#
# 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 PIL import Image
from PIL._util import isStringType
import operator
from functools import reduce


#
# 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 PIL 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 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: What resampling filter to use.
    :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 = (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.
    """
    "Add border to 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 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)

    # make the crop
    leftSide = int(liveArea[0] + (float(liveSize[0]-cropWidth) * centering[0]))
    if leftSide < 0:
        leftSide = 0
    topSide = int(liveArea[1] + (float(liveSize[1]-cropHeight) * centering[1]))
    if topSide < 0:
        topSide = 0

    out = image.crop(
        (leftSide, topSide, leftSide + cropWidth, topSide + cropHeight)
        )

    # resize the image and return it
    return out.resize(size, method)


def flip(image):
    """
    Flip the image vertically (top to bottom).

    :param image: The image to flip.
    :return: An image.
    """
    return image.transpose(Image.FLIP_TOP_BOTTOM)


def grayscale(image):
    """
    Convert the image to grayscale.

    :param image: The image to convert.
    :return: An image.
    """
    return image.convert("L")


def invert(image):
    """
    Invert (negate) the image.

    :param image: The image to invert.
    :return: An image.
    """
    lut = []
    for i in range(256):
        lut.append(255-i)
    return _lut(image, lut)


def mirror(image):
    """
    Flip image horizontally (left to right).

    :param image: The image to mirror.
    :return: An image.
    """
    return image.transpose(Image.FLIP_LEFT_RIGHT)


def posterize(image, bits):
    """
    Reduce the number of bits for each color channel.

    :param image: The image to posterize.
    :param bits: The number of bits to keep for each channel (1-8).
    :return: An image.
    """
    lut = []
    mask = ~(2**(8-bits)-1)
    for i in range(256):
        lut.append(i & mask)
    return _lut(image, lut)


def solarize(image, threshold=128):
    """
    Invert all pixel values above a threshold.

    :param image: The image to solarize.
    :param threshold: All pixels above this greyscale level are inverted.
    :return: An image.
    """
    lut = []
    for i in range(256):
        if i < threshold:
            lut.append(i)
        else:
            lut.append(255-i)
    return _lut(image, lut)


# --------------------------------------------------------------------
# PIL USM components, from Kevin Cazabon.

def gaussian_blur(im, radius=None):
    """ PIL_usm.gblur(im, [radius])"""

    if radius is None:
        radius = 5.0

    im.load()

    return im.im.gaussian_blur(radius)

gblur = gaussian_blur


def unsharp_mask(im, radius=None, percent=None, threshold=None):
    """ PIL_usm.usm(im, [radius, percent, threshold])"""

    if radius is None:
        radius = 5.0
    if percent is None:
        percent = 150
    if threshold is None:
        threshold = 3

    im.load()

    return im.im.unsharp_mask(radius, percent, threshold)

usm = unsharp_mask


def box_blur(image, radius):
    """
    Blur 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 image: The image to blur.
    :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.
    :return: An image.
    """
    image.load()

    return image._new(image.im.box_blur(radius))
