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 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 __init__(self):
self.aug = iaa.Sequential([
iaa.Scale((224, 224)),
iaa.Sometimes(0.30, iaa.GaussianBlur(sigma=(0, 3.0))),
iaa.Sometimes(0.25, iaa.Multiply((0.5, 1.5), per_channel=0.5)),
iaa.Sometimes(0.20, iaa.Invert(0.25, per_channel=0.5)),
iaa.Sometimes(0.25, iaa.ReplaceElementwise(
iap.FromLowerResolution(iap.Binomial(0.1), size_px=8),
iap.Normal(128, 0.4*128),
per_channel=0.5)
),
iaa.Sometimes(0.30, iaa.AdditivePoissonNoise(40)),
iaa.Fliplr(0.5),
iaa.Affine(rotate=(-20, 20), mode='symmetric'),
iaa.Sometimes(0.30,
iaa.OneOf([iaa.Dropout(p=(0, 0.1)),
iaa.CoarseDropout(0.1, size_percent=0.5)])),
iaa.AddToHueAndSaturation(value=(-10, 10), per_channel=True)
])
def aug_data(x_data, y_data, percent, aug_percent):
rand_vector = np.linspace(0,
np.shape(x_data)[0],
num=int(percent*np.shape(x_data)[0]/100),
endpoint=False,
dtype = int)
for count in range(0, len(rand_vector)):
ppl = Image.fromarray(x_data[rand_vector[count],:,:,:].astype('uint8'))
ppl = np.array(ppl)
seq1 = iaa.Sequential([
iaa.ReplaceElementwise(
iap.FromLowerResolution(iap.Binomial(aug_percent), size_px=4),
iap.Normal(128, 0.4*128),
per_channel=1)
])
aug_image = seq1(images=ppl)
x_data[rand_vector[count],:,:,:] = np.copy(aug_image)
return x_data, y_data
def generate_batch_augmented(X, y, batch_size=32, crop_size=130):
while True:
batch_X, batch_y = next(generate_batch(X, y, batch_size))
seq = iaa.Sequential(
[
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Affine(
rotate=(-180, 180), # rotate by -45 to +45 degrees
order=[3], # use nearest neighbour or bilinear interpolation (fast)
),
iaa.CropToFixedSize(crop_size, crop_size),
]
)
seq_X = iaa.Sometimes(0.5, [iaa.ReplaceElementwise(0.01, iap.Uniform(0, 2000))])
if batch_X[0].ndim == 2:
channels = 1
else:
channels = batch_X[0].shape[-1]
batch_aug_X = np.empty((batch_size, crop_size, crop_size, channels))
batch_aug_y = np.empty((batch_size, crop_size, crop_size, 1))
for i in range(batch_size):
seq_det = (
seq.to_deterministic()
) # call this for each batch again, NOT only once at the start
images_aug = seq_det.augment_image(batch_X[i])
forces_aug = seq_det.augment_image(batch_y[i])
images_aug = seq_X.augment_image(images_aug)
if images_aug.ndim == 2:
batch_aug_X[i] = np.expand_dims(images_aug, axis=-1)
else:
batch_aug_X[i] = images_aug
if forces_aug.ndim == 2:
batch_aug_y[i] = np.expand_dims(forces_aug, axis=-1)
else:
batch_aug_y[i] = forces_aug
yield batch_aug_X, batch_aug_y
def __init__(self,
base_data_path,
train,
transform,
id_name_path,
device,
little_train=False,
read_mode='jpeg4py',
input_size=224,
C=2048,
test_mode=False):
print('data init')
self.train = train
self.base_data_path = base_data_path
self.transform = transform
self.fnames = []
self.resize = input_size
self.little_train = little_train
self.id_name_path = id_name_path
self.C = C
self.read_mode = read_mode
self.device = device
self._test = test_mode
self.fnames = self.get_data_list(base_data_path)
self.num_samples = len(self.fnames)
self.get_id_map()
self.cls_path_map = self.get_cls_pathlist_map()
self.img_augsometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.augmentation = iaa.Sequential(
[
# augment without change bboxes
self.img_augsometimes(
iaa.SomeOf(
(1, 4),
[
iaa.Dropout([0.05, 0.2
]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.1, .8)), # sharpen the image
# iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.OneOf([
iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.AverageBlur(k=(2, 5)),
iaa.BilateralBlur(d=(7, 12),
sigma_color=(10, 250),
sigma_space=(10, 250)),
iaa.MedianBlur(k=(3, 7)),
]),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
iaa.JpegCompression(compression=(80, 95)),
iaa.Multiply((0.5, 1.5)),
iaa.MultiplyElementwise((0.5, 1.5)),
iaa.ReplaceElementwise(0.05, [0, 255]),
# iaa.WithColorspace(to_colorspace="HSV", from_colorspace="RGB",
# children=iaa.WithChannels(2, iaa.Add((-10, 50)))),
iaa.OneOf([
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
1, iaa.Add((-10, 50)))),
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
2, iaa.Add((-10, 50)))),
]),
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=(-25, 25),
shear=(-8, 8))
],
random_order=True)),
iaa.Fliplr(.5),
iaa.Flipud(.25),
],
random_order=True)
"Elastic_Transformation": lambda alpha_lo, alpha_hi, sigma_lo, sigma_hi:
iaa.ElasticTransformation(alpha=(alpha_lo, alpha_hi), sigma=(sigma_lo, sigma_hi)),
