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 aug_probaV(bg_color=255):
aug_list = []
# Compatible with 16-bit image
aug_list.append(
augmenters.AllChannelsCLAHE(clip_limit=(10, 80),
tile_grid_size_px=(4, 8)))
# aug_list.append(augmenters.normalize_images())
return augmenters.Sequential(aug_list, random_order=False)
def chapter_augmenters_allchannelsclahe():
fn_start = "contrast/allchannelsclahe"
aug = iaa.AllChannelsCLAHE()
run_and_save_augseq(fn_start + ".jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.AllChannelsCLAHE(clip_limit=(1, 10))
run_and_save_augseq(fn_start + "_random_clip_limit.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True)
run_and_save_augseq(fn_start + "_per_channel.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
def main():
parser = argparse.ArgumentParser(description="Contrast check script")
parser.add_argument("--per_channel", dest="per_channel", action="store_true")
args = parser.parse_args()
augs = []
for p in [0.25, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("GammaContrast " + str(p), iaa.GammaContrast(p, per_channel=args.per_channel)))
for cutoff in [0.25, 0.5, 0.75]:
for gain in [5, 10, 15, 20, 25]:
augs.append(("SigmoidContrast " + str(cutoff) + " " + str(gain), iaa.SigmoidContrast(gain, cutoff, per_channel=args.per_channel)))
for gain in [0.0, 0.25, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("LogContrast " + str(gain), iaa.LogContrast(gain, per_channel=args.per_channel)))
for alpha in [-1.0, 0.5, 0, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("LinearContrast " + str(alpha), iaa.LinearContrast(alpha, per_channel=args.per_channel)))
augs.append(("AllChannelsHistogramEqualization", iaa.AllChannelsHistogramEqualization()))
augs.append(("HistogramEqualization (Lab)", iaa.HistogramEqualization(to_colorspace=iaa.HistogramEqualization.Lab)))
augs.append(("HistogramEqualization (HSV)", iaa.HistogramEqualization(to_colorspace=iaa.HistogramEqualization.HSV)))
augs.append(("HistogramEqualization (HLS)", iaa.HistogramEqualization(to_colorspace=iaa.HistogramEqualization.HLS)))
for clip_limit in [0.1, 1, 5, 10]:
for tile_grid_size_px in [3, 7]:
augs.append(("AllChannelsCLAHE %d %dx%d" % (clip_limit, tile_grid_size_px, tile_grid_size_px),
iaa.AllChannelsCLAHE(clip_limit=clip_limit, tile_grid_size_px=tile_grid_size_px,
per_channel=args.per_channel)))
for clip_limit in [1, 5, 10, 100, 200]:
for tile_grid_size_px in [3, 7, 15]:
augs.append(("CLAHE %d %dx%d" % (clip_limit, tile_grid_size_px, tile_grid_size_px),
iaa.CLAHE(clip_limit=clip_limit, tile_grid_size_px=tile_grid_size_px)))
images = [data.astronaut()] * 16
images = ia.imresize_many_images(np.uint8(images), (128, 128))
for name, aug in augs:
print("-----------")
print(name)
print("-----------")
images_aug = aug.augment_images(images)
images_aug[0] = images[0]
grid = ia.draw_grid(images_aug, rows=4, cols=4)
ia.imshow(grid)
"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
import random
from imgaug import augmenters as iaa
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
import cv2
from verifier import fitCoords
aug = iaa.Sequential(
[
iaa.Sometimes(0.5, iaa.Crop(percent=(0.1, 0.3), keep_size=False)),
iaa.Sometimes(0.5, iaa.MotionBlur(15, random.randint(0, 360))),
iaa.OneOf([
iaa.AllChannelsCLAHE(clip_limit=10),
iaa.AdditiveGaussianNoise(scale=(10, 35)),
iaa.FastSnowyLandscape(lightness_threshold=(50, 115), from_colorspace="BGR")
]),
# iaa.Sometimes(0.25, iaa.Affine(scale={"x": (1.0, 1.2), "y": (1.0, 1.2)})),
iaa.Sometimes(0.25, iaa.Multiply((0.85, 1.15))),
iaa.Sometimes(0.25, iaa.ContrastNormalization((0.85, 1.15))),
# iaa.Affine(rotate=(0, 360))
], random_order=False
)
def customAugmentations(image, box):
y1, x1, y2, x2 = box
bb = BoundingBox(x1=x1, x2=x2, y1=y1, y2=y2)
augImage, augBox = aug(image=image, bounding_boxes=bb)
augBox = fitCoords([augBox.y1_int, augBox.x1_int, augBox.y2_int, augBox.x2_int], augImage.shape[:2])
def transform(self, image: np.ndarray, target: str,
condition: int) -> Tuple[torch.Tensor, torch.Tensor, int]:
"""Transforms and normalizes the data. If in training mode the data is augmentated.
