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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
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__":
parser = argparse.ArgumentParser()
parser.add_argument("-r", "--reference", type=str, help="ruta de la imagen de referencia")
parser.add_argument("-o", "--output", type=str, help="carpeta de salida para las imágenes coloreadas")
parser.add_argument("-ckpt", "--model_checkpoint", type=str, help="ruta del modelo de checkpoint")
args = parser.parse_args()
device = "cuda"
ckpt_path = args.model_checkpoint or 'experiments/Color2Manga_gray/074000_gray.pt'
test_dir_path = 'test_datasets/gray_test'
no_extractor = False
# ... (resto del código)
while True:
# ... (resto del código)
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.join(output_folder, 'color')
if not os.path.exists(out_folder):
os.makedirs(out_folder)
out_img_path = os.path.join(out_folder, f'{img_name}_color.png')
# show image
Image.fromarray(fake_img).show()
# save image
io.imsave(out_img_path, fake_img)
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