scipy.ndimage.gaussian_laplace

scipy.ndimage.gaussian_laplace(input, sigma, output=None, mode='reflect', cval=0.0, **kwargs)

Multidimensional Laplace filter using gaussian second derivatives.

Parameters:

input : array_like

Input array to filter.

sigma : scalar or sequence of scalars

The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

output : array, optional

The output parameter passes an array in which to store the filter output. Output array should have different name as compared to input array to avoid aliasing errors.

mode : str or sequence, optional

The mode parameter determines how the array borders are handled. Valid modes are {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}. cval is the value used when mode is equal to ‘constant’. A list of modes with length equal to the number of axes can be provided to specify different modes for different axes. Default is ‘reflect’

cval : scalar, optional

Value to fill past edges of input if mode is ‘constant’. Default is 0.0

Extra keyword arguments will be passed to gaussian_filter().

Examples

>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()

>>> fig = plt.figure()
>>> plt.gray()  # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121)  # left side
>>> ax2 = fig.add_subplot(122)  # right side

>>> result = ndimage.gaussian_laplace(ascent, sigma=1)
>>> ax1.imshow(result)

>>> result = ndimage.gaussian_laplace(ascent, sigma=3)
>>> ax2.imshow(result)
>>> plt.show()

(Source code)