Learning OpenCV, 2nd Edition

both U and V to NULL and then just take the products of the matrix W to obtain the

determinant.

cv::divide()

void cv::divide(

cv::InputArray src1, // First input array (numerators)

cv::InputArray src2, // Second input array (denominators)

cv::OutputArray dst, // Results array (scale*src1/src2)

double scale = 1.0, // Multiplicative scale factor

int dtype = -1 // Data type for dst, -1 to get from src2

)

void cv::divide(

double scale, // Numerator for all divisions

cv::InputArray src2, // Input array (denominators)

cv::OutputArray dst, // Results array (scale/src2)

int dtype = -1 // Data type for dst, -1 to get from src2

)

dst

i

= saturate scale *†src1

i

/ src2

i

( )

dst

i

= saturate scale / src2

i

( )

cv::divide() is a simple division function; it divides all of the elements in src1 by the corresponding

elements in src2 and puts the results in dst.

cv::eigen()

bool cv::eigen(

cv::InputArray src,

cv::OutputArray eigenvalues,

int lowindex = -1,

int highindex = -1

);

bool cv::eigen(

cv::InputArray src,

cv::OutputArray eigenvalues,

cv::OutputArray eigenvectors,

int lowindex = -1,

int highindex = -1

);

Given a symmetric matrix mat, cv::eigen() will compute the eigenvectors and eigenvalues of that

matrix. The matrix must be of one of the floating-point types. The results array eigenvalues will

contain the