def test__draw_samples__tuple_as_hysteresis(self):
seed = 1
nb_images = 10
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
iap.DiscreteUniform(5, 100)),
sobel_kernel_size=[3, 5, 7],
random_state=iarandom.RNG(seed))
aug.alpha = remove_prefetching(aug.alpha)
aug.hysteresis_thresholds = (
remove_prefetching(aug.hysteresis_thresholds[0]),
remove_prefetching(aug.hysteresis_thresholds[1])
)
aug.sobel_kernel_size = remove_prefetching(aug.sobel_kernel_size)
example_image = np.zeros((5, 5, 3), dtype=np.uint8)
samples = aug._draw_samples([example_image] * nb_images,
random_state=iarandom.RNG(seed))
alpha_samples = samples[0]
hthresh_samples = samples[1]
sobel_samples = samples[2]
rss = iarandom.RNG(seed).duplicate(4)
alpha_expected = iap.Deterministic(0.2).draw_samples((nb_images,),
rss[0])
hthresh_expected = [None, None]
hthresh_expected[0] = iap.Choice(
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).draw_samples((nb_images,),
rss[1])
# TODO simplify this to rss[2].randint(5, 100+1)
# would currenlty be a bit more ugly, because DiscrUniform
# samples two values for a and b first from rss[2]
hthresh_expected[1] = iap.DiscreteUniform(5, 100).draw_samples(
(nb_images,), rss[2])
hthresh_expected = np.stack(hthresh_expected, axis=-1)
sobel_expected = iap.Choice([3, 5, 7]).draw_samples((nb_images,),
rss[3])
invalid = hthresh_expected[:, 0] > hthresh_expected[:, 1]
hthresh_expected[invalid, :] = hthresh_expected[invalid, :][:, [1, 0]]
assert hthresh_expected.shape == (nb_images, 2)
assert not np.any(hthresh_expected[:, 0] > hthresh_expected[:, 1])
assert np.allclose(alpha_samples, alpha_expected)
assert np.allclose(hthresh_samples, hthresh_expected)
assert np.allclose(sobel_samples, sobel_expected)
def example_unusual_distributions():
print("Example: Unusual Distributions")
from imgaug import augmenters as iaa
from imgaug import parameters as iap
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
# Blur by a value sigma which is sampled from a uniform distribution
# of range 0.1 <= x < 3.0.
# The convenience shortcut for this is: iaa.GaussianBlur((0.1, 3.0))
blurer = iaa.GaussianBlur(iap.Uniform(0.1, 3.0))
images_aug = blurer.augment_images(images)
# Blur by a value sigma which is sampled from a normal distribution N(1.0, 0.1),
# i.e. sample a value that is usually around 1.0.
# Clip the resulting value so that it never gets below 0.1 or above 3.0.
blurer = iaa.GaussianBlur(iap.Clip(iap.Normal(1.0, 0.1), 0.1, 3.0))
images_aug = blurer.augment_images(images)
# Same again, but this time the mean of the normal distribution is not constant,
# but comes itself from a uniform distribution between 0.5 and 1.5.
blurer = iaa.GaussianBlur(
iap.Clip(iap.Normal(iap.Uniform(0.5, 1.5), 0.1), 0.1, 3.0))
images_aug = blurer.augment_images(images)
# Use for sigma one of exactly three allowed values: 0.5, 1.0 or 1.5.
blurer = iaa.GaussianBlur(iap.Choice([0.5, 1.0, 1.5]))
images_aug = blurer.augment_images(images)
