import os
import numpy as np
from skimage import color, io

import torch
import torch.nn.functional as F

from PIL import Image
from models import ColorEncoder, ColorUNet
from extractor.manga_panel_extractor import PanelExtractor
import argparse

os.environ["CUDA_VISIBLE_DEVICES"] = '0'

def mkdirs(path):
    if not os.path.exists(path):
        os.makedirs(path)

def Lab2RGB_out(img_lab):
    img_lab = img_lab.detach().cpu()
    img_l = img_lab[:,:1,:,:]
    img_ab = img_lab[:,1:,:,:]
    img_l = img_l + 50
    pred_lab = torch.cat((img_l, img_ab), 1)[0,...].numpy()
    out = (np.clip(color.lab2rgb(pred_lab.transpose(1, 2, 0)), 0, 1)* 255).astype("uint8")
    return out

def RGB2Lab(inputs):
    return color.rgb2lab(inputs)

def Normalize(inputs):
    l = inputs[:, :, 0:1]
    ab = inputs[:, :, 1:3]
    l = l - 50
    lab = np.concatenate((l, ab), 2)
    return lab.astype('float32')

def numpy2tensor(inputs):
    out = torch.from_numpy(inputs.transpose(2,0,1))
    return out

def tensor2numpy(inputs):
    out = inputs[0,...].detach().cpu().numpy().transpose(1,2,0)
    return out

def preprocessing(inputs):
    img_lab = Normalize(RGB2Lab(inputs))
    img = np.array(inputs, 'float32')
    img = numpy2tensor(img)
    img_lab = numpy2tensor(img_lab)
    return img.unsqueeze(0), img_lab.unsqueeze(0)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Colorize manga images based on a single reference image.")
    parser.add_argument("-i", "--input_folder", type=str, required=True, help="Path to the input folder containing images to be colorized.")
    parser.add_argument("-r", "--reference_image", type=str, required=True, help="Path to the reference image.")
    parser.add_argument("-c", "--ckpt", type=str, required=True, help="Path to the model checkpoint file.")
    parser.add_argument("-o", "--output_folder", type=str, required=True, help="Path to the output folder to save colorized images.")
    args = parser.parse_args()

    device = "cuda"

    input_folder = args.input_folder
    reference_image_path = args.reference_image
    ckpt_path = args.ckpt
    output_folder = args.output_folder

    imgsize = 256

    ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage)

    colorEncoder = ColorEncoder().to(device)
    colorEncoder.load_state_dict(ckpt["colorEncoder"])
    colorEncoder.eval()

    colorUNet = ColorUNet().to(device)
    colorUNet.load_state_dict(ckpt["colorUNet"])
    colorUNet.eval()

    # Recorre recursivamente el directorio de entrada y procesa cada imagen encontrada
    for root, dirs, files in os.walk(input_folder):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
                input_image_path = os.path.join(root, file)
                img_name = os.path.splitext(os.path.basename(input_image_path))[0]

                img1 = Image.open(input_image_path).convert("RGB")
                width, height = img1.size
                img1, img1_lab = preprocessing(img1)
                img2, img2_lab = preprocessing(Image.open(reference_image_path).convert("RGB"))

                img1 = img1.to(device)
                img1_lab = img1_lab.to(device)
                img2 = img2.to(device)
                img2_lab = img2_lab.to(device)

                with torch.no_grad():
                    img2_resize = F.interpolate(img2 / 255., size=(imgsize, imgsize), mode='bilinear', recompute_scale_factor=False, align_corners=False)
                    img1_L_resize = F.interpolate(img1_lab[:,:1,:,:] / 50., size=(imgsize, imgsize), mode='bilinear', recompute_scale_factor=False, align_corners=False)

                    color_vector = colorEncoder(img2_resize)

                    fake_ab = colorUNet((img1_L_resize, color_vector))
                    fake_ab = F.interpolate(fake_ab*110, size=(height, width), mode='bilinear', recompute_scale_factor=False, align_corners=False)

                    fake_img = torch.cat((img1_lab[:,:1,:,:], fake_ab), 1)
                    fake_img = Lab2RGB_out(fake_img)

                    out_subfolder = os.path.join(output_folder, os.path.relpath(root, input_folder))
                    out_folder = os.path.join(out_subfolder, 'color')
                    mkdirs(out_folder)
                    out_img_path = os.path.join(out_folder, f'{img_name}_color.png')
                    io.imsave(out_img_path, fake_img)

    print(f'Colored images have been saved to {output_folder}.')