def main(argv):
experiment = Experiment()
parser = ArgumentParser(argv[0], description=__doc__)
parser.add_argument('--model', '-m', type=str, required=True)
parser.add_argument('--data',
'-d',
type=str,
default='data/deadleaves_train.mat')
parser.add_argument('--width', '-W', type=int, default=512)
parser.add_argument('--height', '-H', type=int, default=512)
parser.add_argument('--crop', '-C', type=int, default=16)
parser.add_argument('--log', '-L', type=int, default=0)
parser.add_argument('--output',
'-o',
type=str,
default='results/sample.png')
args = parser.parse_args(argv[1:])
images = loadmat(args.data)['data']
vmin = percentile(images, 0.02)
vmax = percentile(images, 98.)
experiment = Experiment(args.model)
img = empty([args.height + args.crop, args.width + 2 * args.crop])
img.ravel()[:] = images.ravel()[random_select(img.size, images.size)]
img = sample_image(img,
experiment['model'],
experiment['input_mask'],
experiment['output_mask'],
experiment['preconditioner'],
min_value=vmin,
max_value=vmax)
if args.log:
# linearize and gamma-correct
img = power(exp(img), .45)
vmin = power(exp(vmin), .45)
vmax = power(exp(vmax), .45)
imwrite(
args.output,
imformat(img[args.crop:, args.crop:-args.crop],
vmin=vmin,
vmax=vmax,
symmetric=False))
savez('sample.npz', sample=img)
return 0
def main(argv):
experiment = Experiment()
parser = ArgumentParser(argv[0], description=__doc__)
parser.add_argument('--model', '-m', type=str, required=True)
parser.add_argument('--data', '-d', type=str, default='data/deadleaves_train.mat')
parser.add_argument('--width', '-W', type=int, default=512)
parser.add_argument('--height', '-H', type=int, default=512)
parser.add_argument('--crop', '-C', type=int, default=16)
parser.add_argument('--log', '-L', type=int, default=0)
parser.add_argument('--output', '-o', type=str, default='results/sample.png')
args = parser.parse_args(argv[1:])
images = loadmat(args.data)['data']
vmin = percentile(images, 0.02)
vmax = percentile(images, 98.)
experiment = Experiment(args.model)
img = empty([args.height + args.crop, args.width + 2 * args.crop])
img.ravel()[:] = images.ravel()[random_select(img.size, images.size)]
img = sample_image(
img,
experiment['model'],
experiment['input_mask'],
experiment['output_mask'],
experiment['preconditioner'],
min_value=vmin,
max_value=vmax)
if args.log:
# linearize and gamma-correct
img = power(exp(img), .45)
vmin = power(exp(vmin), .45)
vmax = power(exp(vmax), .45)
imwrite(args.output, imformat(img[args.crop:, args.crop:-args.crop], vmin=vmin, vmax=vmax, symmetric=False))
savez('sample.npz', sample=img)
return 0
def main(argv):
parser = ArgumentParser(argv[0], description=__doc__)
parser.add_argument('model', type=str)
parser.add_argument('--num_rows', '-r', type=int, default=256)
parser.add_argument('--num_cols', '-c', type=int, default=256)
parser.add_argument('--data', '-d', type=str, default=None)
parser.add_argument('--log', '-L', type=int, default=0)
parser.add_argument('--output', '-o', type=str, default='sample.png')
parser.add_argument('--margin', '-M', type=int, default=8)
args = parser.parse_args(argv[1:])
model = Experiment(args.model)['model']
if isinstance(model, PatchRIDE):
img = model.sample()[0]
imwrite(args.output, imformat(img, vmin=0, vmax=255, symmetric=False))
else:
if args.data is None:
# initialize image with white noise
img_init = randn(1, args.num_rows + args.margin * 2, args.num_cols
+ args.margin * 2, sum(model.num_channels)) / 10.
img = model.sample(img_init)
if args.log:
# linearize and gamma-correct
img = power(exp(img), .45)
if args.margin > 0:
img = img[:, args.margin:-args.margin,
args.margin:-args.margin]
if img.shape[-1] == 3:
img[img > 255.] = 255.
