pysteps.feature#
Implementations of feature detection methods.
pysteps.feature.interface#
Interface for the feature detection module. It returns a callable function for detecting features from two-dimensional images.
The feature detectors implement the following interface:
detection(input_image, **keywords)
The input is a two-dimensional image. Additional arguments to the specific
method can be given via **keywords
. The output is an array of shape (n, m),
where each row corresponds to one of the n features. The first two columns
contain the coordinates (x, y) of the features, and additional information can
be specified in the remaining columns.
All implemented methods support the following keyword arguments:
Key |
Value |
---|---|
max_num_features |
maximum number of features to detect |
|
Return a callable function for feature detection. |
pysteps.feature.blob#
Blob detection methods.
|
pysteps.feature.tstorm#
Thunderstorm cell detection module, part of Thunderstorm Detection and Tracking (DATing) This module was implemented following the procedures used in the TRT Thunderstorms Radar Tracking algorithm ([HMG+04]) used operationally at MeteoSwiss. Full documentation is published in [FGGB21]. Modifications include advecting the identified thunderstorms with the optical flow obtained from pysteps, as well as additional options in the thresholding.
References#
@author: mfeldman
|
This function detects thunderstorms using a multi-threshold approach. |
|
This function segments the entire 2-D array into areas belonging to each identified maximum according to a watershed algorithm. |
|
This function computes the distance between all maxima and rejects maxima that are less than a minimum distance apart. |
|
This function returns the identified cells in a dataframe including their x,y locations, location of their maxima, maximum reflectivity and contours. |
pysteps.feature.shitomasi#
Shi-Tomasi features detection method to detect corners in an image.
|