albumentations.augmentations.functional

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Functional implementations of image augmentation operations. This module contains low-level functions for various image augmentation techniques including color transformations, blur effects, tone curve adjustments, noise additions, and other visual modifications. These functions form the foundation for the transform classes and provide the core functionality for manipulating image data during the augmentation process.

Members

classMacenkoNormalizer
classSimpleNMF
classStainNormalizer
classVahadaneNormalizer
functionadd_fog
functionadd_gravel
functionadd_noise
functionadd_rain
functionadd_shadow
functionadd_snow_bleach
functionadd_snow_texture
functionadd_sun_flare_overlay
functionadd_sun_flare_physics_based
functionadjust_brightness_torchvision
functionadjust_contrast_torchvision
functionadjust_hue_torchvision
functionadjust_saturation_torchvision
functionapply_corner_illumination
functionapply_gaussian_illumination
functionapply_he_stain_augmentation
functionapply_linear_illumination
functionapply_plasma_brightness_contrast
functionapply_plasma_shadow
functionapply_salt_and_pepper
functionauto_contrast
functionbboxes_morphology
functionchannel_shuffle
functionchromatic_aberration
functionclahe
functionconvolve
functioncreate_contrast_lut
functioncreate_directional_gradient
functiondilate
functiondownscale
functionequalize
functionerode
functionfancy_pca
functiongamma_transform
functiongenerate_constant_noise
functiongenerate_noise
functiongenerate_per_pixel_noise
functiongenerate_plasma_pattern
functiongenerate_random_values
functiongenerate_shared_noise
functiongenerate_snow_textures
functionget_drop_mask
functionget_fog_particle_radiuses
functionget_histogram_bounds
functionget_mask_array
functionget_mud_params
functionget_normalizer
functionget_rain_params
functionget_safe_brightness_contrast_params
functionget_tissue_mask
functiongrayscale_to_multichannel
functionimage_compression
functioninvert
functioniso_noise
functionlinear_transformation_rgb
functionmorphology
functionmove_tone_curve
functionnoop
functionnormalize_vectors
functionorder_stains_combined
functionpixel_dropout
functionplanckian_jitter
functionposterize
functionprepare_drop_values
functionrgb_to_optical_density
functionsample_beta
functionsample_gaussian
functionsample_laplace
functionsample_noise
functionsample_uniform
functionseparable_convolve
functionsharpen_gaussian
functionshift_hsv
functionshot_noise
functionslic
functionsolarize
functionspatter_mud
functionspatter_rain
functionsuperpixels
functionto_gray
functionto_gray_average
functionto_gray_desaturation
functionto_gray_from_lab
functionto_gray_max
functionto_gray_pca
functionto_gray_weighted_average
functionunsharp_mask
functionvolume_channel_shuffle
functionvolumes_channel_shuffle

MacenkoNormalizerclass

MacenkoNormalizer(
    angular_percentile: float = 99
)

Macenko stain normalizer with optimized computations.

Parameters

Name	Type	Default	Description
angular_percentile	float	99	-

SimpleNMFclass

SimpleNMF(
    n_iter: int = 100
)

Parameters

Name	Type	Default	Description
n_iter	int	100	-

StainNormalizerclass

StainNormalizer()

Base class for stain normalizers.

VahadaneNormalizerclass

VahadaneNormalizer()

add_fogfunction

add_fog(
    img: np.ndarray,
    fog_intensity: float,
    alpha_coef: float,
    fog_particle_positions: list[tuple[int, int]],
    fog_particle_radiuses: list[int]
)

Add fog to an image. This function adds fog to an image by drawing fog particles on the image. The fog particles are drawn using the OpenCV function cv2.circle.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to add fog to.
fog_intensity	float	-	The intensity of the fog effect, between 0 and 1.
alpha_coef	float	-	The coefficient for the alpha blending.
fog_particle_positions	list[tuple[int, int]]	-	The positions of the fog particles.
fog_particle_radiuses	list[int]	-	The radiuses of the fog particles.

Returns

np.ndarray: The image with fog added.

add_gravelfunction

add_gravel(
    img: np.ndarray,
    gravels: list[Any]
)

Add gravel to an image. This function adds gravel to an image by drawing gravel particles on the image. The gravel particles are drawn using the OpenCV function cv2.circle.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to add gravel to.
gravels	list[Any]	-	The gravel particles to draw on the image.

Returns

np.ndarray: The image with gravel added.

add_noisefunction

add_noise(
    img: np.ndarray,
    noise: np.ndarray
)

Add noise to an image. This function adds noise to an image by adding the noise to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
noise	np.ndarray	-	Noise as a numpy array.

Returns

np.ndarray: The noise added to the image.

add_rainfunction

add_rain(
    img: np.ndarray,
    slant: float,
    drop_length: int,
    drop_width: int,
    drop_color: tuple[int, int, int],
    blur_value: int,
    brightness_coefficient: float,
    rain_drops: np.ndarray
)

Add rain to an image. This function adds rain to an image by drawing rain drops on the image. The rain drops are drawn using the OpenCV function cv2.polylines.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to add rain to.
slant	float	-	The slant of the rain drops.
drop_length	int	-	The length of the rain drops.
drop_width	int	-	The width of the rain drops.
drop_color	tuple[int, int, int]	-	The color of the rain drops.
blur_value	int	-	The blur value of the rain drops.
brightness_coefficient	float	-	The brightness coefficient of the rain drops.
rain_drops	np.ndarray	-	The rain drops to draw on the image.

Returns

np.ndarray: The image with rain added.

add_shadowfunction

add_shadow(
    img: np.ndarray,
    vertices_list: list[np.ndarray],
    intensities: np.ndarray
)

Add shadows to the image by reducing the intensity of the pixel values in specified regions.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image. Multichannel images are supported.
vertices_list	list[np.ndarray]	-	List of vertices for shadow polygons.
intensities	np.ndarray	-	Array of shadow intensities. Range is [0, 1].

Returns

np.ndarray: Image with shadows added.

References

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

add_snow_bleachfunction

add_snow_bleach(
    img: np.ndarray,
    snow_point: float,
    brightness_coeff: float
)

Adds a simple snow effect to the image by bleaching out pixels. This function simulates a basic snow effect by increasing the brightness of pixels that are above a certain threshold (snow_point). It operates in the HLS color space to modify the lightness channel.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image. Can be either RGB uint8 or float32.
snow_point	float	-	A float in the range [0, 1], scaled and adjusted to determine the threshold for pixel modification. Higher values result in less snow effect.
brightness_coeff	float	-	Coefficient applied to increase the brightness of pixels below the snow_point threshold. Larger values lead to more pronounced snow effects. Should be greater than 1.0 for a visible effect.

Returns

np.ndarray: Image with simulated snow effect. The output has the same dtype as the input.

Notes

- This function converts the image to the HLS color space to modify the lightness channel. - The snow effect is created by selectively increasing the brightness of pixels. - This method tends to create a 'bleached' look, which may not be as realistic as more advanced snow simulation techniques. - The function automatically handles both uint8 and float32 input images.

