def augment():
with open(CONFIG, "r") as file:
config = json.loads(file.read())
if config[ImageAugmentation.AUGMENT_DATA]:
matrix = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
return iaa.Sometimes(ImageAugmentation.AUG_PERCENTAGE, [
iaa.GaussianBlur(sigma=2.0),
iaa.Sequential([iaa.Affine(rotate=45),
iaa.Sharpen(alpha=1.0)]),
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
0, iaa.Add((10, 50)))),
iaa.AdditiveGaussianNoise(scale=0.2 * 255),
iaa.Add(50, per_channel=True),
iaa.Sharpen(alpha=0.5),
iaa.WithChannels(0, iaa.Add((10, 100))),
iaa.WithChannels(0, iaa.Affine(rotate=(0, 45))),
iaa.Noop(),
iaa.Superpixels(p_replace=0.5, n_segments=64),
iaa.Superpixels(p_replace=(0.1, 1.0), n_segments=(16, 128)),
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((50, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.AverageBlur(k=(2, 11)),
iaa.AverageBlur(k=((5, 11), (1, 3))),
iaa.MedianBlur(k=(3, 11)),
iaa.Convolve(matrix=matrix),
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)),
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)),
iaa.EdgeDetect(alpha=(0.0, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.0, 1.0), direction=(0.0, 1.0)),
iaa.Add((-40, 40)),
iaa.Add((-40, 40), per_channel=0.5),
iaa.AddElementwise((-40, 40)),
iaa.AddElementwise((-40, 40), per_channel=0.5),
iaa.AdditiveGaussianNoise(scale=(0, 0.05 * 255)),
iaa.AdditiveGaussianNoise(scale=0.05 * 255),
iaa.AdditiveGaussianNoise(scale=0.05 * 255, per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.Dropout(p=(0, 0.2)),
iaa.Dropout(p=(0, 0.2), per_channel=0.5),
iaa.CoarseDropout(0.02, size_percent=0.5),
iaa.CoarseDropout((0.0, 0.05), size_percent=(0.02, 0.25)),
iaa.CoarseDropout(0.02, size_percent=0.15, per_channel=0.5),
iaa.Invert(0.25, per_channel=0.5),
iaa.Invert(0.5),
iaa.ContrastNormalization((0.5, 1.5)),
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
iaa.ElasticTransformation(alpha=(0, 5.0), sigma=0.25)
])
else:
return None
def __init__(self, ratio=0.5):
self.ratio = ratio
self.noise_list = [
iaa.AddElementwise((-40, 40)),
iaa.AddElementwise((-40, 40), per_channel=0.5),
iaa.AdditiveGaussianNoise(scale=(0, 0.05 * 255)),
iaa.Dropout(p=(0, 0.2)),
iaa.Dropout(p=(0, 0.2), per_channel=0.5),
iaa.CoarseDropout(0.02, size_percent=0.5),
iaa.CoarseDropout((0.0, 0.05), size_percent=(0.02, 0.25)),
iaa.CoarseDropout(0.02, size_percent=0.15, per_channel=0.5)
]
def chapter_augmenters_addelementwise():
aug = iaa.AddElementwise((-40, 40))
run_and_save_augseq("addelementwise.jpg",
aug, [ia.quokka(size=(512, 512)) for _ in range(1)],
cols=1,
rows=1,
quality=90)
aug = iaa.AddElementwise((-40, 40), per_channel=0.5)
run_and_save_augseq("addelementwise_per_channel.jpg",
aug, [ia.quokka(size=(512, 512)) for _ in range(1)],
cols=1,
rows=1,
quality=90)
def f13():
'''
Add random values between -20 and 20 to images, with each value being sampled per pixel
'''
aug = iaa.AddElementwise((-20, 20))
desc = 'Noise'
return aug, desc
def augmentation_sequence(params):
