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536 lines
16 KiB
Python
536 lines
16 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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import functools
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try:
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import numpy
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except ImportError: # pragma: no cover
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numpy = None
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class Filter:
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pass
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class MultibandFilter(Filter):
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pass
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class BuiltinFilter(MultibandFilter):
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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 Kernel(BuiltinFilter):
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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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name = "Kernel"
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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 = functools.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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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(MultibandFilter):
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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):
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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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class BoxBlur(MultibandFilter):
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"""Blurs the image by setting each pixel to the average value of the pixels
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in a square box extending radius pixels in each direction.
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Supports float radius of arbitrary size. Uses an optimized implementation
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which runs in linear time relative to the size of the image
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for any radius value.
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:param radius: Size of the box in one direction. Radius 0 does not blur,
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returns an identical image. Radius 1 takes 1 pixel
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in each direction, i.e. 9 pixels in total.
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"""
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name = "BoxBlur"
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def __init__(self, radius):
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self.radius = radius
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def filter(self, image):
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return image.box_blur(self.radius)
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class UnsharpMask(MultibandFilter):
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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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:param radius: Blur Radius
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:param percent: Unsharp strength, in percent
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:param threshold: Threshold controls the minimum brightness change that
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will be sharpened
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.. _digital unsharp masking: https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking
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""" # noqa: E501
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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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# fmt: off
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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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# fmt: on
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class CONTOUR(BuiltinFilter):
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name = "Contour"
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# fmt: off
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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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# fmt: on
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class DETAIL(BuiltinFilter):
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name = "Detail"
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# fmt: off
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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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# fmt: on
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class EDGE_ENHANCE(BuiltinFilter):
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name = "Edge-enhance"
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# fmt: off
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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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# fmt: on
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class EDGE_ENHANCE_MORE(BuiltinFilter):
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name = "Edge-enhance More"
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# fmt: off
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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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# fmt: on
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class EMBOSS(BuiltinFilter):
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name = "Emboss"
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# fmt: off
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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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# fmt: on
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class FIND_EDGES(BuiltinFilter):
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name = "Find Edges"
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# fmt: off
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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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# fmt: on
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class SHARPEN(BuiltinFilter):
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name = "Sharpen"
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# fmt: off
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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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# fmt: on
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class SMOOTH(BuiltinFilter):
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name = "Smooth"
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# fmt: off
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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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# fmt: on
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class SMOOTH_MORE(BuiltinFilter):
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name = "Smooth More"
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# fmt: off
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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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# fmt: on
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class Color3DLUT(MultibandFilter):
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"""Three-dimensional color lookup table.
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Transforms 3-channel pixels using the values of the channels as coordinates
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in the 3D lookup table and interpolating the nearest elements.
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This method allows you to apply almost any color transformation
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in constant time by using pre-calculated decimated tables.
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.. versionadded:: 5.2.0
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:param size: Size of the table. One int or tuple of (int, int, int).
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Minimal size in any dimension is 2, maximum is 65.
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:param table: Flat lookup table. A list of ``channels * size**3``
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float elements or a list of ``size**3`` channels-sized
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tuples with floats. Channels are changed first,
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then first dimension, then second, then third.
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Value 0.0 corresponds lowest value of output, 1.0 highest.
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:param channels: Number of channels in the table. Could be 3 or 4.
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Default is 3.
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:param target_mode: A mode for the result image. Should have not less
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than ``channels`` channels. Default is ``None``,
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which means that mode wouldn't be changed.
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"""
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name = "Color 3D LUT"
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def __init__(self, size, table, channels=3, target_mode=None, **kwargs):
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if channels not in (3, 4):
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raise ValueError("Only 3 or 4 output channels are supported")
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self.size = size = self._check_size(size)
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self.channels = channels
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self.mode = target_mode
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# Hidden flag `_copy_table=False` could be used to avoid extra copying
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# of the table if the table is specially made for the constructor.