References

HLS Color Space: https://en.wikipedia.org/wiki/HSL_and_HSV
Original implementation: https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

add_snow_texturefunction

add_snow_texture(
    img: np.ndarray,
    snow_point: float,
    brightness_coeff: float,
    snow_texture: np.ndarray,
    sparkle_mask: np.ndarray
)

Add a realistic snow effect to the input image. This function simulates snowfall by applying multiple visual effects to the image, including brightness adjustment, snow texture overlay, depth simulation, and color tinting. The result is a more natural-looking snow effect compared to simple pixel bleaching methods.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image in RGB format.
snow_point	float	-	Coefficient that controls the amount and intensity of snow. Should be in the range [0, 1], where 0 means no snow and 1 means maximum snow effect.
brightness_coeff	float	-	Coefficient for brightness adjustment to simulate the reflective nature of snow. Should be in the range [0, 1], where higher values result in a brighter image.
snow_texture	np.ndarray	-	Snow texture.
sparkle_mask	np.ndarray	-	Sparkle mask.

Returns

np.ndarray: Image with added snow effect. The output has the same dtype as the input.

Notes

This function works with both uint8 and float32 image types, automatically handling the conversion between them.

References

Perlin Noise: https://en.wikipedia.org/wiki/Perlin_noise
HSV Color Space: https://en.wikipedia.org/wiki/HSL_and_HSV

add_sun_flare_overlayfunction

add_sun_flare_overlay(
    img: np.ndarray,
    flare_center: tuple[float, float],
    src_radius: int,
    src_color: tuple[int, ...],
    circles: list[Any]
)

Add a sun flare effect to an image using a simple overlay technique. This function creates a basic sun flare effect by overlaying multiple semi-transparent circles of varying sizes and intensities on the input image. The effect simulates a simple lens flare caused by bright light sources.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The input image.
flare_center	tuple[float, float]	-	(x, y) coordinates of the flare center in pixel coordinates.
src_radius	int	-	The radius of the main sun circle in pixels.
src_color	tuple[int, ...]	-	The color of the sun, represented as a tuple of RGB values.
circles	list[Any]	-	A list of tuples, each representing a circle that contributes to the flare effect. Each tuple contains: - alpha (float): The transparency of the circle (0.0 to 1.0). - center (tuple[int, int]): (x, y) coordinates of the circle center. - radius (int): The radius of the circle. - color (tuple[int, int, int]): RGB color of the circle.

Returns

np.ndarray: The output image with the sun flare effect added.

Notes

- This function uses a simple alpha blending technique to overlay flare elements. - The main sun is created as a gradient circle, fading from the center outwards. - Additional flare circles are added along an imaginary line from the sun's position. - This method is computationally efficient but may produce less realistic results compared to more advanced techniques.

References

Alpha compositing: https://en.wikipedia.org/wiki/Alpha_compositing
Lens flare: https://en.wikipedia.org/wiki/Lens_flare

add_sun_flare_physics_basedfunction

add_sun_flare_physics_based(
    img: np.ndarray,
    flare_center: tuple[int, int],
    src_radius: int,
    src_color: tuple[int, int, int],
    circles: list[Any]
)

Add a more realistic sun flare effect to the image. This function creates a complex sun flare effect by simulating various optical phenomena that occur in real camera lenses when capturing bright light sources. The result is a more realistic and physically plausible lens flare effect.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image.
flare_center	tuple[int, int]	-	(x, y) coordinates of the sun's center in pixels.
src_radius	int	-	Radius of the main sun circle in pixels.
src_color	tuple[int, int, int]	-	Color of the sun in RGB format.
circles	list[Any]	-	List of tuples, each representing a flare circle with parameters: (alpha, center, size, color) - alpha (float): Transparency of the circle (0.0 to 1.0). - center (tuple[int, int]): (x, y) coordinates of the circle center. - size (float): Size factor for the circle radius. - color (tuple[int, int, int]): RGB color of the circle.

Returns

np.ndarray: Image with added sun flare effect.

References

Lens flare: https://en.wikipedia.org/wiki/Lens_flare
Diffraction: https://en.wikipedia.org/wiki/Diffraction
Chromatic aberration: https://en.wikipedia.org/wiki/Chromatic_aberration
Screen blending: https://en.wikipedia.org/wiki/Blend_modes#Screen

adjust_brightness_torchvisionfunction

adjust_brightness_torchvision(
    img: np.ndarray,
    factor: np.ndarray
)

Adjust the brightness of an image. This function adjusts the brightness of an image by multiplying each pixel value by a factor. The brightness is adjusted by multiplying the image by the factor.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
factor	np.ndarray	-	The factor to adjust the brightness by.

Returns

np.ndarray: The adjusted image.

adjust_contrast_torchvisionfunction

adjust_contrast_torchvision(
    img: np.ndarray,
    factor: float
)

Adjust the contrast of an image. This function adjusts the contrast of an image by multiplying each pixel value by a factor. The contrast is adjusted by multiplying the image by the factor.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
factor	float	-	The factor to adjust the contrast by.

Returns

np.ndarray: The adjusted image.

adjust_hue_torchvisionfunction

adjust_hue_torchvision(
    img: np.ndarray,
    factor: float
)

Adjust the hue of an image. This function adjusts the hue of an image by adding a factor to the hue value.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image.
factor	float	-	The factor to adjust the hue by.

Returns

np.ndarray: The adjusted image.

adjust_saturation_torchvisionfunction

adjust_saturation_torchvision(
    img: np.ndarray,
    factor: float,
    gamma: float = 0
)

Adjust the saturation of an image. This function adjusts the saturation of an image by multiplying each pixel value by a factor. The saturation is adjusted by multiplying the image by the factor.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
factor	float	-	The factor to adjust the saturation by.
gamma	float	0	The gamma value to use for the adjustment.

Returns

np.ndarray: The adjusted image.

apply_corner_illuminationfunction

apply_corner_illumination(
    img: np.ndarray,
    intensity: float,
    corner: Literal[0, 1, 2, 3]
)

Apply corner illumination to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
intensity	float	-	Illumination intensity
corner	One of: 0 1 2 3	-	The corner to apply the illumination to.

Returns

np.ndarray: Image with corner illumination applied.

apply_gaussian_illuminationfunction

apply_gaussian_illumination(
    img: np.ndarray,
    intensity: float,
    center: tuple[float, float],
    sigma: float
)

Apply gaussian illumination to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
intensity	float	-	Illumination intensity
center	tuple[float, float]	-	The center of the illumination.
sigma	float	-	The sigma of the illumination.

apply_he_stain_augmentationfunction

apply_he_stain_augmentation(
    img: np.ndarray,
    stain_matrix: np.ndarray,
    scale_factors: np.ndarray,
    shift_values: np.ndarray,
    augment_background: bool
)

Apply HE stain augmentation to an image. This function applies HE stain augmentation to an image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image.
stain_matrix	np.ndarray	-	Stain matrix.
scale_factors	np.ndarray	-	Scale factors.
shift_values	np.ndarray	-	Shift values.
augment_background	bool	-	Whether to augment the background.

