cornerSubPix
import { cornerSubPix } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Refines the corner locations.
The function iterates to find the sub-pixel accurate location of corners or radial saddle points as described in [forstner1987fast], and as shown on the figure below.
Sub-pixel accurate corner locator is based on the observation that every vector from the center q
to a point p located within a neighborhood of q is orthogonal to the image gradient at p
subject to image and measurement noise. Consider the expression:
\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)
where {DI_{p_i}} is an image gradient at one of the points p_i in a neighborhood of q . The
value of q is to be found so that \epsilon_i is minimized. A system of equations may be set up
with \epsilon_i set to zero:
\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)
where the gradients are summed within a neighborhood ("search window") of q . Calling the first
gradient term G and the second gradient term b gives:
q = G^{-1} \cdot b
The algorithm sets the center of the neighborhood window at this new center q and then iterates
until the center stays within a set threshold.
cornerSubPix(image: Mat, corners: Mat, winSize: Size, zeroZone: Size, criteria: TermCriteria): void;imageInput single-channel, 8-bit or float image.
cornersInput/output value, modified by the native operation. Initial coordinates of the input corners and refined coordinates provided for output.
winSizeHalf of the side length of the search window. For example, if winSize=Size(5,5) , then a
(5*2+1) \times (5*2+1) = 11 \times 11search window is used.zeroZoneHalf of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size.
criteriaCriteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.
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