Domain adaptation transforms (augmentations.domain_adaptation)

class albumentations.augmentations.domain_adaptation.FDA (reference_images, beta_limit=0.1, read_fn=<function read_rgb_image at 0x7f0131353430>, 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	Sequence[Any]	Sequence of objects that will be converted to images by read_fn. By default,
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 an element of reference_images
and	return numpy array of image pixels. Default	takes as input a path to an image and returns a numpy array.

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 0x7f0131353430>, 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	Sequence[Any]	Sequence of objects that will be converted to images by read_fn. By default,
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 an element of reference_images
and	return numpy array of image pixels. Default	takes as input a path to an image and returns a numpy array.
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 0x7f0131353430>, 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	Sequence[Any]	Sequence of objects that will be converted to images by read_fn. By default,
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 an element of reference_images
and	return numpy array of image pixels. Default	takes as input a path to an image and returns a numpy array.
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