#
# 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.
#

import Image
import operator

##
# (New in 1.1.3) The <b>ImageOps</b> module contains a number of
# 'ready-made' image processing operations.  This module is somewhat
# experimental, and most operators only work on L and RGB images.
#
# @since 1.1.3
##

#
# helpers

def _border(border):
    if type(border) is type(()):
        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 Image.isStringType(color):
        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

##
# Maximize (normalize) image contrast.  This function calculates a
# histogram of the input image, removes <i>cutoff</i> 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.

def autocontrast(image, cutoff=0, ignore=None):
    "Maximize image contrast, based on histogram"
    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] = 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] = 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(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)

##
# Colorize grayscale image.  The <i>black</i> and <i>white</i>
# arguments should be RGB tuples; this function calculates a colour
# wedge mapping all black pixels in the source image to the first
# colour, and all white pixels to the second colour.
#
# @param image The image to colourize.
# @param black The colour to use for black input pixels.
# @param white The colour to use for white input pixels.
# @return An image.

def colorize(image, black, white):
    "Colorize a grayscale 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)

##
# Remove border from image.  The same amount of pixels are removed
# from all four sides.  This function works on all image modes.
#
# @param image The image to crop.
# @param border The number of pixels to remove.
# @return An image.
# @see Image#Image.crop

def crop(image, border=0):
    "Crop border off image"
    left, top, right, bottom = _border(border)
    return image.crop(
        (left, top, image.size[0]-right, image.size[1]-bottom)
        )

##
# Deform the image.
#
# @param image The image to deform.
# @param deformer A deformer object.  Any object that implements a
#     <b>getmesh</b> method can be used.
# @param resample What resampling filter to use.
# @return An image.

def deform(image, deformer, resample=Image.BILINEAR):
    "Deform image using the given deformer"
    return image.transform(
        image.size, Image.MESH, deformer.getmesh(image), resample
        )

##
# 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.

def equalize(image, mask=None):
    "Equalize image histogram"
    if image.mode == "P":
        image = image.convert("RGB")
    h = image.histogram(mask)
    lut = []
    for b in range(0, len(h), 256):
        histo = filter(None, h[b:b+256])
        if len(histo) <= 1:
            lut.extend(range(256))
        else:
            step = (reduce(operator.add, histo) - histo[-1]) / 255
            if not step:
                lut.extend(range(256))
            else:
                n = step / 2
                for i in range(256):
                    lut.append(n / step)
                    n = n + h[i+b]
    return _lut(image, lut)

##
# Add border to the image
#
# @param image The image to expand.
# @param border Border width, in pixels.
# @param fill Pixel fill value (a colour value).  Default is 0 (black).
# @return An image.

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

##
# Returns a sized and cropped version of the image, cropped to the
# requested aspect ratio and size.
# <p>
# The <b>fit</b> 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 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.

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

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

    # ensure inputs are valid
    if type(centering) != type([]):
        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 = (
        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)

##
# Flip the image vertically (top to bottom).
#
# @param image The image to flip.
# @return An image.

def flip(image):
    "Flip image vertically"
    return image.transpose(Image.FLIP_TOP_BOTTOM)

##
# Convert the image to grayscale.
#
# @param image The image to convert.
# @return An image.

def grayscale(image):
    "Convert to grayscale"
    return image.convert("L")

##
# Invert (negate) the image.
#
# @param image The image to invert.
# @return An image.

def invert(image):
    "Invert image (negate)"
    lut = []
    for i in range(256):
        lut.append(255-i)
    return _lut(image, lut)

##
# Flip image horizontally (left to right).
#
# @param image The image to mirror.
# @return An image.

def mirror(image):
    "Flip image horizontally"
    return image.transpose(Image.FLIP_LEFT_RIGHT)

##
# Reduce the number of bits for each colour channel.
#
# @param image The image to posterize.
# @param bits The number of bits to keep for each channel (1-8).
# @return An image.

def posterize(image, bits):
    "Reduce the number of bits per color channel"
    lut = []
    mask = ~(2**(8-bits)-1)
    for i in range(256):
        lut.append(i & mask)
    return _lut(image, lut)

##
# Invert all pixel values above a threshold.
#
# @param image The image to posterize.
# @param threshold All pixels above this greyscale level are inverted.
# @return An image.

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