SampleToMasks

class deeptrack.features.SampleToMasks(transformation_function: Callable[[Image], Image], number_of_masks: PropertyLike[int] = 1, output_region: PropertyLike[tuple[int, int, int, int]] = None, merge_method: PropertyLike[str | Callable | list[str | Callable]] = 'add', **kwargs: Any)

Bases: Feature

Creates a mask from a list of images.

This feature applies a transformation function to each input image and merges the resulting masks into a single multi-layer image. Each input image must have a position property that determines its placement within the final mask. When used with scatterers, the voxel_size property must be provided for correct object sizing.

Parameters

transformation_function: Callable[[Image], Image]

A function that transforms each input image into a mask with number_of_masks layers.

number_of_masks: PropertyLike[int], optional

The number of mask layers to generate. Default is 1.

output_region: PropertyLike[tuple[int, int, int, int]], optional

The size and position of the output mask, typically aligned with optics.output_region.

merge_method: PropertyLike[str | Callable | list[str | Callable]], optional

Method for merging individual masks into the final image. Can be: - “add” (default): Sum the masks. - “overwrite”: Later masks overwrite earlier masks. - “or”: Combine masks using a logical OR operation. - “mul”: Multiply masks. - Function: Custom function taking two images and merging them.

**kwargs: dict[str, Any]

Additional keyword arguments passed to the parent Feature class.

Methods

get(image: np.ndarray | Image, transformation_function: Callable[[Image], Image], **kwargs: dict[str, Any]) -> Image

Applies the transformation function to the input image.

_process_and_get(images: list[np.ndarray] | np.ndarray | list[Image] | Image, **kwargs: dict[str, Any]) -> Image | np.ndarray

Processes a list of images and generates a multi-layer mask.

Returns

Image or np.ndarray

The final mask image with the specified number of layers.

Raises

ValueError

If merge_method is invalid.

Examples

>>> import deeptrack as dt
>>> import matplotlib.pyplot as plt
>>> import numpy as np

Define number of particles: >>> n_particles = 12

Define optics and particles: >>> optics = dt.Fluorescence(output_region=(0, 0, 64, 64)) >>> particle = dt.PointParticle( >>> position=lambda: np.random.uniform(5, 55, size=2) >>> ) >>> particles = particle ^ n_particles

Define pipelines: >>> sim_im_pip = optics(particles) >>> sim_mask_pip = particles >> dt.SampleToMasks( … lambda: lambda particles: particles > 0, … output_region=optics.output_region, … merge_method=”or” … ) >>> pipeline = sim_im_pip & sim_mask_pip >>> pipeline.store_properties()

Generate image and mask: >>> image, mask = pipeline.update()()

Get particle positions: >>> positions = np.array(image.get_property(“position”, get_one=False))

Visualize results: >>> plt.subplot(1, 2, 1) >>> plt.imshow(image, cmap=”gray”) >>> plt.title(“Original Image”) >>> plt.subplot(1, 2, 2) >>> plt.imshow(mask, cmap=”gray”) >>> plt.scatter(positions[:,1], positions[:,0], c=”r”, marker=”x”, s = 10) >>> plt.title(“Mask”) >>> plt.show()

Methods Summary

get(image, transformation_function, **kwargs)

Apply the transformation function to a single image.

Methods Documentation

get(image: np.ndarray | Image, transformation_function: Callable[[Image], Image], **kwargs: dict[str, Any]) → Image

Apply the transformation function to a single image.

Parameters

image: np.ndarray | Image

The input image.

transformation_function: Callable[[Image], Image]

Function to transform the image.

**kwargs: dict[str, Any]

Additional parameters.

Returns

Image

The transformed image.