# Weather augmenters are computationally expensive and will not work effectively on certain data sets
# Augmenter to draw clouds in images.
"Clouds": iaa.Clouds(),
# Augmenter to draw fog in images.
"Fog": iaa.Fog(),
# Augmenter to add falling snowflakes to images.
"Snowflakes": iaa.Snowflakes(),
# Replaces percent of all pixels in an image by either x or y
"Replace_Element_Wise": lambda percent, x, y: iaa.ReplaceElementwise(percent, [x, y]),
# Adds laplace noise (somewhere between gaussian and salt and peeper noise) to an image, sampled once per pixel
# from a laplace distribution Laplace(0, s), where s is sampled per image and varies between lo and hi*255 for
# percent of all images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same laplace distribution:
"Additive_Laplace_Noise": lambda lo, hi, percent:
iaa.AdditiveLaplaceNoise(scale=(lo, hi), per_channel=percent),
# Adds poisson noise (similar to gaussian but different distribution) to an image, sampled once per pixel from
# a poisson distribution Poisson(s), where s is sampled per image and varies between lo and hi for percent of
# all images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same poisson distribution:
"Additive_Poisson_Noise": lambda lo, hi, percent:
iaa.AdditivePoissonNoise(lam=(lo, hi), per_channel=percent),
def __init__(self,
list_file,
train,
transform,
device,
little_train=False,
S=7):
print('data init')
self.train = train
self.transform = transform
self.fnames = []
self.boxes = []
self.labels = []
self.S = S
self.B = 2
self.C = 20
self.device = device
self.augmentation = iaa.Sometimes(
0.5,
iaa.SomeOf(
(1, 6),
[
iaa.Dropout([0.05, 0.2]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.1, 1.0)), # sharpen the image
iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.OneOf([
iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.AverageBlur(k=(2, 5)),
iaa.BilateralBlur(d=(7, 12),
sigma_color=(10, 250),
sigma_space=(10, 250)),
iaa.MedianBlur(k=(3, 7)),
]),
# iaa.Fliplr(1.0),
# iaa.Flipud(1.0),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
iaa.JpegCompression(compression=(80, 95)),
iaa.Multiply((0.5, 1.5)),
iaa.MultiplyElementwise((0.5, 1.5)),
iaa.ReplaceElementwise(0.05, [0, 255]),
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
2, iaa.Add((-10, 50)))),
iaa.OneOf([
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
1, iaa.Add((-10, 50)))),
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
2, iaa.Add((-10, 50)))),
]),
],
random_order=True))
torch.manual_seed(23)
with open(list_file) as f:
lines = f.readlines()
if little_train:
lines = lines[:64]
for line in lines:
splited = line.strip().split()
self.fnames.append(splited[0])
self.num_samples = len(self.fnames)
def create_augmenters(height, width, height_augmentable, width_augmentable,
only_augmenters):
def lambda_func_images(images, random_state, parents, hooks):
return images
def lambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return heatmaps
def lambda_func_keypoints(keypoints, random_state, parents, hooks):
return keypoints
def assertlambda_func_images(images, random_state, parents, hooks):
return True
def assertlambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return True
def assertlambda_func_keypoints(keypoints, random_state, parents, hooks):
return True
augmenters_meta = [
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=False,
name="Sequential_2xNoop"),
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=True,
name="Sequential_2xNoop_random_order"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=False,
name="SomeOf_3xNoop"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=True,
name="SomeOf_3xNoop_random_order"),
iaa.OneOf([iaa.Noop(), iaa.Noop(), iaa.Noop()], name="OneOf_3xNoop"),
iaa.Sometimes(0.5, iaa.Noop(), name="Sometimes_Noop"),
iaa.WithChannels([1, 2], iaa.Noop(), name="WithChannels_1_and_2_Noop"),
iaa.Noop(name="Noop"),
iaa.Lambda(func_images=lambda_func_images,
func_heatmaps=lambda_func_heatmaps,
func_keypoints=lambda_func_keypoints,
name="Lambda"),
iaa.AssertLambda(func_images=assertlambda_func_images,
func_heatmaps=assertlambda_func_heatmaps,
func_keypoints=assertlambda_func_keypoints,
name="AssertLambda"),
iaa.AssertShape((None, height_augmentable, width_augmentable, None),
name="AssertShape"),
iaa.ChannelShuffle(0.5, name="ChannelShuffle")
]
augmenters_arithmetic = [
iaa.Add((-10, 10), name="Add"),
iaa.AddElementwise((-10, 10), name="AddElementwise"),
#iaa.AddElementwise((-500, 500), name="AddElementwise"),
iaa.AdditiveGaussianNoise(scale=(5, 10), name="AdditiveGaussianNoise"),
iaa.AdditiveLaplaceNoise(scale=(5, 10), name="AdditiveLaplaceNoise"),
iaa.AdditivePoissonNoise(lam=(1, 5), name="AdditivePoissonNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.01, 0.05), name="Dropout"),
iaa.CoarseDropout((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseDropout"),
iaa.ReplaceElementwise((0.01, 0.05), (0, 255),
name="ReplaceElementwise"),
#iaa.ReplaceElementwise((0.95, 0.99), (0, 255), name="ReplaceElementwise"),
iaa.SaltAndPepper((0.01, 0.05), name="SaltAndPepper"),
iaa.ImpulseNoise((0.01, 0.05), name="ImpulseNoise"),
iaa.CoarseSaltAndPepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.Salt((0.01, 0.05), name="Salt"),
iaa.CoarseSalt((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSalt"),
iaa.Pepper((0.01, 0.05), name="Pepper"),
iaa.CoarsePepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Invert(0.1, name="Invert"),
# ContrastNormalization
iaa.JpegCompression((50, 99), name="JpegCompression")
]
augmenters_blend = [
iaa.Alpha((0.01, 0.99), iaa.Noop(), name="Alpha"),
iaa.AlphaElementwise((0.01, 0.99), iaa.Noop(),
name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha((-2.0, 2.0),
iaa.Noop(),
name="FrequencyNoiseAlpha")
]
augmenters_blur = [
iaa.GaussianBlur(sigma=(1.0, 5.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=(3, 11), name="BilateralBlur"),
iaa.MotionBlur(k=(3, 11), name="MotionBlur")
]
augmenters_color = [
# InColorspace (deprecated)
iaa.WithColorspace(to_colorspace="HSV",
children=iaa.Noop(),
name="WithColorspace"),
iaa.WithHueAndSaturation(children=iaa.Noop(),
name="WithHueAndSaturation"),
iaa.MultiplyHueAndSaturation((0.8, 1.2),
name="MultiplyHueAndSaturation"),
iaa.MultiplyHue((-1.0, 1.0), name="MultiplyHue"),
iaa.MultiplySaturation((0.8, 1.2), name="MultiplySaturation"),
iaa.AddToHueAndSaturation((-10, 10), name="AddToHueAndSaturation"),
iaa.AddToHue((-10, 10), name="AddToHue"),
iaa.AddToSaturation((-10, 10), name="AddToSaturation"),
iaa.ChangeColorspace(to_colorspace="HSV", name="ChangeColorspace"),
iaa.Grayscale((0.01, 0.99), name="Grayscale"),
iaa.KMeansColorQuantization((2, 16), name="KMeansColorQuantization"),
iaa.UniformColorQuantization((2, 16), name="UniformColorQuantization")
]