Returns

np.ndarray: Augmented image.

apply_linear_illuminationfunction

apply_linear_illumination(
    img: np.ndarray,
    intensity: float,
    angle: float
)

Apply linear illumination to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
intensity	float	-	Illumination intensity
angle	float	-	Illumination angle in radians

Returns

np.ndarray: Image with linear illumination

apply_plasma_brightness_contrastfunction

apply_plasma_brightness_contrast(
    img: np.ndarray,
    brightness_factor: float,
    contrast_factor: float,
    plasma_pattern: np.ndarray
)

Apply plasma-based brightness and contrast adjustments. This function applies plasma-based brightness and contrast adjustments to an image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to apply the brightness and contrast adjustments to.
brightness_factor	float	-	The brightness factor to apply.
contrast_factor	float	-	The contrast factor to apply.
plasma_pattern	np.ndarray	-	The plasma pattern to use for the brightness and contrast adjustments.

Returns

np.ndarray: The image with the brightness and contrast adjustments applied.

apply_plasma_shadowfunction

apply_plasma_shadow(
    img: np.ndarray,
    intensity: float,
    plasma_pattern: np.ndarray
)

Apply plasma shadow to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
intensity	float	-	Shadow intensity
plasma_pattern	np.ndarray	-	Plasma pattern to use

Returns

np.ndarray: Image with plasma shadow

apply_salt_and_pepperfunction

apply_salt_and_pepper(
    img: np.ndarray,
    salt_mask: np.ndarray,
    pepper_mask: np.ndarray
)

Apply salt and pepper noise to an image. This function applies salt and pepper noise to an image using pre-computed masks.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to apply salt and pepper noise to.
salt_mask	np.ndarray	-	The salt mask to use for the salt and pepper noise.
pepper_mask	np.ndarray	-	The pepper mask to use for the salt and pepper noise.

Returns

np.ndarray: The image with salt and pepper noise applied.

auto_contrastfunction

auto_contrast(
    img: np.ndarray,
    cutoff: float,
    ignore: int | None,
    method: Literal['cdf', 'pil']
)

Apply automatic contrast enhancement.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
cutoff	float	-	Cutoff percentage for histogram
ignore	One of: int None	-	Value to ignore in histogram
method	One of: 'cdf' 'pil'	-	Method to use for contrast enhancement

Returns

np.ndarray: Image with enhanced contrast

bboxes_morphologyfunction

bboxes_morphology(
    bboxes: np.ndarray,
    kernel: np.ndarray,
    operation: Literal['dilation', 'erosion'],
    image_shape: tuple[int, int]
)

Apply morphology to bounding boxes. This function applies morphology to bounding boxes by first converting the bounding boxes to a mask and then applying the morphology to the mask.

Parameters

Name	Type	Default	Description
bboxes	np.ndarray	-	Bounding boxes as a numpy array.
kernel	np.ndarray	-	Kernel as a numpy array.
operation	One of: 'dilation' 'erosion'	-	The operation to apply.
image_shape	tuple[int, int]	-	The shape of the image.

Returns

np.ndarray: The morphology applied to the bounding boxes.

channel_shufflefunction

channel_shuffle(
    img: np.ndarray,
    channels_shuffled: list[int]
)

Shuffle the channels of an image. This function shuffles the channels of an image by using the cv2.mixChannels function. The channels are shuffled according to the channels_shuffled array.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to shuffle.
channels_shuffled	list[int]	-	The array of channels to shuffle.

Returns

np.ndarray: The shuffled image.

chromatic_aberrationfunction

chromatic_aberration(
    img: np.ndarray,
    primary_distortion_red: float,
    secondary_distortion_red: float,
    primary_distortion_blue: float,
    secondary_distortion_blue: float,
    interpolation: int
)

Apply chromatic aberration to an image. This function applies chromatic aberration to an image by distorting the red and blue channels.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
primary_distortion_red	float	-	The primary distortion of the red channel.
secondary_distortion_red	float	-	The secondary distortion of the red channel.
primary_distortion_blue	float	-	The primary distortion of the blue channel.
secondary_distortion_blue	float	-	The secondary distortion of the blue channel.
interpolation	int	-	The interpolation method to use.

Returns

np.ndarray: The chromatic aberration applied to the image.

clahefunction

clahe(
    img: np.ndarray,
    clip_limit: float,
    tile_grid_size: tuple[int, int]
)

Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input image. This function enhances the contrast of the input image using CLAHE. For color images, it converts the image to the LAB color space, applies CLAHE to the L channel, and then converts the image back to RGB.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image. Can be grayscale (2D array) or RGB (3D array).
clip_limit	float	-	Threshold for contrast limiting. Higher values give more contrast.
tile_grid_size	tuple[int, int]	-	Size of grid for histogram equalization. Width and height of the grid.

Returns

np.ndarray: Image with CLAHE applied. The output has the same dtype as the input.

Example

>>> import numpy as np
>>> img = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> result = clahe(img, clip_limit=2.0, tile_grid_size=(8, 8))
>>> assert result.shape == img.shape
>>> assert result.dtype == img.dtype

Notes

- If the input image is float32, it's temporarily converted to uint8 for processing and then converted back to float32. - For color images, CLAHE is applied only to the luminance channel in the LAB color space.

convolvefunction

convolve(
    img: np.ndarray,
    kernel: np.ndarray
)

Convolve an image with a kernel. This function convolves an image with a kernel.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image.
kernel	np.ndarray	-	Kernel.

Returns

np.ndarray: Convolved image.

create_contrast_lutfunction

create_contrast_lut(
    hist: np.ndarray,
    min_intensity: int,
    max_intensity: int,
    max_value: int,
    method: Literal['cdf', 'pil']
)

Create lookup table for contrast adjustment. This function creates a lookup table for contrast adjustment.

Parameters

Name	Type	Default	Description
hist	np.ndarray	-	Histogram of the image.
min_intensity	int	-	Minimum intensity of the histogram.
max_intensity	int	-	Maximum intensity of the histogram.
max_value	int	-	Maximum value of the lookup table.
method	One of: 'cdf' 'pil'	-	Method to use for contrast enhancement.

Returns

np.ndarray: Lookup table for contrast adjustment.

create_directional_gradientfunction

create_directional_gradient(
    height: int,
    width: int,
    angle: float
)

Create a directional gradient in [0, 1] range. This function creates a directional gradient in the [0, 1] range.

Parameters

Name	Type	Default	Description
height	int	-	The height of the image.
width	int	-	The width of the image.
angle	float	-	The angle of the gradient.

Returns

np.ndarray: The directional gradient.

dilatefunction

dilate(
    img: np.ndarray,
    kernel: np.ndarray
)

Apply dilation to an image. This function applies dilation to an image using the cv2.dilate function.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
kernel	np.ndarray	-	Kernel as a numpy array.

