def show_resolution(seed):
rng = np.random.RandomState(seed)
kwargs = dict(alphabet=LANGUAGE_MAP['english'].get_alphabet(),
is_bold=False,
is_slant=False,
inverse_color=False,
pixel_noise_scale=0)
attr_list = [
basic_attribute_sampler(resolution=(8, 8),
char='b',
font='arial',
scale=0.9,
rotation=0,
background=SolidColor((0, 0, 0)),
foreground=SolidColor((0.5, 0.5, 0)),
**kwargs),
basic_attribute_sampler(resolution=(16, 16),
char='x',
font='time',
scale=0.7,
**kwargs),
basic_attribute_sampler(resolution=(32, 32),
char='g',
font='flavors',
scale=0.6,
rotation=1,
**kwargs),
basic_attribute_sampler(resolution=(64, 64),
scale=0.3,
n_symbols=5,
**kwargs),
basic_attribute_sampler(resolution=(128, 128),
scale=0.1,
n_symbols=30,
**kwargs),
]
def attr_sampler():
for attr in attr_list:
yield attr(seed=rand_seed(rng))
x, y = pack_dataset_resample(dataset_generator(attr_sampler(), 1000))
plot_dataset(x,
y,
h_axis='rotation',
v_axis=None,
n_col=5,
n_row=1,
hide_axis=True)
ax = plt.gca()
ax.set_xticks((np.arange(len(attr_list)) + 0.5) * x.shape[1])
ax.set_xticklabels(['8 x 8', '16 x 16', '32 x 32', '64 x 64', '128 x 128'],
rotation=0)
ax.get_xaxis().set_visible(True)
plt.xlabel('')
def show_background(seed):
rng = np.random.RandomState(seed)
kwargs = dict(resolution=(128, 128),
alphabet=LANGUAGE_MAP['english'].get_alphabet(),
char='a',
inverse_color=False,
pixel_noise_scale=0)
attr_list = [
basic_attribute_sampler(background=SolidColor((0.2, 0.2, 0)),
foreground=SolidColor((0.8, 0, 0.8)),
**kwargs),
basic_attribute_sampler(background=lambda _rng: Gradient(
types=('radial', ), seed=rand_seed(_rng)),
foreground=lambda _rng: Gradient(
types=('radial', ), seed=rand_seed(_rng)),
**kwargs),
basic_attribute_sampler(
background=lambda _rng: Camouflage(stroke_angle=np.pi / 4,
seed=rand_seed(_rng)),
foreground=lambda _rng: Camouflage(stroke_angle=np.pi * 3 / 4,
seed=rand_seed(_rng)),
**kwargs),
basic_attribute_sampler(
background=lambda _rng: ImagePattern(seed=rand_seed(_rng)),
foreground=lambda _rng: ImagePattern(seed=rand_seed(_rng)),
**kwargs),
add_occlusion(basic_attribute_sampler(**kwargs),
n_occlusion=3,
scale=lambda _rng: 0.3 * np.exp(_rng.randn() * 0.1),
translation=lambda _rng: tuple(_rng.rand(2) * 2 - 1))
]
def attr_sampler():
for attr in attr_list:
yield attr(seed=rand_seed(rng))
x, _, y = pack_dataset(
dataset_generator(attr_sampler(), 1000, flatten_mask_except_first))
plot_dataset(x,
y,
h_axis='scale',
v_axis=None,
n_col=5,
n_row=1,
hide_axis=True)
ax = plt.gca()
ax.set_xticks((np.arange(5) + 0.5) * x.shape[1])
ax.set_xticklabels(
['Solid', 'Gradient', 'Camouflage', 'Natural', 'Occlusions'],
rotation=0)
ax.get_xaxis().set_visible(True)
plt.xlabel('')
def show_fonts(seed):
rng = np.random.RandomState(seed)
def attr_sampler():
for char in 'abCD':
for font in font_list:
yield basic_attribute_sampler(
alphabet=LANGUAGE_MAP['english'],
char=char,
font=font,
is_bold=False,
is_slant=False,
resolution=(128, 128),
pixel_noise_scale=0)(seed=rand_seed(rng))
x, _, y = pack_dataset(dataset_generator(attr_sampler(), 1000))
plot_dataset(x, y, h_axis='font', v_axis='char', hide_axis=True)
def main():
# XXX: Imports are here so that they are done
# inside the docker image (synbols [...])
from synbols.data_io import load_h5
from synbols.utils import flatten_attr
from synbols.visualization import plot_dataset
args = parse_args()
if args.data is None:
raise Exception(" path to the data is not defined."
