Python源码示例：imgaug.augmenters.Add()
示例1
def example_withchannels():
print("Example: WithChannels")
import numpy as np
import imgaug.augmenters as iaa
# fake RGB images
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
# add a random value from the range (-30, 30) to the first two channels of
# input images (e.g. to the R and G channels)
aug = iaa.WithChannels(
channels=[0, 1],
children=iaa.Add((-30, 30))
)
images_aug = aug(images=images)
示例2
def main():
image = data.astronaut()
print("image shape:", image.shape)
aug = iaa.WithColorspace(
from_colorspace="RGB",
to_colorspace="HSV",
children=iaa.WithChannels(0, iaa.Add(50))
)
aug_no_colorspace = iaa.WithChannels(0, iaa.Add(50))
img_show = np.hstack([
image,
aug.augment_image(image),
aug_no_colorspace.augment_image(image)
])
ia.imshow(img_show)
示例3
def test_images_factor_is_tuple(self):
image = np.zeros((1, 2, 1), dtype=np.uint8)
nb_iterations = 1000
aug = iaa.BlendAlpha((0.0, 1.0), iaa.Add(10), iaa.Add(110))
values = []
for _ in sm.xrange(nb_iterations):
observed = aug.augment_image(image)
observed_val = np.round(np.average(observed)) - 10
values.append(observed_val / 100)
nb_bins = 5
hist, _ = np.histogram(values, bins=nb_bins, range=(0.0, 1.0),
density=False)
density_expected = 1.0/nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / nb_iterations
assert np.isclose(density, density_expected,
rtol=0, atol=density_tolerance)
示例4
def test_images_float_as_per_channel_tuple_as_factor_two_branches(self):
aug = iaa.BlendAlpha(
(0.0, 1.0),
iaa.Add(100),
iaa.Add(0),
per_channel=0.5)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_image(np.zeros((1, 1, 100), dtype=np.uint8))
uq = np.unique(observed)
if len(uq) == 1:
seen[0] += 1
elif len(uq) > 1:
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
示例5
def test_unusual_channel_numbers(self):
shapes = [
(1, 1, 4),
(1, 1, 5),
(1, 1, 512),
(1, 1, 513)
]
for shape in shapes:
with self.subTest(shape=shape):
image = np.full(shape, 0, dtype=np.uint8)
aug = iaa.BlendAlpha(1.0, iaa.Add(1), iaa.Add(100))
image_aug = aug(image=image)
assert np.all(image_aug == 1)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == shape
示例6
def test_hooks_limiting_propagation(self):
aug = iaa.BlendAlphaElementwise(
0.5,
iaa.Add(100),
iaa.Add(50),
name="AlphaElementwiseTest")
def propagator(images, augmenter, parents, default):
if "AlphaElementwise" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 10
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
示例7
def test_zero_sized_axes(self):
shapes = [
(0, 0),
(0, 1),
(1, 0),
(0, 1, 0),
(1, 0, 0),
(0, 1, 1),
(1, 0, 1)
]
for shape in shapes:
with self.subTest(shape=shape):
image = np.full(shape, 0, dtype=np.uint8)
aug = iaa.BlendAlpha(1.0, iaa.Add(1), iaa.Add(100))
image_aug = aug(image=image)
assert np.all(image_aug == 1)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == shape
示例8
def test_unusual_channel_numbers(self):
shapes = [
(1, 1, 4),
(1, 1, 5),
(1, 1, 512),
(1, 1, 513)
]
for shape in shapes:
with self.subTest(shape=shape):
image = np.full(shape, 0, dtype=np.uint8)
aug = iaa.BlendAlpha(1.0, iaa.Add(1), iaa.Add(100))
image_aug = aug(image=image)
assert np.all(image_aug == 1)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == shape
示例9
def test_pickleable(self):
shape = (15, 15, 3)
iterations = 3
augmenter = iaa.BlendAlphaBoundingBoxes(
["bb1", "bb2", "bb3"],
foreground=iaa.Add((1, 10), seed=1),
background=iaa.Add((11, 20), seed=2),
nb_sample_labels=1,
seed=3)
image = np.mod(np.arange(int(np.prod(shape))), 256).astype(np.uint8)
image = image.reshape(shape)
bbs = [ia.BoundingBox(x1=1, y1=1, x2=5, y2=5, label="bb1"),
ia.BoundingBox(x1=-3, y1=4, x2=20, y2=8, label="bb2")]
augmenter_pkl = pickle.loads(pickle.dumps(augmenter, protocol=-1))
for _ in np.arange(iterations):
image_aug, bbs_aug = augmenter(
image=image, bounding_boxes=[bbs])
image_aug_pkl, bbs_aug_pkl = augmenter_pkl(
image=image, bounding_boxes=[bbs])
assert np.array_equal(image_aug, image_aug_pkl)
示例10
def test_n(self, mock_main, mock_initial):
mock_main.return_value = [iaa.Add(1), iaa.Add(2), iaa.Add(4)]
mock_initial.return_value = []
img = np.zeros((1, 1, 3), dtype=np.uint8)
expected = {
0: [0],
1: [1, 2, 4],
2: [1+1, 1+2, 1+4, 2+2, 2+4, 4+4]
}
for n in [0, 1, 2]:
with self.subTest(n=n):
aug = iaa.RandAugment(n=n)
img_aug = aug(image=img)
assert img_aug[0, 0, 0] in expected[n]
