Full API Reference on a single page

Pixel-level transforms

Here is a list of all available pixel-level transforms. You can apply a pixel-level transform to any target, and under the hood, the transform will change only the input image and return any other input targets such as masks, bounding boxes, or keypoints unchanged.

• AdvancedBlur
• Blur
• CLAHE
• ChannelDropout
• ChannelShuffle
• ColorJitter
• Defocus
• Downscale
• Emboss
• Equalize
• FDA
• FancyPCA
• FromFloat
• GaussNoise
• GaussianBlur
• GlassBlur
• HistogramMatching
• HueSaturationValue
• ISONoise
• ImageCompression
• InvertImg
• MedianBlur
• MotionBlur
• MultiplicativeNoise
• Normalize
• PixelDistributionAdaptation
• Posterize
• RGBShift
• RandomBrightnessContrast
• RandomFog
• RandomGamma
• RandomGravel
• RandomRain
• RandomShadow
• RandomSnow
• RandomSunFlare
• RandomToneCurve
• RingingOvershoot
• Sharpen
• Solarize
• Spatter
• Superpixels
• TemplateTransform
• ToFloat
• ToGray
• ToRGB
• ToSepia
• UnsharpMask
• ZoomBlur

Spatial-level transforms

Here is a table with spatial-level transforms and targets they support. If you try to apply a spatial-level transform to an unsupported target, Albumentations will raise an error.

Transform	Image	Masks	BBoxes	Keypoints
Affine	✓	✓	✓	✓
BBoxSafeRandomCrop	✓	✓	✓
CenterCrop	✓	✓	✓	✓
CoarseDropout	✓	✓		✓
Crop	✓	✓	✓	✓
CropAndPad	✓	✓	✓	✓
CropNonEmptyMaskIfExists	✓	✓	✓	✓
ElasticTransform	✓	✓	✓
Flip	✓	✓	✓	✓
GridDistortion	✓	✓	✓
GridDropout	✓	✓
HorizontalFlip	✓	✓	✓	✓
Lambda	✓	✓	✓	✓
LongestMaxSize	✓	✓	✓	✓
MaskDropout	✓	✓
NoOp	✓	✓	✓	✓
OpticalDistortion	✓	✓	✓
PadIfNeeded	✓	✓	✓	✓
Perspective	✓	✓	✓	✓
PiecewiseAffine	✓	✓	✓	✓
PixelDropout	✓	✓	✓	✓
RandomCrop	✓	✓	✓	✓
RandomCropFromBorders	✓	✓	✓	✓
RandomCropNearBBox	✓	✓	✓	✓
RandomGridShuffle	✓	✓		✓
RandomResizedCrop	✓	✓	✓	✓
RandomRotate90	✓	✓	✓	✓
RandomScale	✓	✓	✓	✓
RandomSizedBBoxSafeCrop	✓	✓	✓
RandomSizedCrop	✓	✓	✓	✓
Resize	✓	✓	✓	✓
Rotate	✓	✓	✓	✓
SafeRotate	✓	✓	✓	✓
ShiftScaleRotate	✓	✓	✓	✓
SmallestMaxSize	✓	✓	✓	✓
Transpose	✓	✓	✓	✓
VerticalFlip	✓	✓	✓	✓

albumentations.augmentations special

albumentations.augmentations.blur special

albumentations.augmentations.blur.transforms

class albumentations.augmentations.blur.transforms.AdvancedBlur (blur_limit=(3, 7), sigmaX_limit=(0.2, 1.0), sigmaY_limit=(0.2, 1.0), rotate_limit=90, beta_limit=(0.5, 8.0), noise_limit=(0.9, 1.1), always_apply=False, p=0.5) [view source on GitHub]

Blur the input image using a Generalized Normal filter with a randomly selected parameters. This transform also adds multiplicative noise to generated kernel before convolution.

Parameters:

Name	Type	Description
blur_limit		maximum Gaussian kernel size for blurring the input image. Must be zero or odd and in range [0, inf). If set to 0 it will be computed from sigma as round(sigma * (3 if img.dtype == np.uint8 else 4) * 2 + 1) + 1. If set single value blur_limit will be in range (0, blur_limit). Default: (3, 7).
sigmaX_limit		Gaussian kernel standard deviation. Must be in range [0, inf). If set single value sigmaX_limit will be in range (0, sigma_limit). If set to 0 sigma will be computed as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8. Default: 0.
sigmaY_limit		Same as sigmaY_limit for another dimension.
rotate_limit		Range from which a random angle used to rotate Gaussian kernel is picked. If limit is a single int an angle is picked from (-rotate_limit, rotate_limit). Default: (-90, 90).
beta_limit		Distribution shape parameter, 1 is the normal distribution. Values below 1.0 make distribution tails heavier than normal, values above 1.0 make it lighter than normal. Default: (0.5, 8.0).
noise_limit		Multiplicative factor that control strength of kernel noise. Must be positive and preferably centered around 1.0. If set single value noise_limit will be in range (0, noise_limit). Default: (0.75, 1.25).
p	float	probability of applying the transform. Default: 0.5.

Reference: https://arxiv.org/abs/2107.10833

Targets: image Image types: uint8, float32

class albumentations.augmentations.blur.transforms.Blur (blur_limit=7, always_apply=False, p=0.5) [view source on GitHub]

Blur the input image using a random-sized kernel.

Parameters:

Name	Type	Description
blur_limit	int, [int, int]	maximum kernel size for blurring the input image. Should be in range [3, inf). Default: (3, 7).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.blur.transforms.Defocus (radius=(3, 10), alias_blur=(0.1, 0.5), always_apply=False, p=0.5) [view source on GitHub]

Apply defocus transform. See https://arxiv.org/abs/1903.12261.

Parameters:

Name	Type	Description
radius	[int, int] or int	range for radius of defocusing. If limit is a single int, the range will be [1, limit]. Default: (3, 10).
alias_blur	[float, float] or float	range for alias_blur of defocusing (sigma of gaussian blur). If limit is a single float, the range will be (0, limit). Default: (0.1, 0.5).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: Any

class albumentations.augmentations.blur.transforms.GaussianBlur (blur_limit=(3, 7), sigma_limit=0, always_apply=False, p=0.5) [view source on GitHub]

Blur the input image using a Gaussian filter with a random kernel size.

Parameters:

Name	Type	Description
blur_limit	int, [int, int]	maximum Gaussian kernel size for blurring the input image. Must be zero or odd and in range [0, inf). If set to 0 it will be computed from sigma as round(sigma * (3 if img.dtype == np.uint8 else 4) * 2 + 1) + 1. If set single value blur_limit will be in range (0, blur_limit). Default: (3, 7).
sigma_limit	float, [float, float]	Gaussian kernel standard deviation. Must be in range [0, inf). If set single value sigma_limit will be in range (0, sigma_limit). If set to 0 sigma will be computed as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8. Default: 0.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.blur.transforms.GlassBlur (sigma=0.7, max_delta=4, iterations=2, always_apply=False, mode='fast', p=0.5) [view source on GitHub]

Apply glass noise to the input image.

Parameters:

Name	Type	Description
sigma	float	standard deviation for Gaussian kernel.
max_delta	int	max distance between pixels which are swapped.
iterations	int	number of repeats. Should be in range [1, inf). Default: (2).
mode	str	mode of computation: fast or exact. Default: "fast".
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

Reference: | https://arxiv.org/abs/1903.12261 | https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py

class albumentations.augmentations.blur.transforms.MedianBlur (blur_limit=7, always_apply=False, p=0.5) [view source on GitHub]

Blur the input image using a median filter with a random aperture linear size.

Parameters:

Name	Type	Description
blur_limit	int	maximum aperture linear size for blurring the input image. Must be odd and in range [3, inf). Default: (3, 7).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.blur.transforms.MotionBlur (blur_limit=7, allow_shifted=True, always_apply=False, p=0.5) [view source on GitHub]

Apply motion blur to the input image using a random-sized kernel.

Parameters:

Name	Type	Description
blur_limit	int	maximum kernel size for blurring the input image. Should be in range [3, inf). Default: (3, 7).
allow_shifted	bool	if set to true creates non shifted kernels only, otherwise creates randomly shifted kernels. Default: True.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.blur.transforms.ZoomBlur (max_factor=1.31, step_factor=(0.01, 0.03), always_apply=False, p=0.5) [view source on GitHub]

Apply zoom blur transform. See https://arxiv.org/abs/1903.12261.

Parameters:

Name	Type	Description
max_factor	[float, float] or float	range for max factor for blurring. If max_factor is a single float, the range will be (1, limit). Default: (1, 1.31). All max_factor values should be larger than 1.
step_factor	[float, float] or float	If single float will be used as step parameter for np.arange. If tuple of float step_factor will be in range [step_factor[0], step_factor[1]). Default: (0.01, 0.03). All step_factor values should be positive.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: Any

albumentations.augmentations.crops special

albumentations.augmentations.crops.functional

def albumentations.augmentations.crops.functional.bbox_crop (bbox, x_min, y_min, x_max, y_max, rows, cols) [view source on GitHub]

Crop a bounding box.

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).
x_min	int
y_min	int
x_max	int
y_max	int
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
tuple	A cropped bounding box (x_min, y_min, x_max, y_max).

def albumentations.augmentations.crops.functional.crop_bbox_by_coords (bbox, crop_coords, crop_height, crop_width, rows, cols) [view source on GitHub]

Crop a bounding box using the provided coordinates of bottom-left and top-right corners in pixels and the required height and width of the crop.

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A cropped box (x_min, y_min, x_max, y_max).
crop_coords	Tuple[int, int, int, int]	Crop coordinates (x1, y1, x2, y2).
crop_height	int
crop_width	int
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
tuple	A cropped bounding box (x_min, y_min, x_max, y_max).

def albumentations.augmentations.crops.functional.crop_keypoint_by_coords (keypoint, crop_coords) [view source on GitHub]

Crop a keypoint using the provided coordinates of bottom-left and top-right corners in pixels and the required height and width of the crop.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).
crop_coords	Tuple[int, int, int, int]	Crop box coords (x1, x2, y1, y2).

Returns:

Type	Description
	A keypoint (x, y, angle, scale).

def albumentations.augmentations.crops.functional.keypoint_center_crop (keypoint, crop_height, crop_width, rows, cols) [view source on GitHub]

Keypoint center crop.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).
crop_height	int	Crop height.
crop_width	int	Crop width.
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
tuple	A keypoint (x, y, angle, scale).

def albumentations.augmentations.crops.functional.keypoint_random_crop (keypoint, crop_height, crop_width, h_start, w_start, rows, cols) [view source on GitHub]

Keypoint random crop.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	(tuple): A keypoint (x, y, angle, scale).
crop_height	int	Crop height.
crop_width	int	Crop width.
h_start	float	Crop height start.
w_start	float	Crop width start.
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
	A keypoint (x, y, angle, scale).

albumentations.augmentations.crops.transforms

class albumentations.augmentations.crops.transforms.BBoxSafeRandomCrop (erosion_rate=0.0, always_apply=False, p=1.0) [view source on GitHub]

Crop a random part of the input without loss of bboxes.

Parameters:

Name	Type	Description
erosion_rate	float	erosion rate applied on input image height before crop.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes Image types: uint8, float32

class albumentations.augmentations.crops.transforms.CenterCrop (height, width, always_apply=False, p=1.0) [view source on GitHub]

Crop the central part of the input.

Parameters:

Name	Type	Description
height	int	height of the crop.
width	int	width of the crop.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

Note: It is recommended to use uint8 images as input. Otherwise the operation will require internal conversion float32 -> uint8 -> float32 that causes worse performance.

class albumentations.augmentations.crops.transforms.Crop (x_min=0, y_min=0, x_max=1024, y_max=1024, always_apply=False, p=1.0) [view source on GitHub]

Crop region from image.

Parameters:

Name	Type	Description
x_min	int	Minimum upper left x coordinate.
y_min	int	Minimum upper left y coordinate.
x_max	int	Maximum lower right x coordinate.
y_max	int	Maximum lower right y coordinate.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.crops.transforms.CropAndPad (px=None, percent=None, pad_mode=0, pad_cval=0, pad_cval_mask=0, keep_size=True, sample_independently=True, interpolation=1, always_apply=False, p=1.0) [view source on GitHub]

Crop and pad images by pixel amounts or fractions of image sizes. Cropping removes pixels at the sides (i.e. extracts a subimage from a given full image). Padding adds pixels to the sides (e.g. black pixels). This transformation will never crop images below a height or width of 1.

Note: This transformation automatically resizes images back to their original size. To deactivate this, add the parameter keep_size=False.

Parameters:

Name	Type	Description
px	int or tuple	The number of pixels to crop (negative values) or pad (positive values) on each side of the image. Either this or the parameter percent may be set, not both at the same time. * If None, then pixel-based cropping/padding will not be used. * If int, then that exact number of pixels will always be cropped/padded. * If a tuple of two int s with values a and b, then each side will be cropped/padded by a random amount sampled uniformly per image and side from the interval [a, b]. If however sample_independently is set to False, only one value will be sampled per image and used for all sides. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single int (always crop/pad by exactly that value), a tuple of two int s a and b (crop/pad by an amount within [a, b]), a list of int s (crop/pad by a random value that is contained in the list).
percent	float or tuple	The number of pixels to crop (negative values) or pad (positive values) on each side of the image given as a fraction of the image height/width. E.g. if this is set to -0.1, the transformation will always crop away 10% of the image's height at both the top and the bottom (both 10% each), as well as 10% of the width at the right and left. Expected value range is (-1.0, inf). Either this or the parameter px may be set, not both at the same time. * If None, then fraction-based cropping/padding will not be used. * If float, then that fraction will always be cropped/padded. * If a tuple of two float s with values a and b, then each side will be cropped/padded by a random fraction sampled uniformly per image and side from the interval [a, b]. If however sample_independently is set to False, only one value will be sampled per image and used for all sides. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single float (always crop/pad by exactly that percent value), a tuple of two float s a and b (crop/pad by a fraction from [a, b]), a list of float s (crop/pad by a random value that is contained in the list).
pad_mode	int	OpenCV border mode.
pad_cval	number, Sequence[number]	The constant value to use if the pad mode is BORDER_CONSTANT. * If number, then that value will be used. * If a tuple of two number s and at least one of them is a float, then a random number will be uniformly sampled per image from the continuous interval [a, b] and used as the value. If both number s are int s, the interval is discrete. * If a list of number, then a random value will be chosen from the elements of the list and used as the value.
pad_cval_mask	number, Sequence[number]	Same as pad_cval but only for masks.
keep_size	bool	After cropping and padding, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the cropped/padded image will be resized to the input image's size, i.e. the output shape is always identical to the input shape.
sample_independently	bool	If False and the values for px/percent result in exactly one probability distribution for all image sides, only one single value will be sampled from that probability distribution and used for all sides. I.e. the crop/pad amount then is the same for all sides. If True, four values will be sampled independently, one per side.
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.

