pintar.py

import os
import argparse
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

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

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Colorize manga images.")
    parser.add_argument("-i", "--input_folder", type=str, required=True, help="Path to the input folder containing manga images.")
    parser.add_argument("-r", "--reference_image", type=str, required=True, help="Path to the reference image for colorization.")
    parser.add_argument("-ckpt", "--model_checkpoint", 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 where colorized images will be saved.")
    parser.add_argument("-ne", "--no_extractor", action="store_true", help="Do not segment the manga panels.")
    args = parser.parse_args()

    device = "cuda"

    ckpt = torch.load(args.model_checkpoint, 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()

    reference_img = Image.open(args.reference_image).convert("RGB")
    reference_img = np.array(reference_img).astype(np.float32) / 255.0  # Asegúrate de que la referencia esté en el rango [0, 1]
    reference_img_lab = RGB2Lab(reference_img)
    reference_img_lab = Normalize(reference_img_lab)
    reference_img_lab = numpy2tensor(reference_img_lab)
    reference_img_lab = reference_img_lab.to(device).unsqueeze(0)

    for root, dirs, files in os.walk(args.input_folder):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
                input_image_path = os.path.join(root, file)

                img = Image.open(input_image_path).convert("RGB")
                img = np.array(img).astype(np.float32) / 255.0  # Asegúrate de que la imagen de entrada esté en el rango [0, 1]
                img_lab = RGB2Lab(img)
                img_lab = Normalize(img_lab)
                img_lab = numpy2tensor(img_lab)
                img_lab = img_lab.to(device).unsqueeze(0)

                with torch.no_grad():
                    img_resize = F.interpolate(img_lab / 110., size=(256, 256), mode='bilinear', recompute_scale_factor=False, align_corners=False)
                    img_L_resize = F.interpolate(img_resize[:, :1, :, :] / 50., size=(256, 256), mode='bilinear', recompute_scale_factor=False, align_corners=False)

                    color_vector = colorEncoder(img_resize)
                    fake_ab = colorUNet((img_L_resize, color_vector))
                    fake_ab = F.interpolate(fake_ab, size=(img.shape[0], img.shape[1]), mode='bilinear', recompute_scale_factor=False, align_corners=False)

                    fake_img = torch.cat((img_lab[:, :1, :, :], fake_ab), 1)
                    fake_img = Lab2RGB_out(fake_img)
                    fake_img = (fake_img * 255).astype(np.uint8)  # Convierte de nuevo a [0, 255]

                    relative_path = os.path.relpath(input_image_path, args.input_folder)
                    output_subfolder = os.path.join(args.output_folder, os.path.dirname(relative_path), 'color')
                    mkdirs(output_subfolder)
                    output_image_path = os.path.join(output_subfolder, f'{os.path.splitext(os.path.basename(input_image_path))[0]}_colorized.png')
                    io.imsave(output_image_path, fake_img)

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