def preprocess(self, ims, augment):
def normalize(batch):
return batch.astype(np.float32) / float(255)
if augment:
augmentations = iaa.Sequential([
iaa.Resize(int(1.1 * hp.img_size)),
iaa.Fliplr(0.5),
iaa.Sometimes(0.4, iaa.Rotate((-30, 30))),
iaa.Sometimes(0.4, iaa.Affine(scale=(0.9, 1.2))),
iaa.Sometimes(0.5,
iaa.PerspectiveTransform(scale=(0.01, 0.20))),
# Crop/resize image to proper dimension
iaa.CropToFixedSize(hp.img_size, hp.img_size),
iaa.Resize(hp.img_size),
iaa.Sometimes(0.3, iaa.SaltAndPepper(0.01)),
iaa.CLAHE(to_colorspace='HSV')
])
else:
augmentations = ia.Sequential([iaa.CLAHE(to_colorspace='HSV')])
augmented = augmentations.augment_images(ims)
for i in augmented:
if i.shape != (hp.img_size, hp.img_size, 3):
print(i.shape)
augmented = np.stack(augmented, axis=0)
return normalize(augmented)
def __init__(self, model_path):
self.model_path = model_path
self.seq_norm = iaa.Sequential([
iaa.CLAHE(),
iaa.LinearContrast(alpha=1.0)])
self.__load_model()
self.img = None
self.pred = None
self.root_coords = None
def __init__(self,
df,
hist_equal=False,
augment_data=False,
batch_size=2,
target_size=Config.SHAPE,
shuffle=True,
save_images=False,
input_channels=3,
output="both"):
'Initialization'
self.df = df
self.hist_equal = hist_equal
self.augment_data = augment_data
self.batch_size = batch_size
self.target_size = target_size
self.shuffle = shuffle
self.save_images = save_images
self.input_channels = input_channels
self.output = output
self.save_index = 0
self.mask_channels = 1
self.__init_info()
self.on_epoch_end()
self.seq = iaa.Sequential(
[
iaa.SomeOf(
(0, 4),
[
iaa.OneOf([
iaa.GaussianBlur(sigma=(0, 0.5)),
iaa.Sharpen(alpha=(0.0, 0.7)),
]),
iaa.Fliplr(0.5, name="to_mask"),
iaa.Flipud(0.5, name="to_mask"),
#iaa.Crop(px=(0, 6), name="to_mask"),
iaa.Affine(scale=(0.7, 1.0),
translate_percent={
"x": (-0.3, 0.3),
"y": (-0.3, 0.3)
},
name="to_mask"),
]),
],
random_order=True)
self.seq_img = iaa.Sequential([
iaa.Add((-60, 60)),
])
self.seq_norm = iaa.Sequential(
[iaa.CLAHE(), iaa.LinearContrast(alpha=1.0)])
def __init__(self,
df,
input_shape,
model,
predictiontype,
masktype='leaf'):
#Visualize.__init__(self,df,model,masktype='hand')
self.df = df
self.input_shape = input_shape
self.model = model
self.predictiontype = predictiontype
self.masktype = masktype
self.prediction_threshold = None
self.seq_norm = iaa.Sequential(
[iaa.CLAHE(), iaa.LinearContrast(alpha=1.0)])
def load_augmentations(flip=0.5,
blur=0.2,
crop=0.5,
contrast=0.3,
elastic=0.2,
affine=0.5):
"""
Loads and configures data augmenter object
Arguements:
flip (float) -- probality of horizontal flip
crop (float) -- probability of random crop
blur (float) -- probability of gaussian blur
contrast (float) -- probability of pixelwise color transformation
elastic (float) -- probability of elastic distortion
affine (float) -- probability of affine transform
noise (float) -- probability of one of noises
"""
aug = iaa.Sequential([
iaa.Fliplr(flip),
iaa.Sometimes(crop, iaa.Crop(px=(0, 20))),
iaa.Sometimes(blur, iaa.GaussianBlur(sigma=(0.5, 5))),
iaa.Sometimes(
contrast,
iaa.SomeOf((1, 5), [
iaa.GammaContrast(per_channel=True, gamma=(0.25, 1.75)),
iaa.LinearContrast(alpha=(0.25, 1.75), per_channel=True),
iaa.HistogramEqualization(to_colorspace="HLS"),
iaa.LogContrast(gain=(0.5, 1.0)),
iaa.CLAHE(clip_limit=(1, 10))
]),
),
iaa.Sometimes(
elastic,
iaa.OneOf([
iaa.ElasticTransformation(alpha=20, sigma=1),