img[img < 0.] = 0.
imwrite(args.output, asarray(img[0, :, :, :], dtype='uint8'))
else:
imwrite(args.output, imformat(img[0, :, :, 0], perc=99))
else:
if args.data.lower()[-4:] in ['.gif', '.png', '.jpg', 'jpeg']:
data = imread(args.data)[None]
vmin, vmax = 0, 255
else:
data = loadmat(args.data)['data']
vmin = percentile(data, 0.02)
vmax = percentile(data, 98.)
if data.ndim < 4:
data = data[:, :, :, None]
if isinstance(model, MultiscaleRIDE):
num_channels = 1
elif isinstance(model, ColorRIDE):
num_channels = 3
else:
num_channels = model.num_channels
num_pixels = (args.num_rows + args.margin) * (args.num_cols +
args.margin * 2)
# initialize image with white noise (but correct marginal distribution)
img_init = []
for c in range(num_channels):
indices = randint(data.size // num_channels, size=num_pixels)
img_init.append(
asarray(data[:, :, :, c].ravel()[indices],
dtype=float).reshape(
1, args.num_rows + args.margin,
args.num_cols + args.margin * 2, 1))
img_init = concatenate(img_init, 3)
img_init[img_init < vmin] = vmin
img_init[img_init > vmax] = vmax
if isinstance(model, MultiscaleRIDE) or isinstance(
model, ColorRIDE):
data = model._transform(data)
idx = randint(data.shape[0])
img = model.sample(
img_init,
# min_values=data[idx].min(1).min(0),
# max_values=data[idx].max(1).max(0))
min_values=data.min(2).min(1).min(0),
max_values=data.max(2).max(1).max(0))
else:
# img_init[:] = img_init.mean()
img = model.sample(img_init,
min_values=percentile(data, .1),
max_values=percentile(data, 99.8))
# min_values=percentile(data, 1.),
# max_values=percentile(data, 96.))
if args.log:
# linearize and gamma-correct
img = power(exp(img), .45)
vmin = power(exp(vmin), .45)
vmax = power(exp(vmax), .45)
try:
savez(args.output.split('.')[0] + '.npz', sample=img)
except:
pass
if args.margin > 0:
img = img[:, args.margin:, args.margin:-args.margin]
if num_channels == 1:
imwrite(
args.output,
imformat(img[0, :, :, 0],
vmin=vmin,
vmax=vmax,
symmetric=False))
else:
imwrite(
args.output,
imformat(img[0], vmin=vmin, vmax=vmax, symmetric=False))
return 0
def main(argv):
parser = ArgumentParser(argv[0], description=__doc__)
parser.add_argument('model', type=str)
parser.add_argument('--num_rows', '-r', type=int, default=256)
parser.add_argument('--num_cols', '-c', type=int, default=256)
parser.add_argument('--data', '-d', type=str, default=None)
parser.add_argument('--log', '-L', type=int, default=0)
parser.add_argument('--output', '-o', type=str, default='sample.png')
parser.add_argument('--margin', '-M', type=int, default=8)
args = parser.parse_args(argv[1:])
model = Experiment(args.model)['model']
if isinstance(model, PatchRIDE):
img = model.sample()[0]
imwrite(args.output, imformat(img, vmin=0, vmax=255, symmetric=False))
else:
if args.data is None:
# initialize image with white noise
img_init = randn(1,
args.num_rows + args.margin * 2,
args.num_cols + args.margin * 2,
sum(model.num_channels)) / 10.
img = model.sample(img_init)
if args.log:
# linearize and gamma-correct
img = power(exp(img), .45)
if args.margin > 0:
img = img[:, args.margin:-args.margin, args.margin:-args.margin]
if img.shape[-1] == 3:
img[img > 255.] = 255.
img[img < 0.] = 0.