augmenters_contrast = [
iaa.GammaContrast(gamma=(0.5, 2.0), name="GammaContrast"),
iaa.SigmoidContrast(gain=(5, 20),
cutoff=(0.25, 0.75),
name="SigmoidContrast"),
iaa.LogContrast(gain=(0.7, 1.0), name="LogContrast"),
iaa.LinearContrast((0.5, 1.5), name="LinearContrast"),
iaa.AllChannelsCLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
name="AllChannelsCLAHE"),
iaa.CLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
to_colorspace="HSV",
name="CLAHE"),
iaa.AllChannelsHistogramEqualization(
name="AllChannelsHistogramEqualization"),
iaa.HistogramEqualization(to_colorspace="HSV",
name="HistogramEqualization"),
]
augmenters_convolutional = [
iaa.Convolve(np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]]),
name="Convolve_3x3"),
iaa.Sharpen(alpha=(0.01, 0.99), lightness=(0.5, 2), name="Sharpen"),
iaa.Emboss(alpha=(0.01, 0.99), strength=(0, 2), name="Emboss"),
iaa.EdgeDetect(alpha=(0.01, 0.99), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.01, 0.99), name="DirectedEdgeDetect")
]
augmenters_edges = [iaa.Canny(alpha=(0.01, 0.99), name="Canny")]
augmenters_flip = [
iaa.Fliplr(1.0, name="Fliplr"),
iaa.Flipud(1.0, name="Flipud")
]
augmenters_geometric = [
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=0,
mode="constant",
cval=(0, 255),
name="Affine_order_0_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
name="Affine_order_1_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=3,
mode="constant",
cval=(0, 255),
name="Affine_order_3_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="edge",
cval=(0, 255),
name="Affine_order_1_edge"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
backend="skimage",
name="Affine_order_1_constant_skimage"),
# TODO AffineCv2
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="constant",
name="PiecewiseAffine_4x4_order_1_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=0,
mode="constant",
name="PiecewiseAffine_4x4_order_0_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="edge",
name="PiecewiseAffine_4x4_order_1_edge"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=8,
nb_cols=8,
order=1,
mode="constant",
name="PiecewiseAffine_8x8_order_1_constant"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=False,
name="PerspectiveTransform"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=True,
name="PerspectiveTransform_keep_size"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=0,
mode="constant",
cval=0,
name="ElasticTransformation_order_0_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="constant",
cval=0,
name="ElasticTransformation_order_1_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="nearest",
cval=0,
name="ElasticTransformation_order_1_nearest"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="reflect",
cval=0,
name="ElasticTransformation_order_1_reflect"),
iaa.Rot90((1, 3), keep_size=False, name="Rot90"),
iaa.Rot90((1, 3), keep_size=True, name="Rot90_keep_size")
]
augmenters_pooling = [
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=False,
name="AveragePooling"),
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=True,
name="AveragePooling_keep_size"),
iaa.MaxPooling(kernel_size=(1, 16), keep_size=False,
name="MaxPooling"),
iaa.MaxPooling(kernel_size=(1, 16),
keep_size=True,
name="MaxPooling_keep_size"),
iaa.MinPooling(kernel_size=(1, 16), keep_size=False,
name="MinPooling"),
iaa.MinPooling(kernel_size=(1, 16),
keep_size=True,
name="MinPooling_keep_size"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=False,
name="MedianPooling"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=True,
name="MedianPooling_keep_size")
]
augmenters_segmentation = [
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="cubic",
name="Superpixels_max_size_64_cubic"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="linear",
name="Superpixels_max_size_64_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=128,
interpolation="linear",
name="Superpixels_max_size_128_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=224,
interpolation="linear",
name="Superpixels_max_size_224_linear"),
iaa.UniformVoronoi(n_points=(250, 1000), name="UniformVoronoi"),
iaa.RegularGridVoronoi(n_rows=(16, 31),
n_cols=(16, 31),
name="RegularGridVoronoi"),
iaa.RelativeRegularGridVoronoi(n_rows_frac=(0.07, 0.14),
n_cols_frac=(0.07, 0.14),
name="RelativeRegularGridVoronoi"),
]
augmenters_size = [
iaa.Resize((0.8, 1.2), interpolation="nearest", name="Resize_nearest"),
iaa.Resize((0.8, 1.2), interpolation="linear", name="Resize_linear"),
iaa.Resize((0.8, 1.2), interpolation="cubic", name="Resize_cubic"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad_edge"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="CropAndPad_keep_size"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="Pad"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="Pad_edge"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="Pad_keep_size"),
iaa.Crop(percent=(0.05, 0.2), keep_size=False, name="Crop"),
iaa.Crop(percent=(0.05, 0.2), name="Crop_keep_size"),
iaa.PadToFixedSize(width=width + 10,
height=height + 10,
pad_mode="constant",
pad_cval=(0, 255),
name="PadToFixedSize"),
iaa.CropToFixedSize(width=width - 10,
height=height - 10,
name="CropToFixedSize"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="nearest",
name="KeepSizeByResize_CropToFixedSize_nearest"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="linear",
name="KeepSizeByResize_CropToFixedSize_linear"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="cubic",
name="KeepSizeByResize_CropToFixedSize_cubic"),
]
augmenters_weather = [
iaa.FastSnowyLandscape(lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0),
name="FastSnowyLandscape"),
iaa.Clouds(name="Clouds"),
iaa.Fog(name="Fog"),
iaa.CloudLayer(intensity_mean=(196, 255),
intensity_freq_exponent=(-2.5, -2.0),
intensity_coarse_scale=10,
alpha_min=0,
alpha_multiplier=(0.25, 0.75),
alpha_size_px_max=(2, 8),
alpha_freq_exponent=(-2.5, -2.0),
sparsity=(0.8, 1.0),
density_multiplier=(0.5, 1.0),
name="CloudLayer"),
iaa.Snowflakes(name="Snowflakes"),
iaa.SnowflakesLayer(density=(0.005, 0.075),
density_uniformity=(0.3, 0.9),
flake_size=(0.2, 0.7),
flake_size_uniformity=(0.4, 0.8),
angle=(-30, 30),
speed=(0.007, 0.03),
blur_sigma_fraction=(0.0001, 0.001),
name="SnowflakesLayer")
]
augmenters = (augmenters_meta + augmenters_arithmetic + augmenters_blend +
augmenters_blur + augmenters_color + augmenters_contrast +
augmenters_convolutional + augmenters_edges +
augmenters_flip + augmenters_geometric + augmenters_pooling +
augmenters_segmentation + augmenters_size +
augmenters_weather)
if only_augmenters is not None:
augmenters_reduced = []
for augmenter in augmenters:
if any([
re.search(pattern, augmenter.name)
for pattern in only_augmenters
]):
augmenters_reduced.append(augmenter)
