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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:,:,:]
# print(torch.max(img_l), torch.min(img_l))
# print(torch.max(img_ab), torch.min(img_ab))
img_l = img_l + 50
pred_lab = torch.cat((img_l, img_ab), 1)[0,...].numpy()
# grid_lab = utils.make_grid(pred_lab, nrow=1).numpy().astype("float64")
# print(grid_lab.shape)
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):
# input: rgb, [0, 255], uint8
img_lab = Normalize(RGB2Lab(inputs))
img = np.array(inputs, 'float32') # [0, 255]
img = numpy2tensor(img)
img_lab = numpy2tensor(img_lab)
return img.unsqueeze(0), img_lab.unsqueeze(0)
if __name__ == "__main__":
device = "cuda"
# model_name = 'Color2Manga_sketch'
ckpt_path = 'experiments/Color2Manga_gray/074000_gray.pt'
test_dir_path = 'test_datasets/gray_test'
no_extractor = False
# imgs_num = len(os.listdir(test_dir_path)) // 2
imgsize = 256
parser = argparse.ArgumentParser()
parser.add_argument("--path", type=str, default=None, help="path of input image")
parser.add_argument("--size", type=int, default=None)
parser.add_argument("--ckpt", type=str, default=None, help="path of model weight")
parser.add_argument("-ne", "--no_extractor", action='store_true',
help="Do not segment the manga panels.")
args = parser.parse_args()
if args.path:
ckpt_path = args.path
if args.size:
imgsize = args.size
if args.ckpt:
test_dir_path = args.ckpt
if args.no_extractor:
no_extractor = args.no_extractor
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()
imgs = []
imgs_lab = []
# for i in range(imgs_num):
# idx = i
# print('Image', idx, 'Input Image', 'in%d.JPEG'%idx, 'Ref Image', 'ref%d.JPEG'%idx)
while 1:
print(f'make sure both manga image and reference 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]
if no_extractor:
ref_img_path = os.path.join(test_dir_path, input(f"{1}/{1} reference image:"))
img1 = Image.open(img_path).convert("RGB")
width, height = img1.size
img2 = Image.open(ref_img_path).convert("RGB")
img1, img1_lab = preprocessing(img1)
img2, img2_lab = preprocessing(img2)
img1 = img1.to(device)
img1_lab = img1_lab.to(device)
img2 = img2.to(device)
img2_lab = img2_lab.to(device)
# print('-------',torch.max(img1_lab[:,:1,:,:]), torch.min(img1_lab[:,1:,:,:]))
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)
# io.imsave(out_img_path, 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)
continue
# extract panels from manga
panel_extractor = PanelExtractor(min_pct_panel=5, max_pct_panel=90)
panels, masks, panel_masks = panel_extractor.extract(img_path)
panel_num = len(panels)
ref_img_paths = []
# ref_img_path = os.path.join(test_dir_path, '%03d_ref.png' % idx)
print("Please enter the name of the reference image in order according to the number prompts on the picture")
for i in range(panel_num):
ref_img_path = os.path.join(test_dir_path, input(f"{i+1}/{panel_num} reference image:"))
ref_img_paths.append(ref_img_path)
fake_imgs = []
for i in range(panel_num):
img1 = Image.fromarray(panels[i]).convert("RGB")
width, height = img1.size
img2 = Image.open(ref_img_paths[i]).convert("RGB")
# img1 = Image.open(img_path).convert("RGB")
# width, height = img1.size
# img2 = Image.open(ref_img_path).convert("RGB")
img1, img1_lab = preprocessing(img1)
img2, img2_lab = preprocessing(img2)
img1 = img1.to(device)
img1_lab = img1_lab.to(device)
img2 = img2.to(device)
img2_lab = img2_lab.to(device)
# print('-------',torch.max(img1_lab[:,:1,:,:]), torch.min(img1_lab[:,1:,:,:]))
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)
# io.imsave(f'test_datasets/gray_test/panels/{i}.png', fake_img)
fake_imgs.append(fake_img)
if panel_num == 1:
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_imgs[0]).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_imgs[0])
else:
panel_extractor.concatPanels(img_path, fake_imgs, masks, panel_masks)
print(f'colored image has been put to: {test_dir_path}color')
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