_32S);
-
-
resize(frame, imagedst, dsize);
-
char image_left[200];
-
char image_right[200];
-
frame_L = imagedst(Rect(0, 0, 640, 360)); //获取缩放后左Camera的图像
-
// namedWindow("Video_L", 1);
-
// imshow("Video_L", frame_L);
-
-
frame_R = imagedst(Rect(640, 0, 640, 360)); //获取缩放后右Camera的图像
-
// namedWindow("Video_R", 2);
-
// imshow("Video_R", frame_R);
-
cap >> frame;
-
/*
-
读取图片
-
*/
-
//rgbImageL = imread("image_left_1.jpg", CV_LOAD_IMAGE_COLOR);
-
cvtColor(frame_L, grayImageL, CV_BGR2GRAY);
-
//rgbImageR = imread("image_right_1.jpg", CV_LOAD_IMAGE_COLOR);
-
cvtColor(frame_R, 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);
-
//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);
-
stereo_match(0, 0);
-
-
waitKey(10);
-
-
} //wheil
-
return 0;
-
}
希望对你有帮助。
补充说明：
1.关于如何求出世界坐标？
1）x,y,z 是由
Vec3f point3;
point3 = xyz.at<Vec3f>(origin); 来转化的。
cout << "x: " << point3[0] << " y: " << point3[1] << " z: " << point3[2] << endl;
2）x,y,z求平方和后开根号，是两点的距离公式，即点（0，0，0）------双目摄像头的中心点，和点（x,y,z）进行两点求距离。
文章来源: guo-pu.blog.csdn.net，作者：一颗小树x，版权归原作者所有，如需转载，请联系作者。
原文链接：guo-pu.blog.csdn.net/article/details/86744936
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