augmenters = augmenters_reduced
return augmenters
# Augmenter to draw clouds in images.
"Clouds":
iaa.Clouds(),
# Augmenter to draw fog in images.
"Fog":
iaa.Fog(),
# Augmenter to add falling snowflakes to images.
"Snowflakes":
iaa.Snowflakes(),
# Replaces percent of all pixels in an image by either x or y
"Replace_Element_Wise":
lambda percent, x, y: iaa.ReplaceElementwise(percent, [x, y]),
# Adds laplace noise (somewhere between gaussian and salt and peeper noise) to an image, sampled once per pixel
# from a laplace distribution Laplace(0, s), where s is sampled per image and varies between lo and hi*255 for
# percent of all images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same laplace distribution:
"Additive_Laplace_Noise":
lambda lo, hi, percent: iaa.AdditiveLaplaceNoise(scale=(lo, hi),
per_channel=percent),
# Adds poisson noise (similar to gaussian but different distribution) to an image, sampled once per pixel from
# a poisson distribution Poisson(s), where s is sampled per image and varies between lo and hi for percent of
# all images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same poisson distribution:
"Additive_Poisson_Noise":
lambda lo, hi, percent: iaa.AdditivePoissonNoise(lam=(lo, hi),
def __init__(self,
imgdirs_list,
annfiles_list,
train,
transform,
device,
little_train=False,
with_file_path=False,
S=7,
B=2,
C=20,
test_mode=False):
print('data init')
self.imgdirs_list = imgdirs_list
self.anns_list = annfiles_list
self.train = train
self.transform = transform
self.fnames = []
self.boxes = []
self.labels = []
self.dataset_list = []
self.resize = 448
self.S = S
self.B = B
self.C = C
self.device = device
self._test = test_mode
self.with_file_path = with_file_path
self.img_augsometimes = lambda aug: iaa.Sometimes(0.25, aug)
self.bbox_augsometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.augmentation = iaa.Sequential(
[
# augment without change bboxes
self.img_augsometimes(
iaa.SomeOf(
(1, 3),
[
iaa.Dropout([0.05, 0.2
]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.1, .8)), # sharpen the image
# iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.OneOf([
iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.AverageBlur(k=(2, 5)),
iaa.BilateralBlur(d=(7, 12),
sigma_color=(10, 250),
sigma_space=(10, 250)),
iaa.MedianBlur(k=(3, 7)),
]),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
# iaa.JpegCompression(compression=(80, 95)),
iaa.Multiply((0.5, 1.5)),
iaa.MultiplyElementwise((0.5, 1.5)),
iaa.ReplaceElementwise(0.05, [0, 255]),
# iaa.WithColorspace(to_colorspace="HSV", from_colorspace="RGB",
# children=iaa.WithChannels(2, iaa.Add((-10, 50)))),
iaa.OneOf([
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
1, iaa.Add((-10, 50)))),
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
2, iaa.Add((-10, 50)))),
]),
],
random_order=True)),
iaa.Fliplr(.5),
iaa.Flipud(.125),
# augment changing bboxes
self.bbox_augsometimes(
iaa.Affine(
# translate_px={"x": 40, "y": 60},
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_percent={
"x": (-0.1, 0.1),
"y": (-0.1, 0.1)
},
rotate=(-5, 5),
))
],
random_order=True)
for imgdir, annfile in zip(self.imgdirs_list, self.anns_list):
print('handle dataset:\n\t' + imgdir + '\n\t' + annfile)
annfile_json = json.load(open(annfile, 'r'))
images = annfile_json['images']
annotations = annfile_json['annotations']
ann_dicts = {}
for ann in annotations:
if ann['image_id'] not in ann_dicts.keys():
ann_dicts[ann['image_id']] = []
ann_dicts[ann['image_id']].append(ann)
for img in images:
img['file_name'] = os.path.join(imgdir, img['file_name'])
if img['id'] in ann_dicts.keys():
anns = ann_dicts[img['id']]
else:
continue
image_ann = {'image_info': img, 'ann': anns}
self.dataset_list.append(image_ann)
self.num_samples = len(self.dataset_list)
print('There are %d pics in datasets.' % (self.num_samples))
def __init__(self,list_file,train,transform, device, little_train=False, with_file_path=False, with_mask=False, S=7, B = 2, C = 20, test_mode=False):
print('data init')
self.train = train
self.transform=transform
self.fnames = []
self.boxes = []
self.labels = []
self.resize = 448
self.with_mask = with_mask
self.S = S
self.B = B
self.C = C
self.device = device
self._test = test_mode
self.with_file_path = with_file_path
self.img_augsometimes = lambda aug: iaa.Sometimes(0.25, aug)
self.bbox_augsometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.augmentation = iaa.Sequential(
[
# augment without change bboxes
self.img_augsometimes(
iaa.SomeOf((1, 3), [
iaa.Dropout([0.05, 0.2]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.1, .8)), # sharpen the image
# iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.OneOf([
iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.AverageBlur(k=(2, 5)),
iaa.BilateralBlur(d=(7, 12), sigma_color=(10, 250), sigma_space=(10, 250)),
iaa.MedianBlur(k=(3, 7)),
]),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
iaa.JpegCompression(compression=(80, 95)),
