Pillow/PIL/ImageFilter.py

#
# 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

##
# Convolution filter kernel.

class Kernel(Filter):

    ##
    # Create a convolution kernel.  The current version only
    # supports 3x3 and 5x5 integer and floating point kernels.
    # <p>
    # In the current version, kernels can only be applied to
    # "L" and "RGB" images.
    #
    # @def __init__(size, kernel, **options)
    # @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 **options Optional keyword arguments.
    # @keyparam scale Scale factor.  If given, the result for each
    #    pixel is divided by this value.  The default is the sum
    #    of the kernel weights.
    # @keyparam 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

##
# Rank filter.

class RankFilter(Filter):
    name = "Rank"

    ##
    # 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.

    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)

##
# Median filter.  Picks the median pixel value in a window with the
# given size.

class MedianFilter(RankFilter):
    name = "Median"

    ##
    # Create a median filter.
    #
    # @param size The kernel size, in pixels.

    def __init__(self, size=3):
        self.size = size
        self.rank = size*size//2

##
# Min filter.  Picks the lowest pixel value in a window with the given
# size.

class MinFilter(RankFilter):
    name = "Min"

    ##
    # Create a min filter.
    #
    # @param size The kernel size, in pixels.

    def __init__(self, size=3):
        self.size = size
        self.rank = 0

##
# Max filter.  Picks the largest pixel value in a window with the
# given size.

class MaxFilter(RankFilter):
    name = "Max"

    ##
    # Create a max filter.
    #
    # @param size The kernel size, in pixels.

    def __init__(self, size=3):
        self.size = size
        self.rank = size*size-1

##
# 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.

class ModeFilter(Filter):
    name = "Mode"

    ##
    # Create a mode filter.
    #
    # @param size The kernel size, in pixels.

    def __init__(self, size=3):
        self.size = size
    def filter(self, image):
        return image.modefilter(self.size)

##
# Gaussian blur filter.

class GaussianBlur(Filter):
    name = "GaussianBlur"

    def __init__(self, radius=2):
        self.radius = radius
    def filter(self, image):
        return image.gaussian_blur(self.radius)

##
# Unsharp mask filter.

class UnsharpMask(Filter):
    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)

##
# Simple blur filter.

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
        )

##
# Simple contour filter.

class CONTOUR(BuiltinFilter):
    name = "Contour"
    filterargs = (3, 3), 1, 255, (
        -1, -1, -1,
        -1,  8, -1,
        -1, -1, -1
        )

##
# Simple detail filter.

class DETAIL(BuiltinFilter):
    name = "Detail"
    filterargs = (3, 3), 6, 0, (
        0, -1,  0,
        -1, 10, -1,
        0, -1,  0
        )

##
# Simple edge enhancement filter.

class EDGE_ENHANCE(BuiltinFilter):
    name = "Edge-enhance"
    filterargs = (3, 3), 2, 0, (
        -1, -1, -1,
        -1, 10, -1,
        -1, -1, -1
        )

##
# Simple stronger edge enhancement filter.

class EDGE_ENHANCE_MORE(BuiltinFilter):
    name = "Edge-enhance More"
    filterargs = (3, 3), 1, 0, (
        -1, -1, -1,
        -1,  9, -1,
        -1, -1, -1
        )

##
# Simple embossing filter.

class EMBOSS(BuiltinFilter):
    name = "Emboss"
    filterargs = (3, 3), 1, 128, (
        -1,  0,  0,
        0,  1,  0,
        0,  0,  0
        )

##
# Simple edge-finding filter.

class FIND_EDGES(BuiltinFilter):
    name = "Find Edges"
    filterargs = (3, 3), 1, 0, (
        -1, -1, -1,
        -1,  8, -1,
        -1, -1, -1
        )

##
# Simple smoothing filter.

class SMOOTH(BuiltinFilter):
    name = "Smooth"
    filterargs = (3, 3), 13, 0, (
        1,  1,  1,
        1,  5,  1,
        1,  1,  1
        )

##
# Simple stronger smoothing filter.

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
        )

##
# Simple sharpening filter.

class SHARPEN(BuiltinFilter):
    name = "Sharpen"
    filterargs = (3, 3), 16, 0, (
        -2, -2, -2,
        -2, 32, -2,
        -2, -2, -2
        )