def get_transforms(*, data_type):
if data_type == "light_train":
return Compose([
Resize(CFG.size, CFG.size),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
ShiftScaleRotate(scale_limit=(0, 0), p=0.5),
ToTensorV2(),
])
if data_type == "train":
return Compose([
Resize(CFG.size, CFG.size),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
albumentations.OneOf([
albumentations.ElasticTransform(
alpha=1, sigma=20, alpha_affine=10),
albumentations.GridDistortion(num_steps=6, distort_limit=0.1),
albumentations.OpticalDistortion(distort_limit=0.05,
shift_limit=0.05),
],
p=0.2),
albumentations.core.composition.PerChannel(albumentations.OneOf([
albumentations.MotionBlur(p=.05),
albumentations.MedianBlur(blur_limit=3, p=.05),
albumentations.Blur(blur_limit=3, p=.05),
]),
p=1.0),
albumentations.OneOf([
albumentations.CoarseDropout(max_holes=16,
max_height=CFG.size // 16,
max_width=CFG.size // 16,
fill_value=0,
p=0.5),
albumentations.GridDropout(ratio=0.09, p=0.5),
albumentations.Cutout(num_holes=8,
max_h_size=CFG.size // 16,
max_w_size=CFG.size // 16,
p=0.2),
],
p=0.5),
albumentations.ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.5),
ToTensorV2(),
],
additional_targets={
'r': 'image',
'g': 'image',
'b': 'image',
'y': 'image',
})
elif data_type == 'valid':
return Compose([
Resize(CFG.size, CFG.size),
ToTensorV2(),
])
def aug(self, image):
'''
center crop, resize, affine, hue, saturation,
'''
if self.phase == 'train':
imgaug = Compose([
transforms.Resize(height=self.input_shape[0], width=self.input_shape[1]),
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=.2),
MedianBlur(blur_limit=3, p=.1),
Blur(blur_limit=3, p=.1),
], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=.2),
ToTensorV2(),
], p=1)
else:
imgaug = Compose([transforms.Resize(height=self.input_shape[0], width=self.input_shape[1]),
ToTensorV2()])
image = imgaug(image=image)['image']
return image
def get_train_transforms():
return Compose(
[
#Resize(args.img_size, args.img_size),
RandomResizedCrop(args.img_size, args.img_size),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.25),
ShiftScaleRotate(p=0.25),
HueSaturationValue(hue_shift_limit=0.2,
sat_shift_limit=0.2,
val_shift_limit=0.2,
p=0.25),
RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1),
p=0.5),
Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
p=1.0),
CoarseDropout(p=0.5),
# Cutout(p=0.5),
ToTensorV2(p=1.0),
],
p=1.)
def aug_daniel_prepadded(prob=1.0, image_size=448): return Compose([ RandomRotate90(p=0.5), Transpose(p=0.5), Flip(p=0.5), OneOf([ RandomCrop(height=image_size, width=image_size, p=0.3), Compose([ #3.94 determined by largest angle possible rotatable without introducing nodata pixels into center crop area ShiftScaleRotate(shift_limit=0.0, scale_limit=0.0, rotate_limit=6, border_mode=cv2.BORDER_CONSTANT, p=1.0), CenterCrop(height=int(round(236/224*image_size)), width=int(round(236/224*image_size)), p=1.0), RandomCrop(height=image_size, width=image_size, p=1.0) ], p=0.4), Compose([ #3.94 determined by largest angle possible rotatable without