Targets: image, mask, bboxes, keypoints

Image types: any

class albumentations.augmentations.crops.transforms.CropNonEmptyMaskIfExists (height, width, ignore_values=None, ignore_channels=None, always_apply=False, p=1.0) [view source on GitHub]

Crop area with mask if mask is non-empty, else make random crop.

Parameters:

Name	Type	Description
height	int	vertical size of crop in pixels
width	int	horizontal size of crop in pixels
ignore_values	list of int	values to ignore in mask, 0 values are always ignored (e.g. if background value is 5 set ignore_values=[5] to ignore)
ignore_channels	list of int	channels to ignore in mask (e.g. if background is a first channel set ignore_channels=[0] to ignore)
p	float	probability of applying the transform. Default: 1.0.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.crops.transforms.RandomCrop (height, width, always_apply=False, p=1.0) [view source on GitHub]

Crop a random part of the input.

Parameters:

Name	Type	Description
height	int	height of the crop.
width	int	width of the crop.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.crops.transforms.RandomCropFromBorders (crop_left=0.1, crop_right=0.1, crop_top=0.1, crop_bottom=0.1, always_apply=False, p=1.0) [view source on GitHub]

Crop bbox from image randomly cut parts from borders without resize at the end

Parameters:

Name	Type	Description
crop_left	float	single float value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
crop_right	float	single float value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
crop_top	float	singlefloat value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
crop_bottom	float	single float value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.crops.transforms.RandomCropNearBBox (max_part_shift=(0.3, 0.3), cropping_box_key='cropping_bbox', always_apply=False, p=1.0) [view source on GitHub]

Crop bbox from image with random shift by x,y coordinates

Parameters:

Name	Type	Description
max_part_shift	float, [float, float]	Max shift in height and width dimensions relative to cropping_bbox dimension. If max_part_shift is a single float, the range will be (max_part_shift, max_part_shift). Default (0.3, 0.3).
cropping_box_key	str	Additional target key for cropping box. Default cropping_bbox
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

Examples:

>>> aug = Compose([RandomCropNearBBox(max_part_shift=(0.1, 0.5), cropping_box_key='test_box')],
>>>              bbox_params=BboxParams("pascal_voc"))
>>> result = aug(image=image, bboxes=bboxes, test_box=[0, 5, 10, 20])

class albumentations.augmentations.crops.transforms.RandomResizedCrop (height, width, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1, always_apply=False, p=1.0) [view source on GitHub]

Torchvision's variant of crop a random part of the input and rescale it to some size.

Parameters:

Name	Type	Description
height	int	height after crop and resize.
width	int	width after crop and resize.
scale	[float, float]	range of size of the origin size cropped
ratio	[float, float]	range of aspect ratio of the origin aspect ratio cropped
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.crops.transforms.RandomSizedBBoxSafeCrop (height, width, erosion_rate=0.0, interpolation=1, always_apply=False, p=1.0) [view source on GitHub]

Crop a random part of the input and rescale it to some size without loss of bboxes.

Parameters:

Name	Type	Description
height	int	height after crop and resize.
width	int	width after crop and resize.
erosion_rate	float	erosion rate applied on input image height before crop.
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes Image types: uint8, float32

class albumentations.augmentations.crops.transforms.RandomSizedCrop (min_max_height, height, width, w2h_ratio=1.0, interpolation=1, always_apply=False, p=1.0) [view source on GitHub]

Crop a random part of the input and rescale it to some size.

Parameters:

Name	Type	Description
min_max_height	[int, int]	crop size limits.
height	int	height after crop and resize.
width	int	width after crop and resize.
w2h_ratio	float	aspect ratio of crop.
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

albumentations.augmentations.domain_adaptation

class albumentations.augmentations.domain_adaptation.FDA (reference_images, beta_limit=0.1, read_fn=<function read_rgb_image at 0x7f357e730160>, always_apply=False, p=0.5) [view source on GitHub]

Fourier Domain Adaptation from https://github.com/YanchaoYang/FDA Simple "style transfer".

Parameters:

Name	Type	Description
reference_images	List[str] or List(np.ndarray	List of file paths for reference images or list of reference images.
beta_limit	float or tuple of float	coefficient beta from paper. Recommended less 0.3.
read_fn	Callable	Used-defined function to read image. Function should get image path and return numpy array of image pixels.

Targets: image

Image types: uint8, float32

Reference: https://github.com/YanchaoYang/FDA https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.pdf

Examples:

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
>>> target_image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
>>> aug = A.Compose([A.FDA([target_image], p=1, read_fn=lambda x: x)])
>>> result = aug(image=image)

class albumentations.augmentations.domain_adaptation.HistogramMatching (reference_images, blend_ratio=(0.5, 1.0), read_fn=<function read_rgb_image at 0x7f357e730160>, always_apply=False, p=0.5) [view source on GitHub]

Apply histogram matching. It manipulates the pixels of an input image so that its histogram matches the histogram of the reference image. If the images have multiple channels, the matching is done independently for each channel, as long as the number of channels is equal in the input image and the reference.

Histogram matching can be used as a lightweight normalisation for image processing, such as feature matching, especially in circumstances where the images have been taken from different sources or in different conditions (i.e. lighting).

See: https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_histogram_matching.html

Parameters:

Name	Type	Description
reference_images	List[str] or List(np.ndarray	List of file paths for reference images or list of reference images.
blend_ratio	[float, float]	Tuple of min and max blend ratio. Matched image will be blended with original with random blend factor for increased diversity of generated images.
read_fn	Callable	Used-defined function to read image. Function should get image path and return numpy array of image pixels.
p	float	probability of applying the transform. Default: 1.0.

Targets: image

Image types: uint8, uint16, float32

class albumentations.augmentations.domain_adaptation.PixelDistributionAdaptation (reference_images, blend_ratio=(0.25, 1.0), read_fn=<function read_rgb_image at 0x7f357e730160>, transform_type='pca', always_apply=False, p=0.5) [view source on GitHub]

Another naive and quick pixel-level domain adaptation. It fits a simple transform (such as PCA, StandardScaler or MinMaxScaler) on both original and reference image, transforms original image with transform trained on this image and then performs inverse transformation using transform fitted on reference image.

Parameters:

Name	Type	Description
reference_images	List[str] or List(np.ndarray	List of file paths for reference images or list of reference images.
blend_ratio	[float, float]	Tuple of min and max blend ratio. Matched image will be blended with original with random blend factor for increased diversity of generated images.
read_fn	Callable	Used-defined function to read image. Function should get image path and return numpy array of image pixels. Usually it's default read_rgb_image when images paths are used as reference, otherwise it could be identity function lambda x: x if reference images have been read in advance.
transform_type	str	type of transform; "pca", "standard", "minmax" are allowed.
p	float	probability of applying the transform. Default: 1.0.

Targets: image

Image types: uint8, float32

See also: https://github.com/arsenyinfo/qudida

def albumentations.augmentations.domain_adaptation.fourier_domain_adaptation (img, target_img, beta) [view source on GitHub]

Fourier Domain Adaptation from https://github.com/YanchaoYang/FDA

Parameters:

Name	Type	Description
img	ndarray	source image
target_img	ndarray	target image for domain adaptation
beta	float	coefficient from source paper

Returns:

Type	Description
ndarray	transformed image

albumentations.augmentations.dropout special

albumentations.augmentations.dropout.channel_dropout

class albumentations.augmentations.dropout.channel_dropout.ChannelDropout (channel_drop_range=(1, 1), fill_value=0, always_apply=False, p=0.5) [view source on GitHub]

Randomly Drop Channels in the input Image.

Parameters:

Name	Type	Description
channel_drop_range	[int, int]	range from which we choose the number of channels to drop.
fill_value	int, float	pixel value for the dropped channel.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, uint16, unit32, float32

albumentations.augmentations.dropout.coarse_dropout

class albumentations.augmentations.dropout.coarse_dropout.CoarseDropout (max_holes=8, max_height=8, max_width=8, min_holes=None, min_height=None, min_width=None, fill_value=0, mask_fill_value=None, always_apply=False, p=0.5) [view source on GitHub]

CoarseDropout of the rectangular regions in the image.

Parameters:

Name	Type	Description
max_holes	int	Maximum number of regions to zero out.
max_height	int, float	Maximum height of the hole.
max_width	int, float	Maximum width of the hole.
min_holes	int	Minimum number of regions to zero out. If None, min_holes is be set to max_holes. Default: None.
min_height	int, float	Minimum height of the hole. Default: None. If None, min_height is set to max_height. Default: None. If float, it is calculated as a fraction of the image height.
min_width	int, float	Minimum width of the hole. If None, min_height is set to max_width. Default: None. If float, it is calculated as a fraction of the image width.
fill_value	int, float, list of int, list of float	value for dropped pixels.
mask_fill_value	int, float, list of int, list of float	fill value for dropped pixels in mask. If None - mask is not affected. Default: None.

Targets: image, mask, keypoints

Image types: uint8, float32

Reference: | https://arxiv.org/abs/1708.04552 | https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py | https://github.com/aleju/imgaug/blob/master/imgaug/augmenters/arithmetic.py

albumentations.augmentations.dropout.cutout

class albumentations.augmentations.dropout.cutout.Cutout (num_holes=8, max_h_size=8, max_w_size=8, fill_value=0, always_apply=False, p=0.5) [view source on GitHub]

CoarseDropout of the square regions in the image.

Parameters:

Name	Type	Description
num_holes	int	number of regions to zero out
max_h_size	int	maximum height of the hole
max_w_size	int	maximum width of the hole
fill_value	int, float, list of int, list of float	value for dropped pixels.

Targets: image

Image types: uint8, float32

Reference: | https://arxiv.org/abs/1708.04552 | https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py | https://github.com/aleju/imgaug/blob/master/imgaug/augmenters/arithmetic.py

albumentations.augmentations.dropout.grid_dropout

class albumentations.augmentations.dropout.grid_dropout.GridDropout (ratio=0.5, unit_size_min=None, unit_size_max=None, holes_number_x=None, holes_number_y=None, shift_x=0, shift_y=0, random_offset=False, fill_value=0, mask_fill_value=None, always_apply=False, p=0.5) [view source on GitHub]

GridDropout, drops out rectangular regions of an image and the corresponding mask in a grid fashion.

Parameters:

Name	Type	Description
ratio	float	the ratio of the mask holes to the unit_size (same for horizontal and vertical directions). Must be between 0 and 1. Default: 0.5.
unit_size_min	int	minimum size of the grid unit. Must be between 2 and the image shorter edge. If 'None', holes_number_x and holes_number_y are used to setup the grid. Default: None.
unit_size_max	int	maximum size of the grid unit. Must be between 2 and the image shorter edge. If 'None', holes_number_x and holes_number_y are used to setup the grid. Default: None.
holes_number_x	int	the number of grid units in x direction. Must be between 1 and image width//2. If 'None', grid unit width is set as image_width//10. Default: None.
holes_number_y	int	the number of grid units in y direction. Must be between 1 and image height//2. If None, grid unit height is set equal to the grid unit width or image height, whatever is smaller.
shift_x	int	offsets of the grid start in x direction from (0,0) coordinate. Clipped between 0 and grid unit_width - hole_width. Default: 0.
shift_y	int	offsets of the grid start in y direction from (0,0) coordinate. Clipped between 0 and grid unit height - hole_height. Default: 0.
random_offset	boolean	weather to offset the grid randomly between 0 and grid unit size - hole size If 'True', entered shift_x, shift_y are ignored and set randomly. Default: False.
fill_value	int	value for the dropped pixels. Default = 0
mask_fill_value	int	value for the dropped pixels in mask. If None, transformation is not applied to the mask. Default: None.

Targets: image, mask

Image types: uint8, float32

References: https://arxiv.org/abs/2001.04086

albumentations.augmentations.dropout.mask_dropout

class albumentations.augmentations.dropout.mask_dropout.MaskDropout (max_objects=1, image_fill_value=0, mask_fill_value=0, always_apply=False, p=0.5) [view source on GitHub]

Image & mask augmentation that zero out mask and image regions corresponding to randomly chosen object instance from mask.

Mask must be single-channel image, zero values treated as background. Image can be any number of channels.

Inspired by https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114254

Parameters:

Name	Type	Description
max_objects		Maximum number of labels that can be zeroed out. Can be tuple, in this case it's [min, max]
image_fill_value		Fill value to use when filling image. Can be 'inpaint' to apply inpaining (works only for 3-chahnel images)
mask_fill_value		Fill value to use when filling mask.

Targets: image, mask

Image types: uint8, float32

albumentations.augmentations.functional

def albumentations.augmentations.functional.add_fog (img, fog_coef, alpha_coef, haze_list) [view source on GitHub]

Add fog to the image.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
img	numpy.ndarray	Image.
fog_coef	float	Fog coefficient.
alpha_coef	float	Alpha coefficient.
haze_list	list

Returns:

Type	Description
numpy.ndarray	Image.

def albumentations.augmentations.functional.add_gravel (img, gravels) [view source on GitHub]

Add gravel to the image.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
img	ndarray	image to add gravel to
gravels	list	list of gravel parameters. (float, float, float, float): (top-left x, top-left y, bottom-right x, bottom right y)

Returns:

Type	Description
numpy.ndarray

def albumentations.augmentations.functional.add_rain (img, slant, drop_length, drop_width, drop_color, blur_value, brightness_coefficient, rain_drops) [view source on GitHub]

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
img	numpy.ndarray	Image.
slant	int
drop_length
drop_width
drop_color
blur_value	int	Rainy view are blurry.
brightness_coefficient	float	Rainy days are usually shady.
rain_drops

Returns:

Type	Description
numpy.ndarray	Image.

def albumentations.augmentations.functional.add_shadow (img, vertices_list) [view source on GitHub]

Add shadows to the image.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
img	numpy.ndarray
vertices_list	list

Returns:

Type	Description
numpy.ndarray

def albumentations.augmentations.functional.add_snow (img, snow_point, brightness_coeff) [view source on GitHub]

Bleaches out pixels, imitation snow.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
img	numpy.ndarray	Image.
snow_point		Number of show points.
brightness_coeff		Brightness coefficient.