iaa.ElasticTransformation(alpha=200, sigma=20)
])),
iaa.Sometimes(
affine,
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
rotate=(-30, 30),
order=[0, 1]))
])
return aug
def get_augmentations():
def sometimes(aug, p=0.5):
return iaa.Sometimes(p, aug)
return iaa.Sequential([
# sometimes(iaa.Affine(scale=(1, 1.6), translate_percent=0.2)),
iaa.SomeOf(
2,
[
sometimes(iaa.Multiply()),
sometimes(iaa.GammaContrast()),
sometimes(iaa.AddToSaturation()),
sometimes(iaa.AddToBrightness()),
# sometimes(iaa.AddToHue()),
sometimes(iaa.CLAHE())
])
])
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)
def _load_augmentation_aug_non_geometric():
return iaa.Sequential([
iaa.Sometimes(0.3, iaa.Multiply((0.5, 1.5), per_channel=0.5)),
iaa.Sometimes(0.2, iaa.JpegCompression(compression=(70, 99))),
iaa.Sometimes(0.2, iaa.GaussianBlur(sigma=(0, 3.0))),
iaa.Sometimes(0.2, iaa.MotionBlur(k=15, angle=[-45, 45])),
iaa.Sometimes(0.2, iaa.MultiplyHue((0.5, 1.5))),
iaa.Sometimes(0.2, iaa.MultiplySaturation((0.5, 1.5))),
iaa.Sometimes(
0.34, iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True)),
iaa.Sometimes(0.34, iaa.Grayscale(alpha=(0.0, 1.0))),
iaa.Sometimes(0.2, iaa.ChangeColorTemperature((1100, 10000))),
iaa.Sometimes(0.1, iaa.GammaContrast((0.5, 2.0))),
iaa.Sometimes(0.2, iaa.SigmoidContrast(gain=(3, 10),
cutoff=(0.4, 0.6))),
iaa.Sometimes(0.1, iaa.CLAHE()),
iaa.Sometimes(0.1, iaa.HistogramEqualization()),
iaa.Sometimes(0.2, iaa.LinearContrast((0.5, 2.0), per_channel=0.5)),
iaa.Sometimes(0.1, iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)))
])
def chapter_augmenters_clahe():
fn_start = "contrast/clahe"
aug = iaa.CLAHE()
run_and_save_augseq(fn_start + ".jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.CLAHE(clip_limit=(1, 10))
run_and_save_augseq(fn_start + "_clip_limit.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.CLAHE(tile_grid_size_px=(3, 21))
run_and_save_augseq(fn_start + "_grid_sizes_uniform.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.CLAHE(tile_grid_size_px=iap.Discretize(iap.Normal(loc=7,
scale=2)),
tile_grid_size_px_min=3)
run_and_save_augseq(fn_start + "_grid_sizes_gaussian.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.CLAHE(tile_grid_size_px=((3, 21), [3, 5, 7]))
run_and_save_augseq(fn_start + "_grid_sizes.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.CLAHE(from_colorspace=iaa.CLAHE.BGR, to_colorspace=iaa.CLAHE.HSV)
quokka_bgr = cv2.cvtColor(ia.quokka(size=(128, 128)), cv2.COLOR_RGB2BGR)
run_and_save_augseq(fn_start + "_bgr_to_hsv.jpg",
aug, [quokka_bgr for _ in range(4 * 2)],
cols=4,
rows=2,
image_colorspace="BGR")
def __init__(self,
df,
model,
predictiontype='leaf',
input_shape=(512, 768, 3),
masktype='leaf'):
self.df = df
self.model = model
self.predictiontype = predictiontype
self.input_shape = input_shape
self.figsize = (15, 15)
self.prediction_threshold = None
self.selected_row = None
self.mode = None
self.img = None
self.msk = None
self.prediction = None
self.dice_score = None
self.img_shape = None
self.model_shape = None
assert masktype == 'leaf' or masktype == 'root', 'Masktype not allowed'
self.masktype = masktype
self.seq_norm = iaa.Sequential(
[iaa.CLAHE(), iaa.LinearContrast(alpha=1.0)])
iaa.GaussianBlur(sigma=(0, 1.5)),
iaa.AverageBlur(k=(1, 5)),
iaa.MedianBlur(k=(1, 5)),
iaa.MotionBlur(k=(3, 5)),
]))
])
contrasts = iaa.Sequential([
sometimes_010(
iaa.OneOf([
iaa.LogContrast((0.8, 1.2)),
iaa.GammaContrast((0.8, 1.2)),