iaa.Multiply((0.5, 1.5)),
iaa.MultiplyElementwise((0.5, 1.5)),
iaa.ReplaceElementwise(0.05, [0, 255]),
# iaa.WithColorspace(to_colorspace="HSV", from_colorspace="RGB",
# children=iaa.WithChannels(2, iaa.Add((-10, 50)))),
iaa.OneOf([
iaa.WithColorspace(to_colorspace="HSV", from_colorspace="RGB",
children=iaa.WithChannels(1, iaa.Add((-10, 50)))),
iaa.WithColorspace(to_colorspace="HSV", from_colorspace="RGB",
children=iaa.WithChannels(2, iaa.Add((-10, 50)))),
]),
], random_order=True)
),
# iaa.Fliplr(.5),
# iaa.Flipud(.125),
# # augment changing bboxes
# self.bbox_augsometimes(
# iaa.Affine(
# # translate_px={"x": 40, "y": 60},
# scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
# translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)},
# rotate=(-5, 5),
# )
# )
],
random_order=True
)
# torch.manual_seed(23)
with open(list_file) as f:
lines = f.readlines()
if little_train:
lines = lines[:64*8]
for line in lines:
splited = line.strip().split()
self.fnames.append(splited[0])
self.num_samples = len(self.fnames)
transformed_image = transform(image=image)
elif augmentation == 'salt_and_papper':
transform = iaa.SaltAndPepper(0.1)
transformed_image = transform(image=image)
elif augmentation == 'coarse_salt_and_papper':
transform = iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.1))
transformed_image = transform(image=image)
elif augmentation == 'impulse_noise':
transform = iaa.ImpulseNoise(0.1)
transformed_image = transform(image=image)
elif augmentation == 'replace_elementwise':
transform = iaa.ReplaceElementwise(0.1, [0, 255])
transformed_image = transform(image=image)
elif augmentation == 'cutout':
transform = iaa.Cutout(nb_iterations=5)
transformed_image = transform(image=image)
elif augmentation == 'solarize':
transform = Solarize(always_apply=True)
transformed_image = transform(image=image)['image']
elif augmentation == 'invert_img':
transform = InvertImg(always_apply=True)
transformed_image = transform(image=image)['image']
## Artistic
iaa.OneOf([
iaa.imgcorruptlike.ShotNoise(severity=(1, 2)),
iaa.imgcorruptlike.ImpulseNoise(severity=(1, 2)),
iaa.imgcorruptlike.SpeckleNoise(severity=(1, 2)),
iaa.imgcorruptlike.Spatter(severity=(1, 3)),
iaa.AdditivePoissonNoise((1, 20), per_channel=0.5),
iaa.AdditiveLaplaceNoise(scale=(0.005 * 255, 0.03 * 255),
per_channel=0.5),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.03 * 255),
per_channel=0.5),
iaa.BlendAlphaElementwise((0.0, 1.0),
foreground=iaa.Add((-15, 15)),
background=iaa.Multiply((0.8, 1.2))),
iaa.ReplaceElementwise(0.05,
iap.Normal(128, 0.4 * 128),
per_channel=0.5),
iaa.Dropout(p=(0, 0.05), per_channel=0.5),
])),
# Brightness + Color + Contrast
iaa.Sometimes(
0.5,
iaa.OneOf([
iaa.Add(iap.Normal(iap.Choice([-30, 30]), 10)),
iaa.Multiply((0.75, 1.25)),
iaa.AddToBrightness((-35, 35)),
iaa.MultiplyBrightness((0.85, 1.15)),
iaa.MultiplyAndAddToBrightness(mul=(0.85, 1.15), add=(-10, 10)),
iaa.BlendAlphaHorizontalLinearGradient(iaa.Add(
iap.Normal(iap.Choice([-40, 40]), 10)),
start_at=(0, 0.2),
def data_aug(images):
seq = iaa.Sometimes(
0.5, iaa.Identity(),
iaa.Sometimes(
0.5,
iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Sometimes(
0.5,
iaa.OneOf([
iaa.Add((-40, 40)),
iaa.AddElementwise((-40, 40)),
iaa.AdditiveGaussianNoise(scale=(0, 0.2 * 255)),
iaa.AdditiveLaplaceNoise(scale=(0, 0.2 * 255)),
iaa.AdditivePoissonNoise((0, 40)),
iaa.MultiplyElementwise((0.5, 1.5)),
iaa.ReplaceElementwise(0.1, [0, 255]),
iaa.SaltAndPepper(0.1)
])),
iaa.OneOf([
iaa.Cutout(nb_iterations=2,
size=0.15,
cval=0,
squared=False),
iaa.CoarseDropout((0.0, 0.05), size_percent=(0.02, 0.25)),
iaa.Dropout(p=(0, 0.2)),
iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.1)),
iaa.Cartoon(),
iaa.BlendAlphaVerticalLinearGradient(iaa.TotalDropout(1.0),
min_value=0.2,
max_value=0.8),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.AverageBlur(k=(2, 11)),
iaa.MedianBlur(k=(3, 11)),
iaa.BilateralBlur(d=(3, 10),
sigma_color=(10, 250),
sigma_space=(10, 250)),
iaa.MotionBlur(k=20),
iaa.AllChannelsCLAHE(),
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.Affine(scale=(0.5, 1.5)),
iaa.Affine(translate_px={
"x": (-20, 20),
"y": (-20, 20)
}),
iaa.Affine(shear=(-16, 16)),
iaa.pillike.EnhanceSharpness()
]),
iaa.OneOf([
iaa.GammaContrast((0.5, 2.0)),
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)),
iaa.LogContrast(gain=(0.6, 1.4)),
iaa.LinearContrast((0.4, 1.6)),
iaa.pillike.EnhanceBrightness()
])
]),
iaa.Sometimes(0.5, iaa.RandAugment(n=2, m=9),
iaa.RandAugment(n=(0, 3), m=(0, 9)))))
images = seq(images=images)
return images
class AugmentationScheme:
# Dictionary containing all possible augmentation functions
Augmentations = {
# Convert images to HSV, then increase each pixel's Hue (H), Saturation (S) or Value/lightness (V) [0, 1, 2]
# value by an amount in between lo and hi:
"HSV":
lambda channel, lo, hi: iaa.WithColorspace(
to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(channel, iaa.Add((lo, hi)))),