introducing nodata pixels into center crop area ShiftScaleRotate(shift_limit=0.0, scale_limit=0.0, rotate_limit=12, border_mode=cv2.BORDER_CONSTANT, p=1.0), CenterCrop(height=image_size, width=image_size, p=1.0) ], p=0.3) ], p=1.0), #OneOf([ #IAASharpen(), #IAAEmboss(), # RandomBrightnessContrast(brightness_limit=0.01, contrast_limit=0.01) # This causes a blackout for some reason #Blur(), #GaussNoise() #], p=0.5), IAASharpen(p=0.2), IAAAdditiveGaussianNoise(p=0.2), # HueSaturationValue(p=0.3) #ShiftScaleRotate(shift_limit=0.0, scale_limit=0.0, rotate_limit=2, border_mode=cv2.BORDER_CONSTANT, p=.75), #ChannelShuffle(p=0.33) ], p=prob)
def hard_aug(original_height=128, original_width=128, k=4):
aug = Compose([
OneOf([
RandomSizedCrop(
min_max_height=(original_height // k, original_height),
height=original_height,
width=original_width,
p=0.5),
PadIfNeeded(
min_height=original_height, min_width=original_width, p=0.5)
],
p=1),
VerticalFlip(p=0.5),
HorizontalFlip(p=0.5),
RandomRotate90(p=0.5),
Transpose(p=0.5),
OneOf([
ElasticTransform(
p=0.5, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03),
GridDistortion(p=0.5),
OpticalDistortion(p=1, distort_limit=2, shift_limit=0.5)
],
p=0.8),
CLAHE(p=0.8),
RandomBrightnessContrast(p=0.8),
RandomGamma(p=0.8)
])
return aug
def __init__(self, root_dir, partition, augment=True):
self.root_dir = root_dir
self.list_IDs = os.listdir(
os.path.join(self.root_dir, 'x_{}'.format(partition)))
self.partition = partition
self.augment = augment
self.augmentator = Compose([
# Non destructive transformations
VerticalFlip(p=0.6),
HorizontalFlip(p=0.6),
RandomRotate90(),
Transpose(p=0.6),
ShiftScaleRotate(p=0.45, scale_limit=(0.1, 0.3)),
# # Non-rigid transformations
ElasticTransform(p=0.25,
alpha=160,
sigma=180 * 0.05,
alpha_affine=120 * 0.03),
Blur(blur_limit=3, p=0.2),
# Color augmentation
RandomBrightness(p=0.5),
RandomContrast(p=0.5),
RandomGamma(p=0.5),
CLAHE(p=0.5)
])
def aug_daniel(prob=0.8):
return Compose(
[
RandomRotate90(p=0.5),
Transpose(p=0.5),
Flip(p=0.5),
OneOf(
[
IAAAdditiveGaussianNoise(),
GaussNoise(),
#Blur(),
],
p=0.3),
OneOf(
[
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
OneOf([
RandomContrast(),
RandomBrightness(),
]),
#Blur(),
#GaussNoise()
],
p=0.5),
HueSaturationValue(p=0.5)
],
p=prob)
def transform(config, image, mask):
try:
p = config["train"]["dap"]["p"]
except:
p = 1
assert 0 <= p <= 1
# Inspire by: https://albumentations.readthedocs.io/en/latest/examples.html
return Compose([
Flip(),
Transpose(),
OneOf([IAAAdditiveGaussianNoise(),
GaussNoise()], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1)
],
p=0.2),
ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.2,
rotate_limit=45,
p=0.2),
OneOf([IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast()], p=0.3),
HueSaturationValue(p=0.3),
])(image=image, mask=mask, p=p)
def create_train_transforms(conf):
height = conf['crop_height']
width = conf['crop_width']
return Compose([
SafeRotate(45, p=0.4, border_mode=cv2.BORDER_CONSTANT),
OneOf([
RandomSizedCrop(min_max_height=(int(height * 0.7), int(
