def _test_very_roughly(self, nb_channels):
if nb_channels is None:
img = np.zeros((100, 100), dtype=np.uint8)
else:
img = np.zeros((100, 100, nb_channels), dtype=np.uint8)
img_aug = iaa.Snowflakes().augment_image(img)
assert 0.01 < np.average(img_aug) < 100
assert np.max(img_aug) > 100
img_aug_f32 = img_aug.astype(np.float32)
grad_x = img_aug_f32[:, 1:] - img_aug_f32[:, :-1]
grad_y = img_aug_f32[1:, :] - img_aug_f32[:-1, :]
assert np.sum(np.abs(grad_x)) > 5 * img.shape[1]
assert np.sum(np.abs(grad_y)) > 5 * img.shape[0]
# test density
img_aug_undense = iaa.Snowflakes(
density=0.001, density_uniformity=0.99).augment_image(img)
img_aug_dense = iaa.Snowflakes(
density=0.1, density_uniformity=0.99).augment_image(img)
assert np.average(img_aug_undense) < np.average(img_aug_dense)
# test density_uniformity
img_aug_ununiform = iaa.Snowflakes(
density=0.4, density_uniformity=0.1).augment_image(img)
img_aug_uniform = iaa.Snowflakes(
density=0.4, density_uniformity=0.9).augment_image(img)
assert (self._measure_uniformity(img_aug_ununiform) <
self._measure_uniformity(img_aug_uniform))
def test_Snowflakes():
# rather rough test as fairly hard to test more detailed
reseed()
img = np.zeros((100, 100, 3), dtype=np.uint8)
img_aug = iaa.Snowflakes().augment_image(img)
assert 0.01 < np.average(img_aug) < 100
assert np.max(img_aug) > 100
grad_x = img_aug[:, 1:].astype(np.float32) - img_aug[:, :-1].astype(
np.float32)
grad_y = img_aug[1:, :].astype(np.float32) - img_aug[:-1, :].astype(
np.float32)
assert np.sum(np.abs(grad_x)) > 5 * img.shape[1]
assert np.sum(np.abs(grad_y)) > 5 * img.shape[0]
# test density
img_aug_undense = iaa.Snowflakes(
density=0.001, density_uniformity=0.99).augment_image(img)
img_aug_dense = iaa.Snowflakes(density=0.1,
density_uniformity=0.99).augment_image(img)
assert np.average(img_aug_undense) < np.average(img_aug_dense)
# test density_uniformity
img_aug_ununiform = iaa.Snowflakes(
density=0.4, density_uniformity=0.1).augment_image(img)
img_aug_uniform = iaa.Snowflakes(density=0.4,
density_uniformity=0.9).augment_image(img)
def _measure_uniformity(image, patch_size=5, n_patches=100):
pshalf = (patch_size - 1) // 2
grad_x = image[:, 1:].astype(np.float32) - image[:, :-1].astype(
np.float32)
grad_y = image[1:, :].astype(np.float32) - image[:-1, :].astype(
np.float32)
grad = np.abs(grad_x[1:, :] + grad_y[:, 1:])
points_y = np.random.randint(0, image.shape[0], size=(n_patches, ))
points_x = np.random.randint(0, image.shape[0], size=(n_patches, ))
stds = []
for y, x in zip(points_y, points_x):
bb = ia.BoundingBox(x1=x - pshalf,
y1=y - pshalf,
x2=x + pshalf,
y2=y + pshalf)
patch = bb.extract_from_image(grad)
stds.append(np.std(patch))
return 1 / (1 + np.std(stds))
assert _measure_uniformity(img_aug_ununiform) < _measure_uniformity(
img_aug_uniform)
def get_seq(flag_normal, flag_affine, flag_noise, flag_snow, flag_cloud,
flag_fog, flag_snowflakes, flag_rain, flag_dropout):
if flag_normal:
seq_list = [
iaa.SomeOf((1, 2), [
iaa.LinearContrast((0.5, 2.0), per_channel=0.5),
