使用 OpenCV 生成 3d 点(假设正面平行配置)

本文介绍了使用 OpenCV 生成 3d 点(假设正面平行配置)的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我目前正在尝试在 OpenCV 中生成立体图像对的 3D 点.据我所知，这已经完成了很多.

I am currently trying to generate 3D points given stereo image pair in OpenCV. This has been done quite a bit as far as I can search.

我知道我假设的立体声设置的外部参数是正面平行配置(真的，还不错！).我知道焦距、基线，我将假设主要点作为图像的中心(我知道，我知道......).

I know the extrinsic parameters of the stereo setup which I'm going to assume is in frontal parallel configuration (really, it isn't that bad!). I know the focal length, baseline, and I'm going to assume the principal point as the center of the image (I know, I know...).

我使用 StereoSGBM 计算了一个伪像样的视差图，并按照 O'Reilly 的 Learning OpenCV 书籍手动编码了 Q 矩阵，其中指定:

I calculate a psuedo-decent disparity map using StereoSGBM and hand coded the Q matrix following O'Reilly's Learning OpenCV book which specifies:

Q = [ 1 0 0      -c_x
      0 1 0      -c_y
      0 0 0      f
      0 0 -1/T_x (c_x - c_x')/T_x ]

我认为 ( c_x, c_y ) 是主要点(我在图像坐标中指定)，f 是焦距(我以 mm 为单位描述)，T_x 是两个相机或基线之间的平移(我也用 mm 来描述).

I'll take that ( c_x, c_y ) is the principal point (which I specified in image coordinates), f is the focal length (which I described in mm), and T_x is the translation between the two cameras or baseline (which I also described in mm).

int type = CV_STEREO_BM_BASIC;
double rescx = 0.25, rescy = 0.25;
Mat disparity, vdisparity, depthMap;

Mat frame1 = imread( "C:\Users\Administrator\Desktop\Flow\IMG137.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat frame1L = frame1( Range( 0, frame1.rows ), Range( 0, frame1.cols/2 ));
Mat frame1R = frame1( Range( 0, frame1.rows ), Range( frame1.cols/2, frame1.cols ));

resize( frame1L, frame1L, Size(), rescx, rescy );
resize( frame1R, frame1R, Size(), rescx, rescy );

int preFilterSize = 9, preFilterCap = 32, disparityRange = 4;
int minDisparity = 2, textureThreshold = 12, uniquenessRatio = 3;
int windowSize = 21, smoothP1 = 0, smoothP2 = 0, dispMaxDiff = 32;
int speckleRange = 0, speckleWindowSize = 0;

bool dynamicP = false;

StereoSGBM stereo( minDisparity*-16, disparityRange*16, windowSize,
    smoothP1, smoothP2, dispMaxDiff,
    preFilterCap, uniquenessRatio,
    speckleRange*16, speckleWindowSize, dynamicP );

stereo( frame1L, frame1R, disparity );

double m1[3][3] = { { 46, 0, frame1L.cols/2 }, { 0, 46, frame1L.rows/2 }, { 0, 0, 1 } };
double t1[3] = { 65, 0, 0 };
double q[4][4] = {{ 1, 0, 0, -frame1L.cols/2.0 }, { 0, 1, 0, -frame1L.rows/2.0 }, { 0, 0, 0, 46 }, { 0, 0, -1.0/65, 0 }};
Mat cm1( 3, 3, CV_64F, m1), cm2( 3, 3, CV_64F, m1), T( 3, 1, CV_64F, t1 );
Mat R1, R2, P1, P2;
Mat Q( 4, 4, CV_64F, q );

//stereoRectify( cm1, Mat::zeros( 5, 1, CV_64F ), cm2, Mat::zeros( 5, 1, CV_64F ),  frame1L.size(), Mat::eye( 3, 3, CV_64F ), T, R1, R2, P1, P2, Q );

normalize( disparity, vdisparity, 0, 256, NORM_MINMAX );
//convertScaleAbs( disparity, disparity, 1/16.0 );
reprojectImageTo3D( disparity, depthMap, Q, true );
imshow( "Disparity", vdisparity );
imshow( "3D", depthMap );

因此，我从 StereoSGBM 和 Q 矩阵中输入生成的视差图以获得 3D 点，然后将其写入 ply 文件.

So I feed the resulting disparity map from StereoSGBM and that Q matrix to get 3D points, which I write out to a ply file.

但结果是这样的:http://i.stack.imgur.com/7eH9V.png

看起来很有趣，但不是我需要的:(.我在网上读到，在将视差图除以 16 后得到了更好的结果，确实看起来稍微好一些(实际上看起来像是有一个相机拍摄了这张照片)!).

Fun to look at, but not what I need :(. I read online that it gets better results after dividing the disparity map by 16 and indeed it looked marginally better (it actually looks like there was a camera that took the shot!).

如果你有兴趣，这是我的视差图:http://i.stack.imgur.com/lNPkO.png

This is my disparity map if you're interested: http://i.stack.imgur.com/lNPkO.png

我知道如果没有校准，它几乎不会看起来像最好的 3d 投影，但我期待一些……更好的东西.

I understand that without callibration, it's hardly going to look like the best 3d projection, but I was expecting something a bit... better.

有什么建议吗?

StereoSGBM

使用 OpenCV 生成 3d 点(假设正面平行配置)

问题描述

推荐答案