def main():
if len(sys.argv) < 3:
raise RuntimeError(
'Command Line Argument Must be (sketch file, style file)')
style_f = './data/styles/%s' % sys.argv[2]
test_f = './data/test/%s' % sys.argv[1]
filename = sys.argv[1][:-4] + sys.argv[2][:-4] + '.png'
style = Image.open(style_f).convert('RGB')
style = transforms.Resize((512, 512))(style)
style_pil = style
test = Image.open(test_f).convert('RGB')
test_pil = transforms.Resize((512, 512))(test)
transform = transforms.Compose(
[transforms.Resize((512, 512)),
transforms.ToTensor()])
test = transform(test)
test = scale(test)
test = test.unsqueeze(0).to(device)
to_pil = transforms.ToPILImage()
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)
}
color_tensor = make_colorgram_tensor(result)
color_tensor = color_tensor.unsqueeze(0).to(device)
fakeB, _ = model(test, color_tensor)
fakeB = fakeB.squeeze(0)
fakeB = re_scale(fakeB.detach().cpu())
fakeB = to_pil(fakeB)
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))
except IndexError:
exit(1)
def actbycolor(stylelist, testfile):
print(stylelist)
print(stylelist[0])
if len(sys.argv) < 3:
raise RuntimeError(
'Command Line Argument Must be (sketch file, style file)')
result = {}
j = 0
for i in range(1, 5):
temp = {}
for k in range(1, 5):
if j >= len(stylelist):
j = 0
temp[str(k)] = stylelist[j]
j = j + 1
result[str(i)] = temp
print(result)
test_f = testfile
# filename = sys.argv[1][:-4] + sys.argv[2][:-4] + '.png'
test = Image.open(test_f).convert('RGB')
test_pil = transforms.Resize((256, 256))(test)
style = Image.open('media/immm.jpg').convert('RGB')
style = transforms.Resize((256, 256))(style)
print(style)
style_pil = style
transform = transforms.Compose(
[transforms.Resize((256, 256)),
transforms.ToTensor()])
test = transform(test)
test = scale(test)
test = test.unsqueeze(0).to(device)
to_pil = transforms.ToPILImage()
nowtime = time.time()
try:
nowtime2 = time.time()
print(result)
print(nowtime2 - nowtime)
color_tensor = make_colorgram_tensor(result)
color_tensor = color_tensor.unsqueeze(0).to(device)
fakeB, _ = model(test, color_tensor)
fakeB = fakeB.squeeze(0)
fakeB = re_scale(fakeB.detach().cpu())
fakeB = to_pil(fakeB)
fakeB.save(os.path.expanduser('media/result.jpg'))
result_image = Image.new('RGB', (256 * 3, 256))
result_image.paste(test_pil, (256 * 0, 0, 256 * 1, 256))
result_image.paste(style_pil, (256 * 1, 0, 256 * 2, 256))
result_image.paste(fakeB, (256 * 2, 0, 256 * 3, 256))
result_image.save(os.path.expanduser('media/compareresult.jpg'))
# save_image(result_image, os.path.join(out_root, filename))
except IndexError:
exit(1)
nowtime2 = time.time()
print(nowtime2 - nowtime)
# if __name__ == "__main__":
# main()
image = skimage.transform.pyramid_expand(
image,
upscale=512 // (image.shape[-1]),
multichannel=False,
)
return to_pil(torch.Tensor(image).unsqueeze(0))
attentions = list(map(lambda img: interpolate(img), attentions))
result = Image.new('RGBA', (4 * 512, 512))
styleB = styleB.squeeze()
fakeB = fakeB.squeeze()
imageA = imageA.squeeze()
imageB = imageB.squeeze()
imageA = to_pil(re_scale(imageA).detach().cpu())
imageB = to_pil(re_scale(imageB).detach().cpu())
styleB = to_pil(re_scale(styleB).detach().cpu())
fakeB = to_pil(re_scale(fakeB).detach().cpu())
result.paste(imageA, (0, 0))
result.paste(styleB, (512, 0))
result.paste(fakeB, (512 * 2, 0))
result.paste(imageB, (512 * 3, 0))