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
])
]
else:
seq_list = []
if flag_affine:
seq_list.append(
iaa.Sometimes(
0.7,
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))))
if flag_noise:
seq_list.append(
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 11)),
]))
if flag_snow:
seq_list.append(
iaa.FastSnowyLandscape(lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0)))
elif flag_cloud:
seq_list.append(iaa.Clouds())
elif flag_fog:
seq_list.append(iaa.Fog())
elif flag_snowflakes:
seq_list.append(
iaa.Snowflakes(flake_size=(0.2, 0.7), speed=(0.007, 0.03)))
elif flag_rain:
seq_list.append(iaa.Rain())
if flag_dropout:
seq_list.append(
iaa.OneOf([
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),
]))
return iaa.Sequential(seq_list, random_order=True)
def chapter_augmenters_snowflakes():
fn_start = "weather/snowflakes"
image = LANDSCAPE_IMAGE
aug = iaa.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.05))
run_and_save_augseq(
fn_start + ".jpg", aug,
[image for _ in range(4*2)], cols=4, rows=2)
def test_zero_sized_axes(self):
shapes = [(0, 0, 3), (0, 1, 3), (1, 0, 3)]
for shape in shapes:
with self.subTest(shape=shape):
image = np.zeros(shape, dtype=np.uint8)
aug = iaa.Snowflakes()
image_aug = aug(image=image)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == shape
def main():
for size in [0.1, 0.2, 1.0]:
image = imageio.imread(
"https://upload.wikimedia.org/wikipedia/commons/8/89/Kukle%2CCzech_Republic..jpg",
format="jpg")
image = ia.imresize_single_image(image, size, "cubic")
print(image.shape)
augs = [("iaa.Snowflakes()", iaa.Snowflakes())]
for descr, aug in augs:
print(descr)
images_aug = aug.augment_images([image] * 64)
ia.imshow(ia.draw_grid(images_aug))
def imgaug_snowflake(images, base_save_path):
snowflake = iaa.Snowflakes((0.01, 0.04), (0.1, 0.5), (0.3, 0.6),
(0.4, 0.8), (-30, 30), (0.007, 0.03))
snowflake_imgs = snowflake.augment_images(images)
snowflake_path = '\\snowflake\\'
if not os.path.exists(base_save_path + snowflake_path):
os.mkdir(base_save_path + snowflake_path)
name_index = 0
for img in snowflake_imgs:
name_index += 1
imageio.imwrite(base_save_path + snowflake_path + 'img_aug_snowflake_' + time.strftime('%Y%m%d_%H',time.localtime()) \
+ '_' + str(name_index) + '.jpg', img)
def imgaug_leftrotate_plus_snowflake(images, rotate_degree, base_save_path):
left_rotate = iaa.Affine(rotate=rotate_degree)
snowflake = iaa.Snowflakes((0.01, 0.04), (0.1, 0.5), (0.3, 0.6),
(0.4, 0.8), (-30, 30), (0.007, 0.03))
seq = iaa.Sequential([left_rotate, snowflake])
dst_imgs = seq.augment_images(images)
left_rotate_path = '\\l_rotate_plus_snow\\'
if not os.path.exists(base_save_path + left_rotate_path):
os.mkdir(base_save_path + left_rotate_path)
name_index = 0
for img in dst_imgs:
name_index += 1
imageio.imwrite(base_save_path + left_rotate_path + 'img_aug_l_rotate_snow_' + time.strftime('%Y%m%d_%H',time.localtime()) \
+ '_' + str(name_index) + '.jpg', img)
def do_all_aug(image):
do_aug(image, iaa.Noop(name="origin"))
do_aug(image, iaa.Crop((0, 10))) # 切边
