def main():
nb_rows = 8
nb_cols = 8
h, w = (128, 128)
sample_size = 128
noise_gens = [
iap.SimplexNoise(),
iap.FrequencyNoise(exponent=-4, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=-2, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=0, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=2, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=4, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
iap.IterativeNoiseAggregator(
other_param=iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
iterations=(1, 3),
aggregation_method=["max", "avg"]
),
iap.IterativeNoiseAggregator(
other_param=iap.Sigmoid(
iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
threshold=(-10, 10),
activated=0.33,
mul=20,
add=-10
),
iterations=(1, 3),
aggregation_method=["max", "avg"]
)
]
samples = [[] for _ in range(len(noise_gens))]
for _ in range(nb_rows * nb_cols):
for i, noise_gen in enumerate(noise_gens):
samples[i].append(noise_gen.draw_samples((h, w)))
rows = [np.hstack(row) for row in samples]
grid = np.vstack(rows)
misc.imshow((grid*255).astype(np.uint8))
images = [ia.quokka_square(size=(128, 128)) for _ in range(16)]
seqs = [
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0)),
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0)),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True)
]
images_aug = []
for seq in seqs:
images_aug.append(np.hstack(seq.augment_images(images)))
images_aug = np.vstack(images_aug)
misc.imshow(images_aug)
def chapter_alpha_masks_sigmoid():
# -----------------------------------------
# Sigmoid varying on/off
# -----------------------------------------
import imgaug as ia
from imgaug import parameters as iap
seed = 1
ia.seed(seed)
masks = []
for activated in [False, True]:
noise = iap.Sigmoid.create_for_noise(other_param=iap.FrequencyNoise(
exponent=(-4.0, 4.0), upscale_method="linear"),
activated=activated)
row = [
noise.draw_samples((64, 64),
random_state=ia.new_random_state(seed + 1 + i))
for i in range(16)
]
row = np.hstack(row)
row = np.tile(row[:, :, np.newaxis], (1, 1, 3))
row = (row * 255).astype(np.uint8)
row = np.pad(row, ((0, 0), (90, 0), (0, 0)),
mode="constant",
constant_values=255)
row = ia.draw_text(row,
y=17,
x=2,
text="activated=\n%s" % (activated, ),
size=14,
color=[0, 0, 0])
masks.append(row)
# ------------
save("alpha", "sigmoid_vary_activated.jpg", grid(masks, cols=1, rows=2))
# -----------------------------------------
# Sigmoid varying on/off
# -----------------------------------------
import imgaug as ia
from imgaug import parameters as iap
seed = 1
ia.seed(seed)
masks = []
nb_rows = 3
class ConstantNoise(iap.StochasticParameter):
def __init__(self, noise, seed):
super(ConstantNoise, self).__init__()
self.noise = noise
self.seed = seed
def _draw_samples(self, size, random_state):
return self.noise.draw_samples(size,
random_state=ia.new_random_state(
self.seed))
for rowidx in range(nb_rows):
row = []
for tidx, threshold in enumerate(np.linspace(-10.0, 10.0, 10)):
noise = iap.Sigmoid.create_for_noise(other_param=ConstantNoise(
iap.FrequencyNoise(exponent=(-4.0, 4.0),
upscale_method="linear"),
seed=seed + 100 + rowidx),
activated=True,
threshold=threshold)
cell = noise.draw_samples(
(64, 64), random_state=ia.new_random_state(seed + tidx))
cell = np.tile(cell[:, :, np.newaxis], (1, 1, 3))
cell = (cell * 255).astype(np.uint8)
if rowidx == 0:
cell = np.pad(cell, ((20, 0), (0, 0), (0, 0)),
mode="constant",
constant_values=255)
cell = ia.draw_text(cell,
y=2,
x=15,
text="%.1f" % (threshold, ),
size=14,
color=[0, 0, 0])
row.append(cell)
row = np.hstack(row)
masks.append(row)
gridarr = np.vstack(masks)
# ------------
save("alpha", "sigmoid_vary_threshold.jpg", gridarr)
def chapter_alpha_masks_iterative():
# -----------------------------------------
# IterativeNoiseAggregator varying number of iterations
# -----------------------------------------
import imgaug as ia
from imgaug import parameters as iap
seed = 1
ia.seed(seed)
masks = []
iterations_all = [1, 2, 3, 4]
for iterations in iterations_all:
noise = iap.IterativeNoiseAggregator(other_param=iap.FrequencyNoise(
exponent=(-4.0, 4.0), upscale_method=["linear", "nearest"]),
iterations=iterations,
aggregation_method="max")
row = [
noise.draw_samples((64, 64),
random_state=ia.new_random_state(seed + 1 + i))
for i in range(16)
]
row = np.hstack(row)
row = np.tile(row[:, :, np.newaxis], (1, 1, 3))
row = (row * 255).astype(np.uint8)
row = np.pad(row, ((0, 0), (50, 0), (0, 0)),
mode="constant",
constant_values=255)
row = ia.draw_text(row,
y=24,
x=2,
text="%d iter." % (iterations, ),
size=14,
color=[0, 0, 0])
masks.append(row)
# ------------
save("alpha", "iterative_vary_iterations.jpg",
grid(masks, cols=1, rows=len(iterations_all)))
# -----------------------------------------
# IterativeNoiseAggregator varying methods
# -----------------------------------------
import imgaug as ia
from imgaug import parameters as iap
seed = 1
ia.seed(seed)
iterations_all = [1, 2, 3, 4, 5, 6]
methods = ["min", "avg", "max"]
cell_idx = 0
rows = []
for method_idx, method in enumerate(methods):
row = []
for iterations in iterations_all:
noise = iap.IterativeNoiseAggregator(
other_param=iap.FrequencyNoise(
exponent=-2.0,
size_px_max=32,
upscale_method=["linear", "nearest"]),
iterations=iterations,
aggregation_method=method)
cell = noise.draw_samples(
(64, 64),
random_state=ia.new_random_state(seed + 1 + method_idx))
cell = np.tile(cell[:, :, np.newaxis], (1, 1, 3))
cell = (cell * 255).astype(np.uint8)
if iterations == 1:
cell = np.pad(cell, ((0, 0), (40, 0), (0, 0)),
mode="constant",
constant_values=255)
cell = ia.draw_text(cell,
y=27,
x=2,
text="%s" % (method, ),
size=14,
color=[0, 0, 0])
if method_idx == 0:
cell = np.pad(cell, ((20, 0), (0, 0), (0, 0)),
mode="constant",
constant_values=255)
cell = ia.draw_text(cell,
y=0,
x=12 + 40 * (iterations == 1),
text="%d iter." % (iterations, ),
size=14,
color=[0, 0, 0])
cell = np.pad(cell, ((0, 1), (0, 1), (0, 0)),
mode="constant",
constant_values=255)
row.append(cell)
cell_idx += 1
rows.append(np.hstack(row))
gridarr = np.vstack(rows)
# ------------
save("alpha", "iterative_vary_methods.jpg", gridarr)