# Sample sigma from a discrete uniform distribution of range 1 <= sigma <= 5,
# i.e. sigma will have any of the following values: 1, 2, 3, 4, 5.
blurer = iaa.GaussianBlur(iap.DiscreteUniform(1, 5))
images_aug = blurer.augment_images(images)
def test___init___custom_settings(self):
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=([0, 1, 2], iap.DiscreteUniform(1, 10)),
sobel_kernel_size=[3, 5],
colorizer=iaa.RandomColorsBinaryImageColorizer(
color_true=10, color_false=20)
)
assert is_parameter_instance(aug.alpha, iap.Deterministic)
assert isinstance(aug.hysteresis_thresholds, tuple)
assert is_parameter_instance(aug.sobel_kernel_size, iap.Choice)
assert isinstance(aug.colorizer, iaa.RandomColorsBinaryImageColorizer)
assert np.isclose(aug.alpha.value, 0.2)
assert len(aug.hysteresis_thresholds) == 2
assert is_parameter_instance(aug.hysteresis_thresholds[0], iap.Choice)
assert aug.hysteresis_thresholds[0].a == [0, 1, 2]
assert is_parameter_instance(aug.hysteresis_thresholds[1],
iap.DiscreteUniform)
assert np.isclose(aug.hysteresis_thresholds[1].a.value, 1)
assert np.isclose(aug.hysteresis_thresholds[1].b.value, 10)
assert is_parameter_instance(aug.sobel_kernel_size, iap.Choice)
assert aug.sobel_kernel_size.a == [3, 5]
assert is_parameter_instance(aug.colorizer.color_true,
iap.Deterministic)
assert is_parameter_instance(aug.colorizer.color_false,
iap.Deterministic)
assert aug.colorizer.color_true.value == 10
assert aug.colorizer.color_false.value == 20
def test___init___stochastic_parameters(self):
colorizer = iaa.RandomColorsBinaryImageColorizer(
color_true=iap.DiscreteUniform(0, 100),
color_false=iap.Choice([200, 201, 202]))
assert isinstance(colorizer.color_true, iap.DiscreteUniform)
assert isinstance(colorizer.color_false, iap.Choice)
assert colorizer.color_true.a.value == 0
assert colorizer.color_true.b.value == 100
assert colorizer.color_false.a[0] == 200
assert colorizer.color_false.a[1] == 201
assert colorizer.color_false.a[2] == 202
def test__draw_samples__tuple_as_hysteresis(self):
seed = 1
nb_images = 10
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=([0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10], iap.DiscreteUniform(5, 100)),
sobel_kernel_size=[3, 5, 7],
random_state=np.random.RandomState(seed))
example_image = np.zeros((5, 5, 3), dtype=np.uint8)
samples = aug._draw_samples([example_image] * nb_images,
random_state=np.random.RandomState(seed))
alpha_samples = samples[0]
hthresh_samples = samples[1]
sobel_samples = samples[2]
rss = ia.derive_random_states(np.random.RandomState(seed), 4)
alpha_expected = [0.2] * nb_images
hthresh_expected = (
rss[1].choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
size=(nb_images, )),
# TODO simplify this to rss[2].randint(5, 100+1)
# would currenlty be a bit more ugly, because DiscrUniform
# samples two values for a and b first from rss[2]
iap.DiscreteUniform(5, 100).draw_samples((nb_images, ), rss[2]))
hthresh_expected = np.stack(hthresh_expected, axis=-1)
sobel_expected = rss[3].choice([3, 5, 7], size=(nb_images, ))
invalid = hthresh_expected[:, 0] > hthresh_expected[:, 1]
hthresh_expected[invalid, :] = hthresh_expected[invalid, :][:, [1, 0]]
assert hthresh_expected.shape == (nb_images, 2)
assert not np.any(hthresh_expected[:, 0] > hthresh_expected[:, 1])
assert np.allclose(alpha_samples, alpha_expected)
assert np.allclose(hthresh_samples, hthresh_expected)
assert np.allclose(sobel_samples, sobel_expected)
def chapter_parameters_discrete():
ia.seed(1)
# -----------------------
# Binomial
# -----------------------
from imgaug import parameters as iap
params = [iap.Binomial(0.5), iap.Binomial(0.9)]
gridarr = draw_distributions_grid(params, rows=1)
save("parameters", "continuous_binomial.jpg", gridarr)
