""" Imagekit Image "ImageProcessors"

A processor accepts an image, does some stuff, and returns a new image.
Processors can do anything with the image you want, but their responsibilities
should be limited to image manipulations--they should be completely decoupled
from both the filesystem and the ORM.

"""
from imagekit.lib import *


class ImageProcessor(object):
    """ Base image processor class """

    def process(self, img):
        return img


class ProcessorPipeline(ImageProcessor, list):
    """A processor that just runs a bunch of other processors. This class allows
    any object that knows how to deal with a single processor to deal with a
    list of them.

    """
    def process(self, img):
        for proc in self:
            img = proc.process(img)
        return img


class Adjust(ImageProcessor):
    
    def __init__(self, color=1.0, brightness=1.0, contrast=1.0, sharpness=1.0):
        self.color = color
        self.brightness = brightness
        self.contrast = contrast
        self.sharpness = sharpness

    def process(self, img):
        img = img.convert('RGB')
        for name in ['Color', 'Brightness', 'Contrast', 'Sharpness']:
            factor = getattr(self, name.lower())
            if factor != 1.0:
                try:
                    img = getattr(ImageEnhance, name)(img).enhance(factor)
                except ValueError:
                    pass
        return img


class Reflection(ImageProcessor):
    background_color = '#FFFFFF'
    size = 0.0
    opacity = 0.6

    def process(self, img):
        # convert bgcolor string to rgb value
        background_color = ImageColor.getrgb(self.background_color)
        # handle palleted images
        img = img.convert('RGB')
        # copy orignial image and flip the orientation
        reflection = img.copy().transpose(Image.FLIP_TOP_BOTTOM)
        # create a new image filled with the bgcolor the same size
        background = Image.new("RGB", img.size, background_color)
        # calculate our alpha mask
        start = int(255 - (255 * self.opacity)) # The start of our gradient
        steps = int(255 * self.size) # the number of intermedite values
        increment = (255 - start) / float(steps)
        mask = Image.new('L', (1, 255))
        for y in range(255):
            if y < steps:
                val = int(y * increment + start)
            else:
                val = 255
            mask.putpixel((0, y), val)
        alpha_mask = mask.resize(img.size)
        # merge the reflection onto our background color using the alpha mask
        reflection = Image.composite(background, reflection, alpha_mask)
        # crop the reflection
        reflection_height = int(img.size[1] * self.size)
        reflection = reflection.crop((0, 0, img.size[0], reflection_height))
        # create new image sized to hold both the original image and the reflection
        composite = Image.new("RGB", (img.size[0], img.size[1]+reflection_height), background_color)
        # paste the orignal image and the reflection into the composite image
        composite.paste(img, (0, 0))
        composite.paste(reflection, (0, img.size[1]))
        # return the image complete with reflection effect
        return composite


class _Resize(ImageProcessor):
    
    width = None
    height = None
    
    def __init__(self, width=None, height=None):
        if width is not None:
            self.width = width
        if height is not None:
            self.height = height

    def process(self, img):
        raise NotImplementedError('process must be overridden by subclasses.')


class Crop(_Resize):

    TOP_LEFT = 'tl'
    TOP = 't'
    TOP_RIGHT = 'tr'
    BOTTOM_LEFT = 'bl'
    BOTTOM = 'b'
    BOTTOM_RIGHT = 'br'
    CENTER = 'c'
    LEFT = 'l'
    RIGHT = 'r'