do_aug(image, iaa.GaussianBlur((0, 3)))
do_aug(image, iaa.AverageBlur(1, 7))
do_aug(image, iaa.MedianBlur(1, 7))
do_aug(image, iaa.Sharpen())
do_aug(image, iaa.BilateralBlur()) # 既噪音又模糊，叫双边
do_aug(image, iaa.MotionBlur())
do_aug(image, iaa.MeanShiftBlur())
do_aug(image, iaa.GammaContrast())
do_aug(image, iaa.SigmoidContrast())
do_aug(image,
iaa.Affine(shear={
'x': (-10, 10),
'y': (-10, 10)
}, mode="edge")) # shear：x轴往左右偏离的像素书，(a,b)是a,b间随机值，[a,b]是二选一
do_aug(image,
iaa.Affine(shear={
'x': (-10, 10),
'y': (-10, 10)
}, mode="edge")) # shear：x轴往左右偏离的像素书，(a,b)是a,b间随机值，[a,b]是二选一
do_aug(image, iaa.Rotate(rotate=(-10, 10), mode="edge"))
do_aug(image, iaa.PiecewiseAffine()) # 局部点变形
do_aug(image, iaa.Fog())
do_aug(image, iaa.Clouds())
do_aug(image, iaa.Snowflakes(flake_size=(0.1, 0.2),
density=(0.005, 0.025)))
do_aug(
image,
iaa.Rain(
nb_iterations=1,
drop_size=(0.05, 0.1),
speed=(0.04, 0.08),
))
do_aug(
image,
iaa.ElasticTransformation(alpha=(0.0, 20.0),
sigma=(3.0, 5.0),
mode="nearest"))
do_aug(image, iaa.AdditiveGaussianNoise(scale=(0, 10)))
do_aug(image, iaa.AdditiveLaplaceNoise(scale=(0, 10)))
do_aug(image, iaa.AdditivePoissonNoise(lam=(0, 10)))
do_aug(image, iaa.Salt((0, 0.02)))
do_aug(image, iaa.Pepper((0, 0.02)))
iaa.AllChannelsCLAHE(clip_limit=(1, 10)),
iaa.AllChannelsHistogramEqualization(),
iaa.GammaContrast((0.5, 1.5), per_channel=True),
iaa.GammaContrast((0.5, 1.5)),
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6), per_channel=True),
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)),
iaa.HistogramEqualization(),
iaa.Sharpen(alpha=0.5)
]),
iaa.OneOf([
iaa.AveragePooling([2, 3]),
iaa.MaxPooling(([2, 3], [2, 3])),
]),
iaa.OneOf([
iaa.Clouds(),
iaa.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.05)),
iaa.Rain(speed=(0.1, 0.3))
])
],
random_order=True)
def get_color_augmentation(augment_prob):
return iaa.Sometimes(augment_prob, aug_transform).augment_image
class SegCompose(object):
def __init__(self, augmenters):
super().__init__()
self.augmenters = augmenters
# 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:
def save_aug_file(seq:iaa.Sequential, pathstr:str):
for dirr in os.listdir(directory):
for image in os.listdir(directory + dirr):
imgname1 = directory + dirr+"/"+image
img = cv2.imread(imgname1)
img_aug = seq.augment_image(img)
path = dir_res + pathstr+"/"+dirr+"/"
pathname = os.makedirs(path, exist_ok=True)
cv2.imwrite(path+image, img_aug)
seq = iaa.Sequential([iaa.Snowflakes(flake_size=(0.7, 0.95), speed=(0.001, 0.03))])
save_aug_file(seq,"demoSnow")
# seq = iaa.Sequential([iaa.FastSnowyLandscape(lightness_threshold=(50, 195), lightness_multiplier=(3.0, 4.0))])
# save_aug_file(seq,"demoSnowLand")
# In[43]:
seq = iaa.Sequential([iaa.Clouds()])
save_aug_file(seq,"demoClouds")
import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np
import imageio
ia.seed(1)
img = imageio.imread("test.jpg") #read you image
images = np.array(
[img for _ in range(32)], dtype=np.uint8) # 32 means creat 32 enhanced images using following methods.