Returns:

Type	Description
numpy.ndarray	Image.

def albumentations.augmentations.functional.add_sun_flare (img, flare_center_x, flare_center_y, src_radius, src_color, circles) [view source on GitHub]

Add sun flare.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
img	numpy.ndarray
flare_center_x	float
flare_center_y	float
src_radius
src_color	int, int, int
circles	list

Returns:

Type	Description
numpy.ndarray

def albumentations.augmentations.functional.bbox_from_mask (mask) [view source on GitHub]

Create bounding box from binary mask (fast version)

Parameters:

Name	Type	Description
mask	numpy.ndarray	binary mask.

Returns:

Type	Description
tuple	A bounding box tuple (x_min, y_min, x_max, y_max).

def albumentations.augmentations.functional.equalize (img, mask=None, mode='cv', by_channels=True) [view source on GitHub]

Equalize the image histogram.

Parameters:

Name	Type	Description
img	numpy.ndarray	RGB or grayscale image.
mask	numpy.ndarray	An optional mask. If given, only the pixels selected by the mask are included in the analysis. Maybe 1 channel or 3 channel array.
mode	str	{'cv', 'pil'}. Use OpenCV or Pillow equalization method.
by_channels	bool	If True, use equalization by channels separately, else convert image to YCbCr representation and use equalization by Y channel.

Returns:

Type	Description
numpy.ndarray	Equalized image.

def albumentations.augmentations.functional.fancy_pca (img, alpha=0.1) [view source on GitHub]

Perform 'Fancy PCA' augmentation from: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

Parameters:

Name	Type	Description
img	numpy.ndarray	numpy array with (h, w, rgb) shape, as ints between 0-255
alpha	float	how much to perturb/scale the eigen vecs and vals the paper used std=0.1

Returns:

Type	Description
numpy.ndarray	numpy image-like array as uint8 range(0, 255)

def albumentations.augmentations.functional.iso_noise (image, color_shift=0.05, intensity=0.5, random_state=None, ** kwargs) [view source on GitHub]

Apply poisson noise to image to simulate camera sensor noise.

Parameters:

Name	Type	Description
image	numpy.ndarray	Input image, currently, only RGB, uint8 images are supported.
color_shift	float
intensity	float	Multiplication factor for noise values. Values of ~0.5 are produce noticeable, yet acceptable level of noise.
random_state
**kwargs

Returns:

Type	Description
numpy.ndarray	Noised image

def albumentations.augmentations.functional.mask_from_bbox (img, bbox) [view source on GitHub]

Create binary mask from bounding box

Parameters:

Name	Type	Description
img	numpy.ndarray	input image
bbox		A bounding box tuple (x_min, y_min, x_max, y_max)

Returns:

Type	Description
mask (numpy.ndarray)	binary mask

def albumentations.augmentations.functional.move_tone_curve (img, low_y, high_y) [view source on GitHub]

Rescales the relationship between bright and dark areas of the image by manipulating its tone curve.

Parameters:

Name	Type	Description
img	numpy.ndarray	RGB or grayscale image.
low_y	float	y-position of a Bezier control point used to adjust the tone curve, must be in range [0, 1]
high_y	float	y-position of a Bezier control point used to adjust image tone curve, must be in range [0, 1]

def albumentations.augmentations.functional.multiply (img, multiplier) [view source on GitHub]

Parameters:

Name	Type	Description
img	numpy.ndarray	Image.
multiplier	numpy.ndarray	Multiplier coefficient.

Returns:

Type	Description
numpy.ndarray	Image multiplied by multiplier coefficient.

def albumentations.augmentations.functional.posterize (img, bits) [view source on GitHub]

Reduce the number of bits for each color channel.

Parameters:

Name	Type	Description
img	numpy.ndarray	image to posterize.
bits	int	number of high bits. Must be in range [0, 8]

Returns:

Type	Description
numpy.ndarray	Image with reduced color channels.

def albumentations.augmentations.functional.solarize (img, threshold=128) [view source on GitHub]

Invert all pixel values above a threshold.

Parameters:

Name	Type	Description
img	numpy.ndarray	The image to solarize.
threshold	int	All pixels above this greyscale level are inverted.

Returns:

Type	Description
numpy.ndarray	Solarized image.

def albumentations.augmentations.functional.swap_tiles_on_image (image, tiles) [view source on GitHub]

Swap tiles on image.

Parameters:

Name	Type	Description
image	np.ndarray	Input image.
tiles	np.ndarray	array of tuples( current_left_up_corner_row, current_left_up_corner_col, old_left_up_corner_row, old_left_up_corner_col, height_tile, width_tile)

Returns:

Type	Description
np.ndarray	Output image.

albumentations.augmentations.geometric special

albumentations.augmentations.geometric.functional

def albumentations.augmentations.geometric.functional.bbox_flip (bbox, d, rows, cols) [view source on GitHub]

Flip a bounding box either vertically, horizontally or both depending on the value of d.

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).
d	int	dimension. 0 for vertical flip, 1 for horizontal, -1 for transpose
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).

Exceptions:

Type	Description
ValueError	if value of d is not -1, 0 or 1.

def albumentations.augmentations.geometric.functional.bbox_hflip (bbox, rows, cols) [view source on GitHub]

Flip a bounding box horizontally around the y-axis.

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).

def albumentations.augmentations.geometric.functional.bbox_rot90 (bbox, factor, rows, cols) [view source on GitHub]

Rotates a bounding box by 90 degrees CCW (see np.rot90)

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A bounding box tuple (x_min, y_min, x_max, y_max).
factor	int	Number of CCW rotations. Must be in set {0, 1, 2, 3} See np.rot90.
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
Tuple[float, float, float, float]	tuple: A bounding box tuple (x_min, y_min, x_max, y_max).

def albumentations.augmentations.geometric.functional.bbox_rotate (bbox, angle, method, rows, cols) [view source on GitHub]

Rotates a bounding box by angle degrees.

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).
angle	float	Angle of rotation in degrees.
method	str	Rotation method used. Should be one of: "largest_box", "ellipse". Default: "largest_box".
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).

References: https://arxiv.org/abs/2109.13488

def albumentations.augmentations.geometric.functional.bbox_shift_scale_rotate (bbox, angle, scale, dx, dy, rotate_method, rows, cols, ** kwargs) [view source on GitHub]

Rotates, shifts and scales a bounding box. Rotation is made by angle degrees, scaling is made by scale factor and shifting is made by dx and dy.

Parameters:

Name	Type	Description
bbox	tuple	A bounding box (x_min, y_min, x_max, y_max).
angle	int	Angle of rotation in degrees.
scale	int	Scale factor.
dx	int	Shift along x-axis in pixel units.
dy	int	Shift along y-axis in pixel units.
rotate_method(str)		Rotation method used. Should be one of: "largest_box", "ellipse". Default: "largest_box".
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
	A bounding box (x_min, y_min, x_max, y_max).

def albumentations.augmentations.geometric.functional.bbox_transpose (bbox, axis, rows, cols) [view source on GitHub]

Transposes a bounding box along given axis.

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).
axis	int	0 - main axis, 1 - secondary axis.
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
Tuple[float, float, float, float]	A bounding box tuple (x_min, y_min, x_max, y_max).

Exceptions:

Type	Description
ValueError	If axis not equal to 0 or 1.

def albumentations.augmentations.geometric.functional.bbox_vflip (bbox, rows, cols) [view source on GitHub]

Flip a bounding box vertically around the x-axis.

Parameters:

Name	Type	Description
bbox	Tuple[float, float, float, float]	A bounding box (x_min, y_min, x_max, y_max).
rows	int	Image rows.
cols	int	Image cols.

Returns:

Type	Description
Tuple[float, float, float, float]	tuple: A bounding box (x_min, y_min, x_max, y_max).

def albumentations.augmentations.geometric.functional.elastic_transform (img, alpha, sigma, alpha_affine, interpolation=1, border_mode=4, value=None, random_state=None, approximate=False, same_dxdy=False) [view source on GitHub]

Elastic deformation of images as described in [Simard2003]_ (with modifications). Based on https://gist.github.com/ernestum/601cdf56d2b424757de5

.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for Convolutional Neural Networks applied to Visual Document Analysis", in Proc. of the International Conference on Document Analysis and Recognition, 2003.

def albumentations.augmentations.geometric.functional.elastic_transform_approx (img, alpha, sigma, alpha_affine, interpolation=1, border_mode=4, value=None, random_state=None) [view source on GitHub]

Elastic deformation of images as described in [Simard2003]_ (with modifications for speed). Based on https://gist.github.com/ernestum/601cdf56d2b424757de5

.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for Convolutional Neural Networks applied to Visual Document Analysis", in Proc. of the International Conference on Document Analysis and Recognition, 2003.

def albumentations.augmentations.geometric.functional.from_distance_maps (distance_maps, inverted, if_not_found_coords, threshold=None) [view source on GitHub]

Convert outputs of to_distance_maps() to KeypointsOnImage. This is the inverse of to_distance_maps.

Parameters:

Name	Type	Description
distance_maps	ndarray	The distance maps. N is the number of keypoints.
inverted	bool	Whether the given distance maps were generated in inverted mode (i.e. :func:KeypointsOnImage.to_distance_maps was called with inverted=True) or in non-inverted mode.
if_not_found_coords	Union[Sequence[int], dict]	Coordinates to use for keypoints that cannot be found in distance_maps. If this is a list/tuple, it must contain two int values. If it is a dict, it must contain the keys x and y with each containing one int value. If this is None, then the keypoint will not be added.
threshold	Optional[float]	The search for keypoints works by searching for the argmin (non-inverted) or argmax (inverted) in each channel. This parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit as a keypoint. Use None to use no min/max.
nb_channels	None, int	Number of channels of the image on which the keypoints are placed. Some keypoint augmenters require that information. If set to None, the keypoint's shape will be set to (height, width), otherwise (height, width, nb_channels).

def albumentations.augmentations.geometric.functional.grid_distortion (img, num_steps=10, xsteps=(), ysteps=(), interpolation=1, border_mode=4, value=None) [view source on GitHub]

Perform a grid distortion of an input image.

Reference: http://pythology.blogspot.sg/2014/03/interpolation-on-regular-distorted-grid.html

def albumentations.augmentations.geometric.functional.keypoint_flip (keypoint, d, rows, cols) [view source on GitHub]

Flip a keypoint either vertically, horizontally or both depending on the value of d.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).
d	int	Number of flip. Must be -1, 0 or 1: * 0 - vertical flip, * 1 - horizontal flip, * -1 - vertical and horizontal flip.
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).

Exceptions:

Type	Description
ValueError	if value of d is not -1, 0 or 1.

def albumentations.augmentations.geometric.functional.keypoint_hflip (keypoint, rows, cols) [view source on GitHub]

Flip a keypoint horizontally around the y-axis.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).

def albumentations.augmentations.geometric.functional.keypoint_rot90 (keypoint, factor, rows, cols, ** params) [view source on GitHub]

Rotates a keypoint by 90 degrees CCW (see np.rot90)

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).
factor	int	Number of CCW rotations. Must be in range [0;3] See np.rot90.
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
Tuple[float, float, float, float]	tuple: A keypoint (x, y, angle, scale).

Exceptions:

Type	Description
ValueError	if factor not in set {0, 1, 2, 3}

def albumentations.augmentations.geometric.functional.keypoint_rotate (keypoint, angle, rows, cols, ** params) [view source on GitHub]

Rotate a keypoint by angle.

Parameters:

Name	Type	Description
keypoint	tuple	A keypoint (x, y, angle, scale).
angle	float	Rotation angle.
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
tuple	A keypoint (x, y, angle, scale).

def albumentations.augmentations.geometric.functional.keypoint_scale (keypoint, scale_x, scale_y) [view source on GitHub]

Scales a keypoint by scale_x and scale_y.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).
scale_x	float	Scale coefficient x-axis.
scale_y	float	Scale coefficient y-axis.

Returns:

Type	Description
Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).

def albumentations.augmentations.geometric.functional.keypoint_transpose (keypoint) [view source on GitHub]

Rotate a keypoint by angle.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).

Returns:

Type	Description
Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).

def albumentations.augmentations.geometric.functional.keypoint_vflip (keypoint, rows, cols) [view source on GitHub]

Flip a keypoint vertically around the x-axis.

Parameters:

Name	Type	Description
keypoint	Tuple[float, float, float, float]	A keypoint (x, y, angle, scale).
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
Tuple[float, float, float, float]	tuple: A keypoint (x, y, angle, scale).

def albumentations.augmentations.geometric.functional.optical_distortion (img, k=0, dx=0, dy=0, interpolation=1, border_mode=4, value=None) [view source on GitHub]

Barrel / pincushion distortion. Unconventional augment.

Reference: | https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion | https://stackoverflow.com/questions/10364201/image-transformation-in-opencv | https://stackoverflow.com/questions/2477774/correcting-fisheye-distortion-programmatically | http://www.coldvision.io/2017/03/02/advanced-lane-finding-using-opencv/

def albumentations.augmentations.geometric.functional.py3round (number) [view source on GitHub]

Unified rounding in all python versions.

def albumentations.augmentations.geometric.functional.rotation2DMatrixToEulerAngles (matrix, y_up=False) [view source on GitHub]

Parameters:

Name	Type	Description
matrix	ndarray	Rotation matrix
y_up	bool	is Y axis looks up or down

def albumentations.augmentations.geometric.functional.to_distance_maps (keypoints, height, width, inverted=False) [view source on GitHub]

Generate a (H,W,N) array of distance maps for N keypoints.