iaa.LinearContrast((0.8, 1.2)),
iaa.Alpha((0.0, 1.0), iaa.AllChannelsHistogramEqualization()),
iaa.Alpha((0.0, 1.0), iaa.HistogramEqualization()),
iaa.CLAHE(clip_limit=(1, 3)),
iaa.AllChannelsCLAHE(clip_limit=(1, 3)),
]))
])
dropouts = iaa.Sequential([
sometimes_010(
iaa.OneOf([
iaa.Dropout(p=0.01, per_channel=True),
iaa.Dropout(p=0.01, per_channel=False),
iaa.Cutout(fill_mode="constant",
cval=(0, 255),
size=(0.1, 0.4),
fill_per_channel=0.5),
iaa.CoarseDropout((0.0, 0.08),
size_percent=(0.02, 0.25),
##### Define (light) augemntations
BRIGHTEN = iaa.Multiply(
(0.8, 1.4)) # make upper edge larger to get brighter images
SHARPEN = iaa.Sharpen(
alpha=(0, 0.82)) # the larger alpha, the larger the effect. alpha <= 1
GAMMA_CONTRAST = iaa.GammaContrast((0.6, 1.3))
# param = Exponent for the contrast adjustment. Higher values darken the image. They seem to also remove
# some of the white clouds so careful with that
SHEAR = iaa.Affine(shear=(-10, 10))
ROTATE = iaa.Affine(rotate=(-10, 10))
SUBTLE_CLAHE = iaa.CLAHE(clip_limit=(0.5, 2), tile_grid_size_px=(6, 10))
# previous, less pronounced : iaa.CLAHE(clip_limit=(0.5,2), tile_grid_size_px = (5, 8))
GBLUR = iaa.GaussianBlur((
0,
1.9)) # Values in the range 0.0 (no blur) to 3.0 (strong blur) are common.
SCALE = iaa.Affine(scale={"x": (0.9, 1.1), "y": (0.9, 1.1)})
AUG_NAMES = [
'brighten', 'gamma_contrast', 'clahe', 'rotate', 'shear', 'scale',
'fliplr', 'gblur', 'sharpen'
]
######
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 _load_augmentation_aug_Clashe():
return iaa.Sequential([iaa.Sometimes(1, iaa.CLAHE())])
# 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),
def __init__(self, configuration):
"""
Initialized the configuration prameters
Arguments:
configuration: file pointer
The hitif configuration file
"""
import configparser
config = configparser.ConfigParser()
config.read(configuration)
#Parse the augmentation parameters
aug_prms = config['augmentation']
self.CLAHE = eval(aug_prms['AllChannelsCLAHE'])
self.Saturation = eval(aug_prms['Saturation'])
self.impulse_noise = eval(aug_prms['ImpulseNoise'])
self.gaussian_blur = eval(aug_prms['GaussianBlur'])
self.poisson = eval(aug_prms['AdditivePoissonNoise'])
self.median = eval(aug_prms['MedianBlur'])
self.flip = float(aug_prms["flip"])
self.rotate = eval(aug_prms["rotate"])
self.gamma = eval(aug_prms["GammaContrast"])
self.gaussian_noise = eval(aug_prms["AdditiveGaussianNoise"])
self.dropout = eval(aug_prms["Dropout"])
self.salt_peper = eval(aug_prms["SaltAndPepper"])
from imgaug import augmenters as iaa
import imgaug as ia
import numpy as np
seed = np.random.randint(0, 2**31 - 1)
ia.seed(seed)
self.augmenters = {}
augmenters = self.augmenters
#Affine augmentation
augmenters["fliplr"] = iaa.Fliplr(self.flip)
augmenters["flipud"] = iaa.Flipud(self.flip)
augmenters["rotate"] = iaa.Affine(rotate=[self.rotate[0],\
self.rotate[1],\
self.rotate[2]])
#Contrast augmentation
#augmenters["CLAHE"] = iaa.AllChannelsCLAHE(self.CLAHE)
augmenters["CLAHE"] = iaa.CLAHE(self.CLAHE)
#augmenters["CLAHE"] = iaa.AllChannelsCLAHE(self.CLAHE[0], self.CLAHE[1], self.CLAHE[2],self.CLAHE[3])
augmenters["gamma"] = iaa.GammaContrast(self.gamma, True)
#augmenters['saturation'] = iaa.Lambda(func_images=self.saturate_images, func_heatmaps=self.func_heatmaps, func_keypoints=self.func_keypoints)
augmenters['Saturation'] = iaa.Saturation(self.Saturation)
#Blur augmenters
augmenters["median_blur"] = iaa.MedianBlur(self.median)
