text: #
  # 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.
  #
  import functools
  
  
  class Filter:
      pass
  
  
  class MultibandFilter(Filter):
      pass
  
  
  class BuiltinFilter(MultibandFilter):
      def filter(self, image):
          if image.mode == "P":
              raise ValueError("cannot filter palette images")
          return image.filter(*self.filterargs)
  
  
  class Kernel(BuiltinFilter):
      """
      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.
      """
  
      name = "Kernel"
  
      def __init__(self, size, kernel, scale=None, offset=0):
          if scale is None:
              # default scale is sum of kernel
              scale = functools.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
  
  
  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(MultibandFilter):
      """Blurs the image with a sequence of extended box filters, which
      approximates a Gaussian kernel. For details on accuracy see
      <https://www.mia.uni-saarland.de/Publications/gwosdek-ssvm11.pdf>
  
      :param radius: Standard deviation of the Gaussian kernel.
      """
  
      name = "GaussianBlur"
  
      def __init__(self, radius=2):
          self.radius = radius
  
      def filter(self, image):
          return image.gaussian_blur(self.radius)
  
  
  class BoxBlur(MultibandFilter):
      """Blurs the image by setting each pixel to the average value of the pixels
      in a square box extending radius pixels in each direction.
      Supports float radius of arbitrary size. Uses an optimized implementation
      which runs in linear time relative to the size of the image
      for any radius value.
  
      :param radius: Size of the box in one direction. Radius 0 does not blur,
                     returns an identical image. Radius 1 takes 1 pixel
                     in each direction, i.e. 9 pixels in total.
      """
  
      name = "BoxBlur"
  
      def __init__(self, radius):
          self.radius = radius
  
      def filter(self, image):
          return image.box_blur(self.radius)
  
  
  class UnsharpMask(MultibandFilter):
      """Unsharp mask filter.
  
      See Wikipedia's entry on `digital unsharp masking`_ for an explanation of
      the parameters.
  
      :param radius: Blur Radius
      :param percent: Unsharp strength, in percent
      :param threshold: Threshold controls the minimum brightness change that
        will be sharpened
  
      .. _digital unsharp masking: https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking
  
      """  # noqa: E501
  
      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"
      # fmt: off
      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,
      )
      # fmt: on
  
  
  class CONTOUR(BuiltinFilter):
      name = "Contour"
      # fmt: off
      filterargs = (3, 3), 1, 255, (
          -1, -1, -1,
          -1,  8, -1,
          -1, -1, -1,
      )
      # fmt: on
  
  
  class DETAIL(BuiltinFilter):
      name = "Detail"
      # fmt: off
      filterargs = (3, 3), 6, 0, (
          0,  -1,  0,
          -1, 10, -1,
          0,  -1,  0,
      )
      # fmt: on
  
  
  class EDGE_ENHANCE(BuiltinFilter):
      name = "Edge-enhance"
      # fmt: off
      filterargs = (3, 3), 2, 0, (
          -1, -1, -1,
          -1, 10, -1,
          -1, -1, -1,
      )
      # fmt: on
  
  
  class EDGE_ENHANCE_MORE(BuiltinFilter):
      name = "Edge-enhance More"
      # fmt: off
      filterargs = (3, 3), 1, 0, (
          -1, -1, -1,
          -1,  9, -1,
          -1, -1, -1,
      )
      # fmt: on
  
  
  class EMBOSS(BuiltinFilter):
      name = "Emboss"
      # fmt: off
      filterargs = (3, 3), 1, 128, (
          -1, 0, 0,
          0,  1, 0,
          0,  0, 0,
      )
      # fmt: on
  
  
  class FIND_EDGES(BuiltinFilter):
      name = "Find Edges"
      # fmt: off
      filterargs = (3, 3), 1, 0, (
          -1, -1, -1,
          -1,  8, -1,
          -1, -1, -1,
      )
      # fmt: on
  
  
  class SHARPEN(BuiltinFilter):
      name = "Sharpen"
      # fmt: off
      filterargs = (3, 3), 16, 0, (
          -2, -2, -2,
          -2, 32, -2,
          -2, -2, -2,
      )
      # fmt: on
  
  
  class SMOOTH(BuiltinFilter):
      name = "Smooth"
      # fmt: off
      filterargs = (3, 3), 13, 0, (
          1, 1, 1,
          1, 5, 1,
          1, 1, 1,
      )
      # fmt: on
  
  
  class SMOOTH_MORE(BuiltinFilter):
      name = "Smooth More"
      # fmt: off
      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,
      )
      # fmt: on
  
  
  class Color3DLUT(MultibandFilter):
      """Three-dimensional color lookup table.
  
      Transforms 3-channel pixels using the values of the channels as coordinates
      in the 3D lookup table and interpolating the nearest elements.
  
      This method allows you to apply almost any color transformation
      in constant time by using pre-calculated decimated tables.
  