if params is None:
params = dicto.load_("params.yml")
n_augmenters = params.data_augmentation.n_augmenters
return iaa.SomeOf(n_augmenters, [
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.AverageBlur(k=(2, 9)),
iaa.MedianBlur(k=(3, 9)),
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)),
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)),
iaa.Add((-40, 40), per_channel=0.5),
iaa.AddElementwise((-40, 40), per_channel=0.5),
iaa.AdditiveGaussianNoise(scale=0.05 * 255, per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.MultiplyElementwise((0.5, 1.5), per_channel=0.5),
iaa.Dropout(p=(0, 0.2), per_channel=0.5),
iaa.CoarseDropout(0.05, size_percent=0.1),
iaa.Invert(1.0, per_channel=0.5),
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
iaa.ElasticTransformation(alpha=(0, 5.0), sigma=0.25),
iaa.PiecewiseAffine(scale=(0.01, 0.05)),
])
def f14():
'''
Add random values between -20 and 20 to images, with each value being sampled per pixel
'''
aug = iaa.AddElementwise((-20, 20), per_channel=0.5)
desc = 'ColorNoise'
return aug, desc
def __init__(self,
blur=True,
flip=False,
rotate=10,
shear=10,
**kwargs):
from imgaug import augmenters as iaa
sequence = []
sequence += [
iaa.Scale((INPUT_SIZE, INPUT_SIZE)),
iaa.ContrastNormalization((0.75, 1.25)),
iaa.AddElementwise((-10, 10), per_channel=0.5),
iaa.AddToHueAndSaturation(value=(-20, 20),
per_channel=True),
iaa.Multiply((0.75, 1.25)),
]
sequence += [
iaa.PiecewiseAffine(scale=(0.0005, 0.005)),
iaa.Affine(rotate=(-rotate, rotate),
shear=(-shear, shear),
mode='symmetric'),
iaa.Grayscale(alpha=(0.0, 0.5)),
]
if flip:
sequence += [
iaa.Fliplr(0.5),
]
if blur:
sequence += [
iaa.Sometimes(0.01, iaa.GaussianBlur(sigma=(0, 1.0))),
]
self.aug = iaa.Sequential(sequence)
def data_aug(images):
seq = iaa.Sometimes(
0.5, iaa.Identity(),
iaa.Sometimes(
0.5,
iaa.Sequential([
iaa.Sometimes(
0.5,
iaa.OneOf([
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
iaa.AdditiveLaplaceNoise(scale=(0, 0.1 * 255)),
iaa.ReplaceElementwise(0.03, [0, 255]),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.BilateralBlur(d=(3, 10),
sigma_color=(10, 250),
sigma_space=(10, 250))
])),
iaa.OneOf([
iaa.Add((-40, 40)),
iaa.AddElementwise((-20, 20)),
iaa.pillike.EnhanceBrightness()
]),
iaa.OneOf([
iaa.GammaContrast((0.2, 2.0)),
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)),
iaa.LogContrast(gain=(0.6, 1.4)),
iaa.AllChannelsCLAHE(),
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)),
])
])))
images = seq(images=images)
return images
def noise():
return iaa.OneOf([
iaa.Add((-10, 10), per_channel=0.5),
iaa.AddElementwise((-10, 10), per_channel=0.5),
iaa.AdditiveGaussianNoise(scale=0.1 * 255, per_channel=0.5),
iaa.AdditiveLaplaceNoise(scale=0.1 * 255, per_channel=0.5),
iaa.AdditivePoissonNoise(lam=2, per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5)
])
def add_element_noise(origImage, value):
'''
Add values to the pixels of images with possibly different values for neighbouring pixels.
