scipy.ndimage.gaussian_gradient_magnitude¶
- scipy.ndimage.gaussian_gradient_magnitude(input, sigma, output=None, mode='reflect', cval=0.0, **kwargs)[source]¶
Multidimensional gradient magnitude using Gaussian 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().
Returns: gaussian_gradient_magnitude : ndarray
Filtered array. Has the same shape as input.
Examples
>>> from scipy import ndimage, misc >>> import matplotlib.pyplot as plt >>> 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 >>> ascent = misc.ascent() >>> result = ndimage.gaussian_gradient_magnitude(ascent, sigma=5) >>> ax1.imshow(ascent) >>> ax2.imshow(result) >>> plt.show()