# The augmenter first transforms images to HSV color space, then adds random values (lo to hi)
# to the H and S channels and afterwards converts back to RGB.
# (independently per channel and the same value for all pixels within that channel)
"Add_To_Hue_And_Saturation":
lambda lo, hi: iaa.AddToHueAndSaturation((lo, hi), per_channel=True),
# Increase each pixel’s channel-value (redness/greenness/blueness) [0, 1, 2] by value in between lo and hi:
"Increase_Channel":
lambda channel, lo, hi: iaa.WithChannels(channel, iaa.Add((lo, hi))),
# Rotate each image’s channel [R=0, G=1, B=2] by value in between lo and hi degrees:
"Rotate_Channel":
lambda channel, lo, hi: iaa.WithChannels(channel,
iaa.Affine(rotate=(lo, hi))),
# Augmenter that never changes input images (“no operation”).
"No_Operation":
iaa.Noop(),
# Pads images, i.e. adds columns/rows to them. Pads image by value in between lo and hi
# percent relative to its original size (only accepts positive values in range[0, 1]):
# If s_i is false, The value will be sampled once per image and used for all sides
# (i.e. all sides gain/lose the same number of rows/columns)
# NOTE: automatically resizes images back to their original size after it has augmented them.
"Pad_Percent":
lambda lo, hi, s_i: iaa.Pad(
percent=(lo, hi), keep_size=True, sample_independently=s_i),
# Pads images by a number of pixels between lo and hi
# If s_i is false, The value will be sampled once per image and used for all sides
# (i.e. all sides gain/lose the same number of rows/columns)
"Pad_Pixels":
lambda lo, hi, s_i: iaa.Pad(
px=(lo, hi), keep_size=True, sample_independently=s_i),
# Crops/cuts away pixels at the sides of the image.
# Crops images by value in between lo and hi (only accepts positive values in range[0, 1]):
# If s_i is false, The value will be sampled once per image and used for all sides
# (i.e. all sides gain/lose the same number of rows/columns)
# NOTE: automatically resizes images back to their original size after it has augmented them.
"Crop_Percent":
lambda lo, hi, s_i: iaa.Crop(
percent=(lo, hi), keep_size=True, sample_independently=s_i),
# Crops images by a number of pixels between lo and hi
# If s_i is false, The value will be sampled once per image and used for all sides
# (i.e. all sides gain/lose the same number of rows/columns)
"Crop_Pixels":
lambda lo, hi, s_i: iaa.Crop(
px=(lo, hi), keep_size=True, sample_independently=s_i),
# Flip/mirror percent (i.e 0.5) of the input images horizontally
# The default probability is 0, so to flip all images, percent=1
"Flip_lr":
iaa.Fliplr(1),
# Flip/mirror percent (i.e 0.5) of the input images vertically
# The default probability is 0, so to flip all images, percent=1
"Flip_ud":
iaa.Flipud(1),
# Completely or partially transform images to their superpixel representation.
# Generate s_pix_lo to s_pix_hi superpixels per image. Replace each superpixel with a probability between
# prob_lo and prob_hi with range[0, 1] (sampled once per image) by its average pixel color.
"Superpixels":
lambda prob_lo, prob_hi, s_pix_lo, s_pix_hi: iaa.Superpixels(
p_replace=(prob_lo, prob_hi), n_segments=(s_pix_lo, s_pix_hi)),
# Change images to grayscale and overlay them with the original image by varying strengths,
# effectively removing alpha_lo to alpha_hi of the color:
"Grayscale":
lambda alpha_lo, alpha_hi: iaa.Grayscale(alpha=(alpha_lo, alpha_hi)),
# Blur each image with a gaussian kernel with a sigma between sigma_lo and sigma_hi:
"Gaussian_Blur":
lambda sigma_lo, sigma_hi: iaa.GaussianBlur(sigma=(sigma_lo, sigma_hi)
),
# Blur each image using a mean over neighbourhoods that have random sizes,
# which can vary between h_lo and h_hi in height and w_lo and w_hi in width:
"Average_Blur":
lambda h_lo, h_hi, w_lo, w_hi: iaa.AverageBlur(k=((h_lo, h_hi),
(w_lo, w_hi))),
# Blur each image using a median over neighbourhoods that have a random size between lo x lo and hi x hi:
"Median_Blur":
lambda lo, hi: iaa.MedianBlur(k=(lo, hi)),
# Sharpen an image, then overlay the results with the original using an alpha between alpha_lo and alpha_hi:
"Sharpen":
lambda alpha_lo, alpha_hi, lightness_lo, lightness_hi: iaa.
Sharpen(alpha=(alpha_lo, alpha_hi),
lightness=(lightness_lo, lightness_hi)),
# Emboss an image, then overlay the results with the original using an alpha between alpha_lo and alpha_hi:
"Emboss":
lambda alpha_lo, alpha_hi, strength_lo, strength_hi: iaa.Emboss(
alpha=(alpha_lo, alpha_hi), strength=(strength_lo, strength_hi)),
# Detect edges in images, turning them into black and white images and
# then overlay these with the original images using random alphas between alpha_lo and alpha_hi:
"Detect_Edges":
lambda alpha_lo, alpha_hi: iaa.EdgeDetect(alpha=(alpha_lo, alpha_hi)),
# Detect edges having random directions between dir_lo and dir_hi (i.e (0.0, 1.0) = 0 to 360 degrees) in
# images, turning the images into black and white versions and then overlay these with the original images
# using random alphas between alpha_lo and alpha_hi:
"Directed_edge_Detect":
lambda alpha_lo, alpha_hi, dir_lo, dir_hi: iaa.DirectedEdgeDetect(
alpha=(alpha_lo, alpha_hi), direction=(dir_lo, dir_hi)),
# Add random values between lo and hi to images. In percent of all images the values differ per channel
# (3 sampled value). In the rest of the images the value is the same for all channels:
"Add":