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copy_table = kwargs.get("_copy_table", True)
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items = size[0] * size[1] * size[2]
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wrong_size = False
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if numpy and isinstance(table, numpy.ndarray):
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if copy_table:
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table = table.copy()
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if table.shape in [
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(items * channels,),
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(items, channels),
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(size[2], size[1], size[0], channels),
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]:
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table = table.reshape(items * channels)
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else:
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wrong_size = True
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else:
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if copy_table:
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table = list(table)
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# Convert to a flat list
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if table and isinstance(table[0], (list, tuple)):
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table, raw_table = [], table
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for pixel in raw_table:
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if len(pixel) != channels:
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raise ValueError(
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"The elements of the table should "
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"have a length of {}.".format(channels)
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)
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table.extend(pixel)
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if wrong_size or len(table) != items * channels:
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raise ValueError(
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"The table should have either channels * size**3 float items "
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"or size**3 items of channels-sized tuples with floats. "
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"Table should be: {}x{}x{}x{}. Actual length: {}".format(
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channels, size[0], size[1], size[2], len(table)
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)
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)
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self.table = table
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@staticmethod
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def _check_size(size):
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try:
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_, _, _ = size
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except ValueError:
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raise ValueError(
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"Size should be either an integer or a tuple of three integers."
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)
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except TypeError:
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size = (size, size, size)
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size = [int(x) for x in size]
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for size1D in size:
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if not 2 <= size1D <= 65:
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raise ValueError("Size should be in [2, 65] range.")
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return size
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@classmethod
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def generate(cls, size, callback, channels=3, target_mode=None):
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"""Generates new LUT using provided callback.
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:param size: Size of the table. Passed to the constructor.
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:param callback: Function with three parameters which correspond
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three color channels. Will be called ``size**3``
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times with values from 0.0 to 1.0 and should return
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a tuple with ``channels`` elements.
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:param channels: The number of channels which should return callback.
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:param target_mode: Passed to the constructor of the resulting
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lookup table.
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"""
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size1D, size2D, size3D = cls._check_size(size)
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if channels not in (3, 4):
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raise ValueError("Only 3 or 4 output channels are supported")
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table = [0] * (size1D * size2D * size3D * channels)
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idx_out = 0
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for b in range(size3D):
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for g in range(size2D):
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for r in range(size1D):
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table[idx_out : idx_out + channels] = callback(
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r / (size1D - 1), g / (size2D - 1), b / (size3D - 1)
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)
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idx_out += channels
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return cls(
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(size1D, size2D, size3D),
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table,
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channels=channels,
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target_mode=target_mode,
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_copy_table=False,
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)
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def transform(self, callback, with_normals=False, channels=None, target_mode=None):
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"""Transforms the table values using provided callback and returns
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a new LUT with altered values.
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:param callback: A function which takes old lookup table values
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and returns a new set of values. The number
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of arguments which function should take is
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``self.channels`` or ``3 + self.channels``
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if ``with_normals`` flag is set.
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Should return a tuple of ``self.channels`` or
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``channels`` elements if it is set.
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:param with_normals: If true, ``callback`` will be called with
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coordinates in the color cube as the first
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three arguments. Otherwise, ``callback``
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will be called only with actual color values.
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:param channels: The number of channels in the resulting lookup table.
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:param target_mode: Passed to the constructor of the resulting
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lookup table.
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"""
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if channels not in (None, 3, 4):
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raise ValueError("Only 3 or 4 output channels are supported")
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ch_in = self.channels
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ch_out = channels or ch_in
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size1D, size2D, size3D = self.size
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table = [0] * (size1D * size2D * size3D * ch_out)
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idx_in = 0
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idx_out = 0
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for b in range(size3D):
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for g in range(size2D):
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for r in range(size1D):
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values = self.table[idx_in : idx_in + ch_in]
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if with_normals:
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values = callback(
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r / (size1D - 1),
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g / (size2D - 1),
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b / (size3D - 1),
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*values,
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)
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else:
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values = callback(*values)
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table[idx_out : idx_out + ch_out] = values
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idx_in += ch_in
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idx_out += ch_out
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return type(self)(
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self.size,
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table,
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channels=ch_out,
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target_mode=target_mode or self.mode,
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_copy_table=False,
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)
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def __repr__(self):
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r = [
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"{} from {}".format(self.__class__.__name__, self.table.__class__.__name__),
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"size={:d}x{:d}x{:d}".format(*self.size),
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"channels={:d}".format(self.channels),
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]
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if self.mode:
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r.append("target_mode={}".format(self.mode))
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return "<{}>".format(" ".join(r))
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def filter(self, image):
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from . import Image
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return image.color_lut_3d(
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self.mode or image.mode,
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Image.LINEAR,
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self.channels,
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self.size[0],
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self.size[1],
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self.size[2],
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self.table,
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)
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