height * 1.3)),
w2h_ratio=1.,
height=height,
width=width,
p=0.8),
RandomCrop(height=height, width=width, p=0.2)
],
p=1),
HorizontalFlip(),
VerticalFlip(),
RandomRotate90(),
Transpose(),
ImageCompression(p=0.1),
Lighting(alphastd=0.3),
RandomBrightnessContrast(p=0.4),
RandomGamma(p=0.4),
OneOf([RGBShift(), HueSaturationValue()], p=0.2)
],
additional_targets={'image1': 'image'})
def strong_aug(p=1):
return Compose([
ToFloat(),
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
],
p=0.2),
ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=0.1),
IAAPiecewiseAffine(p=0.3),
],
p=0.2),
FromFloat(dtype='uint16', max_value=65535.0)
],
p=p)
def aug_train(resolution, p=1):
return Compose([Resize(resolution, resolution),
OneOf([
HorizontalFlip(),
VerticalFlip(),
RandomRotate90(),
Transpose()], p=0.5),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.5),
OneOf([
MotionBlur(p=.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
], p=0.5),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=.1),
IAAPiecewiseAffine(p=0.3),
], p=0.5),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
], p=0.5),
HueSaturationValue(p=0.3),
Normalize()
], p=p)
def get_light_augmentations(width, height):
return [
HorizontalFlip(p=1),
VerticalFlip(p=1),
Transpose(p=1),
RandomSizedCrop((height - 4, height - 2), height, width),
]
def get_train_transforms():
return Compose(
[
# RandomResizedCrop(CFG['img_size'], CFG['img_size']),
SmallestMaxSize(max_size=512),
# OneOf([RandomCrop(500, 500, p=0.4),
# CenterCrop(500, 500, p=0.5),
# RandomResizedCrop(512, 512, p=0.1)]),
RandomCrop(320, 320),
OneOf(
[Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5)]),
# ShiftScaleRotate(p=0.5),
HueSaturationValue(hue_shift_limit=0.2,
sat_shift_limit=0.2,
val_shift_limit=0.2,
p=0.5),
RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1),
p=0.5),
Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
p=1.0),
# Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_pixel_value=255.0, p=1.0),
# CoarseDropout(p=0.5),
ToTensorV2(p=1.0),
],
p=1.)
def aug_with_crop(width=640, height=480, crop_prob=1):
return Compose(
[
# RandomCrop(width=480, height=640, p=crop_prob),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
RandomRotate90(p=0.5),
Transpose(p=0.5),
ShiftScaleRotate(
shift_limit=0.01, scale_limit=0.04, rotate_limit=0, p=0.25),
RandomBrightnessContrast(p=0.5),
RandomGamma(p=0.25),
IAAEmboss(p=0.25),
Blur(p=0.01, blur_limit=3),
OneOf([
ElasticTransform(p=0.5,
alpha=120,
sigma=120 * 0.05,
alpha_affine=120 * 0.03),
GridDistortion(p=0.5),
OpticalDistortion(p=1, distort_limit=2, shift_limit=0.5)
],
p=0.8)
],
p=1)
def get_inference_transforms():
return Compose(
[
RandomResizedCrop(CFG['img_size'], CFG['img_size']),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
HueSaturationValue(
hue_shift_limit=0.2,
sat_shift_limit=0.2,
val_shift_limit=0.2,
p=0.5,
),
RandomBrightnessContrast(
brightness_limit=(-0.1,0.1),
contrast_limit=(-0.1, 0.1),
p=0.5,
),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
p=1.0,
),
ToTensorV2(p=1.0),
],
p=1.
