Functional transforms (augmentations.functional)

`add_fog(img, fog_coef, alpha_coef, haze_list)`

Add fog to the image.

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

Parameters:

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

Returns:

Type	Description
`numpy.ndarray`	Image.

`add_rain(img, slant, drop_length, drop_width, drop_color, blur_value, brightness_coefficient, rain_drops)`

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

Parameters:

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

Returns:

Type	Description
`numpy.ndarray`	Image.

`add_shadow(img, vertices_list)`

Add shadows to the image.

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

Parameters:

Name	Type	Description	Default
`img`	`numpy.ndarray`		required
`vertices_list`	`list`		required

Returns:

Type	Description
`numpy.ndarray`

`add_snow(img, snow_point, brightness_coeff)`

Bleaches out pixels, imitation snow.

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

Parameters:

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

Returns:

Type	Description
`numpy.ndarray`	Image.

`add_sun_flare(img, flare_center_x, flare_center_y, src_radius, src_color, circles)`

Add sun flare.

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

Parameters:

Name	Type	Default
`img`	`numpy.ndarray`	required
`flare_center_x`	`float`	required
`flare_center_y`	`float`	required
`src_radius`		required
`src_color`	`int, int, int`	required
`circles`	`list`	required

Returns:

Type	Description
`numpy.ndarray`

`bbox_crop(bbox, x_min, y_min, x_max, y_max, rows, cols)`

Crop a bounding box.

Parameters:

Name	Type	Description	Default
`bbox`	`tuple`	A bounding box `(x_min, y_min, x_max, y_max)`.	required
`x_min`	`int`		required
`y_min`	`int`		required
`x_max`	`int`		required
`y_max`	`int`		required
`rows`	`int`	Image rows.	required
`cols`	`int`	Image cols.	required

Returns:

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

`bbox_flip(bbox, d, rows, cols)`

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

Parameters:

Name	Type	Description	Default
`bbox`	`tuple`	A bounding box `(x_min, y_min, x_max, y_max)`.	required
`d`	`int`		required
`rows`	`int`	Image rows.	required
`cols`	`int`	Image cols.	required

Returns:

Type	Description
`tuple`	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.

`bbox_hflip(bbox, rows, cols)`

Flip a bounding box horizontally around the y-axis.

Parameters:

Name	Type	Description	Default
`bbox`	`tuple`	A bounding box `(x_min, y_min, x_max, y_max)`.	required
`rows`	`int`	Image rows.	required
`cols`	`int`	Image cols.	required

Returns:

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

`bbox_rot90(bbox, factor, rows, cols)`

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

Parameters:

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

Returns:

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

`bbox_rotate(bbox, angle, rows, cols)`

Rotates a bounding box by angle degrees.

Parameters:

Name	Type	Description	Default
`bbox`	`tuple`	A bounding box `(x_min, y_min, x_max, y_max)`.	required
`angle`	`int`	Angle of rotation in degrees.	required
`rows`	`int`	Image rows.	required
`cols`	`int`	Image cols.	required

Returns:

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

`bbox_transpose(bbox, axis, rows, cols)`

Transposes a bounding box along given axis.

Parameters:

Name	Type	Description	Default
`bbox`	`tuple`	A bounding box `(x_min, y_min, x_max, y_max)`.	required
`axis`	`int`	0 - main axis, 1 - secondary axis.	required
`rows`	`int`	Image rows.	required
`cols`	`int`	Image cols.	required

Returns:

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

Exceptions:

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

`bbox_vflip(bbox, rows, cols)`

Flip a bounding box vertically around the x-axis.

Parameters:

Name	Type	Description	Default
`bbox`	`tuple`	A bounding box `(x_min, y_min, x_max, y_max)`.	required
`rows`	`int`	Image rows.	required
`cols`	`int`	Image cols.	required

Returns:

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

`crop_bbox_by_coords(bbox, crop_coords, crop_height, crop_width, rows, cols)`

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	Default
`bbox`	`tuple`	A cropped box `(x_min, y_min, x_max, y_max)`.	required
`crop_coords`	`tuple`	Crop coordinates `(x1, y1, x2, y2)`.	required
`crop_height`	`int`		required
`crop_width`	`int`		required
`rows`	`int`	Image rows.	required
`cols`	`int`	Image cols.	required

Returns:

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

`crop_keypoint_by_coords(keypoint, crop_coords, crop_height, crop_width, rows, cols)`

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	Default
`keypoint`	`tuple`	A keypoint `(x, y, angle, scale)`.	required
`crop_coords`	`tuple`	Crop box coords `(x1, x2, y1, y2)`.	required
`crop`	`height (int`	Crop height.	required
`crop_width`	`int`	Crop width.	required
`rows`	`int`	Image height.	required
`cols`	`int`	Image width.	required

Returns:

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

`elastic_transform(img, alpha, sigma, alpha_affine, interpolation=1, border_mode=4, value=None, random_state=None, approximate=False)`

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

`elastic_transform_approx(img, alpha, sigma, alpha_affine, interpolation=1, border_mode=4, value=None, random_state=None)`

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

`equalize(img, mask=None, mode='cv', by_channels=True)`

Equalize the image histogram.

