def get_image(gen, point):
images = list(map(lambda x: x.detach(), gen(point)))[1:]
images = [adjust_dynamic_range(image, (-1, 1), (0, 1)) for image in images]
images = progressive_upscaling(images)
images = list(map(lambda x: x.squeeze(dim=0), images))
image = make_grid(images, nrow=int(ceil(sqrt(len(images)))), padding=0)
return image.cpu().numpy().transpose(1, 2, 0)
def main(args):
"""
Main function for the script
:param args: parsed command line arguments
:return: None
"""
img_file = os.path.join(args.image_path)
if args.npz_file:
img = np.load(img_file)
else:
img = np.array(Image.open(img_file))
img = np.expand_dims(img, axis=0).transpose(0, 3, 1, 2)
img = th.from_numpy(img).float() # make a tensor out of image
# progressively downsample the image and save it at the given location
dim = img.shape[-1] # gives the highest dimension
ds_img = img # start from the highest resolution image
images = [ds_img] # original image already in
while dim > 4:
ds_img = avg_pool2d(ds_img, kernel_size=2, stride=2)
images.append(ds_img)
dim //= 2
images = progressive_upscaling(list(reversed(images)))
# save the images:
for count in range(len(images)):
imsave(os.path.join(args.out_dir,
str(count + 1) + ".png"),
images[count].squeeze(0).permute(1, 2, 0))
def get_image(gen, point):
images = list(map(lambda x: x.detach(), gen(point)))
images = progressive_upscaling(images)
images = list(map(lambda x: x.squeeze(dim=0), images))
image = make_grid(images,
nrow=int(ceil(sqrt(len(images)))),
normalize=True,
scale_each=True)
return image.cpu().numpy().transpose(1, 2, 0)
def get_image(gen, point):
images = list(map(lambda x: x.detach(), gen(point)))
images = [adjust_dynamic_range(image) for image in images]
images = progressive_upscaling(images)
# discard 128_x_128 resolution (temporarily)
images = images[:-2] + images[-1:]
images = list(map(lambda x: x.squeeze(dim=0), images))
image = make_grid(images, nrow=int(ceil(sqrt(len(images)))), padding=0)
return image.cpu().numpy().transpose(1, 2, 0)
def get_image(gen, point):
"""
obtain an All-resolution grid of images from the given point
:param gen: the generator object
:param point: random latent point for generation
:return: img => generated image
"""
images = list(map(lambda x: x.detach(), gen(point)))[1:]
images = [adjust_dynamic_range(image) for image in images]
images = progressive_upscaling(images)
images = list(map(lambda x: x.squeeze(dim=0), images))
image = make_grid(images, nrow=int(ceil(sqrt(len(images)))))
return image.cpu().numpy().transpose(1, 2, 0)
