#
# The Python Imaging Library.
# $Id$
#
# standard image operations
#
# History:
# 2001-10-20 fl   Created
# 2001-10-23 fl   Added autocontrast operator
# 2001-12-18 fl   Added Kevin's fit operator
# 2004-03-14 fl   Fixed potential division by zero in equalize
# 2005-05-05 fl   Fixed equalize for low number of values
#
# Copyright (c) 2001-2004 by Secret Labs AB
# Copyright (c) 2001-2004 by Fredrik Lundh
#
# See the README file for information on usage and redistribution.
#

from . import Image
from ._util import isStringType
import operator
import functools
import warnings


#
# helpers

def _border(border):
    if isinstance(border, tuple):
        if len(border) == 2:
            left, top = right, bottom = border
        elif len(border) == 4:
            left, top, right, bottom = border
    else:
        left = top = right = bottom = border
    return left, top, right, bottom


def _color(color, mode):
    if isStringType(color):
        from . import ImageColor
        color = ImageColor.getcolor(color, mode)
    return color


def _lut(image, lut):
    if image.mode == "P":
        # FIXME: apply to lookup table, not image data
        raise NotImplementedError("mode P support coming soon")
    elif image.mode in ("L", "RGB"):
        if image.mode == "RGB" and len(lut) == 256:
            lut = lut + lut + lut
        return image.point(lut)
    else:
        raise IOError("not supported for this image mode")

#
# actions


def autocontrast(image, cutoff=0, ignore=None):
    """
    Maximize (normalize) image contrast. This function calculates a
    histogram of the input image, removes **cutoff** percent of the
    lightest and darkest pixels from the histogram, and remaps the image
    so that the darkest pixel becomes black (0), and the lightest
    becomes white (255).

    :param image: The image to process.
    :param cutoff: How many percent to cut off from the histogram.
    :param ignore: The background pixel value (use None for no background).
    :return: An image.
    """
    histogram = image.histogram()
    lut = []
    for layer in range(0, len(histogram), 256):
        h = histogram[layer:layer+256]
        if ignore is not None:
            # get rid of outliers
            try:
                h[ignore] = 0
            except TypeError:
                # assume sequence
                for ix in ignore:
                    h[ix] = 0
        if cutoff:
            # cut off pixels from both ends of the histogram
            # get number of pixels
            n = 0
            for ix in range(256):
                n = n + h[ix]
            # remove cutoff% pixels from the low end
            cut = n * cutoff // 100
            for lo in range(256):
                if cut > h[lo]:
                    cut = cut - h[lo]
                    h[lo] = 0
                else:
                    h[lo] -= cut
                    cut = 0
                if cut <= 0:
                    break
            # remove cutoff% samples from the hi end
            cut = n * cutoff // 100
            for hi in range(255, -1, -1):
                if cut > h[hi]:
                    cut = cut - h[hi]
                    h[hi] = 0
                else:
                    h[hi] -= cut
                    cut = 0
                if cut <= 0:
                    break
        # find lowest/highest samples after preprocessing
        for lo in range(256):
            if h[lo]:
                break
        for hi in range(255, -1, -1):
            if h[hi]:
                break
        if hi <= lo:
            # don't bother
            lut.extend(list(range(256)))
        else:
            scale = 255.0 / (hi - lo)
            offset = -lo * scale
            for ix in range(256):
                ix = int(ix * scale + offset)
                if ix < 0:
                    ix = 0
                elif ix > 255:
                    ix = 255
                lut.append(ix)
    return _lut(image, lut)


def colorize(image, black, white):
    """
    Colorize grayscale image.  The **black** and **white**
    arguments should be RGB tuples; this function calculates a color
    wedge mapping all black pixels in the source image to the first
    color, and all white pixels to the second color.

    :param image: The image to colorize.
    :param black: The color to use for black input pixels.
    :param white: The color to use for white input pixels.
    :return: An image.
    """
    assert image.mode == "L"
    black = _color(black, "RGB")
    white = _color(white, "RGB")
    red = []
    green = []
    blue = []
    for i in range(256):
        red.append(black[0]+i*(white[0]-black[0])//255)
        green.append(black[1]+i*(white[1]-black[1])//255)
        blue.append(black[2]+i*(white[2]-black[2])//255)
    image = image.convert("RGB")
    return _lut(image, red + green + blue)


def crop(image, border=0):
    """
    Remove border from image.  The same amount of pixels are removed
    from all four sides.  This function works on all image modes.

    .. seealso:: :py:meth:`~PIL.Image.Image.crop`

    :param image: The image to crop.
    :param border: The number of pixels to remove.
    :return: An image.
    """
    left, top, right, bottom = _border(border)
    return image.crop(
        (left, top, image.size[0]-right, image.size[1]-bottom)
        )


def scale(image, factor, resample=Image.NEAREST):
    """
    Returns a rescaled image by a specific factor given in parameter.
    A factor greater than 1 expands the image, between 0 and 1 contracts the
    image.