origImage : Array of image pixels as numpy array
value : Is a postive integer than is used as the upper bound to a random value generator for noise
Returns: Augmented image array with added pixel noise
'''
return (iaa.AddElementwise((-value, value)), value)
def augmentor(self, images, targets):
'''Augments each batch of data with random transformations'''
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
seq = iaa.Sequential([
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
sometimes(
iaa.SomeOf((0, 2), [
iaa.Affine(translate_percent={
"x": (-0.1, 0.1),
"y": (-0.1, 0.1)
},
rotate=(-25, 25),
name="Affine"),
iaa.ElasticTransformation(alpha=(0.01, 0.1),
sigma=0.15,
name="ElasticTransformation"),
iaa.PiecewiseAffine(scale=(0.001, 0.03),
name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
name="PerspectiveTransform"),
],
random_order=True)),
sometimes(
iaa.OneOf([
iaa.GaussianBlur(sigma=(0, 0.2)),
iaa.AverageBlur(k=3),
iaa.MedianBlur(k=3),
])),
sometimes(
iaa.OneOf([
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.01 * 255)),
iaa.AddElementwise((-5, 5)),
])),
sometimes(
iaa.OneOf([
iaa.GammaContrast(gamma=(0.75, 1.50)),
iaa.HistogramEqualization(),
iaa.Multiply((0.80, 1.15)),
iaa.Add((-20, 15)),
iaa.Sharpen(alpha=(0, 0.5), lightness=(0.7, 1.5)),
iaa.Emboss(alpha=(0, 0.5), strength=(0.7, 1.5)),
])),
],
random_order=True)
seq_det = seq.to_deterministic()
images = seq_det.augment_images(images)
targets = seq_det.augment_segmentation_maps([
ia.SegmentationMapOnImage(t.astype(bool), shape=t.shape)
for t in targets
])
targets = np.array([t.get_arr_int() for t in targets])
return images, targets
def __init__(self):
self._add_augmentation_list = [
iaa.Add((-30, 30), per_channel=True),
iaa.Add((-30, 30), per_channel=False),
iaa.AddElementwise((-30, 30), per_channel=False),
iaa.AddElementwise((-30, 30), per_channel=True),
iaa.Invert(p=0.2, per_channel=True),
iaa.Invert(p=0.2, per_channel=False),
iaa.AddToHueAndSaturation((0, 80), True),
iaa.Multiply((0.8, 1.2), per_channel=True),
iaa.Multiply((0.8, 1.2), per_channel=False),
iaa.MultiplyElementwise((0.8, 1.2), per_channel=True),
iaa.MultiplyElementwise((0.8, 1.2), per_channel=False)
]
self._blur_augmentation_list = [
iaa.GaussianBlur((2, 3)),
iaa.AverageBlur((2, 3)),
iaa.MedianBlur((3, 5)),
iaa.BilateralBlur((2, 3))
]
self._noise_augmentation_list = [
iaa.AdditiveGaussianNoise(0, (5, 20), per_channel=True),
iaa.AdditiveGaussianNoise(0, (5, 20), per_channel=False),
iaa.Dropout((0.05, 0.15), per_channel=False),
iaa.Dropout((0.05, 0.15), per_channel=True),
iaa.CoarseDropout((0.05, 0.15), size_percent=(0.65, 0.85))
]
self._other_augmentation_list = [
iaa.Sharpen((0.9, 0.11), (0.8, 1.2)),
iaa.Emboss((0.9, 0.11), (0.3, 1.6)),
iaa.EdgeDetect((0, 0.4)),
iaa.Grayscale((0, 1))
]
self.noise_list = self._add_augmentation_list +\
self._blur_augmentation_list +\
self._noise_augmentation_list +\
self._other_augmentation_list
def test_augmentations_with_seed_match_for_images_and_keypoints(self):
augseq = iaa.AddElementwise((0, 255))
image = np.zeros((10, 10, 1), dtype=np.uint8)
# keypoints here will not be changed by augseq, but they will induce
# deterministic mode to start in augment_batches() as each batch
# contains images AND keypoints
kps = ia.KeypointsOnImage([ia.Keypoint(x=2, y=0)], shape=(10, 10, 1))
batch = ia.Batch(images=np.uint8([image, image]), keypoints=[kps, kps])
batches = [batch.deepcopy() for _ in sm.xrange(60)]
# seed=1
with multicore.Pool(augseq, processes=2, maxtasksperchild=30,
seed=1) as pool:
batches_aug1 = pool.map_batches(batches, chunksize=2)
# seed=1
with multicore.Pool(augseq, processes=2, seed=1) as pool:
batches_aug2 = pool.map_batches(batches, chunksize=1)