The n-th distance map contains at every location (y, x) the euclidean distance to the n-th keypoint.

This function can be used as a helper when augmenting keypoints with a method that only supports the augmentation of images.

Parameters:

Name	Type	Description
keypoint		keypoint coordinates
height	int	image height
width	int	image width
inverted	bool	If True, inverted distance maps are returned where each distance value d is replaced by d/(d+1), i.e. the distance maps have values in the range (0.0, 1.0] with 1.0 denoting exactly the position of the respective keypoint.

Returns:

Type	Description
ndarray	(H, W, N) ndarray A float32 array containing N distance maps for N keypoints. Each location (y, x, n) in the array denotes the euclidean distance at (y, x) to the n-th keypoint. If inverted is True, the distance d is replaced by d/(d+1). The height and width of the array match the height and width in KeypointsOnImage.shape.

albumentations.augmentations.geometric.resize

class albumentations.augmentations.geometric.resize.LongestMaxSize (max_size=1024, interpolation=1, always_apply=False, p=1) [view source on GitHub]

Rescale an image so that maximum side is equal to max_size, keeping the aspect ratio of the initial image.

Parameters:

Name	Type	Description
max_size	int, list of int	maximum size of the image after the transformation. When using a list, max size will be randomly selected from the values in the list.
interpolation	OpenCV flag	interpolation method. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.resize.RandomScale (scale_limit=0.1, interpolation=1, always_apply=False, p=0.5) [view source on GitHub]

Randomly resize the input. Output image size is different from the input image size.

Parameters:

Name	Type	Description
scale_limit	[float, float] or float	scaling factor range. If scale_limit is a single float value, the range will be (-scale_limit, scale_limit). Note that the scale_limit will be biased by 1. If scale_limit is a tuple, like (low, high), sampling will be done from the range (1 + low, 1 + high). Default: (-0.1, 0.1).
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.resize.Resize (height, width, interpolation=1, always_apply=False, p=1) [view source on GitHub]

Resize the input to the given height and width.

Parameters:

Name	Type	Description
height	int	desired height of the output.
width	int	desired width of the output.
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.resize.SmallestMaxSize (max_size=1024, interpolation=1, always_apply=False, p=1) [view source on GitHub]

Rescale an image so that minimum side is equal to max_size, keeping the aspect ratio of the initial image.

Parameters:

Name	Type	Description
max_size	int, list of int	maximum size of smallest side of the image after the transformation. When using a list, max size will be randomly selected from the values in the list.
interpolation	OpenCV flag	interpolation method. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 1.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

albumentations.augmentations.geometric.rotate

class albumentations.augmentations.geometric.rotate.RandomRotate90 [view source on GitHub]

Randomly rotate the input by 90 degrees zero or more times.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

albumentations.augmentations.geometric.rotate.RandomRotate90.apply (self, img, factor=0, **params)

Parameters:

Name	Type	Description
factor	int	number of times the input will be rotated by 90 degrees.

class albumentations.augmentations.geometric.rotate.Rotate (limit=90, interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box', crop_border=False, always_apply=False, p=0.5) [view source on GitHub]

Rotate the input by an angle selected randomly from the uniform distribution.

Parameters:

Name	Type	Description
limit	[int, int] or int	range from which a random angle is picked. If limit is a single int an angle is picked from (-limit, limit). Default: (-90, 90)
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
border_mode	OpenCV flag	flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101
value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
rotate_method	str	rotation method used for the bounding boxes. Should be one of "largest_box" or "ellipse". Default: "largest_box"
crop_border	bool	If True would make a largest possible crop within rotated image
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.rotate.SafeRotate (limit=90, interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, p=0.5) [view source on GitHub]

Rotate the input inside the input's frame by an angle selected randomly from the uniform distribution.

The resulting image may have artifacts in it. After rotation, the image may have a different aspect ratio, and after resizing, it returns to its original shape with the original aspect ratio of the image. For these reason we may see some artifacts.

Parameters:

Name	Type	Description
limit	[int, int] or int	range from which a random angle is picked. If limit is a single int an angle is picked from (-limit, limit). Default: (-90, 90)
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
border_mode	OpenCV flag	flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101
value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

albumentations.augmentations.geometric.transforms

class albumentations.augmentations.geometric.transforms.Affine (scale=None, translate_percent=None, translate_px=None, rotate=None, shear=None, interpolation=1, mask_interpolation=0, cval=0, cval_mask=0, mode=0, fit_output=False, keep_ratio=False, rotate_method='largest_box', always_apply=False, p=0.5) [view source on GitHub]

Augmentation to apply affine transformations to images. This is mostly a wrapper around the corresponding classes and functions in OpenCV.

Affine transformations involve:

- Translation ("move" image on the x-/y-axis)
- Rotation
- Scaling ("zoom" in/out)
- Shear (move one side of the image, turning a square into a trapezoid)

All such transformations can create "new" pixels in the image without a defined content, e.g. if the image is translated to the left, pixels are created on the right. A method has to be defined to deal with these pixel values. The parameters cval and mode of this class deal with this.

Some transformations involve interpolations between several pixels of the input image to generate output pixel values. The parameters interpolation and mask_interpolation deals with the method of interpolation used for this.

Parameters:

Name	Type	Description
scale	number, tuple of number or dict	Scaling factor to use, where 1.0 denotes "no change" and 0.5 is zoomed out to 50 percent of the original size. * If a single number, then that value will be used for all images. * If a tuple (a, b), then a value will be uniformly sampled per image from the interval [a, b]. That the same range will be used for both x- and y-axis. To keep the aspect ratio, set keep_ratio=True, then the same value will be used for both x- and y-axis. * If a dictionary, then it is expected to have the keys x and/or y. Each of these keys can have the same values as described above. Using a dictionary allows to set different values for the two axis and sampling will then happen independently per axis, resulting in samples that differ between the axes. Note that when the keep_ratio=True, the x- and y-axis ranges should be the same.
translate_percent	None, number, tuple of number or dict	Translation as a fraction of the image height/width (x-translation, y-translation), where 0 denotes "no change" and 0.5 denotes "half of the axis size". * If None then equivalent to 0.0 unless translate_px has a value other than None. * If a single number, then that value will be used for all images. * If a tuple (a, b), then a value will be uniformly sampled per image from the interval [a, b]. That sampled fraction value will be used identically for both x- and y-axis. * If a dictionary, then it is expected to have the keys x and/or y. Each of these keys can have the same values as described above. Using a dictionary allows to set different values for the two axis and sampling will then happen independently per axis, resulting in samples that differ between the axes.
translate_px	None, int, tuple of int or dict	Translation in pixels. * If None then equivalent to 0 unless translate_percent has a value other than None. * If a single int, then that value will be used for all images. * If a tuple (a, b), then a value will be uniformly sampled per image from the discrete interval [a..b]. That number will be used identically for both x- and y-axis. * If a dictionary, then it is expected to have the keys x and/or y. Each of these keys can have the same values as described above. Using a dictionary allows to set different values for the two axis and sampling will then happen independently per axis, resulting in samples that differ between the axes.
rotate	number or tuple of number	Rotation in degrees (NOT radians), i.e. expected value range is around [-360, 360]. Rotation happens around the center of the image, not the top left corner as in some other frameworks. * If a number, then that value will be used for all images. * If a tuple (a, b), then a value will be uniformly sampled per image from the interval [a, b] and used as the rotation value.
shear	number, tuple of number or dict	Shear in degrees (NOT radians), i.e. expected value range is around [-360, 360], with reasonable values being in the range of [-45, 45]. * If a number, then that value will be used for all images as the shear on the x-axis (no shear on the y-axis will be done). * If a tuple (a, b), then two value will be uniformly sampled per image from the interval [a, b] and be used as the x- and y-shear value. * If a dictionary, then it is expected to have the keys x and/or y. Each of these keys can have the same values as described above. Using a dictionary allows to set different values for the two axis and sampling will then happen independently per axis, resulting in samples that differ between the axes.
interpolation	int	OpenCV interpolation flag.
mask_interpolation	int	OpenCV interpolation flag.
cval	number or sequence of number	The constant value to use when filling in newly created pixels. (E.g. translating by 1px to the right will create a new 1px-wide column of pixels on the left of the image). The value is only used when mode=constant. The expected value range is [0, 255] for uint8 images.
cval_mask	number or tuple of number	Same as cval but only for masks.
mode	int	OpenCV border flag.
fit_output	bool	If True, the image plane size and position will be adjusted to tightly capture the whole image after affine transformation (translate_percent and translate_px are ignored). Otherwise (False), parts of the transformed image may end up outside the image plane. Fitting the output shape can be useful to avoid corners of the image being outside the image plane after applying rotations. Default: False
keep_ratio	bool	When True, the original aspect ratio will be kept when the random scale is applied. Default: False.
rotate_method	str	rotation method used for the bounding boxes. Should be one of "largest_box" or "ellipse"[1]. Default: "largest_box"
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, keypoints, bboxes

Image types: uint8, float32

Reference: [1] https://arxiv.org/abs/2109.13488

class albumentations.augmentations.geometric.transforms.ElasticTransform (alpha=1, sigma=50, alpha_affine=50, interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, approximate=False, same_dxdy=False, p=0.5) [view source on GitHub]

Elastic deformation of images as described in [Simard2003]_ (with modifications). Based on https://gist.github.com/ernestum/601cdf56d2b424757de5

.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for Convolutional Neural Networks applied to Visual Document Analysis", in Proc. of the International Conference on Document Analysis and Recognition, 2003.

Parameters:

Name	Type	Description
alpha	float
sigma	float	Gaussian filter parameter.
alpha_affine	float	The range will be (-alpha_affine, alpha_affine)
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
border_mode	OpenCV flag	flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101
value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
approximate	boolean	Whether to smooth displacement map with fixed kernel size. Enabling this option gives ~2X speedup on large images.
same_dxdy	boolean	Whether to use same random generated shift for x and y. Enabling this option gives ~2X speedup.

Targets: image, mask, bbox

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.Flip [view source on GitHub]

Flip the input either horizontally, vertically or both horizontally and vertically.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

albumentations.augmentations.geometric.transforms.Flip.apply (self, img, d=0, **params)

d (int): code that specifies how to flip the input. 0 for vertical flipping, 1 for horizontal flipping, -1 for both vertical and horizontal flipping (which is also could be seen as rotating the input by 180 degrees).

class albumentations.augmentations.geometric.transforms.GridDistortion (num_steps=5, distort_limit=0.3, interpolation=1, border_mode=4, value=None, mask_value=None, normalized=False, always_apply=False, p=0.5) [view source on GitHub]

Parameters:

Name	Type	Description
num_steps	int	count of grid cells on each side.
distort_limit	float, [float, float]	If distort_limit is a single float, the range will be (-distort_limit, distort_limit). Default: (-0.03, 0.03).
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
border_mode	OpenCV flag	flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101
value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
normalized	bool	if true, distortion will be normalized to do not go outside the image. Default: False See for more information: https://github.com/albumentations-team/albumentations/pull/722

Targets: image, mask

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.HorizontalFlip [view source on GitHub]

Flip the input horizontally around the y-axis.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.OpticalDistortion (distort_limit=0.05, shift_limit=0.05, interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, p=0.5) [view source on GitHub]

Parameters:

Name	Type	Description
distort_limit	float, [float, float]	If distort_limit is a single float, the range will be (-distort_limit, distort_limit). Default: (-0.05, 0.05).
shift_limit	float, [float, float]	If shift_limit is a single float, the range will be (-shift_limit, shift_limit). Default: (-0.05, 0.05).
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
border_mode	OpenCV flag	flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101
value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value	int, float, list of ints, list of float	padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.

Targets: image, mask, bbox

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.PadIfNeeded (min_height=1024, min_width=1024, pad_height_divisor=None, pad_width_divisor=None, position=<PositionType.CENTER: 'center'>, border_mode=4, value=None, mask_value=None, always_apply=False, p=1.0) [view source on GitHub]

Pad side of the image / max if side is less than desired number.

Parameters:

Name	Type	Description
min_height	int	minimal result image height.
min_width	int	minimal result image width.
pad_height_divisor	int	if not None, ensures image height is dividable by value of this argument.
pad_width_divisor	int	if not None, ensures image width is dividable by value of this argument.
position	Union[str, PositionType]	Position of the image. should be PositionType.CENTER or PositionType.TOP_LEFT or PositionType.TOP_RIGHT or PositionType.BOTTOM_LEFT or PositionType.BOTTOM_RIGHT. or PositionType.RANDOM. Default: PositionType.CENTER.
border_mode	OpenCV flag	OpenCV border mode.
value	int, float, list of int, list of float	padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value	int, float, list of int, list of float	padding value for mask if border_mode is cv2.BORDER_CONSTANT.
p	float	probability of applying the transform. Default: 1.0.

Targets: image, mask, bbox, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.PadIfNeeded.PositionType

An enumeration.

class albumentations.augmentations.geometric.transforms.Perspective (scale=(0.05, 0.1), keep_size=True, pad_mode=0, pad_val=0, mask_pad_val=0, fit_output=False, interpolation=1, always_apply=False, p=0.5) [view source on GitHub]

Perform a random four point perspective transform of the input.

Parameters:

Name	Type	Description
scale	float or [float, float]	standard deviation of the normal distributions. These are used to sample the random distances of the subimage's corners from the full image's corners. If scale is a single float value, the range will be (0, scale). Default: (0.05, 0.1).
keep_size	bool	Whether to resize image’s back to their original size after applying the perspective transform. If set to False, the resulting images may end up having different shapes and will always be a list, never an array. Default: True
pad_mode	OpenCV flag	OpenCV border mode.
pad_val	int, float, list of int, list of float	padding value if border_mode is cv2.BORDER_CONSTANT. Default: 0
mask_pad_val	int, float, list of int, list of float	padding value for mask if border_mode is cv2.BORDER_CONSTANT. Default: 0
fit_output	bool	If True, the image plane size and position will be adjusted to still capture the whole image after perspective transformation. (Followed by image resizing if keep_size is set to True.) Otherwise, parts of the transformed image may be outside of the image plane. This setting should not be set to True when using large scale values as it could lead to very large images. Default: False
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, keypoints, bboxes

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.PiecewiseAffine (scale=(0.03, 0.05), nb_rows=4, nb_cols=4, interpolation=1, mask_interpolation=0, cval=0, cval_mask=0, mode='constant', absolute_scale=False, always_apply=False, keypoints_threshold=0.01, p=0.5) [view source on GitHub]

Apply affine transformations that differ between local neighbourhoods. This augmentation 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.

