albumentations.augmentations.blur.functional
Functional implementations of various blur operations for image processing. This module provides a collection of low-level functions for applying different blur effects to images, including standard blur, median blur, glass blur, defocus, and zoom effects. These functions form the foundation for the corresponding transform classes.
Members
- functionblur
- functioncentral_zoom
- functioncreate_gaussian_kernel
- functioncreate_gaussian_kernel_1d
- functioncreate_gaussian_kernel_input_array
- functioncreate_motion_kernel
- functiondefocus
- functionglass_blur
- functionmedian_blur
- functionprocess_blur_limit
- functionsample_odd_from_range
- functionzoom_blur
blurfunction
blur(
img: np.ndarray,
ksize: int
)Blur an image. This function applies a blur to an image.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| img | np.ndarray | - | Input image. |
| ksize | int | - | Kernel size. |
Returns
- np.ndarray: Blurred image.
central_zoomfunction
central_zoom(
img: np.ndarray,
zoom_factor: int
)Central zoom an image. This function zooms an image.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| img | np.ndarray | - | Input image. |
| zoom_factor | int | - | Zoom factor. |
Returns
- np.ndarray: Zoomed image.
create_gaussian_kernelfunction
create_gaussian_kernel(
sigma: float,
ksize: int = 0
)Create a Gaussian kernel following PIL's approach.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| sigma | float | - | Standard deviation for Gaussian kernel. |
| ksize | int | 0 | Kernel size. If 0, size is computed as int(sigma * 3.5) * 2 + 1 to match PIL's implementation. Otherwise, must be positive and odd. |
Returns
- np.ndarray: 2D normalized Gaussian kernel.
create_gaussian_kernel_1dfunction
create_gaussian_kernel_1d(
sigma: float,
ksize: int = 0
)Create a 1D Gaussian kernel following PIL's approach.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| sigma | float | - | Standard deviation for Gaussian kernel. |
| ksize | int | 0 | Kernel size. If 0, size is computed as int(sigma * 3.5) * 2 + 1 to match PIL's implementation. Otherwise, must be positive and odd. |
Returns
- np.ndarray: 1D normalized Gaussian kernel.
create_gaussian_kernel_input_arrayfunction
create_gaussian_kernel_input_array(
size: int
)Creates a 1-D array which will create an array of x-coordinates which will be input for the gaussian function (values from -size/2 to size/2 with step size of 1) Piecewise function is needed as equivalent python list comprehension is faster than np.linspace for values of size < 100
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| size | int | - | kernel size |
Returns
- np.ndarray: x-coordinate array which will be input for gaussian function that will be used for
create_motion_kernelfunction
create_motion_kernel(
kernel_size: int,
angle: float,
direction: float,
allow_shifted: bool,
random_state: random.Random
)Create a motion blur kernel.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| kernel_size | int | - | Size of the kernel (must be odd) |
| angle | float | - | Angle in degrees (counter-clockwise) |
| direction | float | - | Blur direction (-1.0 to 1.0) |
| allow_shifted | bool | - | Allow kernel to be randomly shifted from center |
| random_state | random.Random | - | Python's random.Random instance |
Returns
- np.ndarray: Motion blur kernel
defocusfunction
defocus(
img: np.ndarray,
radius: int,
alias_blur: float
)Defocus an image. This function defocuses an image.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| img | np.ndarray | - | Input image. |
| radius | int | - | Radius. |
| alias_blur | float | - | Alias blur. |
Returns
- np.ndarray: Defocused image.
glass_blurfunction
glass_blur(
img: np.ndarray,
sigma: float,
max_delta: int,
iterations: int,
dxy: np.ndarray,
mode: Literal['fast', 'exact']
)Glass blur an image. This function applies a glass blur to an image.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| img | np.ndarray | - | Input image. |
| sigma | float | - | Sigma. |
| max_delta | int | - | Maximum delta. |
| iterations | int | - | Number of iterations. |
| dxy | np.ndarray | - | Dxy. |
| mode | One of:
| - | Mode. |
Returns
- np.ndarray: Glass blurred image.
median_blurfunction
median_blur(
img: np.ndarray,
ksize: int
)Median blur an image. This function applies a median blur to an image.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| img | np.ndarray | - | Input image. |
| ksize | int | - | Kernel size. |
Returns
- np.ndarray: Median blurred image.
process_blur_limitfunction
process_blur_limit(
value: int | tuple[int, int],
info: ValidationInfo,
min_value: int = 0
)Process blur limit to ensure valid kernel sizes.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| value | One of:
| - | - |
| info | ValidationInfo | - | - |
| min_value | int | 0 | - |
sample_odd_from_rangefunction
sample_odd_from_range(
random_state: random.Random,
low: int,
high: int
)Sample an odd number from the range [low, high] (inclusive).
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| random_state | random.Random | - | instance of random.Random |
| low | int | - | lower bound (will be converted to nearest valid odd number) |
| high | int | - | upper bound (will be converted to nearest valid odd number) |
Returns
- int: Randomly sampled odd number from the range
Notes
- Input values will be converted to nearest valid odd numbers: * Values less than 3 will become 3 * Even values will be rounded up to next odd number - After normalization, high must be >= low
zoom_blurfunction
zoom_blur(
img: np.ndarray,
zoom_factors: np.ndarray | Sequence[int]
)Zoom blur an image. This function zooms and blurs an image.
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
| img | np.ndarray | - | Input image. |
| zoom_factors | One of:
| - | Zoom factors. |
Returns
- np.ndarray: Zoomed and blurred image.