playnano.analysis.modules.log_blob_detection module

Module for LoG blob detection.

Detect “blobs” in each frame of an AFM image stack using the Laplacian-of-Gaussian method. Provides automatic multi-scale blob detection and optional radius estimation.

class playnano.analysis.modules.log_blob_detection.LoGBlobDetectionModule[source]

Bases: AnalysisModule

Detect blobs in AFM image stacks using the Laplacian-of-Gaussian (LoG) method.

This module applies multi-scale blob detection to each frame in an AFM image stack using the Laplacian-of-Gaussian algorithm from skimage.feature.blob_log. It supports automatic scale selection and optional estimation of blob radii.

run(stack, previous_results=None, \*, min_sigma=1.0, max_sigma=5.0, num_sigma=10,

threshold=0.1, overlap=0.5, include_radius=True) Detects blobs in each frame of the AFM image stack and returns per-frame features and a summary.

Version()
-------
0.1.0()

Examples

>>> module = LoGBlobDetectionModule()
>>> result = module.run(stack, min_sigma=1.0, max_sigma=5.0, num_sigma=10)
>>> result['summary']['total_blobs']
42
property name: str

Name of the analysis module.

Returns:

The string identifier for this module: “log_blob_detection”.

Return type:

str

run(stack, previous_results: dict[str, Any] | None = None, *, min_sigma: float = 1.0, max_sigma: float = 5.0, num_sigma: int = 10, threshold: float = 0.1, overlap: float = 0.5, include_radius: bool = True) → dict[str, Any][source]

Detect “blobs” in each frame via a Laplacian-of-Gaussian filter.

Parameters:

  • stack (AFMImageStack) – Must have stack.data of shape (n_frames, H, W).

  • min_sigma (float) – Parameters passed to skimage.feature.blob_log.

  • max_sigma (float) – Parameters passed to skimage.feature.blob_log.

  • num_sigma (int) – Parameter passed to skimage.feature.blob_log.

  • threshold (float) – Absolute intensity threshold for LoG response.

  • overlap (float) – If two detected blobs overlap more than this fraction, only the larger is kept.

  • include_radius (bool) – If True, append the estimated blob radius in each feature-dict.

Returns:

Dictionary with keys:

  • features_per_framelist of list of dict

    Per-frame list of detected blobs. Each dict contains: - frame_timestamp : float - y, x : float - sigma : float - radius : float, optional (if include_radius=True)

  • summarydict

    Aggregate metrics: - total_frames : int - total_blobs : int - avg_blobs_per_frame : float

Return type:

dict[str, Any]

version = '0.1.0'