Returns

np.ndarray: The dilated image.

downscalefunction

downscale(
    img: np.ndarray,
    scale: float,
    down_interpolation: int,
    up_interpolation: int
)

Downscale and upscale an image. This function downscales and upscales an image using the specified interpolation methods. The downscaling and upscaling are performed using the cv2.resize function.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
scale	float	-	The scale factor for the downscaling and upscaling.
down_interpolation	int	-	The interpolation method for the downscaling.
up_interpolation	int	-	The interpolation method for the upscaling.

Returns

np.ndarray: The downscaled and upscaled image.

equalizefunction

equalize(
    img: np.ndarray,
    mask: np.ndarray | None = None,
    mode: Literal['cv', 'pil'] = cv,
    by_channels: bool = True
)

Apply histogram equalization to the input image. This function enhances the contrast of the input image by equalizing its histogram. It supports both grayscale and color images, and can operate on individual channels or on the luminance channel of the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image. Can be grayscale (2D array) or RGB (3D array).
mask	One of: np.ndarray None	None	Optional mask to apply the equalization selectively. If provided, must have the same shape as the input image. Default: None.
mode	One of: 'cv' 'pil'	cv	The backend to use for equalization. Can be either "cv" for OpenCV or "pil" for Pillow-style equalization. Default: "cv".
by_channels	bool	True	If True, applies equalization to each channel independently. If False, converts the image to YCrCb color space and equalizes only the luminance channel. Only applicable to color images. Default: True.

Returns

np.ndarray: Equalized image. The output has the same dtype as the input.

Example

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> equalized = A.equalize(image, mode="cv", by_channels=True)
>>> assert equalized.shape == image.shape
>>> assert equalized.dtype == image.dtype

Notes

- If the input image is not uint8, it will be temporarily converted to uint8 for processing and then converted back to its original dtype. - For color images, when by_channels=False, the image is converted to YCrCb color space, equalized on the Y channel, and then converted back to RGB. - The function preserves the original number of channels in the image.

erodefunction

erode(
    img: np.ndarray,
    kernel: np.ndarray
)

Apply erosion to an image. This function applies erosion to an image using the cv2.erode function.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
kernel	np.ndarray	-	Kernel as a numpy array.

Returns

np.ndarray: The eroded image.

fancy_pcafunction

fancy_pca(
    img: np.ndarray,
    alpha_vector: np.ndarray
)

Perform 'Fancy PCA' augmentation on an image with any number of channels.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
alpha_vector	np.ndarray	-	Vector of scale factors for each principal component. Should have the same length as the number of channels in the image.

Returns

np.ndarray: Augmented image of the same shape, type, and range as the input.

Notes

- This function generalizes the Fancy PCA augmentation to work with any number of channels. - It preserves the original range of the image ([0, 255] for uint8, [0, 1] for float32). - For single-channel images, the augmentation is applied as a simple scaling of pixel intensity variation. - For multi-channel images, PCA is performed on the entire image, treating each pixel as a point in N-dimensional space (where N is the number of channels). - The augmentation preserves the correlation between channels while adding controlled noise. - Computation time may increase significantly for images with a large number of channels.

References

ImageNet classification with deep convolutional neural networks: Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012): In Advances in neural information processing systems (pp. 1097-1105).

gamma_transformfunction

gamma_transform(
    img: np.ndarray,
    gamma: float
)

Apply gamma transformation to an image. This function applies gamma transformation to an image by raising each pixel value to the power of gamma. The result is a non-linear transformation that can enhance or reduce the contrast of the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to apply gamma transformation to.
gamma	float	-	The gamma value to apply.

Returns

np.ndarray: The gamma transformed image.

generate_constant_noisefunction

generate_constant_noise(
    noise_type: Literal['uniform', 'gaussian', 'laplace', 'beta'],
    shape: tuple[int, ...],
    params: dict[str, Any],
    max_value: float,
    random_generator: np.random.Generator
)

Generate constant noise. This function generates constant noise by sampling from the noise distribution.

Parameters

Name	Type	Default	Description
noise_type	One of: 'uniform' 'gaussian' 'laplace' 'beta'	-	The type of noise to generate.
shape	tuple[int, ...]	-	The shape of the noise to generate.
params	dict[str, Any]	-	The parameters of the noise to generate.
max_value	float	-	The maximum value of the noise to generate.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The constant noise generated.

generate_noisefunction

generate_noise(
    noise_type: Literal['uniform', 'gaussian', 'laplace', 'beta'],
    spatial_mode: Literal['constant', 'per_pixel', 'shared'],
    shape: tuple[int, ...],
    params: dict[str, Any] | None,
    max_value: float,
    approximation: float,
    random_generator: np.random.Generator
)

Generate noise with optional approximation for speed. This function generates noise with optional approximation for speed.

Parameters

Name	Type	Default	Description
noise_type	One of: 'uniform' 'gaussian' 'laplace' 'beta'	-	The type of noise to generate.
spatial_mode	One of: 'constant' 'per_pixel' 'shared'	-	The spatial mode to use.
shape	tuple[int, ...]	-	The shape of the noise to generate.
params	One of: dict[str, Any] None	-	The parameters of the noise to generate.
max_value	float	-	The maximum value of the noise to generate.
approximation	float	-	The approximation to use for the noise to generate.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The noise generated.

generate_per_pixel_noisefunction

generate_per_pixel_noise(
    noise_type: Literal['uniform', 'gaussian', 'laplace', 'beta'],
    shape: tuple[int, ...],
    params: dict[str, Any],
    max_value: float,
    random_generator: np.random.Generator
)

Generate per-pixel noise. This function generates per-pixel noise by sampling from the noise distribution.

Parameters

Name	Type	Default	Description
noise_type	One of: 'uniform' 'gaussian' 'laplace' 'beta'	-	The type of noise to generate.
shape	tuple[int, ...]	-	The shape of the noise to generate.
params	dict[str, Any]	-	The parameters of the noise to generate.
max_value	float	-	The maximum value of the noise to generate.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The per-pixel noise generated.

generate_plasma_patternfunction

generate_plasma_pattern(
    target_shape: tuple[int, int],
    roughness: float,
    random_generator: np.random.Generator
)

Generate a plasma pattern. This function generates a plasma pattern using the diamond-square algorithm.

Parameters

Name	Type	Default	Description
target_shape	tuple[int, int]	-	The shape of the plasma pattern to generate.
roughness	float	-	The roughness of the plasma pattern.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The plasma pattern generated.

generate_random_valuesfunction

generate_random_values(
    channels: int,
    dtype: np.dtype,
    random_generator: np.random.Generator
)

Generate random values.

Parameters

Name	Type	Default	Description
channels	int	-	Number of channels
dtype	np.dtype	-	Data type of the output array
random_generator	np.random.Generator	-	Random number generator

Returns

np.ndarray: Random values

generate_shared_noisefunction

generate_shared_noise(
    noise_type: Literal['uniform', 'gaussian', 'laplace', 'beta'],
    shape: tuple[int, ...],
    params: dict[str, Any],
    max_value: float,
    random_generator: np.random.Generator
)

Generate shared noise.