This is mostly a wrapper around scikit-image's PiecewiseAffine. See also Affine for a similar technique.

Note: This augmenter is very slow. Try to use ElasticTransformation instead, which is at least 10x faster.

Note: For coordinate-based inputs (keypoints, bounding boxes, polygons, ...), this augmenter still has to perform an image-based augmentation, which will make it significantly slower and not fully correct for such inputs than other transforms.

Parameters:

Name	Type	Description
scale	float, tuple of float	Each point on the regular grid is moved around via a normal distribution. This scale factor is equivalent to the normal distribution's sigma. Note that the jitter (how far each point is moved in which direction) is multiplied by the height/width of the image if absolute_scale=False (default), so this scale can be the same for different sized images. Recommended values are in the range 0.01 to 0.05 (weak to strong augmentations). * If a single float, then that value will always be used as the scale. * If a tuple (a, b) of float s, then a random value will be uniformly sampled per image from the interval [a, b].
nb_rows	int, tuple of int	Number of rows of points that the regular grid should have. Must be at least 2. For large images, you might want to pick a higher value than 4. You might have to then adjust scale to lower values. * If a single int, then that value will always be used as the number of rows. * If a tuple (a, b), then a value from the discrete interval [a..b] will be uniformly sampled per image.
nb_cols	int, tuple of int	Number of columns. Analogous to nb_rows.
interpolation	int	The order of interpolation. The order has to be in the range 0-5: - 0: Nearest-neighbor - 1: Bi-linear (default) - 2: Bi-quadratic - 3: Bi-cubic - 4: Bi-quartic - 5: Bi-quintic
mask_interpolation	int	same as interpolation but for mask.
cval	number	The constant value to use when filling in newly created pixels.
cval_mask	number	Same as cval but only for masks.
mode	str	{'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad.
absolute_scale	bool	Take scale as an absolute value rather than a relative value.
keypoints_threshold	float	Used as threshold in conversion from distance maps to keypoints. The search for keypoints works by searching for the argmin (non-inverted) or argmax (inverted) in each channel. This parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit as a keypoint. Use None to use no min/max. Default: 0.01

Targets: image, mask, keypoints, bboxes

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.ShiftScaleRotate (shift_limit=0.0625, scale_limit=0.1, rotate_limit=45, interpolation=1, border_mode=4, value=None, mask_value=None, shift_limit_x=None, shift_limit_y=None, rotate_method='largest_box', always_apply=False, p=0.5) [view source on GitHub]

Randomly apply affine transforms: translate, scale and rotate the input.

Parameters:

Name	Type	Description
shift_limit	[float, float] or float	shift factor range for both height and width. If shift_limit is a single float value, the range will be (-shift_limit, shift_limit). Absolute values for lower and upper bounds should lie in range [0, 1]. Default: (-0.0625, 0.0625).
scale_limit	[float, float] or float	scaling factor range. If scale_limit is a single float value, the range will be (-scale_limit, scale_limit). Note that the scale_limit will be biased by 1. If scale_limit is a tuple, like (low, high), sampling will be done from the range (1 + low, 1 + high). Default: (-0.1, 0.1).
rotate_limit	[int, int] or int	rotation range. If rotate_limit is a single int value, the range will be (-rotate_limit, rotate_limit). Default: (-45, 45).
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
border_mode	OpenCV flag	flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101
value	int, float, list of int, list of float	padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value	int, float, list of int, list of float	padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
shift_limit_x	[float, float] or float	shift factor range for width. If it is set then this value instead of shift_limit will be used for shifting width. If shift_limit_x is a single float value, the range will be (-shift_limit_x, shift_limit_x). Absolute values for lower and upper bounds should lie in the range [0, 1]. Default: None.
shift_limit_y	[float, float] or float	shift factor range for height. If it is set then this value instead of shift_limit will be used for shifting height. If shift_limit_y is a single float value, the range will be (-shift_limit_y, shift_limit_y). Absolute values for lower and upper bounds should lie in the range [0, 1]. Default: None.
rotate_method	str	rotation method used for the bounding boxes. Should be one of "largest_box" or "ellipse". Default: "largest_box"
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.Transpose [view source on GitHub]

Transpose the input by swapping rows and columns.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

class albumentations.augmentations.geometric.transforms.VerticalFlip [view source on GitHub]

Flip the input vertically around the x-axis.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask, bboxes, keypoints

Image types: uint8, float32

albumentations.augmentations.transforms

class albumentations.augmentations.transforms.ChannelShuffle [view source on GitHub]

Randomly rearrange channels of the input RGB image.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.CLAHE (clip_limit=4.0, tile_grid_size=(8, 8), always_apply=False, p=0.5) [view source on GitHub]

Apply Contrast Limited Adaptive Histogram Equalization to the input image.

Parameters:

Name	Type	Description
clip_limit	float or [float, float]	upper threshold value for contrast limiting. If clip_limit is a single float value, the range will be (1, clip_limit). Default: (1, 4).
tile_grid_size	[int, int]	size of grid for histogram equalization. Default: (8, 8).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8

class albumentations.augmentations.transforms.ColorJitter (brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2, always_apply=False, p=0.5) [view source on GitHub]

Randomly changes the brightness, contrast, and saturation of an image. Compared to ColorJitter from torchvision, this transform gives a little bit different results because Pillow (used in torchvision) and OpenCV (used in Albumentations) transform an image to HSV format by different formulas. Another difference - Pillow uses uint8 overflow, but we use value saturation.

Parameters:

Name	Type	Description
brightness	float or tuple of float (min, max	How much to jitter brightness. brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness] or the given [min, max]. Should be non negative numbers.
contrast	float or tuple of float (min, max	How much to jitter contrast. contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast] or the given [min, max]. Should be non negative numbers.
saturation	float or tuple of float (min, max	How much to jitter saturation. saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation] or the given [min, max]. Should be non negative numbers.
hue	float or tuple of float (min, max	How much to jitter hue. hue_factor is chosen uniformly from [-hue, hue] or the given [min, max]. Should have 0 <= hue <= 0.5 or -0.5 <= min <= max <= 0.5.

class albumentations.augmentations.transforms.Downscale (scale_min=0.25, scale_max=0.25, interpolation=None, always_apply=False, p=0.5) [view source on GitHub]

Decreases image quality by downscaling and upscaling back.

Parameters:

Name	Type	Description
scale_min	float	lower bound on the image scale. Should be < 1.
scale_max	float	lower bound on the image scale. Should be .
interpolation		cv2 interpolation method. Could be: - single cv2 interpolation flag - selected method will be used for downscale and upscale. - dict(downscale=flag, upscale=flag) - Downscale.Interpolation(downscale=flag, upscale=flag) - Default: Interpolation(downscale=cv2.INTER_NEAREST, upscale=cv2.INTER_NEAREST)

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.Emboss (alpha=(0.2, 0.5), strength=(0.2, 0.7), always_apply=False, p=0.5) [view source on GitHub]

Emboss the input image and overlays the result with the original image.

Parameters:

Name	Type	Description
alpha	[float, float]	range to choose the visibility of the embossed image. At 0, only the original image is visible,at 1.0 only its embossed version is visible. Default: (0.2, 0.5).
strength	[float, float]	strength range of the embossing. Default: (0.2, 0.7).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

class albumentations.augmentations.transforms.Equalize (mode='cv', by_channels=True, mask=None, mask_params=(), always_apply=False, p=0.5) [view source on GitHub]

Equalize the image histogram.

Parameters:

Name	Type	Description
mode	str	{'cv', 'pil'}. Use OpenCV or Pillow equalization method.
by_channels	bool	If True, use equalization by channels separately, else convert image to YCbCr representation and use equalization by Y channel.
mask	np.ndarray, callable	If given, only the pixels selected by the mask are included in the analysis. Maybe 1 channel or 3 channel array or callable. Function signature must include image argument.
mask_params	list of str	Params for mask function.

Targets: image

Image types: uint8

class albumentations.augmentations.transforms.FancyPCA (alpha=0.1, always_apply=False, p=0.5) [view source on GitHub]

Augment RGB image using FancyPCA from Krizhevsky's paper "ImageNet Classification with Deep Convolutional Neural Networks"

Parameters:

Name	Type	Description
alpha	float	how much to perturb/scale the eigen vecs and vals. scale is samples from gaussian distribution (mu=0, sigma=alpha)

Targets: image

Image types: 3-channel uint8 images only

Credit: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf https://deshanadesai.github.io/notes/Fancy-PCA-with-Scikit-Image https://pixelatedbrian.github.io/2018-04-29-fancy_pca/

class albumentations.augmentations.transforms.FromFloat (dtype='uint16', max_value=None, always_apply=False, p=1.0) [view source on GitHub]

Take an input array where all values should lie in the range [0, 1.0], multiply them by max_value and then cast the resulted value to a type specified by dtype. If max_value is None the transform will try to infer the maximum value for the data type from the dtype argument.

This is the inverse transform for :class:~albumentations.augmentations.transforms.ToFloat.

Parameters:

Name	Type	Description
max_value	float	maximum possible input value. Default: None.
dtype	string or numpy data type	data type of the output. See the 'Data types' page from the NumPy docs_. Default: 'uint16'.
p	float	probability of applying the transform. Default: 1.0.

Targets: image

Image types: float32

.. _'Data types' page from the NumPy docs: https://docs.scipy.org/doc/numpy/user/basics.types.html

class albumentations.augmentations.transforms.GaussNoise (var_limit=(10.0, 50.0), mean=0, per_channel=True, always_apply=False, p=0.5) [view source on GitHub]

Apply gaussian noise to the input image.

Parameters:

Name	Type	Description
var_limit	[float, float] or float	variance range for noise. If var_limit is a single float, the range will be (0, var_limit). Default: (10.0, 50.0).
mean	float	mean of the noise. Default: 0
per_channel	bool	if set to True, noise will be sampled for each channel independently. Otherwise, the noise will be sampled once for all channels. Default: True
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.HueSaturationValue (hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, always_apply=False, p=0.5) [view source on GitHub]

Randomly change hue, saturation and value of the input image.

Parameters:

Name	Type	Description
hue_shift_limit	[int, int] or int	range for changing hue. If hue_shift_limit is a single int, the range will be (-hue_shift_limit, hue_shift_limit). Default: (-20, 20).
sat_shift_limit	[int, int] or int	range for changing saturation. If sat_shift_limit is a single int, the range will be (-sat_shift_limit, sat_shift_limit). Default: (-30, 30).
val_shift_limit	[int, int] or int	range for changing value. If val_shift_limit is a single int, the range will be (-val_shift_limit, val_shift_limit). Default: (-20, 20).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.ImageCompression (quality_lower=99, quality_upper=100, compression_type=<ImageCompressionType.JPEG: 0>, always_apply=False, p=0.5) [view source on GitHub]

Decreases image quality by Jpeg, WebP compression of an image.

Parameters:

Name	Type	Description
quality_lower	float	lower bound on the image quality. Should be in [0, 100] range for jpeg and [1, 100] for webp.
quality_upper	float	upper bound on the image quality. Should be in [0, 100] range for jpeg and [1, 100] for webp.
compression_type	ImageCompressionType	should be ImageCompressionType.JPEG or ImageCompressionType.WEBP. Default: ImageCompressionType.JPEG

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.ImageCompression.ImageCompressionType

An enumeration.

class albumentations.augmentations.transforms.InvertImg [view source on GitHub]

Invert the input image by subtracting pixel values from 255.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.ISONoise (color_shift=(0.01, 0.05), intensity=(0.1, 0.5), always_apply=False, p=0.5) [view source on GitHub]

Apply camera sensor noise.

Parameters:

Name	Type	Description
color_shift	[float, float]	variance range for color hue change. Measured as a fraction of 360 degree Hue angle in HLS colorspace.
intensity	[float, float]	Multiplicative factor that control strength of color and luminace noise.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8

class albumentations.augmentations.transforms.JpegCompression (quality_lower=99, quality_upper=100, always_apply=False, p=0.5) [view source on GitHub]

Decreases image quality by Jpeg compression of an image.

Parameters:

Name	Type	Description
quality_lower	float	lower bound on the jpeg quality. Should be in [0, 100] range
quality_upper	float	upper bound on the jpeg quality. Should be in [0, 100] range

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.Lambda (image=None, mask=None, keypoint=None, bbox=None, name=None, always_apply=False, p=1.0) [view source on GitHub]

A flexible transformation class for using user-defined transformation functions per targets. Function signature must include **kwargs to accept optinal arguments like interpolation method, image size, etc:

Parameters:

Name	Type	Description
image	callable	Image transformation function.
mask	callable	Mask transformation function.
keypoint	callable	Keypoint transformation function.
bbox	callable	BBox transformation function.
always_apply	bool	Indicates whether this transformation should be always applied.
p	float	probability of applying the transform. Default: 1.0.

Targets: image, mask, bboxes, keypoints

Image types: Any

class albumentations.augmentations.transforms.MultiplicativeNoise (multiplier=(0.9, 1.1), per_channel=False, elementwise=False, always_apply=False, p=0.5) [view source on GitHub]

Multiply image to random number or array of numbers.

Parameters:

Name	Type	Description
multiplier	float or tuple of floats	If single float image will be multiplied to this number. If tuple of float multiplier will be in range [multiplier[0], multiplier[1]). Default: (0.9, 1.1).
per_channel	bool	If False, same values for all channels will be used. If True use sample values for each channels. Default False.
elementwise	bool	If False multiply multiply all pixels in an image with a random value sampled once. If True Multiply image pixels with values that are pixelwise randomly sampled. Defaule: False.

