mirror of
https://github.com/python-pillow/Pillow.git
synced 2024-09-21 11:28:58 +03:00
a7e3b2e47b
There are two main issues fixed with this commit: * bytes vs. str: All file, image, and palette data are now handled as bytes. A new _binary module consolidates the hacks needed to do this across Python versions. tostring/fromstring methods have been renamed to tobytes/frombytes, but the Python 2.6/2.7 versions alias them to the old names for compatibility. Users should move to tobytes/frombytes. One other potentially-breaking change is that text data in image files (such as tags, comments) are now explicitly handled with a specific character encoding in mind. This works well with the Unicode str in Python 3, but may trip up old code expecting a straight byte-for-byte translation to a Python string. This also required a change to Gohlke's tags tests (in Tests/test_file_png.py) to expect Unicode strings from the code. * True div vs. floor div: Many division operations used the "/" operator to do floor division, which is now the "//" operator in Python 3. These were fixed. As of this commit, on the first pass, I have one failing test (improper handling of a slice object in a C module, test_imagepath.py) in Python 3, and three that that I haven't tried running yet (test_imagegl, test_imagegrab, and test_imageqt). I also haven't tested anything on Windows. All but the three skipped tests run flawlessly against Pythons 2.6 and 2.7.
292 lines
6.5 KiB
Python
292 lines
6.5 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:
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pass
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##
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# Convolution filter kernel.
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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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# <p>
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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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#
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# @def __init__(size, kernel, **options)
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# @param size Kernel size, given as (width, height). In
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# the current 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 **options Optional keyword arguments.
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# @keyparam scale Scale factor. If given, the result for each
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# pixel is divided by this value. The default is the sum
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# of the kernel weights.
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# @keyparam offset Offset. If given, this value is added to the
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# result, after it has been divided by the scale factor.
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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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##
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# Rank filter.
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class RankFilter(Filter):
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name = "Rank"
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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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#
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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 for a max filter,
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# etc.
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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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##
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# Median filter. Picks the median pixel value in a window with the
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# given size.
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class MedianFilter(RankFilter):
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name = "Median"
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##
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# Create a median filter.
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#
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# @param size The kernel size, in pixels.
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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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##
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# Min filter. Picks the lowest pixel value in a window with the given
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# size.
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class MinFilter(RankFilter):
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name = "Min"
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##
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# Create a min filter.
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#
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# @param size The kernel size, in pixels.
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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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##
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# Max filter. Picks the largest pixel value in a window with the
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# given size.
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class MaxFilter(RankFilter):
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name = "Max"
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##
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# Create a max filter.
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#
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# @param size The kernel size, in pixels.
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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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##
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# 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;
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# if no pixel value occurs more than twice, the original pixel value
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# is preserved.
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class ModeFilter(Filter):
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name = "Mode"
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##
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# Create a mode filter.
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#
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# @param size The kernel size, in pixels.
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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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##
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# Gaussian blur filter.
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class GaussianBlur(Filter):
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name = "GaussianBlur"
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def __init__(self, radius=2):
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self.radius = radius
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def filter(self, image):
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return image.gaussian_blur(self.radius)
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##
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# Unsharp mask filter.
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class UnsharpMask(Filter):
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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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##
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# Simple blur filter.
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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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##
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# Simple contour filter.
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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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##
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# Simple detail filter.
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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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##
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# Simple edge enhancement filter.
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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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##
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# Simple stronger edge enhancement filter.
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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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##
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# Simple embossing filter.
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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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##
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# Simple edge-finding filter.
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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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##
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# Simple smoothing filter.
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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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##
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# Simple stronger smoothing filter.
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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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##
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# Simple sharpening filter.
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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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