Args:
image (np.ndarray): Image to transform
target (str): Training target
condition (int): Condition
Returns:
Tuple[torch.Tensor, torch.Tensor, int]: Augmented image, target and condition
"""
# Resize
resize = iaa.Resize({"height": 224, "width": 224})
image = resize.augment_image(image)
# Random horizontal flipping and erase
if self.train:
if random.random() > 0.5:
# flip image
flip = iaa.HorizontalFlip(1.0)
image = flip.augment_image(image)
# flip class
if target == "a":
target = "d"
elif target == "d":
target = "a"
# flip condition
if condition == 2:
condition = 4
elif condition == 4:
condition = 2
#imgaug
seq = iaa.Sequential([
iaa.Sometimes(0.5, iaa.Affine(rotate=(-15, 15))),
iaa.Sometimes(0.3, iaa.EdgeDetect(alpha=(0.3, 0.8))),
iaa.Sometimes(0.5, iaa.MotionBlur(k=iap.Choice([3, 5, 7]))),
iaa.OneOf([
iaa.Dropout(p=(0, 0.3), per_channel=0.5),
iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.09))
]),
iaa.Sometimes(0.5, iaa.AllChannelsCLAHE(clip_limit=(1, 10)))
])
image = seq.augment_image(image)
# Transform to tensor
image = TF.to_tensor(image)
# Transform to one hot encoding
target = torch.tensor(self.target_dict[target])
#normalize image to fit pretrained vgg model
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
image = normalize(image)
return image, target, condition
def build_augmentation_pipeline(self, apply_prob=0.5):
cfg = self.cfg
sometimes = lambda aug: iaa.Sometimes(apply_prob, aug)
pipeline = iaa.Sequential(random_order=False)
pre_resize = cfg.get("pre_resize")
crop_sampling = cfg.get("crop_sampling", "hybrid")
if pre_resize:
width, height = pre_resize
pipeline.add(iaa.Resize({"height": height, "width": width}))
if crop_sampling == "none":
self.default_size = width, height
if crop_sampling != "none":
# Add smart, keypoint-aware image cropping
pipeline.add(iaa.PadToFixedSize(*self.default_size))
pipeline.add(
augmentation.KeypointAwareCropToFixedSize(
*self.default_size,
cfg.get("max_shift", 0.4),
crop_sampling,
))
if cfg.get("fliplr", False):
opt = cfg.get("fliplr", False)
if type(opt) == int:
pipeline.add(sometimes(iaa.Fliplr(opt)))
else:
pipeline.add(sometimes(iaa.Fliplr(0.5)))
if cfg.get("rotation", False):
opt = cfg.get("rotation", False)
if type(opt) == int:
pipeline.add(sometimes(iaa.Affine(rotate=(-opt, opt))))
else:
pipeline.add(sometimes(iaa.Affine(rotate=(-10, 10))))
if cfg.get("hist_eq", False):
pipeline.add(sometimes(iaa.AllChannelsHistogramEqualization()))
if cfg.get("motion_blur", False):
opts = cfg.get("motion_blur", False)
if type(opts) == list:
opts = dict(opts)
pipeline.add(sometimes(iaa.MotionBlur(**opts)))
else:
pipeline.add(sometimes(iaa.MotionBlur(k=7, angle=(-90, 90))))
if cfg.get("covering", False):
pipeline.add(
sometimes(
iaa.CoarseDropout(
(0, 0.02),
size_percent=(0.01, 0.05)))) # , per_channel=0.5)))
if cfg.get("elastic_transform", False):
pipeline.add(sometimes(iaa.ElasticTransformation(sigma=5)))
if cfg.get("gaussian_noise", False):
opt = cfg.get("gaussian_noise", False)