)
def flow(data_dir, Timage, Tmask, batch, size1, size2, augument=False):
images_ = os.listdir(data_dir + Timage)
shuffle(images_)
ids_int = list(range(len(images_)))
NORMALIZE = 127.5
while True:
try:
for start in range(0, len(ids_int), batch):
x_batch = []
y_batch = []
end = min(start + batch, len(images_))
batch_create = ids_int[start:end]
jbs = dict()
for loads in batch_create:
try:
img = cv2.imread(
os.path.join(data_dir, Timage, images_[loads]))
img = image_resize(img, width=param_maps["scale"])
height_o_image, width_o_image = img.shape[
0], img.shape[1]
if height_o_image % 2 != 0:
height_o_image = height_o_image - 1
if width_o_image % 2 != 0:
width_o_image = width_o_image - 1
jbs["width"] = width_o_image * 2
jbs["height"] = height_o_image * 2
img = cv2.resize(img, (width_o_image, height_o_image))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
masks = cv2.imread(
os.path.join(data_dir, Tmask, images_[loads]))
masks = cv2.resize(masks,
(jbs["width"], jbs["height"]))
except:
continue
if augument:
aug = Compose([
VerticalFlip(p=0.1),
Transpose(p=0.01),
RandomGamma(p=0.06),
OpticalDistortion(p=0.00,
distort_limit=0.7,
shift_limit=0.3)
])
augmented = aug(image=img, mask=masks)
img = augmented['image']
masks = augmented['mask']
x_batch.append(img)
y_batch.append(masks)
else:
x_batch.append(img)
y_batch.append(masks)
x_batch = np.array(x_batch) / NORMALIZE
x_batch = x_batch - 1
y_batch = np.array(y_batch) / NORMALIZE
y_batch = y_batch - 1
yield x_batch, y_batch
except:
continue
def get_raw_tta():
transform_test_raw = Compose(
[Resize(RAW_CROP_SIZE, RAW_CROP_SIZE),
ToTensor()])
transform_test_hf = Compose([
Resize(RAW_CROP_SIZE, RAW_CROP_SIZE),
HorizontalFlip(p=1, always_apply=True),
ToTensor()
])
transform_test_vf = Compose([
Resize(RAW_CROP_SIZE, RAW_CROP_SIZE),
VerticalFlip(p=1, always_apply=True),
ToTensor()
])
transform_test_tr = Compose(
[Resize(RAW_CROP_SIZE, RAW_CROP_SIZE),
Transpose(),
ToTensor()])
return [
transform_test_raw, transform_test_hf, transform_test_vf,
transform_test_tr
]
def get_transforms():
return Compose([
RandomRotate90(p=0.5),
Flip(p=0.5),
Transpose(p=0.5),
# OneOf([
# IAAAdditiveGaussianNoise(),
# GaussNoise(),
# ], p=0.2),
# OneOf([
# MotionBlur(p=.2),
# MedianBlur(blur_limit=3, p=0.1),
# Blur(blur_limit=3, p=0.1),
# ], p=0.2),
ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.2,
rotate_limit=45,
p=0.2),
# OneOf([
# CLAHE(clip_limit=2),
# IAASharpen(),
# IAAEmboss(),
# RandomBrightnessContrast(),
# ], p=0.3),
HueSaturationValue(p=0.3),
])
def strong_aug(p=0.5):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=0.1),
IAAPiecewiseAffine(p=0.3),
], p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
], p=0.3),
HueSaturationValue(p=0.3),
], p=p)
def __init__(self,
root_dir,
annotation_lines,
class_number,
transform=None,
loader=default_loader):
self.annotation_lines = annotation_lines
self.class_number = class_number
self.transform = transform
self.loader = loader
self.root_dir = root_dir
curr_size = 512
min_max_height = (curr_size - curr_size // 2, curr_size - 1)
self.transform_strong = Compose([
RandomSizedCrop(min_max_height=min_max_height,
height=curr_size,
width=curr_size,
p=1.0),
OneOf([
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
Rotate(p=0.5),
],
p=1.0),
ElasticTransform(alpha=curr_size,
sigma=curr_size * 0.05,
alpha_affine=10,
p=1.0)
])
def get_transforms(*, data):
if data == 'train':
return Compose([
RandomResizedCrop(CFG.size, CFG.size),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