seq = iaa.Sequential(
[
iaa.FastSnowyLandscape(64,1.5),
iaa.Snowflakes(0.075,0.9,0.7,0.8,30,0.03)
],
random_order=True) # apply augmenters in random order
images_aug = seq.augment_images(images)
for i in range(32):
imageio.imwrite(str(i)+'new.jpg', images_aug[i]) #write all changed images
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
seq = iaa.Sequential([
iaa.Multiply((1.2, 1.5)), # change brightness, doesn't affect keypoints
iaa.Affine(rotate=23, scale=(
0.9, 1.1
)) # rotate by exactly 23 deg and scale to 90-10%, affects keypoints
])
Augmentations.append([augtype, seq])
augtype = 'fog'
seq = iaa.Sequential([iaa.Fog()])
Augmentations.append([augtype, seq])
augtype = 'snow'
seq = iaa.Sequential([
iaa.Snowflakes(flake_size=(.2, .5),
density=(0.005, 0.07),
speed=(0.01, 0.05))
])
Augmentations.append([augtype, seq])
for ind, imname in enumerate(Dataframe.index):
image = imresize(imread(os.path.join('montblanc_images', imname)),
size=scale)
ny, nx, nc = np.shape(image)
kpts = []
for i in individuals:
for b in bodyparts:
x, y = Dataframe.iloc[ind][scorer][i][b]['x'], Dataframe.iloc[ind][
scorer][i][b]['y']
if np.isfinite(x) and np.isfinite(y):
def do_random(image, pos_list):
# 1.先任选5种影响位置的效果之一做位置变换
seq = iaa.Sequential([
iaa.Sometimes(
0.5,
[
iaa.Crop((0, 10)), # 切边, (0到10个像素采样)
]),
iaa.Sometimes(
0.5,
[
iaa.Affine(shear={
'x': (-10, 10),
'y': (-10, 10)
}, mode="edge"),
iaa.Rotate(rotate=(-10, 10), mode="edge"), # 旋转
]),
iaa.Sometimes(
0.5,
[
iaa.PiecewiseAffine(), # 局部仿射
iaa.ElasticTransformation( # distort扭曲变形
alpha=(0.0, 20.0),
sigma=(3.0, 5.0),
mode="nearest"),
]),
# 18种位置不变的效果
iaa.SomeOf(
(1, 3),
[
iaa.GaussianBlur(),
iaa.AverageBlur(),
iaa.MedianBlur(),
iaa.Sharpen(),
iaa.BilateralBlur(), # 既噪音又模糊，叫双边,
iaa.MotionBlur(),
iaa.MeanShiftBlur(),
iaa.GammaContrast(),
iaa.SigmoidContrast(),
iaa.Fog(),
iaa.Clouds(),
iaa.Snowflakes(flake_size=(0.1, 0.2), density=(0.005, 0.025)),
iaa.Rain(nb_iterations=1,
drop_size=(0.05, 0.1),
speed=(0.04, 0.08)),
iaa.AdditiveGaussianNoise(scale=(0, 10)),
iaa.AdditiveLaplaceNoise(scale=(0, 10)),
iaa.AdditivePoissonNoise(lam=(0, 10)),
iaa.Salt((0, 0.02)),
iaa.Pepper((0, 0.02))
])
])
polys = [ia.Polygon(pos) for pos in pos_list]
polygons = ia.PolygonsOnImage(polys, shape=image.shape)
images_aug, polygons_aug = seq(images=[image], polygons=polygons)
image = images_aug[0]
image = polygons_aug.draw_on_image(image, size=2)
new_polys = []
for p in polygons_aug.polygons:
new_polys.append(p.coords)
polys = np.array(new_polys, np.int32).tolist()
return image, polys
def test_pickleable(self):
aug = iaa.Snowflakes(random_state=1)
runtest_pickleable_uint8_img(aug, iterations=3, shape=(20, 20, 3))
def augment(self):
few_instances = self.df[self.df['counts'] < self.df['counts'].median()]
aug_list = list(few_instances['instance_name'])
label_files = Path(self.anno_dir).glob("*.json")
aug = iaa.Sequential([
iaa.AdditiveGaussianNoise(scale=10),
# The following transformations will change the polygon
# iaa.Affine(rotate=(-0.05, 0.05), translate_percent=(-0.05, 0.05), scale=(0.8, 1.2),
# mode=["constant", "edge"], cval=0),
# iaa.CoarseDropout(0.1,size_px=8),
# iaa.Fliplr(0.5),
#iaa.PerspectiveTransform((0.01, 0.01)),
#iaa.LinearContrast((0.8, 1.2), per_channel=0.5),
iaa.Sometimes(0.05, iaa.Snowflakes()),
