pysteps.utils.images.blob_detection

pysteps.utils.images.blob_detection(input_image, method='log', threshold=0.5, min_sigma=3, max_sigma=20, overlap=0.5, return_sigmas=False, **kwargs)

Interface to the feature.blob_* methods implemented in scikit-image. A blob is defined as local a maximum of a Gaussian-filtered image.

Parameters:

input_image : array_like

Array of shape (m, n) containing the input image. Nan values are ignored.

method : {‘log’, ‘dog’, ‘doh’}, optional

The method to use: ‘log’ = Laplacian of Gaussian, ‘dog’ = Difference of Gaussian, ‘’

threshold : float, optional

Detection threshold.

min_sigma : float, optional

The minimum standard deviation for the Gaussian kernel.

max_sigma : float, optional

The maximum standard deviation for the Gaussian kernel.

overlap : float, optional

A value between 0 and 1. If the area of two blobs overlaps by a fraction greater than threshold, the smaller blob is eliminated.

return_sigmas : bool, optional

If True, the return array has a third column indicating the standard deviations of the Gaussian kernels that detected the blobs.