Parameters

Name	Type	Default	Description
noise_type	One of: 'uniform' 'gaussian' 'laplace' 'beta'	-	Type of noise to generate
shape	tuple[int, ...]	-	Shape of the output array
params	dict[str, Any]	-	Distribution parameters
max_value	float	-	Maximum value for the noise
random_generator	np.random.Generator	-	Random number generator

Returns

np.ndarray: Generated noise

generate_snow_texturesfunction

generate_snow_textures(
    img_shape: tuple[int, int],
    random_generator: np.random.Generator
)

Generate snow texture and sparkle mask.

Parameters

Name	Type	Default	Description
img_shape	tuple[int, int]	-	Image shape.
random_generator	np.random.Generator	-	Random generator to use.

Returns

tuple[np.ndarray, np.ndarray]: Tuple of (snow_texture, sparkle_mask) arrays.

get_drop_maskfunction

get_drop_mask(
    shape: tuple[int, ...],
    per_channel: bool,
    dropout_prob: float,
    random_generator: np.random.Generator
)

Generate dropout mask. This function generates a dropout mask.

Parameters

Name	Type	Default	Description
shape	tuple[int, ...]	-	Shape of the output mask
per_channel	bool	-	Whether to apply dropout per channel
dropout_prob	float	-	Dropout probability
random_generator	np.random.Generator	-	Random number generator

Returns

np.ndarray: Dropout mask

get_fog_particle_radiusesfunction

get_fog_particle_radiuses(
    img_shape: tuple[int, int],
    num_particles: int,
    fog_intensity: float,
    random_generator: np.random.Generator
)

Generate radiuses for fog particles.

Parameters

Name	Type	Default	Description
img_shape	tuple[int, int]	-	Image shape.
num_particles	int	-	Number of fog particles.
fog_intensity	float	-	Intensity of the fog effect, between 0 and 1.
random_generator	np.random.Generator	-	Random generator to use.

Returns

list[int]: List of radiuses for each fog particle.

get_histogram_boundsfunction

get_histogram_bounds(
    hist: np.ndarray,
    cutoff: float
)

Get the low and high bounds of the histogram. This function gets the low and high bounds of the histogram.

Parameters

Name	Type	Default	Description
hist	np.ndarray	-	Histogram of the image.
cutoff	float	-	Cutoff percentage for histogram.

Returns

tuple[int, int]: Low and high bounds of the histogram.

get_mask_arrayfunction

get_mask_array(
    data: dict[str, Any]
)

Get mask array from input data if it exists.

Parameters

Name	Type	Default	Description
data	dict[str, Any]	-	-

get_mud_paramsfunction

get_mud_params(
    liquid_layer: np.ndarray,
    color: np.ndarray,
    cutout_threshold: float,
    sigma: float,
    intensity: float,
    random_generator: np.random.Generator
)

Generate parameters for mud effect. This function generates parameters for a mud effect.

Parameters

Name	Type	Default	Description
liquid_layer	np.ndarray	-	Liquid layer of the image.
color	np.ndarray	-	Color of the mud.
cutout_threshold	float	-	Cutout threshold for the mud.
sigma	float	-	Sigma for the Gaussian blur.
intensity	float	-	Intensity of the mud.
random_generator	np.random.Generator	-	Random number generator.

Returns

dict[str, Any]: Parameters for the mud effect.

get_normalizerfunction

get_normalizer(
    method: Literal['vahadane', 'macenko']
)

Get stain normalizer based on method. This function gets a stain normalizer based on a method.

Parameters

Name	Type	Default	Description
method	One of: 'vahadane' 'macenko'	-	Method to use for stain normalization.

Returns

StainNormalizer: Stain normalizer.

get_rain_paramsfunction

get_rain_params(
    liquid_layer: np.ndarray,
    color: np.ndarray,
    intensity: float
)

Generate parameters for rain effect. This function generates parameters for a rain effect.

Parameters

Name	Type	Default	Description
liquid_layer	np.ndarray	-	Liquid layer of the image.
color	np.ndarray	-	Color of the rain.
intensity	float	-	Intensity of the rain.

Returns

dict[str, Any]: Parameters for the rain effect.

get_safe_brightness_contrast_paramsfunction

get_safe_brightness_contrast_params(
    alpha: float,
    beta: float,
    max_value: float
)

Get safe brightness and contrast parameters.

Parameters

Name	Type	Default	Description
alpha	float	-	Contrast factor
beta	float	-	Brightness factor
max_value	float	-	Maximum pixel value

Returns

tuple[float, float]: Safe alpha and beta values

get_tissue_maskfunction

get_tissue_mask(
    img: np.ndarray,
    threshold: float = 0.85
)

Get tissue mask from image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
threshold	float	0.85	Threshold for tissue detection. Default: 0.85

Returns

np.ndarray: Binary mask where True indicates tissue regions

grayscale_to_multichannelfunction

grayscale_to_multichannel(
    grayscale_image: np.ndarray,
    num_output_channels: int = 3
)

Convert a grayscale image to a multi-channel image. This function takes a 2D grayscale image or a 3D image with a single channel and converts it to a multi-channel image by repeating the grayscale data across the specified number of channels.

Parameters

Name	Type	Default	Description
grayscale_image	np.ndarray	-	Input grayscale image. Can be 2D (height, width) or 3D (height, width, 1).
num_output_channels	int	3	Number of channels in the output image. Defaults to 3.

Returns

np.ndarray: Multi-channel image with shape (height, width, num_channels)

image_compressionfunction

image_compression(
    img: np.ndarray,
    quality: int,
    image_type: Literal['.jpg', '.webp']
)

Compress the image using JPEG or WebP compression.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
quality	int	-	Quality of compression in range [1, 100]
image_type	One of: '.jpg' '.webp'	-	Type of compression to use

Returns

np.ndarray: Compressed image

invertfunction

invert(
    img: np.ndarray
)

Invert the colors of an image. This function inverts the colors of an image by subtracting each pixel value from the maximum possible value. The result is a negative of the original image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to invert.

Returns

np.ndarray: The inverted image.

iso_noisefunction

iso_noise(
    image: np.ndarray,
    color_shift: float,
    intensity: float,
    random_generator: np.random.Generator
)

Apply poisson noise to an image to simulate camera sensor noise.

Parameters

Name	Type	Default	Description
image	np.ndarray	-	Input image. Currently, only RGB images are supported.
color_shift	float	-	The amount of color shift to apply.
intensity	float	-	Multiplication factor for noise values. Values of ~0.5 produce a noticeable, yet acceptable level of noise.
random_generator	np.random.Generator	-	If specified, this will be random generator used for noise generation.

Returns

np.ndarray: The noised image.

linear_transformation_rgbfunction

linear_transformation_rgb(
    img: np.ndarray,
    transformation_matrix: np.ndarray
)

Apply a linear transformation to the RGB channels of an image. This function applies a linear transformation matrix to the RGB channels of an image. The transformation matrix is a 3x3 matrix that maps the RGB values to new values.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image. Can be grayscale (2D array) or RGB (3D array).
transformation_matrix	np.ndarray	-	3x3 transformation matrix.

