SVD performance issue #7917
Description
System information (version)
- OpenCV => 3.1
- Operating System / Platform => Ubuntu 16.10 64 Bit
- Compiler => gcc 6.2
Detailed description
On some particular dataset, the method cv::SVD::compute() can take hours where the equivalent eigen method takes only seconds. The enclosed program + data can reproduce the problem.
Note that the performance does not seem to be related to the data volume as I can't reproduce
the issue on a (500 000 x 6) matrix initialized with random values.
Steps to reproduce
Example to run OpenCV and Eigen SVD decompositions on same data.
#include <iostream>
#include "eigen3/Eigen/Core"
#include "eigen3/Eigen/SVD"
#include <opencv2/opencv.hpp>
#include <opencv2/core/eigen.hpp>
cv::Mat load(const std::string & p_filePath)
{
cv::Mat mat;
cv::FileStorage fs;
if (!fs.open(p_filePath, cv::FileStorage::READ))
{
std::cout << "Can't open matrix file" << std::endl;
return cv::Mat();
}
fs["matrix"] >> mat;
fs.release();
return mat;
}
int main (void)
{
cv::Mat src = load("svd_src.xml"); // input (rows: 6 ; cols: 515 808 ; type: CV_64FC1)
cv::Mat u; // output
// Eigen implementation
Eigen::MatrixXd m;
cv::cv2eigen(src, m);
std::cout << "[start] Eigen SVD decomposition" << std::endl;
Eigen::JacobiSVD<Eigen::MatrixXd> svd(m, Eigen::ComputeThinU);
std::cout << "[end] Eigen SVD decomposition" << std::endl;
cv::eigen2cv(svd.matrixU(), u);
// OpenCV implementation
cv::Mat w, vt;
std::cout << "[start] OpenCV SVD decomposition" << std::endl;
cv::SVD::compute(src, w, u, vt);
std::cout << "[end] OpenCV SVD decomposition" << std::endl;
return 0;
}