figure = Image.new('RGB', (9 * 512, 512))
figure.paste(imageA, (0, 0))
figure.paste(styleB, (512 * 1, 0))
figure.paste(attentions[0], (512 * 2, 0))
figure.paste(attentions[1], (512 * 3, 0))
figure.paste(attentions[2], (512 * 4, 0))
def validate(self, dataset, epoch, samples=3):
# self.generator.eval()
# self.discriminator.eval()
length = len(dataset)
# sample images
idxs_total = [
random.sample(range(0, length - 1), samples * 2)
for _ in range(epoch)
]
for j, idxs in enumerate(idxs_total):
styles = idxs[samples:]
targets = idxs[0:samples]
result = Image.new(
'RGB', (5 * self.resolution, samples * self.resolution))
toPIL = transforms.ToPILImage()
G_loss_gan = []
G_loss_l1 = []
D_loss_real = []
D_loss_fake = []
l1_loss = self.losses['L1']
gan_loss = self.losses['GAN']
for i, (target, style) in enumerate(zip(targets, styles)):
sub_result = Image.new('RGB',
(5 * self.resolution, self.resolution))
imageA, imageB, _ = dataset[target]
styleA, styleB, colors = dataset[style]
if self.args.mode == 'B2A':
imageA, imageB = imageB, imageA
styleA, styleB = styleB, styleA
imageA = imageA.unsqueeze(0).to(self.device)
imageB = imageB.unsqueeze(0).to(self.device)
styleB = styleB.unsqueeze(0).to(self.device)
colors = colors.unsqueeze(0).to(self.device)
with torch.no_grad():
fakeB, _ = self.generator(
imageA,
colors,
)
fakeAB = torch.cat([imageA, fakeB], 1)
realAB = torch.cat([imageA, imageB], 1)
G_loss_l1.append(l1_loss(fakeB, imageB).item())
G_loss_gan.append(
gan_loss(self.discriminator(fakeAB), True).item())
D_loss_real.append(
gan_loss(self.discriminator(realAB), True).item())
D_loss_fake.append(
gan_loss(self.discriminator(fakeAB), False).item())
styleB = styleB.squeeze()
fakeB = fakeB.squeeze()
imageA = imageA.squeeze()
imageB = imageB.squeeze()
colors = colors.squeeze()
imageA = toPIL(re_scale(imageA).detach().cpu())
imageB = toPIL(re_scale(imageB).detach().cpu())
styleB = toPIL(re_scale(styleB).detach().cpu())
fakeB = toPIL(re_scale(fakeB).detach().cpu())
# synthesize top-4 colors
color1 = toPIL(re_scale(colors[0:3].detach().cpu()))
color2 = toPIL(re_scale(colors[3:6].detach().cpu()))
color3 = toPIL(re_scale(colors[6:9].detach().cpu()))
color4 = toPIL(re_scale(colors[9:12].detach().cpu()))
color1 = color1.rotate(90)
color2 = color2.rotate(90)
color3 = color3.rotate(90)
color4 = color4.rotate(90)
color_result = Image.new('RGB',
(self.resolution, self.resolution))
color_result.paste(
color1.crop((0, 0, self.resolution, self.resolution // 4)),
(0, 0))
color_result.paste(
color2.crop((0, 0, self.resolution, self.resolution // 4)),
(0, self.resolution // 4))
color_result.paste(
color3.crop((0, 0, self.resolution, self.resolution // 4)),
(0, self.resolution // 4 * 2))
color_result.paste(
color4.crop((0, 0, self.resolution, self.resolution // 4)),
(0, self.resolution // 4 * 3))
sub_result.paste(imageA, (0, 0))
sub_result.paste(styleB, (self.resolution, 0))
sub_result.paste(fakeB, (2 * self.resolution, 0))
sub_result.paste(imageB, (3 * self.resolution, 0))
sub_result.paste(color_result, (4 * self.resolution, 0))
result.paste(sub_result, (0, 0 + self.resolution * i))
print(
'Validate D_loss_real = %f, D_loss_fake = %f, G_loss_l1 = %f, G_loss_gan = %f'
% (
sum(D_loss_real) / samples,
sum(D_loss_fake) / samples,
sum(G_loss_l1) / samples,
sum(G_loss_gan) / samples,
))
save_image(
result,
'deepunetpaint_%03d_%02d' % (epoch, j),
self.save_path,
)
