cv::SVD::vt程序报错怎么改写
时间: 2024-06-06 20:10:56 浏览: 5
很抱歉,作为AI语言模型,我无法确定您遇到的具体错误,也无法直接修改您的代码。建议您在使用SVD和vt时,仔细查看错误提示,检查代码中可能存在的语法错误、数据类型错误等,并参考相关的编程文档和示例代码。如果问题仍然存在,可以在相关的编程论坛或社区寻求帮助,或者请具备相关技术能力的工程师进行诊断和修复。
相关问题
cv::SVD::compute(A, W, U, Vt, cv::SVD::FULL_UV | cv::SVD::vt)在opencv4.55怎么改写
在OpenCV 4.5.5中,可以使用以下方式进行SVD计算:
cv::Mat A = ...; // 输入矩阵
cv::Mat U, W, Vt;
cv::SVD::compute(A, W, U, Vt, cv::SVD::FULL_UV);
其中,cv::SVD::FULL_UV表示计算完整的SVD分解,不需要使用cv::SVD::vt标志。
cv::SVD::V_T
cv::SVD::V_T is a member function of the cv::SVD class in OpenCV library. It returns the transpose of the matrix of right singular vectors of the input matrix. The right singular vectors are the eigenvectors of the transpose of the input matrix multiplied by the input matrix. The transpose of the right singular vectors matrix is also known as the matrix of left singular vectors.
In other words, if we have a matrix A, and we perform the Singular Value Decomposition (SVD) on it: A=U*S*V_T, then cv::SVD::V_T returns the matrix of right singular vectors V_T.
Syntax:
cv::Mat cv::SVD::V_T
Returns:
A cv::Mat object representing the transpose of the matrix of right singular vectors of the input matrix.
Example:
```
cv::Mat A = (cv::Mat_<double>(3,3) << 1, 2, 3, 4, 5, 6, 7, 8, 9);
cv::SVD svd(A);
cv::Mat V_T = svd.vt;
```
This code snippet calculates the SVD of a 3x3 matrix A and returns the transpose of the right singular vectors matrix in V_T.
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