# -*- coding: utf-8 -*-

import os
from PIL import Image
import os.path
import time
import numpy as np

from PIL import Image


# /////////////// Distortion Helpers ///////////////

import skimage as sk
from skimage.filters import gaussian
from io import BytesIO
from wand.image import Image as WandImage
from wand.api import library as wandlibrary
import wand.color as WandColor
import ctypes
from PIL import Image as PILImage
import cv2
from scipy.ndimage import zoom as scizoom
from scipy.ndimage.interpolation import map_coordinates
from tqdm import tqdm
import warnings

warnings.simplefilter("ignore", UserWarning)

resource_path = os.path.join(
    os.path.dirname(os.path.realpath(__file__)),
    "resource"
    )

def disk(radius, alias_blur=0.1, dtype=np.float32):
    if radius <= 8:
        L = np.arange(-8, 8 + 1)
        ksize = (3, 3)
    else:
        L = np.arange(-radius, radius + 1)
        ksize = (5, 5)
    X, Y = np.meshgrid(L, L)
    aliased_disk = np.array((X ** 2 + Y ** 2) <= radius ** 2, dtype=dtype)
    aliased_disk /= np.sum(aliased_disk)

    # supersample disk to antialias
    return cv2.GaussianBlur(aliased_disk, ksize=ksize, sigmaX=alias_blur)


# Tell Python about the C method
wandlibrary.MagickMotionBlurImage.argtypes = (
    ctypes.c_void_p,  # wand
    ctypes.c_double,  # radius
    ctypes.c_double,  # sigma
    ctypes.c_double,
)  # angle


# Extend wand.image.Image class to include method signature
class MotionImage(WandImage):
    def motion_blur(self, radius=0.0, sigma=0.0, angle=0.0):
        wandlibrary.MagickMotionBlurImage(self.wand, radius, sigma, angle)


# modification of https://github.com/FLHerne/mapgen/blob/master/diamondsquare.py
def plasma_fractal(mapsize=32, wibbledecay=3):
    """
    Generate a heightmap using diamond-square algorithm.
    Return square 2d array, side length 'mapsize', of floats in range 0-255.
    'mapsize' must be a power of two.
    """
    assert mapsize & (mapsize - 1) == 0
    maparray = np.empty((mapsize, mapsize), dtype=np.float_)
    maparray[0, 0] = 0
    stepsize = mapsize
    wibble = 100

    def wibbledmean(array):
        return array / 4 + wibble * np.random.uniform(
            -wibble, wibble, array.shape
        )

    def fillsquares():
        """For each square of points stepsize apart,
           calculate middle value as mean of points + wibble"""
        cornerref = maparray[0:mapsize:stepsize, 0:mapsize:stepsize]
        squareaccum = cornerref + np.roll(cornerref, shift=-1, axis=0)
        squareaccum += np.roll(squareaccum, shift=-1, axis=1)
        maparray[
            stepsize // 2 : mapsize : stepsize,
            stepsize // 2 : mapsize : stepsize,
        ] = wibbledmean(squareaccum)

    def filldiamonds():
        """For each diamond of points stepsize apart,
           calculate middle value as mean of points + wibble"""
        mapsize = maparray.shape[0]
        drgrid = maparray[
            stepsize // 2 : mapsize : stepsize,
            stepsize // 2 : mapsize : stepsize,
        ]
        ulgrid = maparray[0:mapsize:stepsize, 0:mapsize:stepsize]
        ldrsum = drgrid + np.roll(drgrid, 1, axis=0)
        lulsum = ulgrid + np.roll(ulgrid, -1, axis=1)
        ltsum = ldrsum + lulsum
        maparray[
            0:mapsize:stepsize, stepsize // 2 : mapsize : stepsize
        ] = wibbledmean(ltsum)
        tdrsum = drgrid + np.roll(drgrid, 1, axis=1)
        tulsum = ulgrid + np.roll(ulgrid, -1, axis=0)
        ttsum = tdrsum + tulsum
        maparray[
            stepsize // 2 : mapsize : stepsize, 0:mapsize:stepsize
        ] = wibbledmean(ttsum)