Returns

np.ndarray: Image with the linear transformation applied. The output has the same dtype as the input.

morphologyfunction

morphology(
    img: np.ndarray,
    kernel: np.ndarray,
    operation: Literal['dilation', 'erosion']
)

Apply morphology to an image. This function applies morphology to an image using the cv2.morphologyEx function.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
kernel	np.ndarray	-	Kernel as a numpy array.
operation	One of: 'dilation' 'erosion'	-	The operation to apply.

Returns

np.ndarray: The morphology applied to the image.

move_tone_curvefunction

move_tone_curve(
    img: np.ndarray,
    low_y: float | np.ndarray,
    high_y: float | np.ndarray
)

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

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Any number of channels
low_y	One of: float np.ndarray	-	per-channel or single y-position of a Bezier control point used to adjust the tone curve, must be in range [0, 1]
high_y	One of: float np.ndarray	-	per-channel or single y-position of a Bezier control point used to adjust image tone curve, must be in range [0, 1]

Returns

np.ndarray: Image with adjusted tone curve

noopfunction

noop(
    input_obj: Any
)

No-op function. This function is a no-op and returns the input object unchanged. It is used to satisfy the type checker requirements for the `noop` function.

Parameters

Name	Type	Default	Description
input_obj	Any	-	The input object to return unchanged.

Returns

Any: The input object unchanged.

normalize_vectorsfunction

normalize_vectors(
    vectors: np.ndarray
)

Normalize vectors. This function normalizes vectors.

Parameters

Name	Type	Default	Description
vectors	np.ndarray	-	Vectors to normalize.

Returns

np.ndarray: Normalized vectors.

order_stains_combinedfunction

order_stains_combined(
    stain_colors: np.ndarray
)

Order stains using a combination of methods. This combines both angular information and spectral characteristics for more robust identification.

Parameters

Name	Type	Default	Description
stain_colors	np.ndarray	-	Stain colors.

Returns

tuple[int, int]: Hematoxylin and eosin indices.

pixel_dropoutfunction

pixel_dropout(
    image: np.ndarray,
    drop_mask: np.ndarray,
    drop_values: np.ndarray
)

Apply pixel dropout to the image.

Parameters

Name	Type	Default	Description
image	np.ndarray	-	Input image
drop_mask	np.ndarray	-	Boolean mask indicating which pixels to drop
drop_values	np.ndarray	-	Values to replace dropped pixels with

Returns

np.ndarray: Image with dropped pixels

planckian_jitterfunction

planckian_jitter(
    img: np.ndarray,
    temperature: int,
    mode: Literal['blackbody', 'cied']
)

Apply Planckian jitter to an image. This function applies Planckian jitter to an image by linearly interpolating between the two closest temperatures in the PLANCKIAN_COEFFS dictionary.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
temperature	int	-	The temperature to apply.
mode	One of: 'blackbody' 'cied'	-	The mode to use.

Returns

np.ndarray: The Planckian jitter applied to the image.

posterizefunction

posterize(
    img: np.ndarray,
    bits: Literal[1, 2, 3, 4, 5, 6, 7] | list[Literal[1, 2, 3, 4, 5, 6, 7]]
)

Reduce the number of bits for each color channel by keeping only the highest N bits.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image. Can be single or multi-channel.
bits	One of: 1 2 3 4 5 6 7] \| list[Literal[1, 2, 3, 4, 5, 6, 7]	-	Number of high bits to keep.. Can be either: - A single value to apply the same bit reduction to all channels - A list of values to apply different bit reduction per channel. Length of list must match number of channels in image.

Returns

np.ndarray: Image with reduced bit depth. Has same shape and dtype as input.

Notes

- The transform keeps the N highest bits and sets all other bits to 0 - For example, if bits=3: - Original value: 11010110 (214) - Keep 3 bits: 11000000 (192) - The number of unique colors per channel will be 2^bits - Higher bits values = more colors = more subtle effect - Lower bits values = fewer colors = more dramatic posterization

prepare_drop_valuesfunction

prepare_drop_values(
    array: np.ndarray,
    value: float | Sequence[float] | np.ndarray | None,
    random_generator: np.random.Generator
)

Prepare values to fill dropped pixels.

Parameters

Name	Type	Default	Description
array	np.ndarray	-	Input array to determine shape and dtype
value	One of: float Sequence[float] np.ndarray None	-	User-specified drop values or None for random
random_generator	np.random.Generator	-	Random number generator

Returns

np.ndarray: Array of values matching input shape

rgb_to_optical_densityfunction

rgb_to_optical_density(
    img: np.ndarray,
    eps: float = 1e-06
)

Convert RGB image to optical density. This function converts an RGB image to optical density.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image.
eps	float	1e-06	Epsilon value.

Returns

np.ndarray: Optical density image.

sample_betafunction

sample_beta(
    size: tuple[int, ...],
    params: dict[str, Any],
    random_generator: np.random.Generator
)

Sample from Beta distribution. This function samples from a Beta distribution.

Parameters

Name	Type	Default	Description
size	tuple[int, ...]	-	The size of the noise to generate.
params	dict[str, Any]	-	The parameters of the noise to generate.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The Beta noise sampled.

sample_gaussianfunction

sample_gaussian(
    size: tuple[int, ...],
    params: dict[str, Any],
    random_generator: np.random.Generator
)

Sample from Gaussian distribution. This function samples from a Gaussian distribution.

Parameters

Name	Type	Default	Description
size	tuple[int, ...]	-	The size of the noise to generate.
params	dict[str, Any]	-	The parameters of the noise to generate.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The Gaussian noise sampled.

sample_laplacefunction

sample_laplace(
    size: tuple[int, ...],
    params: dict[str, Any],
    random_generator: np.random.Generator
)

Sample from Laplace distribution. This function samples from a Laplace distribution.

Parameters

Name	Type	Default	Description
size	tuple[int, ...]	-	The size of the noise to generate.
params	dict[str, Any]	-	The parameters of the noise to generate.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The Laplace noise sampled.

sample_noisefunction

sample_noise(
    noise_type: Literal['uniform', 'gaussian', 'laplace', 'beta'],
    size: tuple[int, ...],
    params: dict[str, Any],
    max_value: float,
    random_generator: np.random.Generator
)

Sample from specific noise distribution. This function samples from a specific noise distribution.

Parameters

Name	Type	Default	Description
noise_type	One of: 'uniform' 'gaussian' 'laplace' 'beta'	-	The type of noise to generate.
size	tuple[int, ...]	-	The size of the noise to generate.
params	dict[str, Any]	-	The parameters of the noise to generate.
max_value	float	-	The maximum value of the noise to generate.
random_generator	np.random.Generator	-	The random number generator to use.

Returns

np.ndarray: The noise sampled.

sample_uniformfunction

sample_uniform(
    size: tuple[int, ...],
    params: dict[str, Any],
    random_generator: np.random.Generator
)

Sample from uniform distribution.

