Functional transforms (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_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.