if type(opt) == int or type(opt) == float:
pipeline.add(
sometimes(
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, opt),
per_channel=0.5)))
else:
pipeline.add(
sometimes(
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.05 * 255),
per_channel=0.5)))
if cfg.get("grayscale", False):
pipeline.add(sometimes(iaa.Grayscale(alpha=(0.5, 1.0))))
def get_aug_param(cfg_value):
if isinstance(cfg_value, dict):
opt = cfg_value
else:
opt = {}
return opt
cfg_cnt = cfg.get("contrast", {})
cfg_cnv = cfg.get("convolution", {})
contrast_aug = ["histeq", "clahe", "gamma", "sigmoid", "log", "linear"]
for aug in contrast_aug:
aug_val = cfg_cnt.get(aug, False)
cfg_cnt[aug] = aug_val
if aug_val:
cfg_cnt[aug + "ratio"] = cfg_cnt.get(aug + "ratio", 0.1)
convolution_aug = ["sharpen", "emboss", "edge"]
for aug in convolution_aug:
aug_val = cfg_cnv.get(aug, False)
cfg_cnv[aug] = aug_val
if aug_val:
cfg_cnv[aug + "ratio"] = cfg_cnv.get(aug + "ratio", 0.1)
if cfg_cnt["histeq"]:
opt = get_aug_param(cfg_cnt["histeq"])
pipeline.add(
iaa.Sometimes(cfg_cnt["histeqratio"],
iaa.AllChannelsHistogramEqualization(**opt)))
if cfg_cnt["clahe"]:
opt = get_aug_param(cfg_cnt["clahe"])
pipeline.add(
iaa.Sometimes(cfg_cnt["claheratio"],
iaa.AllChannelsCLAHE(**opt)))
if cfg_cnt["log"]:
opt = get_aug_param(cfg_cnt["log"])
pipeline.add(
iaa.Sometimes(cfg_cnt["logratio"], iaa.LogContrast(**opt)))
if cfg_cnt["linear"]:
opt = get_aug_param(cfg_cnt["linear"])
pipeline.add(
iaa.Sometimes(cfg_cnt["linearratio"],
iaa.LinearContrast(**opt)))
if cfg_cnt["sigmoid"]:
opt = get_aug_param(cfg_cnt["sigmoid"])
pipeline.add(
iaa.Sometimes(cfg_cnt["sigmoidratio"],
iaa.SigmoidContrast(**opt)))
if cfg_cnt["gamma"]:
opt = get_aug_param(cfg_cnt["gamma"])
pipeline.add(
iaa.Sometimes(cfg_cnt["gammaratio"], iaa.GammaContrast(**opt)))
if cfg_cnv["sharpen"]:
opt = get_aug_param(cfg_cnv["sharpen"])
pipeline.add(
iaa.Sometimes(cfg_cnv["sharpenratio"], iaa.Sharpen(**opt)))
if cfg_cnv["emboss"]:
opt = get_aug_param(cfg_cnv["emboss"])
pipeline.add(
iaa.Sometimes(cfg_cnv["embossratio"], iaa.Emboss(**opt)))
if cfg_cnv["edge"]:
opt = get_aug_param(cfg_cnv["edge"])
pipeline.add(
iaa.Sometimes(cfg_cnv["edgeratio"], iaa.EdgeDetect(**opt)))
return pipeline
y_min = bb_box.y1
x_max = bb_box.x2
y_max = bb_box.y2
cls_id = bb_box.label
x_cen, y_cen, w, h = xyxy2xywh(x_min, y_min, x_max, y_max)
f.write("%d %.06f %.06f %.06f %.06f\n" %
(cls_id, x_cen, y_cen, w, h))
Width = 640
Height = 640
blur = iaa.AverageBlur(k=(2, 11)) #! 2~11 random
emboss = iaa.Emboss(alpha=(1.0, 1.0), strength=(2.0, 2.0))
gray = iaa.RemoveSaturation(from_colorspace=iaa.CSPACE_BGR)
contrast = iaa.AllChannelsCLAHE(clip_limit=(10, 10), per_channel=True)
bright = iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30))
color = iaa.pillike.EnhanceColor()
sharpen = iaa.Sharpen(alpha=(0.5, 1.0)) #! 0.5 ~ 1.0 random
edge = iaa.pillike.FilterEdgeEnhance()
augmentations = [[bright], [emboss], [color],
[edge]] #! choice augmentation ##
