log_gabor¶
-
menpo.math.
log_gabor
(image, **kwargs)[source]¶ Creates a log-gabor filter bank, including smoothing the images via a low-pass filter at the edges.
To create a 2D filter bank, simply specify the number of phi orientations (orientations in the xy-plane).
To create a 3D filter bank, you must specify both the number of phi (azimuth) and theta (elevation) orientations.
This algorithm is directly derived from work by Peter Kovesi.
Parameters: - image (
(M, N, ...)
ndarray) – Image to be convolved - num_scales (int, optional) –
Number of wavelet scales.
Default 2D 4 Default 3D 4 - num_phi_orientations (int, optional) –
Number of filter orientations in the xy-plane
Default 2D 6 Default 3D 6 - num_theta_orientations (int, optional) –
Only required for 3D. Number of filter orientations in the z-plane
Default 2D N/A Default 3D 4 - min_wavelength (int, optional) –
Wavelength of smallest scale filter.
Default 2D 3 Default 3D 3 - scaling_constant (int, optional) –
Scaling factor between successive filters.
Default 2D 2 Default 3D 2 - center_sigma (float, optional) –
Ratio of the standard deviation of the Gaussian describing the Log Gabor filter’s transfer function in the frequency domain to the filter centre frequency.
Default 2D 0.65 Default 3D 0.65 - d_phi_sigma (float, optional) –
Angular bandwidth in xy-plane
Default 2D 1.3 Default 3D 1.5 - d_theta_sigma (float, optional) –
Only required for 3D. Angular bandwidth in z-plane
Default 2D N/A Default 3D 1.5
Returns: - complex_conv (
(num_scales, num_orientations, image.shape)
ndarray) – Complex valued convolution results. The real part is the result of convolving with the even symmetric filter, the imaginary part is the result from convolution with the odd symmetric filter. - bandpass (
(num_scales, image.shape)
ndarray) – Bandpass images corresponding to each scale s - S (
(image.shape,)
ndarray) – Convolved image
Examples
Return the magnitude of the convolution over the image at scale s and orientation o
np.abs(complex_conv[s, o, :, :])
Return the phase angles
np.angle(complex_conv[s, o, :, :])
References
[1] D. J. Field, “Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells”, Journal of The Optical Society of America A, Vol 4, No. 12, December 1987. pp 2379-2394 - image (