# seed=2
with multicore.Pool(augseq, processes=2, seed=2) as pool:
batches_aug3 = pool.map_batches(batches, chunksize=1)
assert len(batches_aug1) == 60
assert len(batches_aug2) == 60
assert len(batches_aug3) == 60
for batches_aug in [batches_aug1, batches_aug2, batches_aug3]:
for batch in batches_aug:
for keypoints_aug in batch.keypoints_aug:
assert keypoints_aug.keypoints[0].x == 2
assert keypoints_aug.keypoints[0].y == 0
for b1, b2, b3 in zip(batches_aug1, batches_aug2, batches_aug3):
# images were augmented
assert not np.array_equal(b1.images_unaug, b1.images_aug)
assert not np.array_equal(b2.images_unaug, b2.images_aug)
assert not np.array_equal(b3.images_unaug, b3.images_aug)
# original images still the same
assert np.array_equal(b1.images_unaug, batch.images_unaug)
assert np.array_equal(b2.images_unaug, batch.images_unaug)
assert np.array_equal(b3.images_unaug, batch.images_unaug)
# augmentations for same seed are the same
assert np.array_equal(b1.images_aug, b2.images_aug)
# augmentations for different seeds are different
assert not np.array_equal(b1.images_aug, b3.images_aug)
# make sure that batches for the two pools with same seed did not
# repeat within results (only between the results of the two pools)
for batches_aug in [batches_aug1, batches_aug2, batches_aug3]:
self._assert_each_augmentation_not_more_than_once(batches_aug)
def __init__(self):
from imgaug import augmenters as iaa
self.aug = iaa.Sequential([
iaa.Scale((INPUT_SIZE, INPUT_SIZE)),
iaa.ContrastNormalization((0.75, 1.25)),
iaa.AddElementwise((-20, 20), per_channel=0.5),
iaa.AddToHueAndSaturation(value=(-5, 5), per_channel=True),
iaa.Multiply((0.75, 1.25)),
# iaa.Dropout(p=(0.0, 0.1)),
iaa.PiecewiseAffine(scale=(0.0001, 0.0005)),
iaa.Affine(rotate=(-1, 1), shear=(-1, 1), mode='symmetric'),
iaa.Grayscale(alpha=(0.0, 0.25)),
])
def create_image_only_augmenter():
aug = iaa.SomeOf((0, None), [
iaa.Noop(),
iaa.GaussianBlur(sigma=(0.0, 1.0)),
iaa.AdditiveGaussianNoise(scale=(0, 0.02 * 255)),
iaa.AdditiveGaussianNoise(scale=0.02 * 255, per_channel=0.5),
iaa.MultiplyElementwise((0.75, 1.25)),
iaa.Dropout(p=(0, 0.1)),
iaa.Add((-20, 20)),
iaa.AddElementwise((-20, 20)),
iaa.ContrastNormalization((0.75, 1.25))
])
return aug
def get_augmenter():
seq = iaa.Sequential([
iaa.SomeOf((1, 4), [
iaa.Fliplr(0.5),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.MotionBlur(angle=(0, 360)),
iaa.Add((-20, 20)),
iaa.AddElementwise((-10, 10)),
iaa.AdditiveGaussianNoise(scale=0.01*255),
iaa.Multiply((0.8, 1.2)),
])
])
return seq
def test_augmentations_without_seed_differ(self):
augseq = iaa.AddElementwise((0, 255))
image = np.zeros((10, 10, 1), dtype=np.uint8)
batch = ia.Batch(images=np.uint8([image, image]))
batches = [batch.deepcopy() for _ in sm.xrange(20)]
with multicore.Pool(augseq, processes=2, maxtasksperchild=5) as pool:
batches_aug = pool.map_batches(batches, chunksize=2)
with multicore.Pool(augseq, processes=2) as pool:
batches_aug.extend(pool.map_batches(batches, chunksize=1))
assert len(batches_aug) == 2 * 20
self._assert_each_augmentation_not_more_than_once(batches_aug)
def DA_AddElementwise(inputimg):
assert inputimg.ndim in [2, 3], "input invalid! Please check input"
values = np.arange(-50,51,10)
p_channels = np.arange(0.01,1.01,0.2)
ret = []
for i in np.arange(len(p_channels)):
for j in np.arange(len(values)):
Name = "AddElementwise_" + str(j) + "_p_channels_" + str(p_channels[i])
VALUE = str(values[i]) + "_" + str(p_channels[j])
aug_img = iaa.AddElementwise(value=(-50, 50), per_channel=p_channels[i]).augment_image(inputimg)
ret.append((Name, VALUE, aug_img))
assert len(ret) == 66, "DA_AddElementwise output size not match!"