rotates = [[iaa.Affine(rotate=90)], [iaa.Affine(rotate=180)],
[iaa.Affine(rotate=270)]]
flip = iaa.Fliplr(1.0) #! 100% left & right
dir = "C:\\Users\\jeongseokoon\\AI-hub\\data\\original\\"
save_aug_dir = "C:\\Users\\jeongseokoon\\AI-hub\\data\\images\\" #! Absolute path
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
def build_augmentation_pipeline(self,
height=None,
width=None,
apply_prob=0.5):
sometimes = lambda aug: iaa.Sometimes(apply_prob, aug)
pipeline = iaa.Sequential(random_order=False)
cfg = self.cfg
if cfg["mirror"]:
opt = cfg["mirror"] # fliplr
if type(opt) == int:
pipeline.add(sometimes(iaa.Fliplr(opt)))
else:
pipeline.add(sometimes(iaa.Fliplr(0.5)))
if cfg["rotation"] > 0:
pipeline.add(
iaa.Sometimes(
cfg["rotratio"],
iaa.Affine(rotate=(-cfg["rotation"], cfg["rotation"])),
))
if cfg["motion_blur"]:
opts = cfg["motion_blur_params"]
pipeline.add(sometimes(iaa.MotionBlur(**opts)))
if cfg["covering"]:
pipeline.add(
sometimes(
iaa.CoarseDropout(0.02, size_percent=0.3,
per_channel=0.5)))
if cfg["elastic_transform"]:
pipeline.add(sometimes(iaa.ElasticTransformation(sigma=5)))
if cfg.get("gaussian_noise", False):
opt = cfg.get("gaussian_noise", False)
if type(opt) == int or type(opt) == float:
pipeline.add(
sometimes(
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, opt),
per_channel=0.5)))
else:
pipeline.add(
sometimes(
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.05 * 255),
per_channel=0.5)))
if cfg.get("grayscale", False):
pipeline.add(sometimes(iaa.Grayscale(alpha=(0.5, 1.0))))
def get_aug_param(cfg_value):
if isinstance(cfg_value, dict):
opt = cfg_value
else:
opt = {}
return opt
cfg_cnt = cfg.get("contrast", {})
cfg_cnv = cfg.get("convolution", {})
contrast_aug = ["histeq", "clahe", "gamma", "sigmoid", "log", "linear"]
for aug in contrast_aug:
aug_val = cfg_cnt.get(aug, False)
cfg_cnt[aug] = aug_val
if aug_val:
cfg_cnt[aug + "ratio"] = cfg_cnt.get(aug + "ratio", 0.1)
convolution_aug = ["sharpen", "emboss", "edge"]
for aug in convolution_aug:
aug_val = cfg_cnv.get(aug, False)
cfg_cnv[aug] = aug_val
if aug_val:
cfg_cnv[aug + "ratio"] = cfg_cnv.get(aug + "ratio", 0.1)
if cfg_cnt["histeq"]:
opt = get_aug_param(cfg_cnt["histeq"])
pipeline.add(
iaa.Sometimes(cfg_cnt["histeqratio"],
iaa.AllChannelsHistogramEqualization(**opt)))
if cfg_cnt["clahe"]:
opt = get_aug_param(cfg_cnt["clahe"])
pipeline.add(
iaa.Sometimes(cfg_cnt["claheratio"],
iaa.AllChannelsCLAHE(**opt)))
if cfg_cnt["log"]:
opt = get_aug_param(cfg_cnt["log"])
pipeline.add(
iaa.Sometimes(cfg_cnt["logratio"], iaa.LogContrast(**opt)))
if cfg_cnt["linear"]:
opt = get_aug_param(cfg_cnt["linear"])
pipeline.add(
iaa.Sometimes(cfg_cnt["linearratio"],
iaa.LinearContrast(**opt)))
if cfg_cnt["sigmoid"]:
opt = get_aug_param(cfg_cnt["sigmoid"])
pipeline.add(
iaa.Sometimes(cfg_cnt["sigmoidratio"],
iaa.SigmoidContrast(**opt)))
if cfg_cnt["gamma"]:
opt = get_aug_param(cfg_cnt["gamma"])
pipeline.add(
iaa.Sometimes(cfg_cnt["gammaratio"], iaa.GammaContrast(**opt)))
