3x1或1x3 cv :: Mat转换为cv :: Point3d? [英] Conversion of 3x1 or 1x3 cv::Mat to cv::Point3d?

问题描述

我正在处理一些代码，其中要做很多3x3矩阵乘法，还使用旋转矩阵等对3d点进行一些平移.我决定将OpenCV核心功能用于数学运算.使用添加到cv::Mat类中的最新构造函数将cv::Point3d直接转换为3x1 cv::Mat的可能性大大减少并简化了代码.

I'm dealing with some code in which a do a lot of 3x3 matrix multiplications an also some translation of 3d points using rotation matrices, etc. I decided to use OpenCV core functionalities for the mathematical operations. The possibility to use the recent constructor added to the cv::Mat class to convert a cv::Point3d directly to a 3x1 cv::Mat reduces and simplifies the code greatly.

我现在想知道的是，是否有一种简单的方法可以将3x1或1x3 cv::Mat转换为cv::Point3d?我总是可以做类似的事情:

What I am wondering now is if there is a simple way to convert a 3x1 or 1x3 cv::Mat to an cv::Point3d? I always can do something like:

cv::Mat mat(3,1,CV_64FC1);
cv::Point3d p (mat.at<double>(0,0), mat.at<double>(1,0), mat.at<double>(2,0));

或

cv::Mat mat(3,1,CV_64FC1);
const double *data = mat.ptr<double>(0);
cv::Point3d p (data[0], data[1], data[2]);

我非常担心性能(避免对at方法的3次调用).

I am very worried about the performance (avoid the 3 calls to at method).

推荐答案

cv::Point3d具有允许直接从cv::Mat创建的构造函数:

cv::Point3d has a constructor which allows direct creation from cv::Mat:

cv::Mat mat(3,1,CV_64FC1);
cv::Point3d p(mat);

您可能尚未考虑的另一种可能性是对数学运算使用cv::Matx而不是cv::Mat.我发现它更易于使用，并提供更多功能，例如Point类型的乘法而无需转换:

Another possibility you may not have considered is using cv::Matx instead of cv::Mat for your mathematical operations. I find it is easier to use, and offers more functionality, like multiplication of Point types without needing a conversion:

cv::Point3d p(1,2,3);
cv::Matx33d m = cv::Matx33d::eye();
cv::Point3d p2 = m * p;

cv::Matx也是静态分配的，而不是动态分配的(例如cv::Mat)，以防您确实需要一点额外的性能.但是，就像所有与性能相关的建议一样:通过进行概要分析，确保要优化的内容实际上是瓶颈.

cv::Matx is also statically allocated, rather than dynamically (like cv::Mat), in case you really need that extra little bit of performance. However, as in all performance-related advice: make sure what you're optimizing is actually a bottleneck by profiling.

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