    while stepsize >= 2:
        fillsquares()
        filldiamonds()
        stepsize //= 2
        wibble /= wibbledecay

    maparray -= maparray.min()
    return maparray / maparray.max()


def clipped_zoom(img, zoom_factor):
    h = img.shape[0]
    # ceil crop height(= crop width)
    ch = int(np.ceil(h / zoom_factor))

    top = (h - ch) // 2
    img = scizoom(
        img[top : top + ch, top : top + ch],
        (zoom_factor, zoom_factor, 1),
        order=1,
    )
    # trim off any extra pixels
    trim_top = (img.shape[0] - h) // 2

    return img[trim_top : trim_top + h, trim_top : trim_top + h]


# /////////////// End Distortion Helpers ///////////////


# /////////////// Distortions ///////////////


def gaussian_noise(x, severity=1):
    c = [0.04, 0.06, 0.08, 0.09, 0.10][severity - 1]

    x = np.array(x) / 255.0
    return np.clip(x + np.random.normal(size=x.shape, scale=c), 0, 1) * 255


def shot_noise(x, severity=1):
    c = [500, 250, 100, 75, 50][severity - 1]

    x = np.array(x) / 255.0
    return np.clip(np.random.poisson(x * c) / c, 0, 1) * 255


def impulse_noise(x, severity=1):
    c = [0.01, 0.02, 0.03, 0.05, 0.07][severity - 1]

    x = sk.util.random_noise(np.array(x) / 255.0, mode="s&p", amount=c)
    return np.clip(x, 0, 1) * 255


def speckle_noise(x, severity=1):
    c = [0.06, 0.1, 0.12, 0.16, 0.2][severity - 1]

    x = np.array(x) / 255.0
    return np.clip(x + x * np.random.normal(size=x.shape, scale=c), 0, 1) * 255


def gaussian_blur(x, severity=1):
    c = [0.4, 0.6, 0.7, 0.8, 1,][severity - 1]

    x = gaussian(np.array(x) / 255.0, sigma=c, multichannel=True)
    return np.clip(x, 0, 1) * 255


def glass_blur(x, severity=1):
    # sigma, max_delta, iterations
    c = [(0.05, 1, 1), (0.25, 1, 1), (0.4, 1, 1), (0.25, 1, 2), (0.4, 1, 2)][
        severity - 1
    ]

    x = np.uint8(
        gaussian(np.array(x) / 255.0, sigma=c[0], multichannel=True) * 255
    )

    # locally shuffle pixels
    for i in range(c[2]):
        for h in range(32 - c[1], c[1], -1):
            for w in range(32 - c[1], c[1], -1):
                dx, dy = np.random.randint(-c[1], c[1], size=(2,))
                h_prime, w_prime = h + dy, w + dx
                # swap
                x[h, w], x[h_prime, w_prime] = x[h_prime, w_prime], x[h, w]

    return (
        np.clip(gaussian(x / 255.0, sigma=c[0], multichannel=True), 0, 1) * 255
    )


def defocus_blur(x, severity=1):
    c = [(0.3, 0.4), (0.4, 0.5), (0.5, 0.6), (1, 0.2), (1.5, 0.1)][severity - 1]

    x = np.array(x) / 255.0
    kernel = disk(radius=c[0], alias_blur=c[1])

    channels = []
    for d in range(3):
        channels.append(cv2.filter2D(x[:, :, d], -1, kernel))
    channels = np.array(channels).transpose((1, 2, 0))  # 3x32x32 -> 32x32x3

    return np.clip(channels, 0, 1) * 255


def motion_blur(x, severity=1):
    c = [(6, 1), (6, 1.5), (6, 2), (8, 2), (9, 2.5)][severity - 1]

    output = BytesIO()
    x.save(output, format="PNG")
    x = MotionImage(blob=output.getvalue())

    x.motion_blur(radius=c[0], sigma=c[1], angle=np.random.uniform(-45, 45))

    x = cv2.imdecode(
        np.fromstring(x.make_blob(), np.uint8), cv2.IMREAD_UNCHANGED
    )