if cfg_cnv["sharpen"]:
opt = get_aug_param(cfg_cnv["sharpen"])
pipeline.add(
iaa.Sometimes(cfg_cnv["sharpenratio"], iaa.Sharpen(**opt)))
if cfg_cnv["emboss"]:
opt = get_aug_param(cfg_cnv["emboss"])
pipeline.add(
iaa.Sometimes(cfg_cnv["embossratio"], iaa.Emboss(**opt)))
if cfg_cnv["edge"]:
opt = get_aug_param(cfg_cnv["edge"])
pipeline.add(
iaa.Sometimes(cfg_cnv["edgeratio"], iaa.EdgeDetect(**opt)))
if height is not None and width is not None:
if not cfg.get("crop_by", False):
crop_by = 0.15
else:
crop_by = cfg.get("crop_by", False)
pipeline.add(
iaa.Sometimes(
cfg.get("cropratio", 0.4),
iaa.CropAndPad(percent=(-crop_by, crop_by),
keep_size=False),
))
pipeline.add(iaa.Resize({"height": height, "width": width}))
return pipeline
def augmentation_of_image(self, test_image, output_path):
self.test_image = test_image
self.output_path = output_path
#define the Augmenters
#properties: A range of values signifies that one of these numbers is randmoly chosen for every augmentation for every batch
# Apply affine transformations to each image.
rotate = iaa.Affine(rotate=(-90, 90))
scale = iaa.Affine(scale={
"x": (0.5, 0.9),
"y": (0.5, 0.9)
})
translation = iaa.Affine(translate_percent={
"x": (-0.15, 0.15),
"y": (-0.15, 0.15)
})
shear = iaa.Affine(shear=(-2, 2))
#plagio parallhlogrammo wihthin a range (-8,8)
zoom = iaa.PerspectiveTransform(
scale=(0.01, 0.15),
keep_size=True) # do not change the output size of the image
h_flip = iaa.Fliplr(1.0)
# flip horizontally all images (100%)
v_flip = iaa.Flipud(1.0)
#flip vertically all images
padding = iaa.KeepSizeByResize(
iaa.CropAndPad(percent=(0.05, 0.25))
) #positive values correspond to padding 5%-25% of the image,but keeping the origial output size of the new image
#More augmentations
blur = iaa.GaussianBlur(
sigma=(0, 1.22)
) # blur images with a sigma 0-2,a number ofthis range is randomly chosen everytime.Low values suggested for this application
contrast = iaa.contrast.LinearContrast((0.75, 1.5))
#change the contrast by a factor of 0.75 and 1.5 sampled randomly per image
contrast_channels = iaa.LinearContrast(
(0.75, 1.5), per_channel=True
) #and for 50% of all images also independently per channel:
sharpen = iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5))
#sharpen with an alpha from 0(no sharpening) - 1(full sharpening) and change the lightness form 0.75 to 1.5
gauss_noise = iaa.AdditiveGaussianNoise(
scale=0.111 * 255, per_channel=True
) #some random gaussian noise might occur in cell images,especially when image quality is poor
laplace_noise = iaa.AdditiveLaplaceNoise(
scale=(0, 0.111 * 255)
) #we choose to be in a small range, as it is logical for training the cell images
#Brightness
brightness = iaa.Multiply(
(0.35, 1.65
)) #change brightness between 35% or 165% of the original image
brightness_channels = iaa.Multiply(
(0.5, 1.5), per_channel=0.75
) # change birghtness for 25% of images.For the remaining 75%, change it, but also channel-wise.
