/usr/lib/python2.7/dist-packages/PIL/ImageFilter.py is in python-pil 2.3.0-1ubuntu3.
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# 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
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
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)
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
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
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
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)
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)
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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