return ret
def get_transforms():
sometimes = lambda aug: iaa.Sometimes(0.2, aug)
seq1 = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
sometimes(
iaa.Affine(
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
},
rotate=(-30, 30),
shear=(-10, 10),
mode='constant',
cval=(0, 255),
)),
sometimes(
iaa.PiecewiseAffine(
scale=(0.01, 0.05),
nb_cols=8,
nb_rows=8,
mode='constant',
cval=(0, 255),
)),
], )
seq2 = iaa.Sequential([
iaa.SomeOf((0, 1), [
sometimes(iaa.MultiplyElementwise((0.8, 1.2))),
sometimes(iaa.AddElementwise((-20, 20))),
sometimes(iaa.ContrastNormalization((0.8, 1.2))),
]),
iaa.SomeOf((0, 1), [
iaa.OneOf([
iaa.GaussianBlur((0, 2.0)),
iaa.AverageBlur(k=2),
iaa.MedianBlur(k=3),
]),
iaa.AdditiveGaussianNoise(0, 10),
iaa.SaltAndPepper(0.01),
iaa.ReplaceElementwise(0.05, (0, 255))
]),
], )
return seq1, seq2
def aug_lv3(images):
seq = iaa.Sequential(
[
# Blur
iaa.OneOf([
iaa.GaussianBlur(sigma=(0, 0.5)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur((3, 11))
]),
iaa.OneOf([
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
iaa.Dropout((0.01, 0.1), per_channel=0.5),
iaa.CoarseDropout(
(0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
]),
iaa.OneOf([
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)),
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)),
# iaa.EdgeDetect(alpha=(0.0 , 0.2))
]),
iaa.SomeOf(2, [
iaa.Add((-40, 40), per_channel=0.5),
iaa.AddElementwise((-40, 40), per_channel=0.5),
iaa.Multiply((0.5, 1.5)),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
],
random_order=True),
iaa.SomeOf((0, None), [
iaa.OneOf([
iaa.ContrastNormalization((0.5, 1.5)),
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
]),
]),
iaa.Affine(scale=(0.8, 1.2),
translate_percent={
"x": (-0.1, 0.1),
"y": (-0.1, 0.1)
},
rotate=(-30, 30)),
iaa.OneOf([
iaa.Affine(shear=(0.01, 0.05)),
iaa.PiecewiseAffine((0.01, 0.05))
]),
],
random_order=True)
augimgs = seq.augment_images(images)
return augimgs
def cpu_augment(self, imgs, boxes):
# for bx in boxes:
# self.assert_bboxes(bx)
ia_bb = []
for n in range(len(imgs)):
c_boxes = []
for i in boxes[n]:
try:
c_boxes.append(
ia.BoundingBox(x1=i[0], y1=i[1], x2=i[2], y2=i[3]))
except AssertionError:
print('Assertion Error: ', i)
ia_bb.append(ia.BoundingBoxesOnImage(c_boxes, shape=imgs[n].shape))
seq = iaa.Sequential([
iaa.Sometimes(0.5, iaa.AddElementwise((-20, 20), per_channel=1)),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=(0, 0.10 * 255))),
iaa.Sometimes(0.5, iaa.Multiply((0.75, 1.25), per_channel=1)),
iaa.Sometimes(0.5, iaa.MultiplyElementwise((0.75, 1.25))),
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0.0, 1.0))),
iaa.Fliplr(0.5),
iaa.Sometimes(
0.95,
iaa.SomeOf(1, [
iaa.CoarseDropout(p=(0.10, 0.25),
size_percent=(0.25, 0.5)),
iaa.CoarseDropout(p=(0.0, 0.15), size_percent=(0.1, 0.25)),
iaa.Dropout(p=(0, 0.25)),
iaa.CoarseSaltAndPepper(p=(0, 0.25),
size_percent=(0.1, 0.2))
])),
iaa.Affine(scale=iap.Choice(