#CHANNELS (RGB)=(Red,Green,Blue)
red = iaa.WithChannels(0, iaa.Add(
(10,
100))) #increase each Red-pixels value within the range 10-100
red_rot = iaa.WithChannels(0, iaa.Affine(
rotate=(0, 45))) #rotate each image's red channel by 0-45 degrees
green = iaa.WithChannels(1, iaa.Add(
(10,
100))) #increase each Green-pixels value within the range 10-100
green_rot = iaa.WithChannels(1, iaa.Affine(
rotate=(0,
45))) #rotate each image's green channel by 0-45 degrees
blue = iaa.WithChannels(2, iaa.Add(
(10,
100))) #increase each Blue-pixels value within the range 10-100
blue_rot = iaa.WithChannels(2, iaa.Affine(
rotate=(0, 45))) #rotate each image's blue channel by 0-45 degrees
#colors
channel_shuffle = iaa.ChannelShuffle(1.0)
#shuffle all images of the batch
grayscale = iaa.Grayscale(1.0)
hue_n_saturation = iaa.MultiplyHueAndSaturation(
(0.5, 1.5), per_channel=True
) #change hue and saturation with this range of values for different values
add_hue_saturation = iaa.AddToHueAndSaturation(
(-50, 50),
per_channel=True) #add more hue and saturation to its pixels
#Quantize colors using k-Means clustering
kmeans_color = iaa.KMeansColorQuantization(
n_colors=(4, 16)
) #quantizes to k means 4 to 16 colors (randomly chosen). Quantizes colors up to 16 colors
#Alpha Blending
blend = iaa.AlphaElementwise((0, 1.0), iaa.Grayscale((0, 1.0)))
#blend depending on which value is greater
#Contrast augmentors
clahe = iaa.CLAHE(tile_grid_size_px=((3, 21), [
0, 2, 3, 4, 5, 6, 7
])) #create a clahe contrast augmentor H=(3,21) and W=(0,7)
histogram = iaa.HistogramEqualization(
) #performs histogram equalization
#Augmentation list of metadata augmentors
OneofRed = iaa.OneOf([red])
OneofGreen = iaa.OneOf([green])
OneofBlue = iaa.OneOf([blue])
contrast_n_shit = iaa.OneOf(
[contrast, brightness, brightness_channels])
SomeAug = iaa.SomeOf(
2, [rotate, scale, translation, shear, h_flip, v_flip],
random_order=True)
SomeClahe = iaa.SomeOf(
2, [
clahe,
iaa.CLAHE(clip_limit=(1, 10)),
iaa.CLAHE(tile_grid_size_px=(3, 21)),
iaa.GammaContrast((0.5, 2.0)),
iaa.AllChannelsCLAHE(),
iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True)
],
random_order=True) #Random selection from clahe augmentors
edgedetection = iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.7)),
iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0))
])
# Search in some images either for all edges or for directed edges.These edges are then marked in a black and white image and overlayed with the original image using an alpha of 0 to 0.7.