      .. versionadded:: 5.2.0
  
      :param size: Size of the table. One int or tuple of (int, int, int).
                   Minimal size in any dimension is 2, maximum is 65.
      :param table: Flat lookup table. A list of ``channels * size**3``
                    float elements or a list of ``size**3`` channels-sized
                    tuples with floats. Channels are changed first,
                    then first dimension, then second, then third.
                    Value 0.0 corresponds lowest value of output, 1.0 highest.
      :param channels: Number of channels in the table. Could be 3 or 4.
                       Default is 3.
      :param target_mode: A mode for the result image. Should have not less
                          than ``channels`` channels. Default is ``None``,
                          which means that mode wouldn't be changed.
      """
  
      name = "Color 3D LUT"
  
      def __init__(self, size, table, channels=3, target_mode=None, **kwargs):
          if channels not in (3, 4):
              raise ValueError("Only 3 or 4 output channels are supported")
          self.size = size = self._check_size(size)
          self.channels = channels
          self.mode = target_mode
  
          # Hidden flag `_copy_table=False` could be used to avoid extra copying
          # of the table if the table is specially made for the constructor.
          copy_table = kwargs.get("_copy_table", True)
          items = size[0] * size[1] * size[2]
          wrong_size = False
  
          numpy = None
          if hasattr(table, "shape"):
              try:
                  import numpy
              except ImportError:  # pragma: no cover
                  pass
  
          if numpy and isinstance(table, numpy.ndarray):
              if copy_table:
                  table = table.copy()
  
              if table.shape in [
                  (items * channels,),
                  (items, channels),
                  (size[2], size[1], size[0], channels),
              ]:
                  table = table.reshape(items * channels)
              else:
                  wrong_size = True
  
          else:
              if copy_table:
                  table = list(table)
  
              # Convert to a flat list
              if table and isinstance(table[0], (list, tuple)):
                  table, raw_table = [], table
                  for pixel in raw_table:
                      if len(pixel) != channels:
                          raise ValueError(
                              "The elements of the table should "
                              "have a length of {}.".format(channels)
                          )
                      table.extend(pixel)
  
          if wrong_size or len(table) != items * channels:
              raise ValueError(
                  "The table should have either channels * size**3 float items "
                  "or size**3 items of channels-sized tuples with floats. "
                  f"Table should be: {channels}x{size[0]}x{size[1]}x{size[2]}. "
                  f"Actual length: {len(table)}"
              )
          self.table = table
  
      @staticmethod
      def _check_size(size):
          try:
              _, _, _ = size
          except ValueError as e:
              raise ValueError(
                  "Size should be either an integer or a tuple of three integers."
              ) from e
          except TypeError:
              size = (size, size, size)
          size = [int(x) for x in size]
          for size1D in size:
              if not 2 <= size1D <= 65:
                  raise ValueError("Size should be in [2, 65] range.")
          return size
  
      @classmethod
      def generate(cls, size, callback, channels=3, target_mode=None):
          """Generates new LUT using provided callback.
  
          :param size: Size of the table. Passed to the constructor.
          :param callback: Function with three parameters which correspond
                           three color channels. Will be called ``size**3``
                           times with values from 0.0 to 1.0 and should return
                           a tuple with ``channels`` elements.
          :param channels: The number of channels which should return callback.
          :param target_mode: Passed to the constructor of the resulting
                              lookup table.
          """
          size1D, size2D, size3D = cls._check_size(size)
          if channels not in (3, 4):
              raise ValueError("Only 3 or 4 output channels are supported")
  
          table = [0] * (size1D * size2D * size3D * channels)
          idx_out = 0
          for b in range(size3D):
              for g in range(size2D):
                  for r in range(size1D):
                      table[idx_out : idx_out + channels] = callback(
                          r / (size1D - 1), g / (size2D - 1), b / (size3D - 1)
                      )
                      idx_out += channels
  
          return cls(
              (size1D, size2D, size3D),
              table,
              channels=channels,
              target_mode=target_mode,
              _copy_table=False,
          )
  
      def transform(self, callback, with_normals=False, channels=None, target_mode=None):
          """Transforms the table values using provided callback and returns
          a new LUT with altered values.
  
          :param callback: A function which takes old lookup table values
                           and returns a new set of values. The number
                           of arguments which function should take is
                           ``self.channels`` or ``3 + self.channels``
                           if ``with_normals`` flag is set.
                           Should return a tuple of ``self.channels`` or
                           ``channels`` elements if it is set.
          :param with_normals: If true, ``callback`` will be called with
                               coordinates in the color cube as the first
                               three arguments. Otherwise, ``callback``
                               will be called only with actual color values.
          :param channels: The number of channels in the resulting lookup table.
          :param target_mode: Passed to the constructor of the resulting
                              lookup table.
          """
          if channels not in (None, 3, 4):
              raise ValueError("Only 3 or 4 output channels are supported")
          ch_in = self.channels
          ch_out = channels or ch_in
          size1D, size2D, size3D = self.size
  
          table = [0] * (size1D * size2D * size3D * ch_out)
          idx_in = 0
          idx_out = 0
          for b in range(size3D):
              for g in range(size2D):
                  for r in range(size1D):
                      values = self.table[idx_in : idx_in + ch_in]
                      if with_normals:
                          values = callback(
                              r / (size1D - 1),
                              g / (size2D - 1),
                              b / (size3D - 1),
                              *values,
                          )
                      else:
                          values = callback(*values)
                      table[idx_out : idx_out + ch_out] = values
                      idx_in += ch_in
                      idx_out += ch_out
  
          return type(self)(
              self.size,
              table,
              channels=ch_out,
              target_mode=target_mode or self.mode,
              _copy_table=False,
          )
  
      def __repr__(self):
          r = [
              f"{self.__class__.__name__} from {self.table.__class__.__name__}",
              "size={:d}x{:d}x{:d}".format(*self.size),
              f"channels={self.channels:d}",
          ]
          if self.mode:
              r.append(f"target_mode={self.mode}")
          return "<{}>".format(" ".join(r))
  
      def filter(self, image):
          from . import Image
  
          return image.color_lut_3d(
              self.mode or image.mode,
              Image.LINEAR,
              self.channels,
              self.size[0],
              self.size[1],
              self.size[2],
              self.table,
          )
  