_generic_edge_filter(image, *, smooth_weights, edge_weights=[1, 0, -1], axis=None, mode='reflect', cval=0.0, mask=None)
The filter is computed by applying the edge weights along one dimension and the smoothing weights along all other dimensions. If no axis is given, or a tuple of axes is given the filter is computed along all axes in turn, and the magnitude is computed as the square root of the average square magnitude of all the axes.
The input image.
The smoothing weights for the filter. These are applied to dimensions orthogonal to the edge axis.
The weights to compute the edge along the chosen axes.
Compute the edge filter along this axis. If not provided, the edge magnitude is computed. This is defined as:
edge_mag = np.sqrt(sum([_generic_edge_filter(image, ..., axis=i)**2
for i in range(image.ndim)]) / image.ndim)
The magnitude is also computed if axis is a sequence.
The boundary mode for the convolution. See scipy.ndimage.convolve
for a description of the modes. This can be either a single boundary mode or one boundary mode per axis.
When :None:None:`mode` is 'constant'
, this is the constant used in values outside the boundary of the image data.
Apply a generic, n-dimensional edge filter.
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