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

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__":
    device = "cuda"

    ckpt_path = 'experiments/Color2Manga_gray/074000_gray.pt'
    test_dir_path = 'test_datasets/gray_test'
    no_extractor = False
    ref_img_path = 'path_to_your_reference_image.jpg'  # Especifica la ruta de tu imagen de referencia aquí

    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()

    img1 = Image.open(ref_img_path).convert("RGB")
    width, height = img1.size

    img1, img1_lab = preprocessing(img1)
    img1 = img1.to(device)
    img1_lab = img1_lab.to(device)

    while True:
        print(f'make sure manga images are under this path: {test_dir_path}')
        img_path = input("please input the name of image needed to be colorized (with file extension): ")
        img_path = os.path.join(test_dir_path, img_path)
        img_name = os.path.basename(img_path)
        img_name = os.path.splitext(img_name)[0]

        img2 = Image.open(img_path).convert("RGB")
        img2, img2_lab = preprocessing(img2)
        img2 = img2.to(device)
        img2_lab = img2_lab.to(device)

        with torch.no_grad():
            img2_resize = F.interpolate(img2 / 255., size=(256, 256), mode='bilinear', recompute_scale_factor=False, align_corners=False)
            img1_L_resize = F.interpolate(img1_lab[:,:1,:,:] / 50., size=(256, 256), 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_folder = os.path.dirname(img_path)
            out_name = os.path.basename(img_path)
            out_name = os.path.splitext(out_name)[0]
            out_img_path = os.path.join(out_folder, 'color', f'{out_name}_color.png')

            # show image
            Image.fromarray(fake_img).show()
            # save image
            folder_path = os.path.join(out_folder, 'color')
            if not os.path.exists(folder_path):
                os.mkdir(folder_path)
            io.imsave(out_img_path, fake_img)