"Please use `--data` and indicate the path.")
print("load dataset in h5 format ...")
x, mask, attr_list, splits = load_h5(args.data)
print("x.shape:", x.shape)
attr_list = [flatten_attr(attr) for attr in attr_list]
all_attr_keys = []
for attr in attr_list[0].keys():
all_attr_keys.append(attr)
check = all(key in all_attr_keys for key in args.attr_keys)
if not check:
raise Exception("One or more of the provided attribute keys are" +
"not valid." +
f"The complete list of keys is {all_attr_keys}")
split_masks = splits[args.split_name]
print("making statistics for %s." % args.split_name)
view_split(split_masks, attr_list, args.attr_keys, args.split_name)
plot_dataset(x, attr_list, v_axis=None, h_axis="font")
if args.save:
plt.savefig(args.save)
plt.show()
def show_languages(seed):
language_list = [
'korean', 'chinese', 'telugu', 'thai', 'gujarati', 'arabic', 'tamil',
'russian'
]
rng = np.random.RandomState(seed)
def attr_sampler():
for lang in language_list:
alphabet = LANGUAGE_MAP[lang].get_alphabet()
for i in range(4):
yield basic_attribute_sampler(
alphabet=alphabet,
char=lambda rng: rng.choice(alphabet.symbols),
font=lambda rng: rng.choice(alphabet.fonts),
is_bold=False,
is_slant=False,
resolution=(128, 128),
pixel_noise_scale=0)(seed=rand_seed(rng))
x, _, y = pack_dataset(dataset_generator(attr_sampler(), 1000))
h_values, v_values = plot_dataset(x,
y,
h_axis='alphabet',
v_axis=None,
n_col=len(language_list),
n_row=4,
hide_axis=True)
# map = {'chinese-simplified': 'chinese'}
# h_values = [map.get(val, val) for val in h_values]
ax = plt.gca()
ax.set_xticks((np.arange(len(h_values)) + 0.5) * x.shape[1])
ax.set_xticklabels(h_values, rotation=0)
ax.get_xaxis().set_visible(True)
plt.xlabel('')
def make_image(attr_sampler, file_name):
x, _, y = pack_dataset(dataset_generator(attr_sampler, 1000))
plot_dataset(x, y, h_axis='font', v_axis='char')
plt.savefig(file_name)
from synbols import generate
from synbols import drawing
from synbols.visualization import plot_dataset
import matplotlib.pyplot as plt
# bg = drawing.Camouflage(stroke_angle=1.)
# bg = drawing.NoPattern()
bg = drawing.MultiGradient(alpha=0.5,
n_gradients=2,
types=('linear', 'radial'))
# bg = drawing.Gradient(types=('linear',), random_color=drawing.color_sampler(brightness_range=(0.1, 0.9)))
# fg = drawing.Camouflage(stroke_angle=0.5)
# fg = drawing.SolidColor((1, 1, 1))
fg = drawing.Gradient(
types=('radial', ),
random_color=drawing.color_sampler(brightness_range=(0.1, 0.9)))
attr_sampler = generate.basic_attribute_sampler(resolution=(64, 64),
foreground=fg,
background=bg)
# attr_sampler = generate.add_occlusion(attr_sampler)
x, mask, y = pack_dataset(
generate.dataset_generator(attr_sampler, 500, generate.flatten_mask))
plt.figure('dataset')
plot_dataset(x, y, h_axis='char', v_axis='font', n_row=10, n_col=20)
plt.savefig('view_generator.png')