viqa.utils.gabor_convolve

viqa.utils.gabor_convolve(img, scales_num: int, orientations_num: int, min_wavelength=3, wavelength_scaling=3, bandwidth_param=0.55, d_theta_on_sigma=1.5)[source]

Compute Log Gabor filter responses.

Parameters:

  • img (np.ndarray) – Image to be filtered

  • scales_num (int) – Number of wavelet scales

  • orientations_num (int) – Number of filter orientations

  • min_wavelength (int, default=3) – Wavelength of smallest scale filter, maximum frequency is set by this value, should be >= 3

  • wavelength_scaling (int, default=3) – Scaling factor between successive filters

  • bandwidth_param (float, default=0.55) – Ratio of standard deviation of the Gaussian describing log Gabor filter’s transfer function in the frequency domain to the filter’s center frequency (0.74 for 1 octave bandwidth, 0.55 for 2 octave bandwidth, 0.41 for 3 octave bandwidth)

  • d_theta_on_sigma (float, default=1.5) – Ratio of angular interval between filter orientations and standard deviation of angular Gaussian spreading function, a value of 1.5 results in approximately the minimum overlap needed to get even spectral coverage

Returns:

Log Gabor filtered image

Return type:

np.ndarray

Notes

Even spectral coverage and independence of filter output are dependent on bandwidth_param vs wavelength_scaling. Some experimental values:

0.85 <–> 1.3

0.74 <–> 1.6 (1 octave bandwidth)

0.65 <–> 2.1

0.55 <–> 3.0 (2 octave bandwidth)

Additionally d_theta_on_sigma should be set to 1.5 for approximately the minimum overlap needed to get even spectral coverage.

For more information see [1]. This code was originally written in Matlab by Peter Kovesi and adapted by Eric Larson. The adaption by Eric Larson is available under [2].

References

Examples

>>> import numpy as np
>>> from viqa.utils import gabor_convolve
>>> img = np.random.rand(128, 128)
>>> res = gabor_convolve(img, scales_num=3, orientations_num=4)