canny_filter = iaa.OneOf([
iaa.Canny(),
iaa.Canny(alpha=(0.5, 1.0), sobel_kernel_size=[3, 7])
])
#choose one of the 2 canny filter options
OneofNoise = iaa.OneOf([blur, gauss_noise, laplace_noise])
Color_1 = iaa.OneOf([
channel_shuffle, grayscale, hue_n_saturation, add_hue_saturation,
kmeans_color
])
Color_2 = iaa.OneOf([
channel_shuffle, grayscale, hue_n_saturation, add_hue_saturation,
kmeans_color
])
Flip = iaa.OneOf([histogram, v_flip, h_flip])
#Define the augmentors used in the DA
Augmentors = [
SomeAug, SomeClahe, SomeClahe, edgedetection, sharpen,
canny_filter, OneofRed, OneofGreen, OneofBlue, OneofNoise, Color_1,
Color_2, Flip, contrast_n_shit
]
for i in range(0, 14):
img = cv2.imread(test_image) #read you image
images = np.array(
[img for _ in range(14)], dtype=np.uint8
) # 12 is the size of the array that will hold 8 different images
images_aug = Augmentors[i].augment_images(
images
) #alternate between the different augmentors for a test image
cv2.imwrite(
os.path.join(output_path,
test_image + "new" + str(i) + '.jpg'),
images_aug[i]) #write all changed images
def build_augmentation_pipeline(self,
height=None,
width=None,
apply_prob=0.5):
sometimes = lambda aug: iaa.Sometimes(apply_prob, aug)
pipeline = iaa.Sequential(random_order=False)
cfg = self.cfg
if cfg.get('fliplr', False):
opt = cfg.get('fliplr', False)
if type(opt) == int:
pipeline.add(sometimes(iaa.Fliplr(opt)))
else:
pipeline.add(sometimes(iaa.Fliplr(0.5)))
if cfg.get('rotation', False):
opt = cfg.get('rotation', False)
if type(opt) == int:
pipeline.add(sometimes(iaa.Affine(rotate=(-opt, opt))))
else:
pipeline.add(sometimes(iaa.Affine(rotate=(-10, 10))))
if cfg.get('motion_blur', False):
opts = cfg.get('motion_blur', False)
if type(opts) == list:
opts = dict(opts)
pipeline.add(sometimes(iaa.MotionBlur(**opts)))
else:
pipeline.add(sometimes(iaa.MotionBlur(k=7, angle=(-90, 90))))
if cfg.get('covering', False):
pipeline.add(
sometimes(
iaa.CoarseDropout(0.02, size_percent=0.3,
per_channel=0.5)))
if cfg.get('elastic_transform', False):
pipeline.add(sometimes(iaa.ElasticTransformation(sigma=5)))
if cfg.get('gaussian_noise', False):
opt = cfg.get('gaussian_noise', False)
if type(opt) == int or type(opt) == float:
pipeline.add(
sometimes(
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, opt),
per_channel=0.5)))
else:
pipeline.add(
sometimes(
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.05 * 255),
per_channel=0.5)))
if cfg.get('grayscale', False):
pipeline.add(sometimes(iaa.Grayscale(alpha=(0.5, 1.0))))
if cfg.get('hist_eq', False):
pipeline.add(sometimes(iaa.AllChannelsHistogramEqualization()))
if height is not None and width is not None:
if not cfg.get('crop_by', False):
crop_by = 0.15
else:
crop_by = cfg.get('crop_by', False)
pipeline.add(
iaa.Sometimes(
cfg.cropratio,
iaa.CropAndPad(percent=(-crop_by, crop_by),
keep_size=False)))
pipeline.add(iaa.Resize({"height": height, "width": width}))
if cfg.get('gamma', False):
pipeline.add(
sometimes(iaa.GammaContrast((0.5, 2.0), per_channel=True)))
if cfg.get('logcontrast', False):
pipeline.add(
sometimes(iaa.LogContrast(gain=(0.6, 1.4), per_channel=True)))
if cfg.get('allchannelsclahe', False):
pipeline.add(
sometimes(
iaa.AllChannelsCLAHE(clip_limit=(1, 10),
per_channel=True)))
return pipeline
transformed_image = transform(image=image)
elif augmentation == 'linear_contrast':
transform = iaa.LinearContrast((0.4, 1.6))
transformed_image = transform(image=image)
elif augmentation == 'histogram_equalization':
transform = iaa.HistogramEqualization()
transformed_image = transform(image=image)
elif augmentation == 'all_channels_he':
transform = iaa.AllChannelsHistogramEqualization()
transformed_image = transform(image=image)
elif augmentation == 'all_channels_clahe':
transform = iaa.AllChannelsCLAHE()
