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