[iap.Uniform(0.4, 1), iap.Uniform(1, 3)]),
rotate=(-180, 180))
])
seq_det = seq.to_deterministic()
image_b_aug = seq_det.augment_images(imgs)
bbs_b_aug = seq_det.augment_bounding_boxes(ia_bb)
bbs_b_aug = [
b.remove_out_of_image().cut_out_of_image() for b in bbs_b_aug
]
return image_b_aug, [
np.array([self.bbox_r(j) for j in i.bounding_boxes])
for i in bbs_b_aug
]
def get_augmenter(self):
seq = iaa.Sequential([
iaa.SomeOf((2, 6), [
iaa.Flipud(0.5),
iaa.Rot90(k=(0, 3)),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.MotionBlur(angle=(0, 360), k=(3, 8)),
iaa.Add((-50, 5)),
iaa.AddElementwise((-20, 2)),
iaa.AdditiveGaussianNoise(scale=0.05 * 255),
iaa.Multiply((0.3, 1.05)),
iaa.SaltAndPepper(p=(0.1, 0.3)),
iaa.JpegCompression(compression=(20, 90)),
iaa.Affine(shear=(-15, 15)),
iaa.Affine(rotate=(-10, 10)),
])
])
return seq
def augment_image(image_file):
im = Image.open(image_file)
im_array = img_to_array(im)
#function returns an array of images (including the first one)
output_images = []
output_images.append(im_array)
#darken image
#aug_dark = iaa.Add(-30)
#aug_image = aug_dark.augment_images([im_array])[0]
#output_images.append(aug_image)
#lighten image
#aug_light = iaa.Add(30)
#aug_image = aug_light.augment_images([im_array])[0]
# output_images.append(aug_image)
augmentations = [
iaa.Fliplr(1),
iaa.PerspectiveTransform(0.03, True),
iaa.PerspectiveTransform(0.04, True),
iaa.PerspectiveTransform(0.045, True),
iaa.Add(30, per_channel=True),
iaa.Add(-20, per_channel=True),
iaa.PiecewiseAffine(scale=0.01),
iaa.AdditiveGaussianNoise(scale=10),
iaa.MedianBlur(k=(3, 3)),
iaa.PerspectiveTransform(0.02, True),
iaa.PerspectiveTransform(0.021, True),
iaa.PerspectiveTransform(0.022, True),
iaa.AddElementwise(20),
iaa.Affine(rotate=3),
iaa.Affine(rotate=-5),
iaa.Affine(rotate=4)
]
#do this 3 times
base = 4
for i in range(7):
#for i in range(1):
#get between 2 and 5 augs
aug = iaa.SomeOf(i + base, augmentations)
#produce more images where there are more augs
for j in range(4):
this_image = aug.augment_images([im_array])[0]
output_images.append(this_image)
return(output_images)
def test_augmentations_with_seed_match(self):
nb_batches = 60
augseq = iaa.AddElementwise((0, 255))
image = np.zeros((10, 10, 1), dtype=np.uint8)
batch = ia.Batch(images=np.uint8([image, image]))
batches = [batch.deepcopy() for _ in sm.xrange(nb_batches)]
# seed=1
with multicore.Pool(augseq, processes=2, maxtasksperchild=30,
seed=1) as pool:
batches_aug1 = pool.map_batches(batches, chunksize=2)
# seed=1
with multicore.Pool(augseq, processes=2, seed=1) as pool:
batches_aug2 = pool.map_batches(batches, chunksize=1)
# seed=2
with multicore.Pool(augseq, processes=2, seed=2) as pool:
batches_aug3 = pool.map_batches(batches, chunksize=1)
assert len(batches_aug1) == nb_batches
assert len(batches_aug2) == nb_batches
assert len(batches_aug3) == nb_batches
for b1, b2, b3 in zip(batches_aug1, batches_aug2, batches_aug3):
