Source code for PIL.ImageFilter

#
# The Python Imaging Library.
# $Id$
#
# standard filters
#
# History:
# 1995-11-27 fl   Created
# 2002-06-08 fl   Added rank and mode filters
# 2003-09-15 fl   Fixed rank calculation in rank filter; added expand call
#
# Copyright (c) 1997-2003 by Secret Labs AB.
# Copyright (c) 1995-2002 by Fredrik Lundh.
#
# See the README file for information on usage and redistribution.
#

from functools import reduce


class Filter(object):
    pass


[docs]class Kernel(Filter): """ Create a convolution kernel. The current version only supports 3x3 and 5x5 integer and floating point kernels. In the current version, kernels can only be applied to "L" and "RGB" images. :param size: Kernel size, given as (width, height). In the current version, this must be (3,3) or (5,5). :param kernel: A sequence containing kernel weights. :param scale: Scale factor. If given, the result for each pixel is divided by this value. the default is the sum of the kernel weights. :param offset: Offset. If given, this value is added to the result, after it has been divided by the scale factor. """ def __init__(self, size, kernel, scale=None, offset=0): if scale is None: # default scale is sum of kernel scale = reduce(lambda a,b: a+b, kernel) if size[0] * size[1] != len(kernel): raise ValueError("not enough coefficients in kernel") self.filterargs = size, scale, offset, kernel def filter(self, image): if image.mode == "P": raise ValueError("cannot filter palette images") return image.filter(*self.filterargs)
class BuiltinFilter(Kernel): def __init__(self): pass
[docs]class RankFilter(Filter): """ Create a rank filter. The rank filter sorts all pixels in a window of the given size, and returns the **rank**'th value. :param size: The kernel size, in pixels. :param rank: What pixel value to pick. Use 0 for a min filter, ``size * size / 2`` for a median filter, ``size * size - 1`` for a max filter, etc. """ name = "Rank" def __init__(self, size, rank): self.size = size self.rank = rank def filter(self, image): if image.mode == "P": raise ValueError("cannot filter palette images") image = image.expand(self.size//2, self.size//2) return image.rankfilter(self.size, self.rank)
[docs]class MedianFilter(RankFilter): """ Create a median filter. Picks the median pixel value in a window with the given size. :param size: The kernel size, in pixels. """ name = "Median" def __init__(self, size=3): self.size = size self.rank = size*size//2
[docs]class MinFilter(RankFilter): """ Create a min filter. Picks the lowest pixel value in a window with the given size. :param size: The kernel size, in pixels. """ name = "Min" def __init__(self, size=3): self.size = size self.rank = 0
[docs]class MaxFilter(RankFilter): """ Create a max filter. Picks the largest pixel value in a window with the given size. :param size: The kernel size, in pixels. """ name = "Max" def __init__(self, size=3): self.size = size self.rank = size*size-1
[docs]class ModeFilter(Filter): """ Create a mode filter. Picks the most frequent pixel value in a box with the given size. Pixel values that occur only once or twice are ignored; if no pixel value occurs more than twice, the original pixel value is preserved. :param size: The kernel size, in pixels. """ name = "Mode" def __init__(self, size=3): self.size = size def filter(self, image): return image.modefilter(self.size)
[docs]class GaussianBlur(Filter): """Gaussian blur filter. :param radius: Blur radius. """ name = "GaussianBlur" def __init__(self, radius=2): self.radius = radius def filter(self, image): return image.gaussian_blur(self.radius)
[docs]class UnsharpMask(Filter): """Unsharp mask filter. See Wikipedia's entry on `digital unsharp masking`_ for an explanation of the parameters. .. _digital unsharp masking: https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking """ name = "UnsharpMask" def __init__(self, radius=2, percent=150, threshold=3): self.radius = radius self.percent = percent self.threshold = threshold def filter(self, image): return image.unsharp_mask(self.radius, self.percent, self.threshold)
class BLUR(BuiltinFilter): name = "Blur" filterargs = (5, 5), 16, 0, ( 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1 ) class CONTOUR(BuiltinFilter): name = "Contour" filterargs = (3, 3), 1, 255, ( -1, -1, -1, -1, 8, -1, -1, -1, -1 ) class DETAIL(BuiltinFilter): name = "Detail" filterargs = (3, 3), 6, 0, ( 0, -1, 0, -1, 10, -1, 0, -1, 0 ) class EDGE_ENHANCE(BuiltinFilter): name = "Edge-enhance" filterargs = (3, 3), 2, 0, ( -1, -1, -1, -1, 10, -1, -1, -1, -1 ) class EDGE_ENHANCE_MORE(BuiltinFilter): name = "Edge-enhance More" filterargs = (3, 3), 1, 0, ( -1, -1, -1, -1, 9, -1, -1, -1, -1 ) class EMBOSS(BuiltinFilter): name = "Emboss" filterargs = (3, 3), 1, 128, ( -1, 0, 0, 0, 1, 0, 0, 0, 0 ) class FIND_EDGES(BuiltinFilter): name = "Find Edges" filterargs = (3, 3), 1, 0, ( -1, -1, -1, -1, 8, -1, -1, -1, -1 ) class SMOOTH(BuiltinFilter): name = "Smooth" filterargs = (3, 3), 13, 0, ( 1, 1, 1, 1, 5, 1, 1, 1, 1 ) class SMOOTH_MORE(BuiltinFilter): name = "Smooth More" filterargs = (5, 5), 100, 0, ( 1, 1, 1, 1, 1, 1, 5, 5, 5, 1, 1, 5, 44, 5, 1, 1, 5, 5, 5, 1, 1, 1, 1, 1, 1 ) class SHARPEN(BuiltinFilter): name = "Sharpen" filterargs = (3, 3), 16, 0, ( -2, -2, -2, -2, 32, -2, -2, -2, -2 )

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