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272 lines
6.4 KiB
Python
272 lines
6.4 KiB
Python
#
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# The Python Imaging Library.
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# $Id$
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#
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# standard filters
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#
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# History:
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# 1995-11-27 fl Created
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# 2002-06-08 fl Added rank and mode filters
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# 2003-09-15 fl Fixed rank calculation in rank filter; added expand call
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#
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# Copyright (c) 1997-2003 by Secret Labs AB.
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# Copyright (c) 1995-2002 by Fredrik Lundh.
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#
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# See the README file for information on usage and redistribution.
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#
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from functools import reduce
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class Filter(object):
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pass
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class Kernel(Filter):
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"""
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Create a convolution kernel. The current version only
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supports 3x3 and 5x5 integer and floating point kernels.
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In the current version, kernels can only be applied to
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"L" and "RGB" images.
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:param size: Kernel size, given as (width, height). In the current
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version, this must be (3,3) or (5,5).
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:param kernel: A sequence containing kernel weights.
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:param scale: Scale factor. If given, the result for each pixel is
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divided by this value. the default is the sum of the
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kernel weights.
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:param offset: Offset. If given, this value is added to the result,
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after it has been divided by the scale factor.
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"""
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def __init__(self, size, kernel, scale=None, offset=0):
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if scale is None:
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# default scale is sum of kernel
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scale = reduce(lambda a, b: a+b, kernel)
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if size[0] * size[1] != len(kernel):
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raise ValueError("not enough coefficients in kernel")
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self.filterargs = size, scale, offset, kernel
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def filter(self, image):
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if image.mode == "P":
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raise ValueError("cannot filter palette images")
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return image.filter(*self.filterargs)
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class BuiltinFilter(Kernel):
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def __init__(self):
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pass
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class RankFilter(Filter):
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"""
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Create a rank filter. The rank filter sorts all pixels in
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a window of the given size, and returns the **rank**'th value.
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:param size: The kernel size, in pixels.
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:param rank: What pixel value to pick. Use 0 for a min filter,
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``size * size / 2`` for a median filter, ``size * size - 1``
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for a max filter, etc.
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"""
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name = "Rank"
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def __init__(self, size, rank):
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self.size = size
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self.rank = rank
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def filter(self, image):
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if image.mode == "P":
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raise ValueError("cannot filter palette images")
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image = image.expand(self.size//2, self.size//2)
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return image.rankfilter(self.size, self.rank)
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class MedianFilter(RankFilter):
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"""
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Create a median filter. Picks the median pixel value in a window with the
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given size.
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:param size: The kernel size, in pixels.
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"""
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name = "Median"
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def __init__(self, size=3):
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self.size = size
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self.rank = size*size//2
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class MinFilter(RankFilter):
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"""
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Create a min filter. Picks the lowest pixel value in a window with the
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given size.
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:param size: The kernel size, in pixels.
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"""
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name = "Min"
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def __init__(self, size=3):
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self.size = size
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self.rank = 0
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class MaxFilter(RankFilter):
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"""
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Create a max filter. Picks the largest pixel value in a window with the
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given size.
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:param size: The kernel size, in pixels.
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"""
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name = "Max"
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def __init__(self, size=3):
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self.size = size
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self.rank = size*size-1
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class ModeFilter(Filter):
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"""
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Create a mode filter. Picks the most frequent pixel value in a box with the
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given size. Pixel values that occur only once or twice are ignored; if no
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pixel value occurs more than twice, the original pixel value is preserved.
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:param size: The kernel size, in pixels.
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"""
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name = "Mode"
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def __init__(self, size=3):
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self.size = size
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def filter(self, image):
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return image.modefilter(self.size)
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class GaussianBlur(Filter):
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"""Gaussian blur filter.
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:param radius: Blur radius.
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"""
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name = "GaussianBlur"
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def __init__(self, radius=2, effective_scale=None):
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self.radius = radius
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self.effective_scale = effective_scale
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def filter(self, image):
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return image.gaussian_blur(self.radius, self.effective_scale or 2.6)
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class UnsharpMask(Filter):
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"""Unsharp mask filter.
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See Wikipedia's entry on `digital unsharp masking`_ for an explanation of
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the parameters.
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.. _digital unsharp masking:
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https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking
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"""
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name = "UnsharpMask"
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def __init__(self, radius=2, percent=150, threshold=3):
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self.radius = radius
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self.percent = percent
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self.threshold = threshold
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def filter(self, image):
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return image.unsharp_mask(self.radius, self.percent, self.threshold)
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class BLUR(BuiltinFilter):
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name = "Blur"
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filterargs = (5, 5), 16, 0, (
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1, 1, 1, 1, 1,
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1, 0, 0, 0, 1,
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1, 0, 0, 0, 1,
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1, 0, 0, 0, 1,
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1, 1, 1, 1, 1
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)
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class CONTOUR(BuiltinFilter):
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name = "Contour"
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filterargs = (3, 3), 1, 255, (
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-1, -1, -1,
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-1, 8, -1,
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-1, -1, -1
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)
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class DETAIL(BuiltinFilter):
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name = "Detail"
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filterargs = (3, 3), 6, 0, (
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0, -1, 0,
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-1, 10, -1,
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0, -1, 0
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)
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class EDGE_ENHANCE(BuiltinFilter):
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name = "Edge-enhance"
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filterargs = (3, 3), 2, 0, (
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-1, -1, -1,
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-1, 10, -1,
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-1, -1, -1
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)
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class EDGE_ENHANCE_MORE(BuiltinFilter):
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name = "Edge-enhance More"
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filterargs = (3, 3), 1, 0, (
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-1, -1, -1,
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-1, 9, -1,
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-1, -1, -1
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)
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class EMBOSS(BuiltinFilter):
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name = "Emboss"
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filterargs = (3, 3), 1, 128, (
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-1, 0, 0,
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0, 1, 0,
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0, 0, 0
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)
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class FIND_EDGES(BuiltinFilter):
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name = "Find Edges"
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filterargs = (3, 3), 1, 0, (
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-1, -1, -1,
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-1, 8, -1,
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-1, -1, -1
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)
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class SMOOTH(BuiltinFilter):
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name = "Smooth"
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filterargs = (3, 3), 13, 0, (
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1, 1, 1,
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1, 5, 1,
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1, 1, 1
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)
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class SMOOTH_MORE(BuiltinFilter):
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name = "Smooth More"
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filterargs = (5, 5), 100, 0, (
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1, 1, 1, 1, 1,
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1, 5, 5, 5, 1,
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1, 5, 44, 5, 1,
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1, 5, 5, 5, 1,
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1, 1, 1, 1, 1
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)
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class SHARPEN(BuiltinFilter):
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name = "Sharpen"
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filterargs = (3, 3), 16, 0, (
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-2, -2, -2,
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-2, 32, -2,
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-2, -2, -2
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)
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