Parameters

Name	Type	Default	Description
size	tuple[int, ...]	-	Size of the output array
params	dict[str, Any]	-	Distribution parameters
random_generator	np.random.Generator	-	Random number generator

Returns

np.ndarray | float: Sampled values

separable_convolvefunction

separable_convolve(
    img: np.ndarray,
    kernel: np.ndarray
)

Convolve an image with a separable kernel. This function convolves an image with a separable kernel.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image.
kernel	np.ndarray	-	Kernel.

Returns

np.ndarray: Convolved image.

sharpen_gaussianfunction

sharpen_gaussian(
    img: np.ndarray,
    alpha: float,
    kernel_size: int,
    sigma: float
)

Sharpen image using Gaussian blur. This function sharpens an image using a Gaussian blur.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to sharpen.
alpha	float	-	The alpha value to use for the sharpening.
kernel_size	int	-	The kernel size to use for the Gaussian blur.
sigma	float	-	The sigma value to use for the Gaussian blur.

Returns

np.ndarray: The sharpened image.

shift_hsvfunction

shift_hsv(
    img: np.ndarray,
    hue_shift: float,
    sat_shift: float,
    val_shift: float
)

Shift the hue, saturation, and value of an image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to shift.
hue_shift	float	-	The amount to shift the hue.
sat_shift	float	-	The amount to shift the saturation.
val_shift	float	-	The amount to shift the value.

Returns

np.ndarray: The shifted image.

shot_noisefunction

shot_noise(
    img: np.ndarray,
    scale: float,
    random_generator: np.random.Generator
)

Apply shot noise to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image
scale	float	-	Scale factor for the noise
random_generator	np.random.Generator	-	Random number generator

Returns

np.ndarray: Image with shot noise

slicfunction

slic(
    image: np.ndarray,
    n_segments: int,
    compactness: float = 10.0,
    max_iterations: int = 10
)

Simple Linear Iterative Clustering (SLIC) superpixel segmentation using OpenCV and NumPy.

Parameters

Name	Type	Default	Description
image	np.ndarray	-	Input image (2D or 3D numpy array).
n_segments	int	-	Approximate number of superpixels to generate.
compactness	float	10.0	Balance between color proximity and space proximity.
max_iterations	int	10	Maximum number of iterations for k-means.

Returns

np.ndarray: Segmentation mask where each superpixel has a unique label.

solarizefunction

solarize(
    img: np.ndarray,
    threshold: float
)

Invert all pixel values above a threshold.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	The image to solarize. Can be uint8 or float32.
threshold	float	-	Normalized threshold value in range [0, 1]. For uint8 images: pixels above threshold * 255 are inverted For float32 images: pixels above threshold are inverted

Returns

np.ndarray: Solarized image.

Notes

The threshold is normalized to [0, 1] range for both uint8 and float32 images. For uint8 images, the threshold is internally scaled by 255.

spatter_mudfunction

spatter_mud(
    img: np.ndarray,
    non_mud: np.ndarray,
    mud: np.ndarray
)

Apply spatter mud to an image. This function applies spatter mud to an image by adding the mud to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
non_mud	np.ndarray	-	Non-mud image as a numpy array.
mud	np.ndarray	-	Mud image as a numpy array.

Returns

np.ndarray: The spatter mud applied to the image.

spatter_rainfunction

spatter_rain(
    img: np.ndarray,
    rain: np.ndarray
)

Apply spatter rain to an image. This function applies spatter rain to an image by adding the rain to the image.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
rain	np.ndarray	-	Rain image as a numpy array.

Returns

np.ndarray: The spatter rain applied to the image.

superpixelsfunction

superpixels(
    image: np.ndarray,
    n_segments: int,
    replace_samples: Sequence[bool],
    max_size: int | None,
    interpolation: int
)

Apply superpixels to an image. This function applies superpixels to an image using the SLIC algorithm. The superpixels are applied by replacing the pixels in the image with the mean intensity of the superpixel.

Parameters

Name	Type	Default	Description
image	np.ndarray	-	Input image as a numpy array.
n_segments	int	-	The number of segments to use for the superpixels.
replace_samples	Sequence[bool]	-	The samples to replace.
max_size	One of: int None	-	The maximum size of the superpixels.
interpolation	int	-	The interpolation method to use.

Returns

np.ndarray: The superpixels applied to the image.

to_grayfunction

to_gray(
    img: np.ndarray,
    num_output_channels: int,
    method: Literal['weighted_average', 'from_lab', 'desaturation', 'average', 'max', 'pca']
)

Convert an image to grayscale using a specified method. This function converts an image to grayscale using a specified method. The method can be one of the following: - "weighted_average": Use the weighted average method. - "from_lab": Use the L channel from the LAB color space. - "desaturation": Use the desaturation method. - "average": Use the average method. - "max": Use the maximum channel value method. - "pca": Use the Principal Component Analysis method.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.
num_output_channels	int	-	The number of channels in the output image.
method	One of: 'weighted_average' 'from_lab' 'desaturation' 'average' 'max' 'pca'	-	The method to use for grayscale conversion.

Returns

np.ndarray: Grayscale image as a 2D numpy array.

to_gray_averagefunction

to_gray_average(
    img: np.ndarray
)

Convert an image to grayscale using the average method. This function computes the arithmetic mean across all channels for each pixel, resulting in a grayscale representation of the image. Key aspects of this method: 1. It treats all channels equally, regardless of their perceptual importance. 2. Works with any number of channels, making it versatile for various image types. 3. Simple and fast to compute, but may not accurately represent perceived brightness. 4. For RGB images, the formula is: Gray = (R + G + B) / 3 Note: This method may produce different results compared to weighted methods (like RGB weighted average) which account for human perception of color brightness. It may also produce unexpected results for images with alpha channels or non-color data in additional channels.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array. Can be any number of channels.

Returns

np.ndarray: Grayscale image as a 2D numpy array. The output data type

to_gray_desaturationfunction

to_gray_desaturation(
    img: np.ndarray
)

Convert an image to grayscale using the desaturation method.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array.

Returns

np.ndarray: Grayscale image as a 2D numpy array.

to_gray_from_labfunction

to_gray_from_lab(
    img: np.ndarray
)

Convert an RGB image to grayscale using the L channel from the LAB color space. This function converts the RGB image to the LAB color space and extracts the L channel. The LAB color space is designed to approximate human vision, where L represents lightness. Key aspects of this method: 1. The L channel represents the lightness of each pixel, ranging from 0 (black) to 100 (white). 2. It's more perceptually uniform than RGB, meaning equal changes in L values correspond to roughly equal changes in perceived lightness. 3. The L channel is independent of the color information (A and B channels), making it suitable for grayscale conversion. This method can be particularly useful when you want a grayscale image that closely matches human perception of lightness, potentially preserving more perceived contrast than simple RGB-based methods.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input RGB image as a numpy array.