    _ANCHOR_PTS = {
        TOP_LEFT: (0, 0),
        TOP: (0.5, 0),
        TOP_RIGHT: (1, 0),
        LEFT: (0, 0.5),
        CENTER: (0.5, 0.5),
        RIGHT: (1, 0.5),
        BOTTOM_LEFT: (0, 1),
        BOTTOM: (0.5, 1),
        BOTTOM_RIGHT: (1, 1),
    }

    def __init__(self, width=None, height=None, anchor=None):
        super(Crop, self).__init__(width, height)
        self.anchor = anchor

    def process(self, img):
        cur_width, cur_height = img.size
        horizontal_anchor, vertical_anchor = Crop._ANCHOR_PTS[self.anchor or \
                Crop.CENTER]
        ratio = max(float(self.width)/cur_width, float(self.height)/cur_height)
        resize_x, resize_y = ((cur_width * ratio), (cur_height * ratio))
        crop_x, crop_y = (abs(self.width - resize_x), abs(self.height - resize_y))
        x_diff, y_diff = (int(crop_x / 2), int(crop_y / 2))
        box_left, box_right = {
            0:   (0, self.width),
            0.5: (int(x_diff), int(x_diff + self.width)),
            1:   (int(crop_x), int(resize_x)),
        }[horizontal_anchor]
        box_upper, box_lower = {
            0:   (0, self.height),
            0.5: (int(y_diff), int(y_diff + self.height)),
            1:   (int(crop_y), int(resize_y)),
        }[vertical_anchor]
        box = (box_left, box_upper, box_right, box_lower)
        img = img.resize((int(resize_x), int(resize_y)), Image.ANTIALIAS).crop(box)
        return img


class Fit(_Resize):
    def __init__(self, width=None, height=None, upscale=None):
        super(Fit, self).__init__(width, height)
        self.upscale = upscale

    def process(self, img):
        cur_width, cur_height = img.size
        if not self.width is None and not self.height is None:
            ratio = min(float(self.width)/cur_width,
                        float(self.height)/cur_height)
        else:
            if self.width is None:
                ratio = float(self.height)/cur_height
            else:
                ratio = float(self.width)/cur_width
        new_dimensions = (int(round(cur_width*ratio)),
                          int(round(cur_height*ratio)))
        if new_dimensions[0] > cur_width or \
           new_dimensions[1] > cur_height:
            if not self.upscale:
                return img
        img = img.resize(new_dimensions, Image.ANTIALIAS)
        return img


class Transpose(ImageProcessor):
    """ Rotates or flips the image

    Possible arguments:
        - Transpose.AUTO 
        - Transpose.FLIP_HORIZONTAL
        - Transpose.FLIP_VERTICAL
        - Transpose.ROTATE_90
        - Transpose.ROTATE_180
        - Transpose.ROTATE_270
    
    The order of the arguments dictates the order in which the Transposition
    steps are taken.

    If Transpose.AUTO is present, all other arguments are ignored, and the 
    processor will attempt to rotate the image according to the 
    EXIF Orientation data.

    """
    AUTO = 'auto'
    FLIP_HORIZONTAL = Image.FLIP_LEFT_RIGHT
    FLIP_VERTICAL = Image.FLIP_TOP_BOTTOM
    ROTATE_90 = Image.ROTATE_90
    ROTATE_180 = Image.ROTATE_180
    ROTATE_270 = Image.ROTATE_270

    methods = [AUTO]
    _EXIF_ORIENTATION_STEPS = {
        1: [],
        2: [FLIP_HORIZONTAL],
        3: [ROTATE_180],
        4: [FLIP_VERTICAL],
        5: [ROTATE_270, FLIP_HORIZONTAL],
        6: [ROTATE_270],
        7: [ROTATE_90, FLIP_HORIZONTAL],
        8: [ROTATE_90],
    }

    def __init__(self, *args):
        super(Transpose, self).__init__()
        if args:
            self.methods = args

    def process(self, img):
        if self.AUTO in self.methods:
            raise Exception("AUTO is not yet supported. Sorry!")
            try:
                orientation = img._getexif()[0x0112]
                ops = self._EXIF_ORIENTATION_STEPS[orientation]
                print 'GOT %s >>>> %s' % (orientation, ops)
            except AttributeError:
                ops = []
        else:
            ops = self.methods
        for method in ops:
            img = img.transpose(method)
        return img