lambda lo, hi, percent: iaa.Add((lo, hi), per_channel=percent),
# Adds random values between lo and hi to images, with each value being sampled per pixel.
# In percent of all images the values differ per channel (3 sampled value). In the rest of the images
# the value is the same for all channels:
"Add_Element_Wise":
lambda lo, hi, percent: iaa.AddElementwise(
(lo, hi), per_channel=percent),
# Add gaussian noise (aka white noise) to an image, sampled once per pixel from a normal
# distribution N(0, s), where s is sampled per image and varies between lo and hi*255 for percent of all
# images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same normal distribution:
"Additive_Gaussian_Noise":
lambda lo, hi, percent: iaa.AdditiveGaussianNoise(scale=(lo, hi),
per_channel=percent),
# Multiply in percent of all images each pixel with random values between lo and hi and multiply
# the pixels in the rest of the images channel-wise,
# i.e. sample one multiplier independently per channel and pixel:
"Multiply":
lambda lo, hi, percent: iaa.Multiply((lo, hi), per_channel=percent),
# Multiply values of pixels with possibly different values for neighbouring pixels,
# making each pixel darker or brighter. Multiply each pixel with a random value between lo and hi:
"Multiply_Element_Wise":
lambda lo, hi, percent: iaa.MultiplyElementwise(
(0.5, 1.5), per_channel=0.5),
# Augmenter that sets a certain fraction of pixels in images to zero.
# Sample per image a value p from the range lo<=p<=hi and then drop p percent of all pixels in the image
# (i.e. convert them to black pixels), but do this independently per channel in percent of all images
"Dropout":
lambda lo, hi, percent: iaa.Dropout(p=(lo, hi), per_channel=percent),
# Augmenter that sets rectangular areas within images to zero.
# Drop d_lo to d_hi percent of all pixels by converting them to black pixels,
# but do that on a lower-resolution version of the image that has s_lo to s_hi percent of the original size,
# Also do this in percent of all images channel-wise, so that only the information of some
# channels is set to 0 while others remain untouched:
"Coarse_Dropout":
lambda d_lo, d_hi, s_lo, s_hi, percent: iaa.CoarseDropout(
(d_lo, d_hi), size_percent=(s_hi, s_hi), per_channel=percent),
# Augmenter that inverts all values in images, i.e. sets a pixel from value v to 255-v.
# For c_percent of all images, invert all pixels in these images channel-wise with probability=i_percent
# (per image). In the rest of the images, invert i_percent of all channels:
"Invert":
lambda i_percent, c_percent: iaa.Invert(i_percent,
per_channel=c_percent),
# Augmenter that changes the contrast of images.
# Normalize contrast by a factor of lo to hi, sampled randomly per image
# and for percent of all images also independently per channel:
"Contrast_Normalisation":
lambda lo, hi, percent: iaa.ContrastNormalization(
(lo, hi), per_channel=percent),
# Scale images to a value of lo to hi percent of their original size but do this independently per axis:
"Scale":
lambda x_lo, x_hi, y_lo, y_hi: iaa.Affine(scale={
"x": (x_lo, x_hi),
"y": (y_lo, y_hi)
}),
# Translate images by lo to hi percent on x-axis and y-axis independently:
"Translate_Percent":
lambda x_lo, x_hi, y_lo, y_hi: iaa.Affine(translate_percent={
"x": (x_lo, x_hi),
"y": (y_lo, y_hi)
}),
# Translate images by lo to hi pixels on x-axis and y-axis independently:
"Translate_Pixels":
lambda x_lo, x_hi, y_lo, y_hi: iaa.Affine(translate_px={
"x": (x_lo, x_hi),
"y": (y_lo, y_hi)
}),
# Rotate images by lo to hi degrees:
"Rotate":
lambda lo, hi: iaa.Affine(rotate=(lo, hi)),
# Shear images by lo to hi degrees:
"Shear":
lambda lo, hi: iaa.Affine(shear=(lo, hi)),
# Augmenter that places a regular grid of points on an image and randomly moves the neighbourhood of
# these point around via affine transformations. This leads to local distortions.
# Distort images locally by moving points around, each with a distance v (percent relative to image size),
# where v is sampled per point from N(0, z) z is sampled per image from the range lo to hi:
"Piecewise_Affine":
lambda lo, hi: iaa.PiecewiseAffine(scale=(lo, hi)),
# Augmenter to transform images by moving pixels locally around using displacement fields.
# Distort images locally by moving individual pixels around following a distortions field with
# strength sigma_lo to sigma_hi. The strength of the movement is sampled per pixel from the range
# alpha_lo to alpha_hi:
"Elastic_Transformation":
lambda alpha_lo, alpha_hi, sigma_lo, sigma_hi: iaa.
ElasticTransformation(alpha=(alpha_lo, alpha_hi),
sigma=(sigma_lo, sigma_hi)),
# Weather augmenters are computationally expensive and will not work effectively on certain data sets
# Augmenter to draw clouds in images.
"Clouds":
iaa.Clouds(),
# Augmenter to draw fog in images.
"Fog":
iaa.Fog(),
# Augmenter to add falling snowflakes to images.
"Snowflakes":
iaa.Snowflakes(),
# Replaces percent of all pixels in an image by either x or y
"Replace_Element_Wise":
lambda percent, x, y: iaa.ReplaceElementwise(percent, [x, y]),
# Adds laplace noise (somewhere between gaussian and salt and peeper noise) to an image, sampled once per pixel
# from a laplace distribution Laplace(0, s), where s is sampled per image and varies between lo and hi*255 for
# percent of all images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same laplace distribution:
"Additive_Laplace_Noise":
lambda lo, hi, percent: iaa.AdditiveLaplaceNoise(scale=(lo, hi),
per_channel=percent),
# Adds poisson noise (similar to gaussian but different distribution) to an image, sampled once per pixel from
# a poisson distribution Poisson(s), where s is sampled per image and varies between lo and hi for percent of
# all images (sampled once for all channels) and sampled three (RGB) times (channel-wise)
# for the rest from the same poisson distribution:
"Additive_Poisson_Noise":