    if x.shape != (32, 32):
        return np.clip(x[..., [2, 1, 0]], 0, 255)  # BGR to RGB
    else:  # greyscale to RGB
        return np.clip(np.array([x, x, x]).transpose((1, 2, 0)), 0, 255)


def zoom_blur(x, severity=1):
    c = [
        np.arange(1, 1.06, 0.01),
        np.arange(1, 1.11, 0.01),
        np.arange(1, 1.16, 0.01),
        np.arange(1, 1.21, 0.01),
        np.arange(1, 1.26, 0.01),
    ][severity - 1]

    x = (np.array(x) / 255.0).astype(np.float32)
    out = np.zeros_like(x)
    for zoom_factor in c:
        out += clipped_zoom(x, zoom_factor)

    x = (x + out) / (len(c) + 1)
    return np.clip(x, 0, 1) * 255


def fog(x, severity=1):
    c = [(0.2, 3), (0.5, 3), (0.75, 2.5), (1, 2), (1.5, 1.75)][severity - 1]

    x = np.array(x) / 255.0
    max_val = x.max()
    x += c[0] * plasma_fractal(wibbledecay=c[1])[:32, :32][..., np.newaxis]
    return np.clip(x * max_val / (max_val + c[0]), 0, 1) * 255


def frost(x, severity=1):
    c = [(1, 0.2), (1, 0.3), (0.9, 0.4), (0.85, 0.4), (0.75, 0.45)][
        severity - 1
    ]
    idx = np.random.randint(5)
    filename = [
        f"{resource_path}/frost1.png",
        f"{resource_path}/frost2.png",
        f"{resource_path}/frost3.png",
        f"{resource_path}/frost4.jpg",
        f"{resource_path}/frost5.jpg",
        f"{resource_path}/frost6.jpg",
    ][idx]
    frost = cv2.imread(filename)
    frost = cv2.resize(frost, (0, 0), fx=0.2, fy=0.2)
    # randomly crop and convert to rgb
    x_start, y_start = (
        np.random.randint(0, frost.shape[0] - 32),
        np.random.randint(0, frost.shape[1] - 32),
    )
    frost = frost[x_start : x_start + 32, y_start : y_start + 32][
        ..., [2, 1, 0]
    ]

    return np.clip(c[0] * np.array(x) + c[1] * frost, 0, 255)


def snow(x, severity=1):
    c = [
        (0.1, 0.2, 1, 0.6, 8, 3, 0.95),
        (0.1, 0.2, 1, 0.5, 10, 4, 0.9),
        (0.15, 0.3, 1.75, 0.55, 10, 4, 0.9),
        (0.25, 0.3, 2.25, 0.6, 12, 6, 0.85),
        (0.3, 0.3, 1.25, 0.65, 14, 12, 0.8),
    ][severity - 1]

    x = np.array(x, dtype=np.float32) / 255.0
    snow_layer = np.random.normal(
        size=x.shape[:2], loc=c[0], scale=c[1]
    )  # [:2] for monochrome

    snow_layer = clipped_zoom(snow_layer[..., np.newaxis], c[2])
    snow_layer[snow_layer < c[3]] = 0

    snow_layer = PILImage.fromarray(
        (np.clip(snow_layer.squeeze(), 0, 1) * 255).astype(np.uint8), mode="L"
    )
    output = BytesIO()
    snow_layer.save(output, format="PNG")
    snow_layer = MotionImage(blob=output.getvalue())

    snow_layer.motion_blur(
        radius=c[4], sigma=c[5], angle=np.random.uniform(-135, -45)
    )

    snow_layer = (
        cv2.imdecode(
            np.fromstring(snow_layer.make_blob(), np.uint8),
            cv2.IMREAD_UNCHANGED,
        )
        / 255.0
    )
    snow_layer = snow_layer[..., np.newaxis]

    x = c[6] * x + (1 - c[6]) * np.maximum(
        x, cv2.cvtColor(x, cv2.COLOR_RGB2GRAY).reshape(32, 32, 1) * 1.5 + 0.5
    )
    return np.clip(x + snow_layer + np.rot90(snow_layer, k=2), 0, 1) * 255


