opencv中reprojectImageTo3D函数的Q矩阵 [英] Q matrix for the reprojectImageTo3D function in opencv

问题描述

我正在opencv中进行一个项目，以使用立体声校准来检测盲人路径中的障碍物.我已经正确计算了视差图.现在要找到障碍物到相机的距离，我希望它的3D坐标[X，Y，Z]可以通过reprojectImageTo3D()找到，但我没有在此函数中使用的Q矩阵，因为我从stereoRectify()获取的Q矩阵可能为空，这可能是因为我使用了预先校准的图像.虽然我确实有相机的固有参数和外在参数. 因此，我的问题是，如果我知道焦距，基线以及有关相机的所有其他信息，该如何手动创建Q矩阵以直接在reprojectImageTo3D()函数中使用? Q矩阵的基本格式是什么?

I am doing a project in opencv to detect obstacle in the path of a blind person using stereo calibration. I have calculated the disparity map correctly. Now to find the distance of obstacle from the camera, I want its 3D coordinates [X,Y,Z] , which I am guessing can be found by reprojectImageTo3D(), but I dont have the Q matrix to use in this function because the Q matrix I am getting from stereoRectify() is coming null probably because I used pre calibrated images. Although I do have the inrinsic and extrinsic parameters of my camera. So my question is that how can I manually create the Q matrix to directly use in the function reprojectImageTo3D(), if I know the focal length, baseline and everything else about my camera? What is the basic format of the Q matrix?

推荐答案

Q矩阵的形式如下:

在该图像中， c _x 和 c _y 是左侧主点的坐标摄像头(如果您使用左摄像头主导进行了立体匹配)， c' _x 是右摄像头( c <如果您为stereoRectify()，指定CV_CALIB_ZERO_DISPARITY标志，则sub> x 和 c' _x 相同f 是焦距， T _x 是基线长度(可能是基线长度的负数，是从一个光学中心到另一个光学中心的平移想).

In that image, c_x and c_y are the coordinates of the principal point in the left camera (if you did stereo matching with the left camera dominant), c'_x is the x-coordinate of the principal point in the right camera (c_x and c'_x will be the same if you specified the CV_CALIB_ZERO_DISPARITY flag for stereoRectify()), f is the focal length and T_x is the baseline length (possibly the negative of the baseline length, it's the translation from one optical centre to the other I think).

我建议您看一本书 学习OpenCV 了解更多信息.它仍然基于较旧的C接口，但是在解释基础理论方面做得很好，也是我从中获得Q矩阵形式的地方.

I would suggest having a look at the book Learning OpenCV for more information. It's still based on the older C interface, but does a good job of explaining the underlying theory, and is where I sourced the form of the Q matrix from.

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