# images were augmented
assert not np.array_equal(b1.images_unaug, b1.images_aug)
assert not np.array_equal(b2.images_unaug, b2.images_aug)
assert not np.array_equal(b3.images_unaug, b3.images_aug)
# original images still the same
assert np.array_equal(b1.images_unaug, batch.images_unaug)
assert np.array_equal(b2.images_unaug, batch.images_unaug)
assert np.array_equal(b3.images_unaug, batch.images_unaug)
# augmentations for same seed are the same
assert np.array_equal(b1.images_aug, b2.images_aug)
# augmentations for different seeds are different
assert not np.array_equal(b1.images_aug, b3.images_aug)
# make sure that batches for the two pools with same seed did not
# repeat within results (only between the results of the two pools)
for batches_aug in [batches_aug1, batches_aug2, batches_aug3]:
self._assert_each_augmentation_not_more_than_once(batches_aug)
def chapter_parameters_introduction():
ia.seed(1)
from imgaug import augmenters as iaa
from imgaug import parameters as iap
seq = iaa.Sequential([
iaa.GaussianBlur(sigma=iap.Uniform(0.0, 1.0)),
iaa.ContrastNormalization(
iap.Choice([1.0, 1.5, 3.0], p=[0.5, 0.3, 0.2])),
iaa.Affine(rotate=iap.Normal(0.0, 30),
translate_px=iap.RandomSign(iap.Poisson(3))),
iaa.AddElementwise(iap.Discretize(
(iap.Beta(0.5, 0.5) * 2 - 1.0) * 64)),
iaa.Multiply(iap.Positive(iap.Normal(0.0, 0.1)) + 1.0)
])
images = np.array([ia.quokka_square(size=(128, 128)) for i in range(16)])
images_aug = [seq.augment_image(images[i]) for i in range(len(images))]
save("parameters", "introduction.jpg", grid(images_aug, cols=4, rows=4))
def from_config(cls, config: AugmentationConfig) -> "ImgaugAugmenter":
"""Create an augmenter from a set of configuration parameters.
Args:
config: An `AugmentationConfig` instance with the desired parameters.
Returns:
An instance of this class with the specified augmentation configuration.
"""
aug_stack = []
if config.rotate:
aug_stack.append(
iaa.Affine(rotate=(config.rotation_min_angle,
config.rotation_max_angle)))
if config.translate:
aug_stack.append(
iaa.Affine(
translate_px={
"x": (config.translate_min, config.translate_max),
"y": (config.translate_min, config.translate_max),
}))
if config.scale:
aug_stack.append(
iaa.Affine(scale=(config.scale_min, config.scale_max)))
if config.uniform_noise:
aug_stack.append(
iaa.AddElementwise(value=(config.uniform_noise_min_val,
config.uniform_noise_max_val)))
if config.gaussian_noise:
aug_stack.append(
iaa.AdditiveGaussianNoise(loc=config.gaussian_noise_mean,
scale=config.gaussian_noise_stddev))
if config.contrast:
aug_stack.append(
iaa.GammaContrast(gamma=(config.contrast_min_gamma,
config.contrast_max_gamma)))
if config.brightness:
aug_stack.append(
iaa.Add(value=(config.brightness_min_val,
config.brightness_max_val)))
return cls(augmenter=iaa.Sequential(aug_stack))
def image_augment(input_dir, augmented_dir, coefficient=10):
# 删除原来的
for file_name in os.listdir(augmented_dir):
os.remove(augmented_dir + '/' + file_name)
