在OpenCV2.x中，有一个称为Mat::inv(int method)计算矩阵逆的新接口。请参阅参考。
C ++：MatExpr Mat :: inv（int method = DECOMP_LU）const
参数：方法–
Matrix inversion method. Possible values are the following:
DECOMP_LU is the LU decomposition. The matrix must be non-
singular. DECOMP_CHOLESKY is the Cholesky LL^T decomposition for symmetrical positively defined matrices only. This type is about twice faster than LU on big matrices. DECOMP_SVD is the SVD decomposition. If the matrix is singular or even non-square, the pseudo inversion is computed.
我对每种方法进行了测试，结果表明DECOMP_CHOLESKY对于测试用例最快，LU提供了类似的结果。
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
int main(void)
{
cv::Mat img1 = cv::imread("2.png");
cv::Mat img2, img3, img;
cv::cvtColor(img1, img2, CV_BGR2GRAY);
img2.convertTo(img3, CV_32FC1);
cv::resize(img3, img, cv::Size(200,200));
double freq = cv::getTickFrequency();
double t1 = 0.0, t2 = 0.0;
t1 = (double)cv::getTickCount();
cv::Mat m4 = img.inv(cv::DECOMP_LU);
t2 = (cv::getTickCount()-t1)/freq;
std::cout << "LU:" << t2 << std::endl;
t1 = (double)cv::getTickCount();
cv::Mat m5 = img.inv(cv::DECOMP_SVD);
t2 = (cv::getTickCount()-t1)/freq;
std::cout << "DECOMP_SVD:" << t2 << std::endl;
t1 = (double)cv::getTickCount();
cv::Mat m6 = img.inv(cv::DECOMP_CHOLESKY);
t2 = (cv::getTickCount()-t1)/freq;
std::cout << "DECOMP_CHOLESKY:" << t2 << std::endl;
cv::waitKey(0);
}
这是正在运行的结果：
LU：0.000423759
DECOMP_SVD：0.0583525
DECOMP_CHOLESKY：9.3453e-05
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