maximum_filter(input, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0)¶
Calculate a multidimensional maximum filter.
The input array.
- sizescalar or tuple, optional
See footprint, below. Ignored if footprint is given.
- footprintarray, optional
Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus
size=(n,m)is equivalent to
footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.
- outputarray or dtype, optional
- modestr or sequence, optional
- ‘reflect’ (d c b a | a b c d | d c b a)
The input is extended by reflecting about the edge of the last pixel.
- ‘constant’ (k k k k | a b c d | k k k k)
- ‘nearest’ (a a a a | a b c d | d d d d)
The input is extended by replicating the last pixel.
- ‘mirror’ (d c b | a b c d | c b a)
The input is extended by reflecting about the center of the last pixel.
- ‘wrap’ (a b c d | a b c d | a b c d)
The input is extended by wrapping around to the opposite edge.
- cvalscalar, optional
Value to fill past edges of input if mode is ‘constant’. Default is 0.0.
- originint or sequence, optional
Controls the placement of the filter on the input array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the input array, different shifts can be specified along each axis.
Filtered array. Has the same shape as input.
A sequence of modes (one per axis) is only supported when the footprint is separable. Otherwise, a single mode string must be provided.
>>> 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.maximum_filter(ascent, size=20) >>> ax1.imshow(ascent) >>> ax2.imshow(result) >>> plt.show()