pysteps.feature.blob.detection

pysteps.feature.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 a scale-space 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, ‘doh’ = Determinant of Hessian.

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 the value for overlap, the smaller blob is eliminated.

return_sigmas: bool, optional

If True, return the standard deviations of the Gaussian kernels corresponding to the detected blobs.

Returns

points: ndarray

Array of shape (p, 2) or (p, 3) indicating the pixel coordinates of p detected blobs. If return_sigmas is True, the third column contains the standard deviations of the Gaussian kernels corresponding to the blobs.