imwrite(args.output, asarray(img[0, :, :, :], dtype='uint8'))
else:
imwrite(args.output, imformat(img[0, :, :, 0], perc=99))
else:
if args.data.lower()[-4:] in ['.gif', '.png', '.jpg', 'jpeg']:
data = imread(args.data)[None]
vmin, vmax = 0, 255
else:
data = loadmat(args.data)['data']
vmin = percentile(data, 0.02)
vmax = percentile(data, 98.)
if data.ndim < 4:
data = data[:, :, :, None]
if isinstance(model, MultiscaleRIDE):
num_channels = 1
elif isinstance(model, ColorRIDE):
num_channels = 3
else:
num_channels = model.num_channels
num_pixels = (args.num_rows + args.margin) * (args.num_cols + args.margin * 2)
# initialize image with white noise (but correct marginal distribution)
img_init = []
for c in range(num_channels):
indices = randint(data.size // num_channels, size=num_pixels)
img_init.append(
asarray(data[:, :, :, c].ravel()[indices], dtype=float).reshape(
1,
args.num_rows + args.margin,
args.num_cols + args.margin * 2, 1))
img_init = concatenate(img_init, 3)
img_init[img_init < vmin] = vmin
img_init[img_init > vmax] = vmax
if isinstance(model, MultiscaleRIDE) or isinstance(model, ColorRIDE):
data = model._transform(data)
idx = randint(data.shape[0])
img = model.sample(img_init,
# min_values=data[idx].min(1).min(0),
# max_values=data[idx].max(1).max(0))
min_values=data.min(2).min(1).min(0),
max_values=data.max(2).max(1).max(0))
else:
# img_init[:] = img_init.mean()
img = model.sample(img_init,
min_values=percentile(data, .1),
max_values=percentile(data, 99.8))
# min_values=percentile(data, 1.),
# max_values=percentile(data, 96.))
if args.log:
# linearize and gamma-correct
img = power(exp(img), .45)
vmin = power(exp(vmin), .45)
vmax = power(exp(vmax), .45)
try:
savez(args.output.split('.')[0] + '.npz', sample=img)
except:
pass
if args.margin > 0:
img = img[:, args.margin:, args.margin:-args.margin]
if num_channels == 1:
imwrite(args.output,
imformat(img[0, :, :, 0], vmin=vmin, vmax=vmax, symmetric=False))
else:
imwrite(args.output,
imformat(img[0], vmin=vmin, vmax=vmax, symmetric=False))
return 0
def main(argv):
parser = ArgumentParser(argv[0], description=__doc__)
parser.add_argument('image', type=str)
parser.add_argument('model', type=str)
parser.add_argument('--init', '-I', type=str, default=None)
parser.add_argument('--index', '-x', type=int, default=0,
help='Determines which image is used when whole dataset is given instead of image.')
parser.add_argument('--fill_region', '-f', type=int, default=71)
parser.add_argument('--outer_patch_size', '-p', type=int, default=19)
parser.add_argument('--inner_patch_size', '-i', type=int, default=5)
parser.add_argument('--stride', '-s', type=int, default=3)
parser.add_argument('--candidates', '-C', type=int, default=5,
help='The best initialization is taken out of this many initializations.')
parser.add_argument('--num_epochs', '-e', type=int, default=1000)
parser.add_argument('--method', '-m', type=str, default='SAMPLE', choices=['SAMPLE', 'MAP'])
parser.add_argument('--step_width', '-l', type=float, default=100.)
parser.add_argument('--output', '-o', type=str, default='results/inpainting/')
parser.add_argument('--flip', '-F', type=int, default=0,
help='If > 0, assume horizontal symmetry. If > 1, assume vertical symmetry.')