iaa.AddToHueAndSaturation((-50, 50)),
])
for lf in label_files:
label_file = json.loads(lf.read_bytes())
img_path = lf.with_suffix('.jpg')
img = imageio.imread(img_path)
image_polys = np.copy(img)
polys = []
is_aug = False
aug_dir = img_path.parent.parent / (img_path.parent.stem + '_aug')
aug_dir.mkdir(exist_ok=True)
for i, shape in enumerate(label_file['shapes']):
label = shape['label']
if label in aug_list:
is_aug = True
points = shape['points']
polygon = Polygon(points, [label])
psoi = ia.PolygonsOnImage([polygon],
shape=image_polys.shape)
instance_counts_median = self.df['counts'].median()
instance_counts = (self.df[self.df['instance_name'] ==
label]['counts'].values[0])
for j in range(
int(instance_counts_median - instance_counts)):
aug_img, psoi_aug = aug(image=image_polys,
polygons=psoi)
aug_img_path = aug_dir / \
(img_path.stem + f'_{j}_aug.jpg')
aug_json_path = aug_img_path.with_suffix('.json')
aug_points = psoi_aug.polygons[0].exterior
imageio.imsave(aug_img_path, aug_img, '.jpg')
label_file["imageData"] = None
label_file['imagePath'] = aug_img_path.name
with open(aug_json_path, "w") as f:
json.dump(label_file,
f,
ensure_ascii=False,
indent=2)
label_file['shapes'][i]['points'] = aug_points.tolist()
self.augment_list.append(lf)
return set(self.augment_list)
def get_aug(self):
#sometimes_bg = lambda aug: iaa.Sometimes(0.3, aug)
sometimes_contrast = lambda aug: iaa.Sometimes(0.3, aug)
sometimes_noise = lambda aug: iaa.Sometimes(0.6, aug)
sometimes_blur = lambda aug: iaa.Sometimes(0.6, aug)
sometimes_degrade_quality = lambda aug: iaa.Sometimes(0.9, aug)
sometimes_blend = lambda aug: iaa.Sometimes(0.2, aug)
seq = iaa.Sequential(
[
# crop some of the images by 0-30% of their height/width
# Execute 0 to 4 of the following (less important) augmenters per
# image. Don't execute all of them, as that would often be way too
# strong.
# iaa.SomeOf((0, 4),
# [
# change the background color of some of the images chosing any one technique
# sometimes_bg(iaa.OneOf([
# iaa.AddToHueAndSaturation((-60, 60)),
# iaa.Multiply((0.6, 1), per_channel=True),
# ])),
#change the contrast of the input images chosing any one technique
sometimes_contrast(iaa.OneOf([
iaa.LinearContrast((0.5,1.5)),
iaa.SigmoidContrast(gain=(3, 5), cutoff=(0.4, 0.6)),
iaa.CLAHE(tile_grid_size_px=(3, 21)),
iaa.GammaContrast((0.5,1.0))
])),
#add noise to the input images chosing any one technique
sometimes_noise(iaa.OneOf([
iaa.AdditiveGaussianNoise(scale=(3,8)),
iaa.CoarseDropout((0.001,0.01), size_percent=0.5),
iaa.AdditiveLaplaceNoise(scale=(3,10)),
iaa.CoarsePepper((0.001,0.01), size_percent=(0.5)),
iaa.AdditivePoissonNoise(lam=(3.0,10.0)),
iaa.Pepper((0.001,0.01)),
iaa.Snowflakes(),
iaa.Dropout(0.01,0.01),
])),
#add blurring techniques to the input image
sometimes_blur(iaa.OneOf([
iaa.AverageBlur(k=(3)),
iaa.GaussianBlur(sigma=(1.0)),
])),
# add techniques to degrade the iamge quality
sometimes_degrade_quality(iaa.OneOf([
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
iaa.Sharpen(alpha=(0.5), lightness=(0.75,1.5)),
iaa.BlendAlphaSimplexNoise(
foreground=iaa.Multiply(iap.Choice([1.5]), per_channel=False)
)
])),
# blend some patterns in the background
sometimes_blend(iaa.OneOf([
iaa.BlendAlpha(
factor=(0.6,0.8),
foreground=iaa.Sharpen(1.0, lightness=1),