ShiftScaleRotate(p=0.5),
HueSaturationValue(hue_shift_limit=0.2,
sat_shift_limit=0.2,
val_shift_limit=0.2,
p=0.5),
RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1),
p=0.5),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2(),
])
elif data == 'valid':
return Compose([
Resize(CFG.size, CFG.size),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2(),
])
def test_transpose_both_image_and_mask():
image = np.ones((8, 6, 3))
mask = np.ones((8, 6))
augmentation = Transpose(p=1)
augmented = augmentation(image=image, mask=mask)
assert augmented['image'].shape == (6, 8, 3)
assert augmented['mask'].shape == (6, 8)
def strong_aug(p=1):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
],
p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
],
p=0.3),
HueSaturationValue(p=0.3),
],
p=p)
def strong_aug(p=0.5, crop_size=(512, 512)):
return Compose([
RandomResizedCrop(crop_size[0],
crop_size[1],
scale=(0.3, 1.0),
ratio=(0.75, 1.3),
interpolation=4,
p=1.0),
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.8),
OneOf([
MotionBlur(p=0.5),
MedianBlur(blur_limit=3, p=0.5),
Blur(blur_limit=3, p=0.5),
],
p=0.3),
ShiftScaleRotate(
shift_limit=0.2, scale_limit=0.5, rotate_limit=180, p=0.8),
OneOf([
OpticalDistortion(p=0.5),
GridDistortion(p=0.5),
IAAPiecewiseAffine(p=0.5),
ElasticTransform(p=0.5),
],
p=0.3),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
],
p=0.3),
OneOf([
GaussNoise(),
RandomRain(
p=0.2, brightness_coefficient=0.9, drop_width=1, blur_value=5),
RandomSnow(p=0.4,
brightness_coeff=0.5,
snow_point_lower=0.1,
snow_point_upper=0.3),
RandomShadow(p=0.2,
num_shadows_lower=1,
num_shadows_upper=1,
shadow_dimension=5,
shadow_roi=(0, 0.5, 1, 1)),
RandomFog(
p=0.5, fog_coef_lower=0.3, fog_coef_upper=0.5, alpha_coef=0.1)
],
p=0.3),
RGBShift(),
HueSaturationValue(p=0.9),
],
p=p)
def light_aug_detection(p=1.0):
return Compose([
Flip(p=1),
Transpose(p=0.8),
ShiftScaleRotate(shift_limit=0.01, scale_limit=0.01, rotate_limit=30, p=1),
RandomBrightnessContrast(brightness_limit=(-0.01, 0.01), contrast_limit=(-0.01, 0.01), p=p),
],
bbox_params=BboxParams(format='pascal_voc', label_fields=['category_ids']),
p=p)
def strong_aug(p=1):
return Compose([
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
Transpose(p=0.5),
ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=20, p=0.5),
RandomBrightnessContrast(brightness_limit=0.15, contrast_limit=0.15, p=0.2),
HueSaturationValue(hue_shift_limit=10, sat_shift_limit=20, p=0.2)
], p=p)
def transpose(image, mask):
aug = Transpose(p=1)
augmented = aug(image=image, mask=mask)
image_transposed = augmented['image']
mask_transposed = augmented['mask']
return image_transposed, mask_transposed
def get_png_tta():
transform_test = Compose([ToTensor()])
tfm_hf = Compose([HorizontalFlip(p=1, always_apply=True), ToTensor()])
tfm_vf = Compose([VerticalFlip(p=1, always_apply=True), ToTensor()])
tfm_tr = Compose([Transpose(), ToTensor()])
return [transform_test, tfm_hf, tfm_vf, tfm_tr]
def augment_flips_color(p=.5):
return Compose([
RandomRotate90(),
Transpose(),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.50, rotate_limit=45, p=.75),
Blur(blur_limit=3),
VerticalFlip(),
HorizontalFlip()
], p=p)
def strong_aug(self,p=.5):
return Compose([
Transpose(),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
HueSaturationValue(p=0.3),
HorizontalFlip(always_apply=False, p=0.5),
Cutout(num_holes=1, max_h_size=8, max_w_size=8, fill_value=0.5*255)
# Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
# ToTensor()
], p=p)