seq = iaa.Sequential(
[
iaa.GaussianBlur(sigma=(0.2, 0.4)), # 高斯模糊
# iaa.Add((-50,50), per_channel=0.5),#为图像中的所有像素添加值
iaa.Sometimes(0.3, iaa.AddElementwise((-40, 40))), # 相邻像素加法增强器
iaa.Sometimes(
0.3,
iaa.AdditiveGaussianNoise(scale=0.05 * 255,
per_channel=0.5)), # 高斯噪声
iaa.Sometimes(0.3, iaa.PiecewiseAffine(scale=0.03)), # 扭曲增强器
iaa.Sometimes(
0.3, iaa.ElasticTransformation(alpha=(0, 5.0),
sigma=0.25)), # 像素位移增强器
# iaa.Sometimes(0.2, iaa.Superpixels(p_replace=0.05, n_segments=8)) #部分地将图像变换为其超像素表示
],
random_order=True) # do all of the above in random order
i = 0
for file_name in os.listdir(input_dir):
image = cv2.imread(input_dir + file_name)
if not (image.shape == (64, 64)):
image = cv2.resize(image, (64, 64))
images_aug = [seq.augment_image(image)
for _ in range(coefficient)] # 应用data augmentation
# ia.imshow(ia.draw_grid(images_aug, cols=4, rows=2)) # 显示图片操作效果
for img in images_aug:
output_file_name = file_name.split("_")[0] + "_" + str(i) + ".jpg"
cv2.imwrite(augmented_dir + output_file_name, img)
i += 1
def test_augmentations_without_seed_differ_for_images_and_keypoints(self):
augseq = iaa.AddElementwise((0, 255))
image = np.zeros((10, 10, 1), dtype=np.uint8)
# keypoints here will not be changed by augseq, but they will induce deterministic mode to start in
# augment_batches() as each batch contains images AND keypoints
kps = ia.KeypointsOnImage([ia.Keypoint(x=2, y=0)], shape=(10, 10, 1))
batch = ia.Batch(images=np.uint8([image, image]), keypoints=[kps, kps])
batches = [batch.deepcopy() for _ in sm.xrange(20)]
with multicore.Pool(augseq, processes=2, maxtasksperchild=5) as pool:
batches_aug = pool.map_batches(batches, chunksize=2)
with multicore.Pool(augseq, processes=2) as pool:
batches_aug.extend(pool.map_batches(batches, chunksize=1))
assert len(batches_aug) == 2 * 20
for batch in batches_aug:
for keypoints_aug in batch.keypoints_aug:
assert keypoints_aug.keypoints[0].x == 2
assert keypoints_aug.keypoints[0].y == 0
self._assert_each_augmentation_not_more_than_once(batches_aug)
def randomIntensityAugmentation(image):
intensity_seq = iaa.Sequential(
[
# iaa.Invert(0.3),
iaa.Sometimes(0.3, iaa.ContrastNormalization((0.5, 1.5))),
iaa.OneOf([
iaa.Noop(),
iaa.Sequential([
iaa.OneOf([
iaa.Add((-10, 10)),
iaa.AddElementwise((-10, 10)),
iaa.Multiply((0.95, 1.05)),
iaa.MultiplyElementwise((0.95, 1.05)),
]),
]),
iaa.OneOf([
iaa.GaussianBlur(sigma=(0.0, 1.0)),
iaa.AverageBlur(k=(2, 5)),
iaa.MedianBlur(k=(3, 5))
])
])
],
random_order=False)
return intensity_seq.augment_images(image)
def stochastic():
return iaa.Sequential([
iaa.GaussianBlur(
sigma=iap.Uniform(0.0, 1.0)
),
iaa.ContrastNormalization(
iap.Choice(
[1.0, 1.5, 3.0],
p=[0.5, 0.3, 0.2]
)
),
iaa.Affine(
rotate=iap.Normal(0.0, 30),
translate_px=iap.RandomSign(iap.Poisson(3))
),
iaa.AddElementwise(
iap.Discretize(
(iap.Beta(0.5, 0.5) * 2 - 1.0) * 64
)
),
iaa.Multiply(
iap.Positive(iap.Normal(0.0, 0.1)) + 1.0
)
])