augmenters["gaussian_blur"] = iaa.GaussianBlur(self.gaussian_blur)
#Noise augmenters
augmenters["impulse_noise"] = iaa.ImpulseNoise(self.impulse_noise)
augmenters["poisson_noise"] = iaa.AdditivePoissonNoise(self.poisson)
augmenters["gaussian_noise"] = iaa.AdditiveGaussianNoise(
scale=self.gaussian_noise)
augmenters["dropout"] = iaa.Dropout(self.dropout)
iaa.Sometimes(0.05,(iaa.Crop(px=(22, 45),keep_size=True))), # crop images from each side by 0 to 16px (randomly chosen)
iaa.Sometimes(0.5,(iaa.Fliplr(1))), # horizontally flip 50% of the images
iaa.Sometimes(0.02,iaa.GaussianBlur(sigma=(5, 7.0))), # blur images with a sigma of 0 to 3.0
iaa.Sometimes(0.02 ,iaa.ImpulseNoise(p=(0.6,1))),
iaa.Sometimes(0.02 ,iaa.EdgeDetect(alpha=(0.09,1))),
#iaa.AddToBrightness(add=(100,124)),
iaa.Sometimes(0.02 ,iaa.Canny(alpha=(0.8,0.9))),
iaa.Sometimes(0.5 ,iaa.Grayscale(alpha=1.00)),
iaa.Sometimes(0.5 ,iaa.ChannelShuffle(p=1)),
#iaa.Sometimes(0.02 ,(iaa.geometric.Affine( scale=2,rotate=22,order=1))),
iaa.Sometimes(0.5 ,iaa.Cartoon(blur_ksize=(11,13))),
iaa.Sometimes(0.02 ,iaa.CenterCropToAspectRatio(1)),
iaa.Sometimes(0.02 ,iaa.CenterCropToFixedSize(100,100)),
iaa.Sometimes(0.12 ,iaa.ChangeColorTemperature(kelvin=(2222,3333))),
#iaa.segmentation(),
iaa.Sometimes(0.12 ,iaa.CLAHE(clip_limit=(4,8))),
iaa.Sometimes(0.8 ,iaa.Rotate(rotate=(-90,90),order=1))
])
plt.figure(figsize=(12,12))
ls=glob.glob(path)
res=[]
for idx,l in enumerate(ls):
res.append(Parser.myType(l,idx,classes=['bird','zebra']))
def our_generator(res):
for i in res:
img= cv2.imread(ls[i['image_id']].replace('.xml','.jpg'))
iaa.Crop(px=(22, 45),keep_size=False), # crop images from each side by 0 to 16px (randomly chosen)
iaa.Fliplr(1), # horizontally flip 50% of the images
iaa.GaussianBlur(sigma=(5, 7.0)), # blur images with a sigma of 0 to 3.0
iaa.ImpulseNoise(p=(0.6,1)),
iaa.EdgeDetect(alpha=(0.9,1)),
#iaa.AddToBrightness(add=(100,124)),
iaa.Canny(alpha=(0.8,0.9)),
iaa.Grayscale(alpha=1.00),
iaa.ChannelShuffle(p=1),
iaa.geometric.Affine( scale=2,rotate=22, backend='cv2'),
iaa.Cartoon(blur_ksize=(11,13)),
iaa.CenterCropToAspectRatio(1),
iaa.CenterCropToFixedSize(100,100),
iaa.ChangeColorTemperature(kelvin=(2222,3333)),
#iaa.segmentation(),
iaa.CLAHE(clip_limit=(4,8)),
iaa.Rotate(rotate=(-30,90))
])
plt.figure(figsize=(12,12))
for idx,Augmentor in enumerate(seq):
# print(1)
ax=plt.subplot(4,4,idx+1)
ax.axis('off')
plt.tight_layout()
title=str(Augmentor).split('(')[0]
#plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
# hspace = 0.2, wspace = 0)
#plt.margins(0,0)
ax.set_title(title)
# @Author : shargootian # @Email : [email protected] # @File : dataset.py import os import json import random import cv2 as cv import numpy as np from imgaug import augmenters as iaa aug = [ iaa.LinearContrast(alpha=2), iaa.SigmoidContrast(gain=10), iaa.GammaContrast(gamma=2), iaa.CLAHE(clip_limit=(1, 5)), iaa.Grayscale(alpha=1.0), iaa.AddToHueAndSaturation((-20, 20), per_channel=True), iaa.BilateralBlur(d=6), iaa.MotionBlur(k=7), iaa.MedianBlur(k=3), iaa.AverageBlur(k=3), iaa.AdditiveGaussianNoise(loc=0.8, scale=(0.01, 0.08 * 255)), iaa.ContrastNormalization((0.3, 1.5)), iaa.Sharpen(alpha=0, lightness=1) ] class Dataset(object): def __init__(self, args): self.args = args
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 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
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