Targets: image

Image types: Any

class albumentations.augmentations.transforms.Normalize (mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0, always_apply=False, p=1.0) [view source on GitHub]

Normalization is applied by the formula: img = (img - mean * max_pixel_value) / (std * max_pixel_value)

Parameters:

Name	Type	Description
mean	float, list of float	mean values
std	(float, list of float	std values
max_pixel_value	float	maximum possible pixel value

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.PixelDropout (dropout_prob=0.01, per_channel=False, drop_value=0, mask_drop_value=None, always_apply=False, p=0.5) [view source on GitHub]

Set pixels to 0 with some probability.

Parameters:

Name	Type	Description
dropout_prob	float	pixel drop probability. Default: 0.01
per_channel	bool	if set to True drop mask will be sampled fo each channel, otherwise the same mask will be sampled for all channels. Default: False
drop_value	number or sequence of numbers or None	Value that will be set in dropped place. If set to None value will be sampled randomly, default ranges will be used: - uint8 - [0, 255] - uint16 - [0, 65535] - uint32 - [0, 4294967295] - float, double - [0, 1] Default: 0
mask_drop_value	number or sequence of numbers or None	Value that will be set in dropped place in masks. If set to None masks will be unchanged. Default: 0
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask Image types: any

class albumentations.augmentations.transforms.Posterize (num_bits=4, always_apply=False, p=0.5) [view source on GitHub]

Reduce the number of bits for each color channel.

Parameters:

Name	Type	Description
num_bits	[int, int] or int, or list of ints [r, g, b], or list of ints [[r1, r1], [g1, g2], [b1, b2]]	number of high bits. If num_bits is a single value, the range will be [num_bits, num_bits]. Must be in range [0, 8]. Default: 4.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8

class albumentations.augmentations.transforms.RandomBrightness (limit=0.2, always_apply=False, p=0.5) [view source on GitHub]

Randomly change brightness of the input image.

Parameters:

Name	Type	Description
limit	[float, float] or float	factor range for changing brightness. If limit is a single float, the range will be (-limit, limit). Default: (-0.2, 0.2).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomBrightnessContrast (brightness_limit=0.2, contrast_limit=0.2, brightness_by_max=True, always_apply=False, p=0.5) [view source on GitHub]

Randomly change brightness and contrast of the input image.

Parameters:

Name	Type	Description
brightness_limit	[float, float] or float	factor range for changing brightness. If limit is a single float, the range will be (-limit, limit). Default: (-0.2, 0.2).
contrast_limit	[float, float] or float	factor range for changing contrast. If limit is a single float, the range will be (-limit, limit). Default: (-0.2, 0.2).
brightness_by_max	Boolean	If True adjust contrast by image dtype maximum, else adjust contrast by image mean.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomContrast (limit=0.2, always_apply=False, p=0.5) [view source on GitHub]

Randomly change contrast of the input image.

Parameters:

Name	Type	Description
limit	[float, float] or float	factor range for changing contrast. If limit is a single float, the range will be (-limit, limit). Default: (-0.2, 0.2).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomFog (fog_coef_lower=0.3, fog_coef_upper=1, alpha_coef=0.08, always_apply=False, p=0.5) [view source on GitHub]

Simulates fog for the image

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
fog_coef_lower	float	lower limit for fog intensity coefficient. Should be in [0, 1] range.
fog_coef_upper	float	upper limit for fog intensity coefficient. Should be in [0, 1] range.
alpha_coef	float	transparency of the fog circles. Should be in [0, 1] range.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomGamma (gamma_limit=(80, 120), eps=None, always_apply=False, p=0.5) [view source on GitHub]

Parameters:

Name	Type	Description
gamma_limit	float or [float, float]	If gamma_limit is a single float value, the range will be (-gamma_limit, gamma_limit). Default: (80, 120).
eps		Deprecated.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomGravel (gravel_roi=(0.1, 0.4, 0.9, 0.9), number_of_patches=2, always_apply=False, p=0.5) [view source on GitHub]

Add gravels.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
gravel_roi	float, float, float, float	(top-left x, top-left y, bottom-right x, bottom right y). Should be in [0, 1] range
number_of_patches	int	no. of gravel patches required

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomGridShuffle (grid=(3, 3), always_apply=False, p=0.5) [view source on GitHub]

Random shuffle grid's cells on image.

Parameters:

Name	Type	Description
grid	[int, int]	size of grid for splitting image.

Targets: image, mask, keypoints

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomRain (slant_lower=-10, slant_upper=10, drop_length=20, drop_width=1, drop_color=(200, 200, 200), blur_value=7, brightness_coefficient=0.7, rain_type=None, always_apply=False, p=0.5) [view source on GitHub]

Adds rain effects.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
slant_lower		should be in range [-20, 20].
slant_upper		should be in range [-20, 20].
drop_length		should be in range [0, 100].
drop_width		should be in range [1, 5].
drop_color	list of (r, g, b	rain lines color.
blur_value	int	rainy view are blurry
brightness_coefficient	float	rainy days are usually shady. Should be in range [0, 1].
rain_type		One of [None, "drizzle", "heavy", "torrential"]

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomShadow (shadow_roi=(0, 0.5, 1, 1), num_shadows_lower=1, num_shadows_upper=2, shadow_dimension=5, always_apply=False, p=0.5) [view source on GitHub]

Simulates shadows for the image

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
shadow_roi	float, float, float, float	region of the image where shadows will appear (x_min, y_min, x_max, y_max). All values should be in range [0, 1].
num_shadows_lower	int	Lower limit for the possible number of shadows. Should be in range [0, num_shadows_upper].
num_shadows_upper	int	Lower limit for the possible number of shadows. Should be in range [num_shadows_lower, inf].
shadow_dimension	int	number of edges in the shadow polygons

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomSnow (snow_point_lower=0.1, snow_point_upper=0.3, brightness_coeff=2.5, always_apply=False, p=0.5) [view source on GitHub]

Bleach out some pixel values simulating snow.

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
snow_point_lower	float	lower_bond of the amount of snow. Should be in [0, 1] range
snow_point_upper	float	upper_bond of the amount of snow. Should be in [0, 1] range
brightness_coeff	float	larger number will lead to a more snow on the image. Should be >= 0

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomSunFlare (flare_roi=(0, 0, 1, 0.5), angle_lower=0, angle_upper=1, num_flare_circles_lower=6, num_flare_circles_upper=10, src_radius=400, src_color=(255, 255, 255), always_apply=False, p=0.5) [view source on GitHub]

Simulates Sun Flare for the image

From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

Parameters:

Name	Type	Description
flare_roi	float, float, float, float	region of the image where flare will appear (x_min, y_min, x_max, y_max). All values should be in range [0, 1].
angle_lower	float	should be in range [0, angle_upper].
angle_upper	float	should be in range [angle_lower, 1].
num_flare_circles_lower	int	lower limit for the number of flare circles. Should be in range [0, num_flare_circles_upper].
num_flare_circles_upper	int	upper limit for the number of flare circles. Should be in range [num_flare_circles_lower, inf].
src_radius	int
src_color	int, int, int	color of the flare

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RandomToneCurve (scale=0.1, always_apply=False, p=0.5) [view source on GitHub]

Randomly change the relationship between bright and dark areas of the image by manipulating its tone curve.

Parameters:

Name	Type	Description
scale	float	standard deviation of the normal distribution. Used to sample random distances to move two control points that modify the image's curve. Values should be in range [0, 1]. Default: 0.1

Targets: image

Image types: uint8

class albumentations.augmentations.transforms.RGBShift (r_shift_limit=20, g_shift_limit=20, b_shift_limit=20, always_apply=False, p=0.5) [view source on GitHub]

Randomly shift values for each channel of the input RGB image.

Parameters:

Name	Type	Description
r_shift_limit	[int, int] or int	range for changing values for the red channel. If r_shift_limit is a single int, the range will be (-r_shift_limit, r_shift_limit). Default: (-20, 20).
g_shift_limit	[int, int] or int	range for changing values for the green channel. If g_shift_limit is a single int, the range will be (-g_shift_limit, g_shift_limit). Default: (-20, 20).
b_shift_limit	[int, int] or int	range for changing values for the blue channel. If b_shift_limit is a single int, the range will be (-b_shift_limit, b_shift_limit). Default: (-20, 20).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.RingingOvershoot (blur_limit=(7, 15), cutoff=(0.7853981633974483, 1.5707963267948966), always_apply=False, p=0.5) [view source on GitHub]

Create ringing or overshoot artefacts by conlvolving image with 2D sinc filter.

Parameters:

Name	Type	Description
blur_limit	int, [int, int]	maximum kernel size for sinc filter. Should be in range [3, inf). Default: (7, 15).
cutoff	float, [float, float]	range to choose the cutoff frequency in radians. Should be in range (0, np.pi) Default: (np.pi / 4, np.pi / 2).
p	float	probability of applying the transform. Default: 0.5.

Reference: dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter https://arxiv.org/abs/2107.10833

Targets: image

class albumentations.augmentations.transforms.Sharpen (alpha=(0.2, 0.5), lightness=(0.5, 1.0), always_apply=False, p=0.5) [view source on GitHub]

Sharpen the input image and overlays the result with the original image.

Parameters:

Name	Type	Description
alpha	[float, float]	range to choose the visibility of the sharpened image. At 0, only the original image is visible, at 1.0 only its sharpened version is visible. Default: (0.2, 0.5).
lightness	[float, float]	range to choose the lightness of the sharpened image. Default: (0.5, 1.0).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

class albumentations.augmentations.transforms.Solarize (threshold=128, always_apply=False, p=0.5) [view source on GitHub]

Invert all pixel values above a threshold.

Parameters:

Name	Type	Description
threshold	[int, int] or int, or [float, float] or float	range for solarizing threshold. If threshold is a single value, the range will be [threshold, threshold]. Default: 128.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: any

class albumentations.augmentations.transforms.Spatter (mean=0.65, std=0.3, gauss_sigma=2, cutout_threshold=0.68, intensity=0.6, mode='rain', color=None, always_apply=False, p=0.5) [view source on GitHub]

Apply spatter transform. It simulates corruption which can occlude a lens in the form of rain or mud.

Parameters:

Name	Type	Description
mean	float, or tuple of floats	Mean value of normal distribution for generating liquid layer. If single float it will be used as mean. If tuple of float mean will be sampled from range [mean[0], mean[1]). Default: (0.65).
std	float, or tuple of floats	Standard deviation value of normal distribution for generating liquid layer. If single float it will be used as std. If tuple of float std will be sampled from range [std[0], std[1]). Default: (0.3).
gauss_sigma	float, or tuple of floats	Sigma value for gaussian filtering of liquid layer. If single float it will be used as gauss_sigma. If tuple of float gauss_sigma will be sampled from range [sigma[0], sigma[1]). Default: (2).
cutout_threshold	float, or tuple of floats	Threshold for filtering liqued layer (determines number of drops). If single float it will used as cutout_threshold. If tuple of float cutout_threshold will be sampled from range [cutout_threshold[0], cutout_threshold[1]). Default: (0.68).
intensity	float, or tuple of floats	Intensity of corruption. If single float it will be used as intensity. If tuple of float intensity will be sampled from range [intensity[0], intensity[1]). Default: (0.6).
mode	string, or list of strings	Type of corruption. Currently, supported options are 'rain' and 'mud'. If list is provided type of corruption will be sampled list. Default: ("rain").
color	list of (r, g, b) or dict or None	Corruption elements color. If list uses provided list as color for specified mode. If dict uses provided color for specified mode. Color for each specified mode should be provided in dict. If None uses default colors (rain: (238, 238, 175), mud: (20, 42, 63)).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

Reference: | https://arxiv.org/pdf/1903.12261.pdf | https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py

class albumentations.augmentations.transforms.Superpixels (p_replace=0.1, n_segments=100, max_size=128, interpolation=1, always_apply=False, p=0.5) [view source on GitHub]

Transform images partially/completely to their superpixel representation. This implementation uses skimage's version of the SLIC algorithm.

Parameters:

Name	Type	Description
p_replace	float or tuple of float	Defines for any segment the probability that the pixels within that segment are replaced by their average color (otherwise, the pixels are not changed). Examples: * A probability of 0.0 would mean, that the pixels in no segment are replaced by their average color (image is not changed at all). * A probability of 0.5 would mean, that around half of all segments are replaced by their average color. * A probability of 1.0 would mean, that all segments are replaced by their average color (resulting in a voronoi image). Behaviour based on chosen data types for this parameter: * If a float, then that flat will always be used. * If tuple (a, b), then a random probability will be sampled from the interval [a, b] per image.
n_segments	int, or tuple of int	Rough target number of how many superpixels to generate (the algorithm may deviate from this number). Lower value will lead to coarser superpixels. Higher values are computationally more intensive and will hence lead to a slowdown * If a single int, then that value will always be used as the number of segments. * If a tuple (a, b), then a value from the discrete interval [a..b] will be sampled per image.
max_size	int or None	Maximum image size at which the augmentation is performed. If the width or height of an image exceeds this value, it will be downscaled before the augmentation so that the longest side matches max_size. This is done to speed up the process. The final output image has the same size as the input image. Note that in case p_replace is below 1.0, the down-/upscaling will affect the not-replaced pixels too. Use None to apply no down-/upscaling.
interpolation	OpenCV flag	flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

class albumentations.augmentations.transforms.TemplateTransform (templates, img_weight=0.5, template_weight=0.5, template_transform=None, name=None, always_apply=False, p=0.5) [view source on GitHub]

Apply blending of input image with specified templates

Parameters:

Name	Type	Description
templates	numpy array or list of numpy arrays	Images as template for transform.
img_weight	[float, float] or float	If single float will be used as weight for input image. If tuple of float img_weight will be in range [img_weight[0], img_weight[1]). Default: 0.5.
template_weight	[float, float] or float	If single float will be used as weight for template. If tuple of float template_weight will be in range [template_weight[0], template_weight[1]). Default: 0.5.
template_transform		transformation object which could be applied to template, must produce template the same size as input image.
name	string	(Optional) Name of transform, used only for deserialization.
p	float	probability of applying the transform. Default: 0.5.

