meanStdDev
import { meanStdDev } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Calculates a mean and standard deviation of array elements.
The function cv::meanStdDev calculates the mean and the standard deviation M of array elements independently for each channel and returns it via the output parameters:
\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}
When all the mask elements are 0's, the function returns mean=stddev=Scalar::all(0).
Note: The calculated standard deviation is only the diagonal of the complete normalized covariance matrix. If the full matrix is needed, you can reshape the multi-channel array M x N to the single-channel array M*N x mtx.channels() (only possible when the matrix is continuous) and then pass the matrix to calcCovarMatrix .
See: countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
meanStdDev(src: Mat, mean: Mat, stddev: Mat, mask: Mat): void;2 available overloads
meanStdDev(src: Mat, mean: Mat, stddev: Mat): void;meanStdDev(src: Mat, mean: Mat, stddev: Mat, mask: Mat): void;srcinput array that should have from 1 to 4 channels so that the results can be stored in Scalar_ 's.
meanOutput destination, filled by the native operation. output parameter: calculated mean value.
stddevOutput destination, filled by the native operation. output parameter: calculated standard deviation.
maskoptional operation mask of type CV_8U, CV_8S or CV_Bool.
These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.