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()
def __getitem__(self, index):
"""
Niko Dataset Get Item
@param index: index
Returns:
if self.color_histogram
tuple: (imageA == original, imageB == sketch, colors)
else:
tuple: (imageA == original, imageB == sketch)
if self.resize
resized image will be appended end of the above tuple
"""
filename = self.image_files[index]
file_id = filename.split('/')[-1][:-4]
if self.color_histogram:
# build colorgram tensor
color_info = self.color_cache.get(file_id, None)
if color_info is None:
with open(
os.path.join('./data/colorgram',
'%s.json' % file_id).replace('\\', '/'),
'r') as json_file:
# load color info dictionary from json file
color_info = json.loads(json_file.read())
self.color_cache[file_id] = color_info
colors = make_colorgram_tensor(color_info)
image = Image.open(filename)
image_width, image_height = image.size
imageA = image.crop((0, 0, image_width // 2, image_height))
imageB = image.crop((image_width // 2, 0, image_width, image_height))
# default transforms, pad if needed and center crop 256
width_pad = self.size - image_width // 2
if width_pad < 0:
# do not pad
width_pad = 0
height_pad = self.size - image_height
if height_pad < 0:
height_pad = 0
# padding as white
padding = transforms.Pad((width_pad // 2, height_pad // 2 + 1,
width_pad // 2 + 1, height_pad // 2),
(255, 255, 255))
# use center crop
crop = transforms.CenterCrop(self.size)
imageA = padding(imageA)
imageA = crop(imageA)
imageB = padding(imageB)
imageB = crop(imageB)
if self.transform is not None:
imageA = self.transform(imageA)
imageB = self.transform(imageB)
# scale image into range [-1, 1]
imageA = scale(imageA)
imageB = scale(imageB)
if not self.color_histogram:
return imageA, imageB
else:
return imageA, imageB, colors