Targets: image Image types: uint8, float32

class albumentations.augmentations.transforms.ToFloat (max_value=None, always_apply=False, p=1.0) [view source on GitHub]

Divide pixel values by max_value to get a float32 output array where all values lie in the range [0, 1.0]. If max_value is None the transform will try to infer the maximum value by inspecting the data type of the input image.

See Also: :class:~albumentations.augmentations.transforms.FromFloat

Parameters:

Name	Type	Description
max_value	float	maximum possible input value. Default: None.
p	float	probability of applying the transform. Default: 1.0.

Targets: image

Image types: any type

class albumentations.augmentations.transforms.ToGray [view source on GitHub]

Convert the input RGB image to grayscale. If the mean pixel value for the resulting image is greater than 127, invert the resulting grayscale image.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.ToRGB (always_apply=True, p=1.0) [view source on GitHub]

Convert the input grayscale image to RGB.

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 1.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.ToSepia (always_apply=False, p=0.5) [view source on GitHub]

Applies sepia filter to the input RGB image

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image

Image types: uint8, float32

class albumentations.augmentations.transforms.UnsharpMask (blur_limit=(3, 7), sigma_limit=0.0, alpha=(0.2, 0.5), threshold=10, always_apply=False, p=0.5) [view source on GitHub]

Sharpen the input image using Unsharp Masking processing and overlays the result with the original image.

Parameters:

Name	Type	Description
blur_limit	int, [int, int]	maximum Gaussian kernel size for blurring the input image. Must be zero or odd and in range [0, inf). If set to 0 it will be computed from sigma as round(sigma * (3 if img.dtype == np.uint8 else 4) * 2 + 1) + 1. If set single value blur_limit will be in range (0, blur_limit). Default: (3, 7).
sigma_limit	float, [float, float]	Gaussian kernel standard deviation. Must be in range [0, inf). If set single value sigma_limit will be in range (0, sigma_limit). If set to 0 sigma will be computed as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8. Default: 0.
alpha	float, [float, float]	range to choose the visibility of the sharpened image. At 0, only the original image is visible, at 1.0 only its sharpened version is visible. Default: (0.2, 0.5).
threshold	int	Value to limit sharpening only for areas with high pixel difference between original image and it's smoothed version. Higher threshold means less sharpening on flat areas. Must be in range [0, 255]. Default: 10.
p	float	probability of applying the transform. Default: 0.5.

Reference: arxiv.org/pdf/2107.10833.pdf

Targets: image

albumentations.augmentations.utils

def albumentations.augmentations.utils.ensure_contiguous (func) [view source on GitHub]

Ensure that input img is contiguous.

def albumentations.augmentations.utils.get_opencv_dtype_from_numpy (value) [view source on GitHub]

Return a corresponding OpenCV dtype for a numpy's dtype :param value: Input dtype of numpy array :return: Corresponding dtype for OpenCV

def albumentations.augmentations.utils.preserve_channel_dim (func) [view source on GitHub]

Preserve dummy channel dim.

def albumentations.augmentations.utils.preserve_shape (func) [view source on GitHub]

Preserve shape of the image

albumentations.core special

albumentations.core.bbox_utils

class albumentations.core.bbox_utils.BboxParams (format, label_fields=None, min_area=0.0, min_visibility=0.0, min_width=0.0, min_height=0.0, check_each_transform=True) [view source on GitHub]

Parameters of bounding boxes

Parameters:

Name	Type	Description
format	str	format of bounding boxes. Should be 'coco', 'pascal_voc', 'albumentations' or 'yolo'. The coco format [x_min, y_min, width, height], e.g. [97, 12, 150, 200]. The pascal_voc format [x_min, y_min, x_max, y_max], e.g. [97, 12, 247, 212]. The albumentations format is like pascal_voc, but normalized, in other words: [x_min, y_min, x_max, y_max], e.g. [0.2, 0.3, 0.4, 0.5]. The yolo format [x, y, width, height], e.g. [0.1, 0.2, 0.3, 0.4]; x, y - normalized bbox center; width, height - normalized bbox width and height.
label_fields	list	list of fields that are joined with boxes, e.g labels. Should be same type as boxes.
min_area	float	minimum area of a bounding box. All bounding boxes whose visible area in pixels is less than this value will be removed. Default: 0.0.
min_visibility	float	minimum fraction of area for a bounding box to remain this box in list. Default: 0.0.
min_width	float	Minimum width of a bounding box. All bounding boxes whose width is less than this value will be removed. Default: 0.0.
min_height	float	Minimum height of a bounding box. All bounding boxes whose height is less than this value will be removed. Default: 0.0.
check_each_transform	bool	if True, then bboxes will be checked after each dual transform. Default: True

def albumentations.core.bbox_utils.calculate_bbox_area (bbox, rows, cols) [view source on GitHub]

Calculate the area of a bounding box in (fractional) pixels.

Parameters:

Name	Type	Description
bbox	Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]	A bounding box (x_min, y_min, x_max, y_max).
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
float	Area in (fractional) pixels of the (denormalized) bounding box.

def albumentations.core.bbox_utils.check_bbox (bbox) [view source on GitHub]

Check if bbox boundaries are in range 0, 1 and minimums are lesser then maximums

def albumentations.core.bbox_utils.check_bboxes (bboxes) [view source on GitHub]

Check if bboxes boundaries are in range 0, 1 and minimums are lesser then maximums

def albumentations.core.bbox_utils.convert_bbox_from_albumentations (bbox, target_format, rows, cols, check_validity=False) [view source on GitHub]

Convert a bounding box from the format used by albumentations to a format, specified in target_format.

Parameters:

Name	Type	Description
bbox	Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]	An albumentations bounding box (x_min, y_min, x_max, y_max).
target_format	str	required format of the output bounding box. Should be 'coco', 'pascal_voc' or 'yolo'.
rows	int	Image height.
cols	int	Image width.
check_validity	bool	Check if all boxes are valid boxes.

Returns:

Type	Description
Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]	tuple: A bounding box.

Note: The coco format of a bounding box looks like [x_min, y_min, width, height], e.g. [97, 12, 150, 200]. The pascal_voc format of a bounding box looks like [x_min, y_min, x_max, y_max], e.g. [97, 12, 247, 212]. The yolo format of a bounding box looks like [x, y, width, height], e.g. [0.3, 0.1, 0.05, 0.07].

Exceptions:

Type	Description
ValueError	if target_format is not equal to coco, pascal_voc or yolo.

def albumentations.core.bbox_utils.convert_bbox_to_albumentations (bbox, source_format, rows, cols, check_validity=False) [view source on GitHub]

Convert a bounding box from a format specified in source_format to the format used by albumentations: normalized coordinates of top-left and bottom-right corners of the bounding box in a form of (x_min, y_min, x_max, y_max) e.g. (0.15, 0.27, 0.67, 0.5).

Parameters:

Name	Type	Description
bbox	Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]	A bounding box tuple.
source_format	str	format of the bounding box. Should be 'coco', 'pascal_voc', or 'yolo'.
check_validity	bool	Check if all boxes are valid boxes.
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]	tuple: A bounding box (x_min, y_min, x_max, y_max).

Note: The coco format of a bounding box looks like (x_min, y_min, width, height), e.g. (97, 12, 150, 200). The pascal_voc format of a bounding box looks like (x_min, y_min, x_max, y_max), e.g. (97, 12, 247, 212). The yolo format of a bounding box looks like (x, y, width, height), e.g. (0.3, 0.1, 0.05, 0.07); where x, y coordinates of the center of the box, all values normalized to 1 by image height and width.

Exceptions:

Type	Description
ValueError	if target_format is not equal to coco or pascal_voc, or yolo.
ValueError	If in YOLO format all labels not in range (0, 1).

def albumentations.core.bbox_utils.convert_bboxes_from_albumentations (bboxes, target_format, rows, cols, check_validity=False) [view source on GitHub]

Convert a list of bounding boxes from the format used by albumentations to a format, specified in target_format.

Parameters:

Name	Type	Description
bboxes	Sequence[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	List of albumentation bounding box (x_min, y_min, x_max, y_max).
target_format	str	required format of the output bounding box. Should be 'coco', 'pascal_voc' or 'yolo'.
rows	int	Image height.
cols	int	Image width.
check_validity	bool	Check if all boxes are valid boxes.

Returns:

Type	Description
List[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	List of bounding boxes.

def albumentations.core.bbox_utils.convert_bboxes_to_albumentations (bboxes, source_format, rows, cols, check_validity=False) [view source on GitHub]

Convert a list bounding boxes from a format specified in source_format to the format used by albumentations

def albumentations.core.bbox_utils.denormalize_bbox (bbox, rows, cols) [view source on GitHub]

Denormalize coordinates of a bounding box. Multiply x-coordinates by image width and y-coordinates by image height. This is an inverse operation for :func:~albumentations.augmentations.bbox.normalize_bbox.

Parameters:

Name	Type	Description
bbox	~TBox	Normalized bounding box (x_min, y_min, x_max, y_max).
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
~TBox	Denormalized bounding box (x_min, y_min, x_max, y_max).

Exceptions:

Type	Description
ValueError	If rows or cols is less or equal zero

def albumentations.core.bbox_utils.denormalize_bboxes (bboxes, rows, cols) [view source on GitHub]

Denormalize a list of bounding boxes.

Parameters:

Name	Type	Description
bboxes	Sequence[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	Normalized bounding boxes [(x_min, y_min, x_max, y_max)].
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
List[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	List: Denormalized bounding boxes [(x_min, y_min, x_max, y_max)].

def albumentations.core.bbox_utils.filter_bboxes (bboxes, rows, cols, min_area=0.0, min_visibility=0.0, min_width=0.0, min_height=0.0) [view source on GitHub]

Remove bounding boxes that either lie outside of the visible area by more then min_visibility or whose area in pixels is under the threshold set by min_area. Also it crops boxes to final image size.

Parameters:

Name	Type	Description
bboxes	Sequence[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	List of albumentation bounding box (x_min, y_min, x_max, y_max).
rows	int	Image height.
cols	int	Image width.
min_area	float	Minimum area of a bounding box. All bounding boxes whose visible area in pixels. is less than this value will be removed. Default: 0.0.
min_visibility	float	Minimum fraction of area for a bounding box to remain this box in list. Default: 0.0.
min_width	float	Minimum width of a bounding box. All bounding boxes whose width is less than this value will be removed. Default: 0.0.
min_height	float	Minimum height of a bounding box. All bounding boxes whose height is less than this value will be removed. Default: 0.0.

Returns:

Type	Description
List[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	List of bounding boxes.

def albumentations.core.bbox_utils.filter_bboxes_by_visibility (original_shape, bboxes, transformed_shape, transformed_bboxes, threshold=0.0, min_area=0.0) [view source on GitHub]

Filter bounding boxes and return only those boxes whose visibility after transformation is above the threshold and minimal area of bounding box in pixels is more then min_area.

Parameters:

Name	Type	Description
original_shape	Sequence[int]	Original image shape (height, width, ...).
bboxes	Sequence[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	Original bounding boxes [(x_min, y_min, x_max, y_max)].
transformed_shape	Sequence[int]	Transformed image shape (height, width).
transformed_bboxes	Sequence[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	Transformed bounding boxes [(x_min, y_min, x_max, y_max)].
threshold	float	visibility threshold. Should be a value in the range [0.0, 1.0].
min_area	float	Minimal area threshold.

Returns:

Type	Description
List[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	Filtered bounding boxes [(x_min, y_min, x_max, y_max)].

def albumentations.core.bbox_utils.normalize_bbox (bbox, rows, cols) [view source on GitHub]

Normalize coordinates of a bounding box. Divide x-coordinates by image width and y-coordinates by image height.

Parameters:

Name	Type	Description
bbox	~TBox	Denormalized bounding box (x_min, y_min, x_max, y_max).
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
~TBox	Normalized bounding box (x_min, y_min, x_max, y_max).

Exceptions:

Type	Description
ValueError	If rows or cols is less or equal zero

def albumentations.core.bbox_utils.normalize_bboxes (bboxes, rows, cols) [view source on GitHub]

Normalize a list of bounding boxes.

Parameters:

Name	Type	Description
bboxes	Sequence[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	Denormalized bounding boxes [(x_min, y_min, x_max, y_max)].
rows	int	Image height.
cols	int	Image width.

Returns:

Type	Description
List[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	Normalized bounding boxes [(x_min, y_min, x_max, y_max)].

def albumentations.core.bbox_utils.union_of_bboxes (height, width, bboxes, erosion_rate=0.0) [view source on GitHub]

Calculate union of bounding boxes.

Parameters:

Name	Type	Description
height	int	Height of image or space.
width	int	Width of image or space.
bboxes	Sequence[Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]]	List like bounding boxes. Format is [(x_min, y_min, x_max, y_max)].
erosion_rate	float	How much each bounding box can be shrinked, useful for erosive cropping. Set this in range [0, 1]. 0 will not be erosive at all, 1.0 can make any bbox to lose its volume.

Returns:

Type	Description
Union[Tuple[float, float, float, float], Tuple[float, float, float, float, Any]]	tuple: A bounding box (x_min, y_min, x_max, y_max).

albumentations.core.composition

class albumentations.core.composition.Compose (transforms, bbox_params=None, keypoint_params=None, additional_targets=None, p=1.0, is_check_shapes=True) [view source on GitHub]

Compose transforms and handle all transformations regarding bounding boxes

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
bbox_params	BboxParams	Parameters for bounding boxes transforms
keypoint_params	KeypointParams	Parameters for keypoints transforms
additional_targets	dict	Dict with keys - new target name, values - old target name. ex: {'image2': 'image'}
p	float	probability of applying all list of transforms. Default: 1.0.
is_check_shapes	bool	If True shapes consistency of images/mask/masks would be checked on each call. If you would like to disable this check - pass False (do it only if you are sure in your data consistency).

class albumentations.core.composition.OneOf (transforms, p=0.5) [view source on GitHub]

Select one of transforms to apply. Selected transform will be called with force_apply=True. Transforms probabilities will be normalized to one 1, so in this case transforms probabilities works as weights.

