# This script performs the linear color transfer step that 
# leongatys/NeuralImageSynthesis' Scale Control code performs.
# https://github.com/leongatys/NeuralImageSynthesis/blob/master/ExampleNotebooks/ScaleControl.ipynb
# Standalone script by github.com/htoyryla, and github.com/ProGamerGov
#based on this: https://github.com/ProGamerGov/Neural-Tools

import numpy as np
# import argparse
import imageio
from skimage import io,transform,img_as_float
from skimage.io import imread,imsave
from PIL import Image
from numpy import eye
import cv2 

# parser = argparse.ArgumentParser()
# parser.add_argument('-t', '--target_image', type=str, help="The image you are transfering color to. Ex: target.png", required=True)
# parser.add_argument('-s', '--source_image', type=str, help="The image you are transfering color from. Ex: source.png", required=True)
# parser.add_argument('-o', '--output_image', default='output.png', help="The name of your output image. Ex: output.png", type=str)
# parser.add_argument('-m', '--mode', default='pca', help="The color transfer mode. Options are pca, chol, or sym.", type=str)
# parser.add_argument('-e', '--eps', default='1e-5', help="Your epsilon value in scientific notation or normal notation. Ex: 1e-5 or 0.00001", type=float)
# parser.parse_args()
# args = parser.parse_args()


Image.MAX_IMAGE_PIXELS = 1000000000 # Support gigapixel images


# def main():   

#     target_img = imageio.v2.imread(args.target_image, pilmode="RGB").astype(float)/256
#     source_img = imageio.v2.imread(args.source_image, pilmode="RGB").astype(float)/256
    
#     output_img = match_color(target_img, source_img, mode=args.mode, eps=args.eps)
#     output_img = (output_img * 255).astype(np.uint8)
#     imsave(args.output_image, output_img)
#     # imsave(args.output_image, output_img)


def match_color(target_img, source_img, mode='pca', eps=1e-5):
    '''
    Matches the colour distribution of the target image to that of the source image
    using a linear transform.
    Images are expected to be of form (w,h,c) and float in [0,1].
    Modes are chol, pca or sym for different choices of basis.
    '''
    mu_t = target_img.mean(0).mean(0)
    t = target_img - mu_t
    t = t.transpose(2,0,1).reshape(3,-1)
    Ct = t.dot(t.T) / t.shape[1] + eps * eye(t.shape[0])
    mu_s = source_img.mean(0).mean(0)
    s = source_img - mu_s
    s = s.transpose(2,0,1).reshape(3,-1)
    Cs = s.dot(s.T) / s.shape[1] + eps * eye(s.shape[0])
    if mode == 'chol':
        chol_t = np.linalg.cholesky(Ct)
        chol_s = np.linalg.cholesky(Cs)
        ts = chol_s.dot(np.linalg.inv(chol_t)).dot(t)
    if mode == 'pca':
        eva_t, eve_t = np.linalg.eigh(Ct)
        Qt = eve_t.dot(np.sqrt(np.diag(eva_t))).dot(eve_t.T)
        eva_s, eve_s = np.linalg.eigh(Cs)
        Qs = eve_s.dot(np.sqrt(np.diag(eva_s))).dot(eve_s.T)
        ts = Qs.dot(np.linalg.inv(Qt)).dot(t)
    if mode == 'sym':
        eva_t, eve_t = np.linalg.eigh(Ct)
        Qt = eve_t.dot(np.sqrt(np.diag(eva_t))).dot(eve_t.T)
        Qt_Cs_Qt = Qt.dot(Cs).dot(Qt)
        eva_QtCsQt, eve_QtCsQt = np.linalg.eigh(Qt_Cs_Qt)
        QtCsQt = eve_QtCsQt.dot(np.sqrt(np.diag(eva_QtCsQt))).dot(eve_QtCsQt.T)
        ts = np.linalg.inv(Qt).dot(QtCsQt).dot(np.linalg.inv(Qt)).dot(t)
    matched_img = ts.reshape(*target_img.transpose(2,0,1).shape).transpose(1,2,0)
    matched_img += mu_s
    matched_img[matched_img>1] = 1
    matched_img[matched_img<0] = 0
    return matched_img


# if __name__ == "__main__":
#     main()

def create_target_image(hex_colors):
    # Convert hex colors to RGB
    rgb_colors = [tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) for hex_color in hex_colors]
    
    # Create an image with the RGB colors
    target_image = np.zeros((100, len(rgb_colors), 3), dtype=np.uint8)
    for i, rgb_color in enumerate(rgb_colors):
        target_image[:, i] = rgb_color
    
    return target_image


def recolor(image_np, colors):
    colors = [color.replace('#', '') for color in colors]
    palette = create_target_image(colors)
    
    # Only take the first three channels (RGB) of the image
    image_np = image_np[:, :, :3]
    
    target_img = image_np.astype(float)/256
    source_img = palette.astype(float)/256
    output_img = match_color(target_img, source_img)
    output_img = (output_img * 255).astype(np.uint8)
    imsave('result.jpg', output_img)
    return output_img