# coding:utf-8
from math import floor

import numpy as np
from colorthief import ColorThief
from PIL import Image
from PySide6.QtGui import QImage, QPixmap
from scipy.ndimage.filters import gaussian_filter

from .exception_handler import exceptionHandler



def gaussianBlur(imagePath, blurRadius=18, brightFactor=1, blurPicSize= None):
    if not imagePath.startswith(':'):
        image = Image.open(imagePath)
    else:
        image = Image.fromqpixmap(QPixmap(imagePath))

    if blurPicSize:
        # adjust image size to reduce computation
        w, h = image.size
        ratio = min(blurPicSize[0] / w, blurPicSize[1] / h)
        w_, h_ = w * ratio, h * ratio

        if w_ < w:
            image = image.resize((int(w_), int(h_)), Image.ANTIALIAS)

    image = np.array(image)

    # handle gray image
    if len(image.shape) == 2:
        image = np.stack([image, image, image], axis=-1)

    # blur each channel
    for i in range(3):
        image[:, :, i] = gaussian_filter(
            image[:, :, i], blurRadius) * brightFactor

    # convert ndarray to QPixmap
    h, w, _ = image.shape
    return QPixmap.fromImage(QImage(image.data, w, h, 3*w, QImage.Format_RGB888))


class DominantColor:
    """ Dominant color class """

    @classmethod
    @exceptionHandler((24, 24, 24))
    def getDominantColor(cls, imagePath):
        """ extract dominant color from image

        Parameters
        ----------
        imagePath: str
            image path

        Returns
        -------
        r, g, b: int
            gray value of each color channel
        """
        if imagePath.startswith(':'):
            return (24, 24, 24)

        colorThief = ColorThief(imagePath)

        # scale image to speed up the computation speed
        if max(colorThief.image.size) > 400:
            colorThief.image = colorThief.image.resize((400, 400))

        palette = colorThief.get_palette(quality=9)

        # adjust the brightness of palette
        palette = cls.__adjustPaletteValue(palette)
        for rgb in palette[:]:
            h, s, v = cls.rgb2hsv(rgb)
            if h < 0.02:
                palette.remove(rgb)
                if len(palette) <= 2:
                    break

        palette = palette[:5]
        palette.sort(key=lambda rgb: cls.colorfulness(*rgb), reverse=True)

        return palette[0]

    @classmethod
    def __adjustPaletteValue(cls, palette):
        """ adjust the brightness of palette """
        newPalette = []
        for rgb in palette:
            h, s, v = cls.rgb2hsv(rgb)
            if v > 0.9:
                factor = 0.8
            elif 0.8 < v <= 0.9:
                factor = 0.9
            elif 0.7 < v <= 0.8:
                factor = 0.95
            else:
                factor = 1
            v *= factor
            newPalette.append(cls.hsv2rgb(h, s, v))

        return newPalette

    @staticmethod
    def rgb2hsv(rgb):
        """ convert rgb to hsv """
        r, g, b = [i / 255 for i in rgb]
        mx = max(r, g, b)
        mn = min(r, g, b)
        df = mx - mn
        if mx == mn:
            h = 0
        elif mx == r:
            h = (60 * ((g - b) / df) + 360) % 360
        elif mx == g:
            h = (60 * ((b - r) / df) + 120) % 360
        elif mx == b:
            h = (60 * ((r - g) / df) + 240) % 360
        s = 0 if mx == 0 else df / mx
        v = mx
        return (h, s, v)

    @staticmethod
    def hsv2rgb(h, s, v):
        """ convert hsv to rgb """
        h60 = h / 60.0
        h60f = floor(h60)
        hi = int(h60f) % 6
        f = h60 - h60f
        p = v * (1 - s)
        q = v * (1 - f * s)
        t = v * (1 - (1 - f) * s)
        r, g, b = 0, 0, 0
        if hi == 0:
            r, g, b = v, t, p
        elif hi == 1:
            r, g, b = q, v, p
        elif hi == 2:
            r, g, b = p, v, t
        elif hi == 3:
            r, g, b = p, q, v
        elif hi == 4:
            r, g, b = t, p, v
        elif hi == 5:
            r, g, b = v, p, q
        r, g, b = int(r * 255), int(g * 255), int(b * 255)
        return (r, g, b)

    @staticmethod
    def colorfulness(r: int, g: int, b: int):
        rg = np.absolute(r - g)
        yb = np.absolute(0.5 * (r + g) - b)

        # Compute the mean and standard deviation of both `rg` and `yb`.
        rg_mean, rg_std = (np.mean(rg), np.std(rg))
        yb_mean, yb_std = (np.mean(yb), np.std(yb))

        # Combine the mean and standard deviations.
        std_root = np.sqrt((rg_std ** 2) + (yb_std ** 2))
        mean_root = np.sqrt((rg_mean ** 2) + (yb_mean ** 2))

        return std_root + (0.3 * mean_root)