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_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 |
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 |
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 |
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 |
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. |