GaussianBlur

class deeptrack.optical.math.GaussianBlur(sigma: float | Callable[[...], float] = 2, channel_axis: int | None = -1, **kwargs: Any)

Bases: Blur

Apply a Gaussian blur over spatial dimensions.

The image is convolved with a Gaussian kernel with standard deviation sigma. If channel_axis is specified, the blur is applied independently per channel. Otherwise, all dimensions (including channels, if present) are treated as spatial, and the filter is applied across them. The implementation uses separable convolution for efficiency. For large sigma relative to the image size, the output approaches

the global mean of the image.

Parameters

sigma: float

Standard deviation of the Gaussian kernel.

channel_axis: int or None, default=-1

Axis corresponding to channels. Set to None to treat all dimensions as spatial.

Methods

get(image, sigma, channel_axis, **kwargs) –> array | tensor

Apply Gaussian blurring to the input image using the selected backend.

Examples

>>> import deeptrack as dt

Create an input image:

>>> import numpy as np
>>>
>>> input_image = np.random.rand(32, 32)

Define a Gaussian blur feature.

>>> gaussian_blur = dt.GaussianBlur(sigma=2, channel_axis=None)
>>> output_image = gaussian_blur(input_image)
>>> print(output_image.shape)
(32, 32)

Visualize the input and output images.

>>> import matplotlib.pyplot as plt
>>>
>>> plt.figure(figsize=(8, 4))
>>> plt.subplot(1, 2, 1)
>>> plt.imshow(input_image, cmap='gray')
>>> plt.subplot(1, 2, 2)
>>> plt.imshow(output_image, cmap='gray')
>>> plt.show()