Python源码示例：imgaug.augmenters.SimplexNoiseAlpha()
示例1
def test_deprecation_warning(self):
aug1 = iaa.Sequential([])
aug2 = iaa.Sequential([])
with warnings.catch_warnings(record=True) as caught_warnings:
warnings.simplefilter("always")
aug = iaa.SimplexNoiseAlpha(first=aug1, second=aug2)
assert (
"is deprecated"
in str(caught_warnings[-1].message)
)
assert isinstance(aug, iaa.BlendAlphaSimplexNoise)
assert aug.foreground is aug1
assert aug.background is aug2
示例2
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)
ia.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)
ia.imshow(images_aug)
示例3
def get_augmentations():
# applies the given augmenter in 50% of all cases,
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
# Define our sequence of augmentation steps that will be applied to every image
seq = iaa.Sequential([
# execute 0 to 5 of the following (less important) augmenters per image
iaa.SomeOf((0, 5),
[
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 11)),
]),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
# search either for all edges or for directed edges,
# blend the result with the original image using a blobby mask
iaa.SimplexNoiseAlpha(iaa.OneOf([
iaa.EdgeDetect(alpha=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)),
])),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),
iaa.OneOf([
iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
]),
iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation
# either change the brightness of the whole image (sometimes
# per channel) or change the brightness of subareas
iaa.OneOf([
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.5, 1.5), per_channel=True),
second=iaa.ContrastNormalization((0.5, 2.0))
)
]),
iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
],
random_order=True
)
],
random_order=True
)
return seq
### data transforms
示例4
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)
