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
