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
        )