# for some reason these mocks don't work with
# imgaug.augmenters.collections.(...)
示例11
def __init__(self):
self.seq = iaa.Sequential([
iaa.Sometimes(0.5, iaa.OneOf([
iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
])),
iaa.Sometimes(0.5, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5)),
iaa.Sometimes(0.5, iaa.Add((-10, 10), per_channel=0.5)),
iaa.Sometimes(0.5, iaa.AddToHueAndSaturation((-20, 20))),
iaa.Sometimes(0.5, iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.5, 1.5), per_channel=True),
second=iaa.LinearContrast((0.5, 2.0))
)),
iaa.Sometimes(0.5, iaa.PiecewiseAffine(scale=(0.01, 0.05))),
iaa.Sometimes(0.5, iaa.PerspectiveTransform(scale=(0.01, 0.1)))
], random_order=True)
示例12
def __init__(self, augmentation_rate):
self.augs = iaa.Sometimes(
augmentation_rate,
iaa.SomeOf(
(4, 7),
[
iaa.Affine(rotate=(-10, 10)),
iaa.Fliplr(0.2),
iaa.AverageBlur(k=(2, 10)),
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.75, 1.25), per_channel=0.5),
iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5),
iaa.Crop(px=(0, 20))
],
random_order=True
)
)
示例13
def main():
image = data.astronaut()
print("image shape:", image.shape)
aug = iaa.WithColorspace(
from_colorspace="RGB",
to_colorspace="HSV",
children=iaa.WithChannels(0, iaa.Add(50))
)
aug_no_colorspace = iaa.WithChannels(0, iaa.Add(50))
img_show = np.hstack([
image,
aug.augment_image(image),
aug_no_colorspace.augment_image(image)
])
misc.imshow(img_show)
示例14
def example_withchannels():
print("Example: WithChannels")
from imgaug import augmenters as iaa
import numpy as np
# fake RGB images
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
# add a random value from the range (-30, 30) to the first two channels of
# input images (e.g. to the R and G channels)
aug = iaa.WithChannels(
channels=[0, 1],
children=iaa.Add((-30, 30))
)
images_aug = aug.augment_images(images)
示例15
def __init__(self):
self.imgaug_transform = iaa.Sequential(
[iaa.Multiply((1.5, 1.5), per_channel=False), iaa.Add((127, 127), per_channel=False)]
)
self.augmentor_pipeline = Pipeline()
self.augmentor_pipeline.add_operation(
Operations.RandomBrightness(probability=1, min_factor=1.5, max_factor=1.5)
)
self.augmentor_pipeline.add_operation(Operations.RandomContrast(probability=1, min_factor=1.5, max_factor=1.5))
self.solt_stream = slc.Stream(
[
slt.ImageRandomBrightness(p=1, brightness_range=(127, 127)),
slt.ImageRandomContrast(p=1, contrast_range=(1.5, 1.5)),
]
)
示例16
def __init__(self,data_dir, back_dir,
batch_size=50,gan=True,imsize=128,
res_x=640,res_y=480,
**kwargs):
'''
data_dir: Folder that contains cropped image+xyz
back_dir: Folder that contains random background images
batch_size: batch size for training
gan: if False, gt for GAN is not yielded
'''
self.data_dir = data_dir
self.back_dir = back_dir
self.imsize=imsize
self.batch_size = batch_size
self.gan = gan
self.backfiles = os.listdir(back_dir)
data_list = os.listdir(data_dir)
self.datafiles=[]
self.res_x=res_x
self.res_y=res_y
for file in data_list:
if(file.endswith(".npy")):
self.datafiles.append(file)
self.n_data = len(self.datafiles)
self.n_background = len(self.backfiles)
print("Total training views:", self.n_data)
self.seq_syn= iaa.Sequential([
iaa.WithChannels(0, iaa.Add((-15, 15))),
iaa.WithChannels(1, iaa.Add((-15, 15))),
iaa.WithChannels(2, iaa.Add((-15, 15))),
iaa.ContrastNormalization((0.8, 1.3)),
iaa.Multiply((0.8, 1.2),per_channel=0.5),
iaa.GaussianBlur(sigma=(0.0, 0.5)),
iaa.Sometimes(0.1, iaa.AdditiveGaussianNoise(scale=10, per_channel=True)),
iaa.Sometimes(0.5, iaa.ContrastNormalization((0.5, 2.2), per_channel=0.3)),
], random_order=True)
示例17
def main():
image = ia.quokka_square(size=(128, 128))
images = []
for i in range(15):
aug = iaa.WithHueAndSaturation(iaa.WithChannels(0, iaa.Add(i*20)))
images.append(aug.augment_image(image))
for i in range(15):
aug = iaa.WithHueAndSaturation(iaa.WithChannels(1, iaa.Add(i*20)))
images.append(aug.augment_image(image))
ia.imshow(ia.draw_grid(images, rows=2))
示例18
def main():
image = data.astronaut()