def spatter(x, severity=1):
    c = [
        (0.62, 0.1, 0.7, 0.7, 0.5, 0),
        (0.65, 0.1, 0.8, 0.7, 0.5, 0),
        (0.65, 0.3, 1, 0.69, 0.5, 0),
        (0.65, 0.1, 0.7, 0.69, 0.6, 1),
        (0.65, 0.1, 0.5, 0.68, 0.6, 1),
    ][severity - 1]
    x = np.array(x, dtype=np.float32) / 255.0

    liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])

    liquid_layer = gaussian(liquid_layer, sigma=c[2])
    liquid_layer[liquid_layer < c[3]] = 0
    if c[5] == 0:
        liquid_layer = (liquid_layer * 255).astype(np.uint8)
        dist = 255 - cv2.Canny(liquid_layer, 50, 150)
        dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
        _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
        dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
        dist = cv2.equalizeHist(dist)
        #     ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
        #     ker -= np.mean(ker)
        ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
        dist = cv2.filter2D(dist, cv2.CV_8U, ker)
        dist = cv2.blur(dist, (3, 3)).astype(np.float32)

        m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
        m /= np.max(m, axis=(0, 1))
        m *= c[4]

        # water is pale turqouise
        color = np.concatenate(
            (
                175 / 255.0 * np.ones_like(m[..., :1]),
                238 / 255.0 * np.ones_like(m[..., :1]),
                238 / 255.0 * np.ones_like(m[..., :1]),
            ),
            axis=2,
        )

        color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
        x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)

        return (
            cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
        )
    else:
        m = np.where(liquid_layer > c[3], 1, 0)
        m = gaussian(m.astype(np.float32), sigma=c[4])
        m[m < 0.8] = 0
        #         m = np.abs(m) ** (1/c[4])

        # mud brown
        color = np.concatenate(
            (
                63 / 255.0 * np.ones_like(x[..., :1]),
                42 / 255.0 * np.ones_like(x[..., :1]),
                20 / 255.0 * np.ones_like(x[..., :1]),
            ),
            axis=2,
        )

        color *= m[..., np.newaxis]
        x *= 1 - m[..., np.newaxis]

        return np.clip(x + color, 0, 1) * 255


def contrast(x, severity=1):
    c = [0.75, 0.5, 0.4, 0.3, 0.15, 0.12][severity - 1]

    x = np.array(x) / 255.0
    means = np.mean(x, axis=(0, 1), keepdims=True)
    return np.clip((x - means) * c + means, 0, 1) * 255


def brightness(x, severity=1):
    c = [0.05, 0.1, 0.15, 0.2, 0.3][severity - 1]

    x = np.array(x) / 255.0
    x = sk.color.rgb2hsv(x)
    x[:, :, 2] = np.clip(x[:, :, 2] + c, 0, 1)
    x = sk.color.hsv2rgb(x)

    return np.clip(x, 0, 1) * 255


def saturate(x, severity=1):
    c = [(0.3, 0), (0.1, 0), (1.5, 0), (2, 0.1), (2.5, 0.2)][severity - 1]

    x = np.array(x) / 255.0
    x = sk.color.rgb2hsv(x)
    x[:, :, 1] = np.clip(x[:, :, 1] * c[0] + c[1], 0, 1)
    x = sk.color.hsv2rgb(x)

    return np.clip(x, 0, 1) * 255


def jpeg_compression(x, severity=1):
    c = [80, 65, 58, 50, 40][severity - 1]

    output = BytesIO()
    x.save(output, "JPEG", quality=c)
    x = np.asarray(PILImage.open(output))

    return x


def pixelate(x, severity=1):
    c = [0.95, 0.9, 0.85, 0.75, 0.65][severity - 1]

    x = x.resize((int(32 * c), int(32 * c)), PILImage.BOX)
    x = x.resize((32, 32), PILImage.BOX)