transformed_image = transform(image=image)
## Compression
elif augmentation == 'image_compression':
transform = ImageCompression(always_apply=True, quality_lower=10)
transformed_image = transform(image=image)['image']
elif augmentation == 'downscale':
transform = Downscale(always_apply=True)
transformed_image = transform(image=image)['image']
elif augmentation == 'pixelate':
transform = iaa.imgcorruptlike.Pixelate(severity=4)
transformed_image = transform(image=image)
def augment(img_data, config, augment=True):
assert 'filepath' in img_data
assert 'bboxes' in img_data
assert 'width' in img_data
assert 'height' in img_data
img_data_aug = copy.deepcopy(img_data)
aug_list = []
img = cv2.imread(img_data_aug['filepath'])
if augment:
rows, cols = img.shape[:2]
#[START] Pallete Augmentation
pallete_augmentation(img=img, img_data=img_data_aug, config=config)
#[END] Pallete Augmentation
if config.use_horizontal_flips and np.random.randint(0, 2) == 0:
img = cv2.flip(img, 1)
for bbox in img_data_aug['bboxes']:
x1 = bbox['x1']
x2 = bbox['x2']
bbox['x2'] = cols - x1
bbox['x1'] = cols - x2
if config.use_vertical_flips and np.random.randint(0, 2) == 0:
img = cv2.flip(img, 0)
for bbox in img_data_aug['bboxes']:
y1 = bbox['y1']
y2 = bbox['y2']
bbox['y2'] = rows - y1
bbox['y1'] = rows - y2
if config.rot_90:
angle = np.random.choice([0, 90, 180, 270], 1)[0]
if angle == 270:
img = np.transpose(img, (1, 0, 2))
img = cv2.flip(img, 0)
elif angle == 180:
img = cv2.flip(img, -1)
elif angle == 90:
img = np.transpose(img, (1, 0, 2))
img = cv2.flip(img, 1)
elif angle == 0:
pass
for bbox in img_data_aug['bboxes']:
x1 = bbox['x1']
x2 = bbox['x2']
y1 = bbox['y1']
y2 = bbox['y2']
if angle == 270:
bbox['x1'] = y1
bbox['x2'] = y2
bbox['y1'] = cols - x2
bbox['y2'] = cols - x1
elif angle == 180:
bbox['x2'] = cols - x1
bbox['x1'] = cols - x2
bbox['y2'] = rows - y1
bbox['y1'] = rows - y2
elif angle == 90:
bbox['x1'] = rows - y2
bbox['x2'] = rows - y1
bbox['y1'] = x1
bbox['y2'] = x2
elif angle == 0:
pass
if config.color:
aug_list.append(
np.random.choice([
iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True),
iaa.AddToHueAndSaturation((-50, 50), per_channel=True),
iaa.KMeansColorQuantization(),
iaa.UniformColorQuantization(),
iaa.Grayscale(alpha=(0.0, 1.0))
]))
if config.contrast:
aug_list.append(
np.random.choice([
iaa.GammaContrast((0.5, 2.0), per_channel=True),
iaa.SigmoidContrast(gain=(3, 10),
cutoff=(0.4, 0.6),
per_channel=True),
iaa.LogContrast(gain=(0.6, 1.4), per_channel=True),
iaa.LinearContrast((0.4, 1.6), per_channel=True),
iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True),
iaa.AllChannelsHistogramEqualization(),
iaa.HistogramEqualization()
]))
## Augmentation
aug = iaa.SomeOf((0, None), aug_list, random_order=True)
seq = iaa.Sequential(aug)
img = seq.augment_image(img)
##
img_data_aug['width'] = img.shape[1]
img_data_aug['height'] = img.shape[0]
return img_data_aug, img
iaa.OneOf([
iaa.MultiplyAndAddToBrightness(mul=(0.3, 1.6), add=(-50, 50)),
iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True),
iaa.ChannelShuffle(0.5),
iaa.RemoveSaturation(),
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.ChangeColorTemperature((1100, 35000)),
]),
iaa.OneOf([
iaa.MedianBlur(k=(3, 7)),
iaa.BilateralBlur(
d=(3, 10), sigma_color=(10, 250), sigma_space=(10, 250)),
iaa.MotionBlur(k=(3, 9), angle=[-45, 45]),
iaa.MeanShiftBlur(spatial_radius=(5.0, 10.0),
color_radius=(5.0, 10.0)),
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)),
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
def __init__(self, AllChannelsCLAHE_ratio=None):
self.AllChannelsCLAHE_ratio = AllChannelsCLAHE_ratio
self.seq = iaa.Sequential([
iaa.AllChannelsCLAHE(clip_limit=(0.8, 0.8)),
])
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