print("image shape:", image.shape)
print("Press ENTER or wait %d ms to proceed to the next image." % (TIME_PER_STEP,))
children_all = [
("hflip", iaa.Fliplr(1)),
("add", iaa.Add(50)),
("dropout", iaa.Dropout(0.2)),
("affine", iaa.Affine(rotate=35))
]
channels_all = [
None,
0,
[],
[0],
[0, 1],
[1, 2],
[0, 1, 2]
]
cv2.namedWindow("aug", cv2.WINDOW_NORMAL)
cv2.imshow("aug", image[..., ::-1])
cv2.waitKey(TIME_PER_STEP)
for children_title, children in children_all:
for channels in channels_all:
aug = iaa.WithChannels(channels=channels, children=children)
img_aug = aug.augment_image(image)
print("dtype", img_aug.dtype, "averages", np.average(img_aug, axis=tuple(range(0, img_aug.ndim-1))))
title = "children=%s " channels=%s" % (children_title, channels)
img_aug = ia.draw_text(img_aug, x=5, y=5, text=title)
cv2.imshow("aug", img_aug[..., ::-1]) # here with rgb2bgr
cv2.waitKey(TIME_PER_STEP)
示例19
def test_images_factor_is_0(self):
aug = iaa.BlendAlpha(0, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(self.image)
expected = np.round(self.image + 20).astype(np.uint8)
assert np.allclose(observed, expected)
示例20
def test_images_factor_is_075(self):
aug = iaa.BlendAlpha(0.75, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(self.image)
expected = np.round(
self.image
+ 0.75 * 10
+ 0.25 * 20
).astype(np.uint8)
assert np.allclose(observed, expected)
示例21
def test_images_factor_is_075_fg_branch_is_none(self):
aug = iaa.BlendAlpha(0.75, None, iaa.Add(20))
observed = aug.augment_image(self.image + 10)
expected = np.round(
self.image
+ 0.75 * 10
+ 0.25 * (10 + 20)
).astype(np.uint8)
assert np.allclose(observed, expected)
示例22
def test_images_factor_is_075_bg_branch_is_none(self):
aug = iaa.BlendAlpha(0.75, iaa.Add(10), None)
observed = aug.augment_image(self.image + 10)
expected = np.round(
self.image
+ 0.75 * (10 + 10)
+ 0.25 * 10
).astype(np.uint8)
assert np.allclose(observed, expected)
示例23
def test_images_with_per_channel_in_both_alpha_and_child(self):
image = np.zeros((1, 1, 1000), dtype=np.uint8)
aug = iaa.BlendAlpha(
1.0,
iaa.Add((0, 100), per_channel=True),
None,
per_channel=True)
observed = aug.augment_image(image)
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
示例24
def test_images_with_per_channel_in_alpha_and_tuple_as_factor(self):
image = np.zeros((1, 1, 1000), dtype=np.uint8)
aug = iaa.BlendAlpha(
(0.0, 1.0),
iaa.Add(100),
None,
per_channel=True)
observed = aug.augment_image(image)
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
示例25
def test_bad_datatype_for_per_channel_fails(self):
# bad datatype for per_channel
got_exception = False
try:
_ = iaa.BlendAlpha(0.5, iaa.Add(10), None, per_channel="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
示例26
def test_hooks_limiting_propagation(self):
aug = iaa.BlendAlpha(0.5, iaa.Add(100), iaa.Add(50), name="AlphaTest")
def propagator(images, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 1
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
示例27
def test_get_parameters(self):
fg = iaa.Identity()
bg = iaa.Sequential([iaa.Add(1)])
aug = iaa.BlendAlpha(0.65, fg, bg, per_channel=1)
params = aug.get_parameters()
assert params[0] is aug.factor
assert params[1] is aug.per_channel
assert 0.65 - 1e-6 < params[0].value < 0.65 + 1e-6
assert params[1].value == 1
示例28
def test_get_children_lists(self):
fg = iaa.Identity()
bg = iaa.Sequential([iaa.Add(1)])
aug = iaa.BlendAlpha(0.65, fg, bg, per_channel=1)
children_lsts = aug.get_children_lists()
assert len(children_lsts) == 2
assert ia.is_iterable([lst for lst in children_lsts])
assert fg in children_lsts[0]
assert bg == children_lsts[1]
示例29
def test_pickleable(self):
aug = iaa.BlendAlpha(
(0.1, 0.9),
iaa.Add((1, 10), seed=1),
iaa.Add((11, 20), seed=2),
per_channel=True,
seed=3)
runtest_pickleable_uint8_img(aug, iterations=10)
示例30
def test_images_factor_is_1(self):
aug = iaa.BlendAlphaElementwise(1, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(self.image)
expected = self.image + 10
assert np.allclose(observed, expected)