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
p	float	probability of applying selected transform. Default: 0.5.

class albumentations.core.composition.OneOrOther (first=None, second=None, transforms=None, p=0.5) [view source on GitHub]

Select one or another transform to apply. Selected transform will be called with force_apply=True.

class albumentations.core.composition.PerChannel (transforms, channels=None, p=0.5) [view source on GitHub]

Apply transformations per-channel

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
channels	sequence	channels to apply the transform to. Pass None to apply to all. Default: None (apply to all)
p	float	probability of applying the transform. Default: 0.5.

class albumentations.core.composition.Sequential (transforms, p=0.5) [view source on GitHub]

Sequentially applies all transforms to targets.

Note: This transform is not intended to be a replacement for Compose. Instead, it should be used inside Compose the same way OneOf or OneOrOther are used. For instance, you can combine OneOf with Sequential to create an augmentation pipeline that contains multiple sequences of augmentations and applies one randomly chose sequence to input data (see the Example section for an example definition of such pipeline).

Examples:

>>> import albumentations as A
>>> transform = A.Compose([
>>>    A.OneOf([
>>>        A.Sequential([
>>>            A.HorizontalFlip(p=0.5),
>>>            A.ShiftScaleRotate(p=0.5),
>>>        ]),
>>>        A.Sequential([
>>>            A.VerticalFlip(p=0.5),
>>>            A.RandomBrightnessContrast(p=0.5),
>>>        ]),
>>>    ], p=1)
>>> ])

class albumentations.core.composition.SomeOf (transforms, n, replace=True, p=1) [view source on GitHub]

Select N transforms to apply. Selected transforms will be called with force_apply=True. Transforms probabilities will be normalized to one 1, so in this case transforms probabilities works as weights.

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
n	int	number of transforms to apply.
replace	bool	Whether the sampled transforms are with or without replacement. Default: True.
p	float	probability of applying selected transform. Default: 1.

albumentations.core.keypoints_utils

class albumentations.core.keypoints_utils.KeypointParams (format, label_fields=None, remove_invisible=True, angle_in_degrees=True, check_each_transform=True) [view source on GitHub]

Parameters of keypoints

Parameters:

Name	Type	Description
format	str	format of keypoints. Should be 'xy', 'yx', 'xya', 'xys', 'xyas', 'xysa'. x - X coordinate, y - Y coordinate s - Keypoint scale a - Keypoint orientation in radians or degrees (depending on KeypointParams.angle_in_degrees)
label_fields	list	list of fields that are joined with keypoints, e.g labels. Should be same type as keypoints.
remove_invisible	bool	to remove invisible points after transform or not
angle_in_degrees	bool	angle in degrees or radians in 'xya', 'xyas', 'xysa' keypoints
check_each_transform	bool	if True, then keypoints will be checked after each dual transform. Default: True

def albumentations.core.keypoints_utils.check_keypoint (kp, rows, cols) [view source on GitHub]

Check if keypoint coordinates are less than image shapes

def albumentations.core.keypoints_utils.check_keypoints (keypoints, rows, cols) [view source on GitHub]

Check if keypoints boundaries are less than image shapes

albumentations.core.serialization

class albumentations.core.serialization.Serializable [view source on GitHub]

albumentations.core.serialization.Serializable.to_dict (self, on_not_implemented_error='raise')

Take a transform pipeline and convert it to a serializable representation that uses only standard python data types: dictionaries, lists, strings, integers, and floats.

Parameters:

Name	Type	Description
self		A transform that should be serialized. If the transform doesn't implement the to_dict method and on_not_implemented_error equals to 'raise' then NotImplementedError is raised. If on_not_implemented_error equals to 'warn' then NotImplementedError will be ignored but no transform parameters will be serialized.
on_not_implemented_error	str	raise or warn.

class albumentations.core.serialization.SerializableMeta [view source on GitHub]

A metaclass that is used to register classes in SERIALIZABLE_REGISTRY or NON_SERIALIZABLE_REGISTRY so they can be found later while deserializing transformation pipeline using classes full names.

albumentations.core.serialization.SerializableMeta.__new__ (mcs, name, bases, *args, **kwargs) special staticmethod

Create and return a new object. See help(type) for accurate signature.

def albumentations.core.serialization.from_dict (transform_dict, nonserializable=None, lambda_transforms='deprecated') [view source on GitHub]

Parameters:

Name	Type	Description
transform_dict	Dict[str, Any]	A dictionary with serialized transform pipeline.
nonserializable	Optional[Dict[str, Any]]	A dictionary that contains non-serializable transforms. This dictionary is required when you are restoring a pipeline that contains non-serializable transforms. Keys in that dictionary should be named same as name arguments in respective transforms from a serialized pipeline.
lambda_transforms	Union[Dict[str, Any], NoneType, str]	Deprecated. Use 'nonserizalizable' instead.

def albumentations.core.serialization.load (filepath, data_format='json', nonserializable=None, lambda_transforms='deprecated') [view source on GitHub]

Load a serialized pipeline from a json or yaml file and construct a transform pipeline.

Parameters:

Name	Type	Description
filepath	str	Filepath to read from.
data_format	str	Serialization format. Should be either json or 'yaml'.
nonserializable	Optional[Dict[str, Any]]	A dictionary that contains non-serializable transforms. This dictionary is required when you are restoring a pipeline that contains non-serializable transforms. Keys in that dictionary should be named same as name arguments in respective transforms from a serialized pipeline.
lambda_transforms	Union[Dict[str, Any], NoneType, str]	Deprecated. Use 'nonserizalizable' instead.

def albumentations.core.serialization.register_additional_transforms () [view source on GitHub]

Register transforms that are not imported directly into the albumentations module.

def albumentations.core.serialization.save (transform, filepath, data_format='json', on_not_implemented_error='raise') [view source on GitHub]

Take a transform pipeline, serialize it and save a serialized version to a file using either json or yaml format.

Parameters:

Name	Type	Description
transform	Serializable	Transform to serialize.
filepath	str	Filepath to write to.
data_format	str	Serialization format. Should be either json or 'yaml'.
on_not_implemented_error	str	Parameter that describes what to do if a transform doesn't implement the to_dict method. If 'raise' then NotImplementedError is raised, if warn then the exception will be ignored and no transform arguments will be saved.

def albumentations.core.serialization.to_dict (transform, on_not_implemented_error='raise') [view source on GitHub]

Take a transform pipeline and convert it to a serializable representation that uses only standard python data types: dictionaries, lists, strings, integers, and floats.

Parameters:

Name	Type	Description
transform	Serializable	A transform that should be serialized. If the transform doesn't implement the to_dict method and on_not_implemented_error equals to 'raise' then NotImplementedError is raised. If on_not_implemented_error equals to 'warn' then NotImplementedError will be ignored but no transform parameters will be serialized.
on_not_implemented_error	str	raise or warn.

albumentations.core.transforms_interface

class albumentations.core.transforms_interface.BasicTransform (always_apply=False, p=0.5) [view source on GitHub]

albumentations.core.transforms_interface.BasicTransform.add_targets (self, additional_targets)

Add targets to transform them the same way as one of existing targets ex: {'target_image': 'image'} ex: {'obj1_mask': 'mask', 'obj2_mask': 'mask'} by the way you must have at least one object with key 'image'

Parameters:

Name	Type	Description
additional_targets	Dict[str, str]	keys - new target name, values - old target name. ex: {'image2': 'image'}

class albumentations.core.transforms_interface.DualTransform [view source on GitHub]

Transform for segmentation task.

class albumentations.core.transforms_interface.ImageOnlyTransform [view source on GitHub]

Transform applied to image only.

class albumentations.core.transforms_interface.NoOp [view source on GitHub]

Does nothing

def albumentations.core.transforms_interface.to_tuple (param, low=None, bias=None) [view source on GitHub]

Convert input argument to min-max tuple

Parameters:

Name	Type	Description
param	scalar, tuple or list of 2+ elements	Input value. If value is scalar, return value would be (offset - value, offset + value). If value is tuple, return value would be value + offset (broadcasted).
low		Second element of tuple can be passed as optional argument
bias		An offset factor added to each element

albumentations.imgaug special

albumentations.imgaug.transforms

class albumentations.imgaug.transforms.IAAAdditiveGaussianNoise (loc=0, scale=(2.5500000000000003, 12.75), per_channel=False, always_apply=False, p=0.5) [view source on GitHub]

Add gaussian noise to the input image.

This augmentation is deprecated. Please use GaussNoise instead.

Parameters:

Name	Type	Description
loc	int	mean of the normal distribution that generates the noise. Default: 0.
scale	[float, float]	standard deviation of the normal distribution that generates the noise. Default: (0.01 * 255, 0.05 * 255).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

class albumentations.imgaug.transforms.IAAAffine (scale=1.0, translate_percent=None, translate_px=None, rotate=0.0, shear=0.0, order=1, cval=0, mode='reflect', always_apply=False, p=0.5) [view source on GitHub]

Place a regular grid of points on the input and randomly move the neighbourhood of these point around via affine transformations.

This augmentation is deprecated. Please use Affine instead.

Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}

Parameters:

Name	Type	Description
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask

class albumentations.imgaug.transforms.IAACropAndPad (px=None, percent=None, pad_mode='constant', pad_cval=0, keep_size=True, always_apply=False, p=1) [view source on GitHub]

This augmentation is deprecated. Please use CropAndPad instead.

class albumentations.imgaug.transforms.IAAEmboss (alpha=(0.2, 0.5), strength=(0.2, 0.7), always_apply=False, p=0.5) [view source on GitHub]

Emboss the input image and overlays the result with the original image. This augmentation is deprecated. Please use Emboss instead.

Parameters:

Name	Type	Description
alpha	[float, float]	range to choose the visibility of the embossed image. At 0, only the original image is visible,at 1.0 only its embossed version is visible. Default: (0.2, 0.5).
strength	[float, float]	strength range of the embossing. Default: (0.2, 0.7).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

class albumentations.imgaug.transforms.IAAFliplr (always_apply=False, p=0.5) [view source on GitHub]

This augmentation is deprecated. Please use HorizontalFlip instead.

class albumentations.imgaug.transforms.IAAFlipud (always_apply=False, p=0.5) [view source on GitHub]

This augmentation is deprecated. Please use VerticalFlip instead.

class albumentations.imgaug.transforms.IAAPerspective (scale=(0.05, 0.1), keep_size=True, always_apply=False, p=0.5) [view source on GitHub]

Perform a random four point perspective transform of the input. This augmentation is deprecated. Please use Perspective instead.

Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}

Parameters:

Name	Type	Description
scale	[float, float]	standard deviation of the normal distributions. These are used to sample the random distances of the subimage's corners from the full image's corners. Default: (0.05, 0.1).
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask

class albumentations.imgaug.transforms.IAAPiecewiseAffine (scale=(0.03, 0.05), nb_rows=4, nb_cols=4, order=1, cval=0, mode='constant', always_apply=False, p=0.5) [view source on GitHub]

Place a regular grid of points on the input and randomly move the neighbourhood of these point around via affine transformations.

This augmentation is deprecated. Please use PiecewiseAffine instead.

Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}

Parameters:

Name	Type	Description
scale	[float, float]	factor range that determines how far each point is moved. Default: (0.03, 0.05).
nb_rows	int	number of rows of points that the regular grid should have. Default: 4.
nb_cols	int	number of columns of points that the regular grid should have. Default: 4.
p	float	probability of applying the transform. Default: 0.5.

Targets: image, mask

class albumentations.imgaug.transforms.IAASharpen (alpha=(0.2, 0.5), lightness=(0.5, 1.0), always_apply=False, p=0.5) [view source on GitHub]

Sharpen the input image and overlays the result with the original image. This augmentation is deprecated. Please use Sharpen instead

Parameters:

Name	Type	Description
alpha	[float, float]	range to choose the visibility of the sharpened image. At 0, only the original image is visible, at 1.0 only its sharpened version is visible. Default: (0.2, 0.5).
lightness	[float, float]	range to choose the lightness of the sharpened image. Default: (0.5, 1.0).
p	float	probability of applying the transform. Default: 0.5.

Targets: image

class albumentations.imgaug.transforms.IAASuperpixels (p_replace=0.1, n_segments=100, always_apply=False, p=0.5) [view source on GitHub]

Completely or partially transform the input image to its superpixel representation. Uses skimage's version of the SLIC algorithm. May be slow.

This augmentation is deprecated. Please use Superpixels instead.

Parameters:

Name	Type	Description
p_replace	float	defines the probability of any superpixel area being replaced by the superpixel, i.e. by the average pixel color within its area. Default: 0.1.
n_segments	int	target number of superpixels to generate. Default: 100.
p	float	probability of applying the transform. Default: 0.5.

Targets: image

albumentations.pytorch special

albumentations.pytorch.transforms

class albumentations.pytorch.transforms.ToTensor (num_classes=1, sigmoid=True, normalize=None) [view source on GitHub]

Convert image and mask to torch.Tensor and divide by 255 if image or mask are uint8 type. This transform is now removed from Albumentations. If you need it downgrade the library to version 0.5.2.

Parameters:

Name	Type	Description
num_classes	int	only for segmentation
sigmoid	bool	only for segmentation, transform mask to LongTensor or not.
normalize	dict	dict with keys [mean, std] to pass it into torchvision.normalize

class albumentations.pytorch.transforms.ToTensorV2 (transpose_mask=False, always_apply=True, p=1.0) [view source on GitHub]

Convert image and mask to torch.Tensor. The numpy HWC image is converted to pytorch CHW tensor. If the image is in HW format (grayscale image), it will be converted to pytorch HW tensor. This is a simplified and improved version of the old ToTensor transform (ToTensor was deprecated, and now it is not present in Albumentations. You should use ToTensorV2 instead).

Parameters:

Name	Type	Description
transpose_mask	bool	If True and an input mask has three dimensions, this transform will transpose dimensions so the shape [height, width, num_channels] becomes [num_channels, height, width]. The latter format is a standard format for PyTorch Tensors. Default: False.
always_apply	bool	Indicates whether this transformation should be always applied. Default: True.
p	float	Probability of applying the transform. Default: 1.0.