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import torch | ||
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import os | ||
import colorgram.colorgram as cgm | ||
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import sys | ||
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from PIL import Image | ||
from torchvision import transforms | ||
from preprocess import re_scale, save_image, make_colorgram_tensor, scale | ||
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from models import DeepUNetPaintGenerator | ||
from utils import load_checkpoints | ||
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topk = 4 | ||
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def get_rgb(colorgram_result): | ||
""" | ||
from colorgram_result, result rgb value as tuple of (r,g,b) | ||
""" | ||
color = colorgram_result.rgb | ||
return (color.r, color.g, color.b) | ||
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def crop_region(image): | ||
""" | ||
from image, crop 4 region and return | ||
""" | ||
width, height = image.size | ||
h1 = height // 4 | ||
h2 = h1 + h1 | ||
h3 = h2 + h1 | ||
h4 = h3 + h1 | ||
image1 = image.crop((0, 0, width, h1)) | ||
image2 = image.crop((0, h1, width, h2)) | ||
image3 = image.crop((0, h2, width, h3)) | ||
image4 = image.crop((0, h3, width, h4)) | ||
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return (image1, image2, image3, image4) | ||
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def get_topk(color_info, k): | ||
colors = list(color_info.values()) | ||
return list(map(lambda x: x[k], colors)) | ||
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | ||
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out_root = './data/colorize_result' | ||
if not os.path.exists(out_root): | ||
os.mkdir(out_root) | ||
generator = 'deepunetG_030.pth.tar' | ||
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model = DeepUNetPaintGenerator() | ||
model = model.to(device) | ||
load_checkpoints(generator, model, device_type=device.type) | ||
for param in model.parameters(): | ||
param.requires_grad = False | ||
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def main(): | ||
if len(sys.argv) < 3: | ||
raise RuntimeError( | ||
'Command Line Argument Must be (sketch file, style file)') | ||
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style_f = './data/styles/%s' % sys.argv[2] | ||
test_f = './data/test/%s' % sys.argv[1] | ||
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filename = sys.argv[1][:-4] + sys.argv[2][:-4] + '.png' | ||
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style = Image.open(style_f) | ||
style_pil = style | ||
test = Image.open(test_f) | ||
test_pil = test | ||
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transform = transforms.Compose( | ||
[transforms.CenterCrop(512), | ||
transforms.ToTensor()]) | ||
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test = transform(test) | ||
test = scale(test) | ||
test = test.unsqueeze(0).to(device) | ||
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to_pil = transforms.ToPILImage() | ||
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try: | ||
images = list(crop_region(style)) | ||
result = {} | ||
for i, img in enumerate(images, 1): | ||
colors = cgm.extract(img, topk + 1) | ||
result[str(i)] = { | ||
'%d' % i: get_rgb(colors[i]) | ||
for i in range(1, topk + 1) | ||
} | ||
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color_tensor = make_colorgram_tensor(result) | ||
color_tensor = color_tensor.unsqueeze(0).to(device) | ||
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fakeB, _ = model(test, color_tensor) | ||
fakeB = fakeB.squeeze(0) | ||
fakeB = re_scale(fakeB.detach().cpu()) | ||
fakeB = to_pil(fakeB) | ||
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result_image = Image.new('RGB', (512 * 3, 512)) | ||
result_image.paste(test_pil, (512 * 0, 0, 512 * 1, 512)) | ||
result_image.paste(style_pil, (512 * 1, 0, 512 * 2, 512)) | ||
result_image.paste(fakeB, (512 * 2, 0, 512 * 3, 512)) | ||
save_image(result_image, os.path.join(out_root, filename)) | ||
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except IndexError: | ||
exit(1) | ||
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if __name__ == "__main__": | ||
main() |