background=iaa.CoarseDropout(p=0.1, size_px=np.random.randint(30))),
iaa.BlendAlphaFrequencyNoise(exponent=(-4),
foreground=iaa.Multiply(iap.Choice([0.5]), per_channel=False)
),
iaa.BlendAlphaSimplexNoise(
foreground=iaa.Multiply(iap.Choice([0.5]), per_channel=True)
)
])),
])
return seq
def augument(self, image, bbox_list):
seq = iaa.Sequential([
# 变形
iaa.Sometimes(
0.6,
[
iaa.OneOf([
iaa.Affine(shear={
'x': (-1.5, 1.5),
'y': (-1.5, 1.5)
},
mode="edge"), # 仿射变化程度，单位像素
iaa.Rotate(rotate=(-1, 1), mode="edge"), # 旋转,单位度
])
]),
# 扭曲
iaa.Sometimes(
0.5,
[
iaa.OneOf([
iaa.PiecewiseAffine(
scale=(0, 0.02), nb_rows=2, nb_cols=2), # 局部仿射
iaa.ElasticTransformation( # distort扭曲变形
alpha=(0, 3), # 扭曲程度
sigma=(0.8, 1), # 扭曲后的平滑程度
mode="nearest"),
]),
]),
# 模糊
iaa.Sometimes(
0.5,
[
iaa.OneOf([
iaa.GaussianBlur(sigma=(0, 0.7)),
iaa.AverageBlur(k=(1, 3)),
iaa.MedianBlur(k=(1, 3)),
iaa.BilateralBlur(
d=(1, 5),
sigma_color=(10, 200),
sigma_space=(10, 200)), # 既噪音又模糊，叫双边,
iaa.MotionBlur(k=(3, 5)),
iaa.Snowflakes(flake_size=(0.1, 0.2),
density=(0.005, 0.025)),
iaa.Rain(nb_iterations=1,
drop_size=(0.05, 0.1),
speed=(0.04, 0.08)),
])
]),
# 锐化
iaa.Sometimes(0.3, [
iaa.OneOf([
iaa.Sharpen(),
iaa.GammaContrast(),
iaa.SigmoidContrast()
])
]),
# 噪音
iaa.Sometimes(0.3, [
iaa.OneOf([
iaa.AdditiveGaussianNoise(scale=(1, 5)),
iaa.AdditiveLaplaceNoise(scale=(1, 5)),
iaa.AdditivePoissonNoise(lam=(1, 5)),
iaa.Salt((0, 0.02)),
iaa.Pepper((0, 0.02))
])
]),
# 剪切
iaa.Sometimes(
0.8,
[
iaa.OneOf([
iaa.Crop((0, 2)), # 切边, (0到10个像素采样)
])
]),
])
assert bbox_list is None or type(bbox_list) == list
if bbox_list is None or len(bbox_list) == 0:
polys = None
else:
polys = [ia.Polygon(pos) for pos in bbox_list]
polys = ia.PolygonsOnImage(polys, shape=image.shape)
# 处理部分或者整体出了图像的范围的多边形，参考：https://imgaug.readthedocs.io/en/latest/source/examples_bounding_boxes.html
polys = polys.remove_out_of_image().clip_out_of_image()
images_aug, polygons_aug = seq(images=[image], polygons=polys)
image = images_aug[0]
if polygons_aug is None:
polys = None
else:
polys = []
for p in polygons_aug.polygons:
polys.append(p.coords)
polys = np.array(polys, np.int32).tolist() # (N,2)
return image, polys
def __init__(self, param=0.5):
self.param = 0.5
self.seq = iaa.Sequential([iaa.Snowflakes(0.4), iaa.Fog()])
iaa.Invert(0.01, per_channel=0.5),
iaa.AddToHueAndSaturation((-1, 1)),
iaa.MultiplyHueAndSaturation((-1, 1))
]),
# Change brightness and contrast
iaa.OneOf([
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.GammaContrast(gamma=(0.5, 1.75), per_channel=0.5),
iaa.SigmoidContrast(cutoff=(0, 1), per_channel=0.5),
iaa.LogContrast(gain=(0.5, 1), per_channel=0.5),
iaa.LinearContrast(alpha=(0.25, 1.75), per_channel=0.5),
iaa.HistogramEqualization()
]),
sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5),
sigma=0.25)),
# move pixels locally around (with random strengths)
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))),
# sometimes move parts of the image around
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.2))),
iaa.JpegCompression((0.1, 1))
]
),
# With 10 % probability apply one the of the weather conditions
iaa.Sometimes(0.2, iaa.OneOf([
iaa.Clouds(),
iaa.Fog(),
iaa.Snowflakes()
]))
])
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