Returns

np.ndarray: Grayscale image as a 2D numpy array, representing the L (lightness) channel.

to_gray_maxfunction

to_gray_max(
    img: np.ndarray
)

Convert an image to grayscale using the maximum channel value method. This function takes the maximum value across all channels for each pixel, resulting in a grayscale image that preserves the brightest parts of the original image. Key aspects of this method: 1. Works with any number of channels, making it versatile for various image types. 2. For 3-channel (e.g., RGB) images, this method is equivalent to extracting the V (Value) channel from the HSV color space. 3. Preserves the brightest parts of the image but may lose some color contrast information. 4. Simple and fast to compute. Note: - This method tends to produce brighter grayscale images compared to other conversion methods, as it always selects the highest intensity value from the channels. - For RGB images, it may not accurately represent perceived brightness as it doesn't account for human color perception.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array. Can be any number of channels.

Returns

np.ndarray: Grayscale image as a 2D numpy array. The output data type

Notes

- This method tends to produce brighter grayscale images compared to other conversion methods, as it always selects the highest intensity value from the channels. - For RGB images, it may not accurately represent perceived brightness as it doesn't account for human color perception.

to_gray_pcafunction

to_gray_pca(
    img: np.ndarray
)

Convert an image to grayscale using Principal Component Analysis (PCA). This function applies PCA to reduce a multi-channel image to a single channel, effectively creating a grayscale representation that captures the maximum variance in the color data.

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input image as a numpy array with shape (height, width, channels).

Returns

np.ndarray: Grayscale image as a 2D numpy array with shape (height, width).

Notes

This method can potentially preserve more information from the original image compared to standard weighted average methods, as it accounts for the correlations between color channels.

to_gray_weighted_averagefunction

to_gray_weighted_average(
    img: np.ndarray
)

Convert an RGB image to grayscale using the weighted average method. This function uses OpenCV's cvtColor function with COLOR_RGB2GRAY conversion, which applies the following formula: Y = 0.299*R + 0.587*G + 0.114*B

Parameters

Name	Type	Default	Description
img	np.ndarray	-	Input RGB image as a numpy array.

Returns

np.ndarray: Grayscale image as a 2D numpy array.

unsharp_maskfunction

unsharp_mask(
    image: np.ndarray,
    ksize: int,
    sigma: float,
    alpha: float,
    threshold: int
)

Apply an unsharp mask to an image. This function applies an unsharp mask to an image using the Gaussian blur function. The unsharp mask is applied by subtracting the blurred image from the original image and then adding the result to the original image.

Parameters

Name	Type	Default	Description
image	np.ndarray	-	Input image as a numpy array.
ksize	int	-	The kernel size to use for the Gaussian blur.
sigma	float	-	The sigma value to use for the Gaussian blur.
alpha	float	-	The alpha value to use for the unsharp mask.
threshold	int	-	The threshold value to use for the unsharp mask.

Returns

np.ndarray: The unsharp mask applied to the image.

volume_channel_shufflefunction

volume_channel_shuffle(
    volume: np.ndarray,
    channels_shuffled: Sequence[int]
)

Shuffle channels of a single volume (D, H, W, C) or (D, H, W).

Parameters

Name	Type	Default	Description
volume	np.ndarray	-	Input volume.
channels_shuffled	Sequence[int]	-	New channel order.

Returns

np.ndarray: Volume with channels shuffled.

volumes_channel_shufflefunction

volumes_channel_shuffle(
    volumes: np.ndarray,
    channels_shuffled: Sequence[int]
)

Shuffle channels of a batch of volumes (B, D, H, W, C) or (B, D, H, W).

Parameters

Name	Type	Default	Description
volumes	np.ndarray	-	Input batch of volumes.
channels_shuffled	Sequence[int]	-	New channel order.

Returns

np.ndarray: Batch of volumes with channels shuffled.

MacenkoNormalizer
SimpleNMF
StainNormalizer
VahadaneNormalizer
add_fog
add_gravel
add_noise
add_rain
add_shadow
add_snow_bleach
add_snow_texture
add_sun_flare_overlay
add_sun_flare_physics_based
adjust_brightness_torchvision
adjust_contrast_torchvision
adjust_hue_torchvision
adjust_saturation_torchvision
apply_corner_illumination
apply_gaussian_illumination
apply_he_stain_augmentation
apply_linear_illumination
apply_plasma_brightness_contrast
apply_plasma_shadow
apply_salt_and_pepper
auto_contrast
bboxes_morphology
channel_shuffle
chromatic_aberration
clahe
convolve
create_contrast_lut
create_directional_gradient
dilate
downscale
equalize
erode
fancy_pca
gamma_transform
generate_constant_noise
generate_noise
generate_per_pixel_noise
generate_plasma_pattern
generate_random_values
generate_shared_noise
generate_snow_textures
get_drop_mask
get_fog_particle_radiuses
get_histogram_bounds
get_mask_array
get_mud_params
get_normalizer
get_rain_params
get_safe_brightness_contrast_params
get_tissue_mask
grayscale_to_multichannel
image_compression
invert
iso_noise
linear_transformation_rgb
morphology
move_tone_curve
noop
normalize_vectors
order_stains_combined
pixel_dropout
planckian_jitter
posterize
prepare_drop_values
rgb_to_optical_density
sample_beta
sample_gaussian
sample_laplace
sample_noise
sample_uniform
separable_convolve
sharpen_gaussian
shift_hsv
shot_noise
slic
solarize
spatter_mud
spatter_rain
superpixels
to_gray
to_gray_average
to_gray_desaturation
to_gray_from_lab
to_gray_max
to_gray_pca
to_gray_weighted_average
unsharp_mask
volume_channel_shuffle
volumes_channel_shuffle

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albumentations.augmentations.functional

Members

MacenkoNormalizerclass

Parameters

SimpleNMFclass

Parameters

StainNormalizerclass

VahadaneNormalizerclass

add_fogfunction

Parameters

Returns

add_gravelfunction

Parameters

Returns

add_noisefunction

Parameters

Returns

add_rainfunction

Parameters

Returns

add_shadowfunction

Parameters

Returns

References

add_snow_bleachfunction

Parameters

Returns

Notes

References

add_snow_texturefunction

Parameters

Returns

Notes

References

add_sun_flare_overlayfunction

Parameters

Returns

Notes

References

add_sun_flare_physics_basedfunction

Parameters

Returns

References

adjust_brightness_torchvisionfunction

Parameters

Returns

adjust_contrast_torchvisionfunction

Parameters

Returns

adjust_hue_torchvisionfunction

Parameters

Returns

adjust_saturation_torchvisionfunction

Parameters

Returns

apply_corner_illuminationfunction

Parameters

Returns

apply_gaussian_illuminationfunction

Parameters

apply_he_stain_augmentationfunction

Parameters

Returns

apply_linear_illuminationfunction

Parameters

Returns

apply_plasma_brightness_contrastfunction

Parameters

Returns

apply_plasma_shadowfunction

Parameters

Returns

apply_salt_and_pepperfunction

Parameters

Returns

auto_contrastfunction

Parameters

Returns

bboxes_morphologyfunction