lambda lo, hi, percent: iaa.AdditivePoissonNoise(lam=(lo, hi),
per_channel=percent),
# Adds salt and pepper noise to an image, i.e. some white-ish and black-ish pixels.
# Replaces percent of all pixels with salt and pepper noise
"Salt_And_Pepper":
lambda percent: iaa.SaltAndPepper(percent),
# Adds coarse salt and pepper noise to image, i.e. rectangles that contain noisy white-ish and black-ish pixels
# Replaces percent of all pixels with salt/pepper in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Salt_And_Pepper":
lambda percent, lo, hi: iaa.CoarseSaltAndPepper(percent,
size_percent=(lo, hi)),
# Adds salt noise to an image, i.e white-ish pixels
# Replaces percent of all pixels with salt noise
"Salt":
lambda percent: iaa.Salt(percent),
# Adds coarse salt noise to image, i.e. rectangles that contain noisy white-ish pixels
# Replaces percent of all pixels with salt in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Salt":
lambda percent, lo, hi: iaa.CoarseSalt(percent, size_percent=(lo, hi)),
# Adds Pepper noise to an image, i.e Black-ish pixels
# Replaces percent of all pixels with Pepper noise
"Pepper":
lambda percent: iaa.Pepper(percent),
# Adds coarse pepper noise to image, i.e. rectangles that contain noisy black-ish pixels
# Replaces percent of all pixels with salt in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Pepper":
lambda percent, lo, hi: iaa.CoarsePepper(percent,
size_percent=(lo, hi)),
# In an alpha blending, two images are naively mixed. E.g. Let A be the foreground image, B be the background
# image and a is the alpha value. Each pixel intensity is then computed as a * A_ij + (1-a) * B_ij.
# Images passed in must be a numpy array of type (height, width, channel)
"Blend_Alpha":
lambda image_fg, image_bg, alpha: iaa.blend_alpha(
image_fg, image_bg, alpha),
# Blur/Denoise an image using a bilateral filter.
# Bilateral filters blur homogeneous and textured areas, while trying to preserve edges.
# Blurs all images using a bilateral filter with max distance d_lo to d_hi with ranges for sigma_colour
# and sigma space being define by sc_lo/sc_hi and ss_lo/ss_hi
"Bilateral_Blur":
lambda d_lo, d_hi, sc_lo, sc_hi, ss_lo, ss_hi: iaa.BilateralBlur(
d=(d_lo, d_hi),
sigma_color=(sc_lo, sc_hi),
sigma_space=(ss_lo, ss_hi)),
# Augmenter that sharpens images and overlays the result with the original image.
# Create a motion blur augmenter with kernel size of (kernel x kernel) and a blur angle of either x or y degrees
# (randomly picked per image).
"Motion_Blur":
lambda kernel, x, y: iaa.MotionBlur(k=kernel, angle=[x, y]),
# Augmenter to apply standard histogram equalization to images (similar to CLAHE)
"Histogram_Equalization":
iaa.HistogramEqualization(),
# Augmenter to perform standard histogram equalization on images, applied to all channels of each input image
"All_Channels_Histogram_Equalization":
iaa.AllChannelsHistogramEqualization(),
# Contrast Limited Adaptive Histogram Equalization (CLAHE). This augmenter applies CLAHE to images, a form of
# histogram equalization that normalizes within local image patches.
# Creates a CLAHE augmenter with clip limit uniformly sampled from [cl_lo..cl_hi], i.e. 1 is rather low contrast
# and 50 is rather high contrast. Kernel sizes of SxS, where S is uniformly sampled from [t_lo..t_hi].
# Sampling happens once per image. (Note: more parameters are available for further specification)
"CLAHE":
lambda cl_lo, cl_hi, t_lo, t_hi: iaa.CLAHE(
clip_limit=(cl_lo, cl_hi), tile_grid_size_px=(t_lo, t_hi)),
# Contrast Limited Adaptive Histogram Equalization (refer above), applied to all channels of the input images.
# CLAHE performs histogram equalization within image patches, i.e. over local neighbourhoods
"All_Channels_CLAHE":
lambda cl_lo, cl_hi, t_lo, t_hi: iaa.AllChannelsCLAHE(
clip_limit=(cl_lo, cl_hi), tile_grid_size_px=(t_lo, t_hi)),
# Augmenter that changes the contrast of images using a unique formula (using gamma).
# Multiplier for gamma function is between lo and hi,, sampled randomly per image (higher values darken image)
# For percent of all images values are sampled independently per channel.
"Gamma_Contrast":
lambda lo, hi, percent: iaa.GammaContrast(
(lo, hi), per_channel=percent),
# Augmenter that changes the contrast of images using a unique formula (linear).
# Multiplier for linear function is between lo and hi, sampled randomly per image
# For percent of all images values are sampled independently per channel.
"Linear_Contrast":
lambda lo, hi, percent: iaa.LinearContrast(
(lo, hi), per_channel=percent),
# Augmenter that changes the contrast of images using a unique formula (using log).
# Multiplier for log function is between lo and hi, sampled randomly per image.
# For percent of all images values are sampled independently per channel.
# Values around 1.0 lead to a contrast-adjusted images. Values above 1.0 quickly lead to partially broken
# images due to exceeding the datatype’s value range.
"Log_Contrast":
lambda lo, hi, percent: iaa.LogContrast((lo, hi), per_channel=percent),
# Augmenter that changes the contrast of images using a unique formula (sigmoid).
# Multiplier for sigmoid function is between lo and hi, sampled randomly per image. c_lo and c_hi decide the
# cutoff value that shifts the sigmoid function in horizontal direction (Higher values mean that the switch
# from dark to light pixels happens later, i.e. the pixels will remain darker).
# For percent of all images values are sampled independently per channel:
"Sigmoid_Contrast":
lambda lo, hi, c_lo, c_hi, percent: iaa.SigmoidContrast(
(lo, hi), (c_lo, c_hi), per_channel=percent),
# Augmenter that calls a custom (lambda) function for each batch of input image.
# Extracts Canny Edges from images (refer to description in CO)
# Good default values for min and max are 100 and 200
'Custom_Canny_Edges':
lambda min_val, max_val: iaa.Lambda(func_images=CO.Edges(
min_value=min_val, max_value=max_val)),
}
# AugmentationScheme objects require images and labels.
# 'augs' is a list that contains all data augmentations in the scheme
def __init__(self):
self.augs = [iaa.Flipud(1)]
def __call__(self, image):
image = np.array(image)
aug_scheme = iaa.Sometimes(
0.5,
iaa.SomeOf(random.randrange(1,
len(self.augs) + 1),
self.augs,
random_order=True))
aug_img = self.aug_scheme.augment_image(image)
# fixes negative strides
aug_img = aug_img[..., ::1] - np.zeros_like(aug_img)
return aug_img