Parameters:

Name	Type	Description	Default
`img`	`numpy.ndarray`	RGB or grayscale image.	required
`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.	`None`
`mode`	`str`	{'cv', 'pil'}. Use OpenCV or Pillow equalization method.	`'cv'`
`by_channels`	`bool`	If True, use equalization by channels separately, else convert image to YCbCr representation and use equalization by `Y` channel.	`True`

Returns:

Type	Description
`numpy.ndarray`	Equalized image.

`fancy_pca(img, alpha=0.1)`

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

Parameters:

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

Returns:

Type	Description
	numpy image-like array as float range(0, 1)

`grid_distortion(img, num_steps=10, xsteps=(), ysteps=(), interpolation=1, border_mode=4, value=None)`

Reference

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

`iso_noise(image, color_shift=0.05, intensity=0.5, random_state=None, **kwargs)`

Apply poisson noise to image to simulate camera sensor noise.

Parameters:

Name	Type	Description	Default
`image`	`numpy.ndarray`	Input image, currently, only RGB, uint8 images are supported.	required
`color_shift`	`float`		`0.05`
`intensity`	`float`	Multiplication factor for noise values. Values of ~0.5 are produce noticeable, yet acceptable level of noise.	`0.5`
`random_state`			`None`
`**kwargs`			`{}`

Returns:

Type	Description
`numpy.ndarray`	Noised image

`keypoint_center_crop(keypoint, crop_height, crop_width, rows, cols)`

Keypoint center crop.

Parameters:

Name	Type	Description	Default
`keypoint`	`tuple`	A keypoint `(x, y, angle, scale)`.	required
`crop_height`	`int`	Crop height.	required
`crop_width`	`int`	Crop width.	required
`h_start`	`int`	Crop height start.	required
`w_start`	`int`	Crop width start.	required
`rows`	`int`	Image height.	required
`cols`	`int`	Image width.	required

Returns:

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

`keypoint_flip(keypoint, d, rows, cols)`

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

Parameters:

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

Returns:

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

Exceptions:

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

`keypoint_hflip(keypoint, rows, cols)`

Flip a keypoint horizontally around the y-axis.

Parameters:

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

Returns:

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

`keypoint_random_crop(keypoint, crop_height, crop_width, h_start, w_start, rows, cols)`

Keypoint random crop.

Parameters:

Name	Type	Description	Default
`keypoint`		(tuple): A keypoint `(x, y, angle, scale)`.	required
`crop_height`	`int`	Crop height.	required
`crop_width`	`int`	Crop width.	required
`h_start`	`int`	Crop height start.	required
`w_start`	`int`	Crop width start.	required
`rows`	`int`	Image height.	required
`cols`	`int`	Image width.	required

Returns:

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

`keypoint_rot90(keypoint, factor, rows, cols, **params)`

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

Parameters:

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

Returns:

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

Exceptions:

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

`keypoint_rotate(keypoint, angle, rows, cols, **params)`

Rotate a keypoint by angle.

Parameters:

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

Returns:

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

`keypoint_scale(keypoint, scale_x, scale_y)`

Scales a keypoint by scale_x and scale_y.

Parameters:

Name	Type	Description	Default
`keypoint`	`tuple`	A keypoint `(x, y, angle, scale)`.	required
`scale_x`	`int`	Scale coefficient x-axis.	required
`scale_y`	`int`	Scale coefficient y-axis.	required

Returns:

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

`keypoint_transpose(keypoint)`

Rotate a keypoint by angle.

Parameters:

Name	Type	Description	Default
`keypoint`	`tuple`	A keypoint `(x, y, angle, scale)`.	required

Returns:

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

`keypoint_vflip(keypoint, rows, cols)`

Flip a keypoint vertically around the x-axis.

Parameters:

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

Returns:

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

`multiply(img, multiplier)`

Parameters:

Name	Type	Description	Default
`img`	`numpy.ndarray`	Image.	required
`multiplier`	`numpy.ndarray`	Multiplier coefficient.	required

Returns:

Type	Description
`numpy.ndarray`	Image multiplied by `multiplier` coefficient.

`optical_distortion(img, k=0, dx=0, dy=0, interpolation=1, border_mode=4, value=None)`

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/

`posterize(img, bits)`

Reduce the number of bits for each color channel.

Parameters:

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

Returns:

Type	Description
`numpy.ndarray`	Image with reduced color channels.

`preserve_channel_dim(func)`

Preserve dummy channel dim.

`preserve_shape(func)`

Preserve shape of the image

`py3round(number)`

Unified rounding in all python versions.

`solarize(img, threshold=128)`

Invert all pixel values above a threshold.

Parameters:

Name	Type	Description	Default
`img`	`numpy.ndarray`	The image to solarize.	required
`threshold`	`int`	All pixels above this greyscale level are inverted.	`128`

Returns:

Type	Description
`numpy.ndarray`	Solarized image.

`swap_tiles_on_image(image, tiles)`

Swap tiles on image.

Parameters:

Name	Type	Description	Default
`image`	`np.ndarray`	Input image.	required
`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)	required

Returns:

Type	Description
`np.ndarray`	Output image.