    return x


# mod of https://gist.github.com/erniejunior/601cdf56d2b424757de5
def elastic_transform(image, severity=1):
    IMSIZE = 32
    c = [
        (IMSIZE * 0, IMSIZE * 0, IMSIZE * 0.08),
        (IMSIZE * 0.05, IMSIZE * 0.2, IMSIZE * 0.07),
        (IMSIZE * 0.08, IMSIZE * 0.06, IMSIZE * 0.06),
        (IMSIZE * 0.1, IMSIZE * 0.04, IMSIZE * 0.05),
        (IMSIZE * 0.1, IMSIZE * 0.03, IMSIZE * 0.03),
    ][severity - 1]

    image = np.array(image, dtype=np.float32) / 255.0
    shape = image.shape
    shape_size = shape[:2]

    # random affine
    center_square = np.float32(shape_size) // 2
    square_size = min(shape_size) // 3
    pts1 = np.float32(
        [
            center_square + square_size,
            [center_square[0] + square_size, center_square[1] - square_size],
            center_square - square_size,
        ]
    )
    pts2 = pts1 + np.random.uniform(-c[2], c[2], size=pts1.shape).astype(
        np.float32
    )
    M = cv2.getAffineTransform(pts1, pts2)
    image = cv2.warpAffine(
        image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101
    )

    dx = (
        gaussian(
            np.random.uniform(-1, 1, size=shape[:2]),
            c[1],
            mode="reflect",
            truncate=3,
        )
        * c[0]
    ).astype(np.float32)
    dy = (
        gaussian(
            np.random.uniform(-1, 1, size=shape[:2]),
            c[1],
            mode="reflect",
            truncate=3,
        )
        * c[0]
    ).astype(np.float32)
    dx, dy = dx[..., np.newaxis], dy[..., np.newaxis]

    x, y, z = np.meshgrid(
        np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2])
    )
    indices = (
        np.reshape(y + dy, (-1, 1)),
        np.reshape(x + dx, (-1, 1)),
        np.reshape(z, (-1, 1)),
    )
    return (
        np.clip(
            map_coordinates(image, indices, order=1, mode="reflect").reshape(
                shape
            ),
            0,
            1,
        )
        * 255
    )


# /////////////// End Distortions ///////////////

import collections

CORRUPTED_CIFAR10_PROTOCOL = collections.OrderedDict()
CORRUPTED_CIFAR10_PROTOCOL["Gaussian Noise"] = gaussian_noise
CORRUPTED_CIFAR10_PROTOCOL["Shot Noise"] = shot_noise
CORRUPTED_CIFAR10_PROTOCOL["Impulse Noise"] = impulse_noise
CORRUPTED_CIFAR10_PROTOCOL["Speckle Noise"] = speckle_noise
CORRUPTED_CIFAR10_PROTOCOL["Gaussian Blur"] = gaussian_blur
CORRUPTED_CIFAR10_PROTOCOL["Defocus Blur"] = defocus_blur
CORRUPTED_CIFAR10_PROTOCOL["Glass Blur"] = glass_blur
CORRUPTED_CIFAR10_PROTOCOL["Motion Blur"] = motion_blur
CORRUPTED_CIFAR10_PROTOCOL["Zoom Blur"] = zoom_blur
CORRUPTED_CIFAR10_PROTOCOL["Snow"] = snow
CORRUPTED_CIFAR10_PROTOCOL["Frost"] = frost
CORRUPTED_CIFAR10_PROTOCOL["Fog"] = fog
CORRUPTED_CIFAR10_PROTOCOL["Brightness"] = brightness
CORRUPTED_CIFAR10_PROTOCOL["Contrast"] = contrast
CORRUPTED_CIFAR10_PROTOCOL["Elastic"] = elastic_transform
CORRUPTED_CIFAR10_PROTOCOL["Pixelate"] = pixelate
CORRUPTED_CIFAR10_PROTOCOL["JPEG"] = jpeg_compression
CORRUPTED_CIFAR10_PROTOCOL["Spatter"] = spatter
CORRUPTED_CIFAR10_PROTOCOL["Saturate"] = saturate
CORRUPTED_CIFAR10_PROTOCOL["Original"] = lambda image, severity: np.array(image)