args = parser.parse_args(argv[1:])
### DATA
# load image
if args.image.lower()[-4:] in ['.gif', '.png', '.jpg', 'jpeg']:
image = imread(args.image)[None]
vmin, vmax = 0, 255
else:
image = loadmat(args.image)['data'][[args.index]]
vmin, vmax = image.min(), image.max()
if image.ndim < 4:
image = image[:, :, :, None]
image = asarray(image, dtype=float)
imwrite(os.path.join(args.output, 'original.png'),
imformat(image[0, :, :, 0], vmin=vmin, vmax=vmax, symmetric=False))
# remove center portion
i_start = (image.shape[1] - args.fill_region) // 2
j_start = (image.shape[2] - args.fill_region) // 2
image[0,
i_start:i_start + args.fill_region,
j_start:j_start + args.fill_region, 0] = vmin + rand(args.fill_region, args.fill_region) * (vmax - vmin)
imwrite(os.path.join(args.output, 'start.png'),
imformat(image[0, :, :, 0], vmin=vmin, vmax=vmax, symmetric=False))
### MODEL
# load model
model = Experiment(args.model)['model']
model.verbosity = False
# use different models for sampling and likelihoods because of SLSTM caching
model_copy = deepcopy(model)
# create mask indicating pixels to replace
M = args.outer_patch_size
N = args.inner_patch_size
m = (M - N) // 2
n = M - N - m
patch_mask = zeros([M, M], dtype=bool)
patch_mask[m:-n, m:-n] = True
if args.init is None:
candidates = []
logliks = []
for _ in range(args.candidates):
# replace missing pixels by ancestral sampling
patch = image[:,
i_start - M:i_start + args.fill_region,
j_start - M:j_start + args.fill_region + M]
sample_mask = zeros([patch.shape[1], patch.shape[2]], dtype=bool)
sample_mask[M:, M:-M] = True
image[:,
i_start - M:i_start + args.fill_region,
j_start - M:j_start + args.fill_region + M] = model.sample(patch, mask=sample_mask,
min_values=vmin, max_values=vmax)
candidates.append(image.copy())
logliks.append(model.loglikelihood(image).sum())
image = candidates[argmax(logliks)]
imwrite(os.path.join(args.output, 'fillin.0.png'),
imformat(image[0, :, :, 0], vmin=vmin, vmax=vmax, symmetric=False))
start_epoch = 0
else:
init = load(args.init)
image = init['image']
start_epoch = init['epoch']
### INPAINTING
try:
for epoch in range(start_epoch, args.num_epochs):
print epoch
h_flipped = False
if args.flip > 0 and rand() < .5:
print 'Horizontal flip.'
# flip image horizontally
image = image[:, :, ::-1]
j_start = image.shape[2] - j_start - args.fill_region
h_flipped = True
v_flipped = False
if args.flip > 0 and rand() < .5:
print 'Vertical flip.'
# flip image vertically
image = image[:, ::-1, :]
i_start = image.shape[1] - i_start - args.fill_region
v_flipped = True
for i in range(i_start - m, i_start - m + args.fill_region - N + 1, args.stride):
for j in range(j_start - m, j_start - m + args.fill_region - N + 1, args.stride):
patch = image[:, i:i + M, j:j + M]
if args.method == 'SAMPLE':
# proposal
patch_pr, logq_pr = model.sample(patch.copy(), mask=patch_mask,
min_values=vmin, max_values=vmax, return_loglik=True)
# conditional log-density
logq = model_copy._logq(patch, patch_mask)
# joint log-densities
logp = model_copy.loglikelihood(patch).sum()
logp_pr = model_copy.loglikelihood(patch_pr).sum()
if rand() < exp(logp_pr - logp - logq_pr + logq):
# accept proposal
patch[:] = patch_pr
else:
# gradient step
grad = model.gradient(patch)[1]
patch[:, patch_mask] += grad[:, patch_mask] * args.step_width
# flip back
if h_flipped:
image = image[:, :, ::-1]
j_start = image.shape[2] - j_start - args.fill_region
if v_flipped:
image = image[:, ::-1, :]
i_start = image.shape[1] - i_start - args.fill_region
imwrite(os.path.join(args.output, 'fillin.{0}.png'.format(epoch + 1)),
imformat(image[0, :, :, 0], vmin=vmin, vmax=vmax, symmetric=False))
except KeyboardInterrupt:
pass
savez(os.path.join(args.output, 'final.npz'), image=image, epoch=epoch)
return 0