# -----------------------
# DiscreteUniform
# -----------------------
from imgaug import parameters as iap
params = [
iap.DiscreteUniform(0, 10),
iap.DiscreteUniform(-10, 10),
iap.DiscreteUniform([-10, -9, -8, -7], 10),
iap.DiscreteUniform((-10, -7), 10)
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_discreteuniform.jpg", gridarr)
# -----------------------
# Poisson
# -----------------------
from imgaug import parameters as iap
params = [
iap.Poisson(1),
iap.Poisson(2.5),
iap.Poisson((1, 2.5)),
iap.RandomSign(iap.Poisson(2.5))
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_poisson.jpg", gridarr)
def main():
image = ia.quokka(size=0.5)
kps = [ia.KeypointsOnImage(
[ia.Keypoint(x=245, y=203, vis=None, label=None), ia.Keypoint(x=365, y=195, vis=None, label=None),
ia.Keypoint(x=313, y=269, vis=None, label=None)],
shape=(image.shape[0]*2, image.shape[1]*2)
)]
kps[0] = kps[0].on(image.shape)
print("image shape:", image.shape)
augs = [
iaa.CropAndPad(px=50, name="pad-by-50px"),
iaa.CropAndPad(px=(10, 20, 30, 40), name="pad-by-10-20-30-40px"),
iaa.CropAndPad(percent=0.1, name="pad-by-01percent"),
iaa.CropAndPad(percent=(0.01, 0.02, 0.03, 0.04), name="pad-by-001-002-003-004percent"),
iaa.CropAndPad(px=-20, name="crop-by-20px"),
iaa.CropAndPad(px=(-10, -20, -30, -40), name="crop-by-10-20-30-40px"),
iaa.CropAndPad(percent=-0.1, name="crop-by-01percent"),
iaa.CropAndPad(percent=(-0.01, -0.02, -0.03, -0.04), name="crop-by-001-002-003-004percent")
]
augs_many = [
iaa.Crop(px=(0, 50), name="native-crop-0-to-50px"),
iaa.Crop(px=iap.DiscreteUniform(0, 50), name="native-crop-0-to-50px-iap"),
iaa.Pad(px=(0, 50), pad_mode="linear_ramp", pad_cval=(0, 255), name="native-pad-0-to-50px-pad-modes"),
iaa.CropAndPad(px=(0, 50), sample_independently=False, name="pad-by-0-to-50px-same"),
iaa.CropAndPad(px=(0, 50), name="pad-by-0-to-50px"),
iaa.CropAndPad(px=(0, 50), pad_mode=ia.ALL, pad_cval=(0, 255), name="pad-by-0-to-50px-random-pad-modes-cvals"),
iaa.CropAndPad(px=((0, 50), (0, 50), (0, 50), (0, 50)), name="pad-by-0-to-50px-each"),
iaa.CropAndPad(percent=(0, 0.1), sample_independently=False, name="pad-by-0-to-01percent-same"),
iaa.CropAndPad(percent=(0, 0.1), name="pad-by-0-to-01percent"),
iaa.CropAndPad(percent=(0, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255),
name="pad-by-0-to-01percent-random-pad-modes-cvals"),
iaa.CropAndPad(percent=((0, 0.1), (0, 0.1), (0, 0.1), (0, 0.1)), name="pad-by-0-to-01percent-each"),
iaa.CropAndPad(px=(-50, 0), name="crop-by-50-to-0px"),
iaa.CropAndPad(px=((-50, 0), (-50, 0), (-50, 0), (-50, 0)), name="crop-by-50-to-0px-each"),
iaa.CropAndPad(percent=(-0.1, 0), name="crop-by-01-to-0percent"),
iaa.CropAndPad(percent=((-0.1, 0), (-0.1, 0), (-0.1, 0), (-0.1, 0)), name="crop-by-01-to-0percent-each"),
iaa.CropAndPad(px=(-50, 50), name="pad-and-crop-by-50px")
]
print("original", image.shape)
ia.imshow(kps[0].draw_on_image(image))
print("-----------------")
print("Same aug per image")
print("-----------------")
for aug in augs:
img_aug = aug.augment_image(image)
kps_aug = aug.augment_keypoints(kps)[0]
img_aug_kps = kps_aug.draw_on_image(img_aug)
print(aug.name, img_aug_kps.shape, img_aug_kps.shape[1]/img_aug_kps.shape[0])
ia.imshow(img_aug_kps)
print("-----------------")
print("Random aug per image")
print("-----------------")
for aug in augs_many:
images_aug = []
for _ in range(64):
aug_det = aug.to_deterministic()
img_aug = aug_det.augment_image(image)
kps_aug = aug_det.augment_keypoints(kps)[0]
img_aug_kps = kps_aug.draw_on_image(img_aug)
img_aug_kps = np.pad(img_aug_kps, ((1, 1), (1, 1), (0, 0)), mode="constant", constant_values=255)
images_aug.append(img_aug_kps)
print(aug.name)
ia.imshow(ia.draw_grid(images_aug))