    :param image: The image to rescale.
    :param factor: The expansion factor, as a float.
    :param resample: An optional resampling filter. Same values possible as
       in the PIL.Image.resize function.
    :returns: An :py:class:`~PIL.Image.Image` object.
    """
    if factor == 1:
        return image.copy()
    elif factor <= 0:
        raise ValueError("the factor must be greater than 0")
    else:
        size = (int(round(factor * image.width)),
                int(round(factor * image.height)))
        return image.resize(size, resample)


def deform(image, deformer, resample=Image.BILINEAR):
    """
    Deform the image.

    :param image: The image to deform.
    :param deformer: A deformer object.  Any object that implements a
                    **getmesh** method can be used.
    :param resample: An optional resampling filter. Same values possible as
       in the PIL.Image.transform function.
    :return: An image.
    """
    return image.transform(
        image.size, Image.MESH, deformer.getmesh(image), resample
        )


def equalize(image, mask=None):
    """
    Equalize the image histogram. This function applies a non-linear
    mapping to the input image, in order to create a uniform
    distribution of grayscale values in the output image.

    :param image: The image to equalize.
    :param mask: An optional mask.  If given, only the pixels selected by
                 the mask are included in the analysis.
    :return: An image.
    """
    if image.mode == "P":
        image = image.convert("RGB")
    h = image.histogram(mask)
    lut = []
    for b in range(0, len(h), 256):
        histo = [_f for _f in h[b:b+256] if _f]
        if len(histo) <= 1:
            lut.extend(list(range(256)))
        else:
            step = (functools.reduce(operator.add, histo) - histo[-1]) // 255
            if not step:
                lut.extend(list(range(256)))
            else:
                n = step // 2
                for i in range(256):
                    lut.append(n // step)
                    n = n + h[i+b]
    return _lut(image, lut)


def expand(image, border=0, fill=0):
    """
    Add border to the image

    :param image: The image to expand.
    :param border: Border width, in pixels.
    :param fill: Pixel fill value (a color value).  Default is 0 (black).
    :return: An image.
    """
    left, top, right, bottom = _border(border)
    width = left + image.size[0] + right
    height = top + image.size[1] + bottom
    out = Image.new(image.mode, (width, height), _color(fill, image.mode))
    out.paste(image, (left, top))
    return out


def fit(image, size, method=Image.NEAREST, bleed=0.0, centering=(0.5, 0.5)):
    """
    Returns a sized and cropped version of the image, cropped to the
    requested aspect ratio and size.

    This function was contributed by Kevin Cazabon.

    :param image: The image to size and crop.
    :param size: The requested output size in pixels, given as a
                 (width, height) tuple.
    :param method: What resampling method to use. Default is
                   :py:attr:`PIL.Image.NEAREST`.
    :param bleed: Remove a border around the outside of the image (from all
                  four edges. The value is a decimal percentage (use 0.01 for
                  one percent). The default value is 0 (no border).
    :param centering: Control the cropping position.  Use (0.5, 0.5) for
                      center cropping (e.g. if cropping the width, take 50% off
                      of the left side, and therefore 50% off the right side).
                      (0.0, 0.0) will crop from the top left corner (i.e. if
                      cropping the width, take all of the crop off of the right
                      side, and if cropping the height, take all of it off the
                      bottom).  (1.0, 0.0) will crop from the bottom left
                      corner, etc. (i.e. if cropping the width, take all of the
                      crop off the left side, and if cropping the height take
                      none from the top, and therefore all off the bottom).
    :return: An image.
    """

    # by Kevin Cazabon, Feb 17/2000
    # kevin@cazabon.com
    # http://www.cazabon.com

    # ensure inputs are valid
    if not isinstance(centering, list):
        centering = [centering[0], centering[1]]

    if centering[0] > 1.0 or centering[0] < 0.0:
        centering[0] = 0.50
    if centering[1] > 1.0 or centering[1] < 0.0:
        centering[1] = 0.50

    if bleed > 0.49999 or bleed < 0.0:
        bleed = 0.0

    # calculate the area to use for resizing and cropping, subtracting
    # the 'bleed' around the edges

    # number of pixels to trim off on Top and Bottom, Left and Right
    bleedPixels = (
        int((float(bleed) * float(image.size[0])) + 0.5),
        int((float(bleed) * float(image.size[1])) + 0.5)
        )

    liveArea = (0, 0, image.size[0], image.size[1])
    if bleed > 0.0:
        liveArea = (
            bleedPixels[0], bleedPixels[1], image.size[0] - bleedPixels[0] - 1,
            image.size[1] - bleedPixels[1] - 1
            )

    liveSize = (liveArea[2] - liveArea[0], liveArea[3] - liveArea[1])

    # calculate the aspect ratio of the liveArea
    liveAreaAspectRatio = float(liveSize[0])/float(liveSize[1])

    # calculate the aspect ratio of the output image
    aspectRatio = float(size[0]) / float(size[1])

    # figure out if the sides or top/bottom will be cropped off
    if liveAreaAspectRatio >= aspectRatio:
        # liveArea is wider than what's needed, crop the sides
        cropWidth = int((aspectRatio * float(liveSize[1])) + 0.5)
        cropHeight = liveSize[1]
    else:
        # liveArea is taller than what's needed, crop the top and bottom
        cropWidth = liveSize[0]
        cropHeight = int((float(liveSize[0])/aspectRatio) + 0.5)