gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0)
Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely proportional to the standard deviation.
Spatial frequency of the harmonic function. Specified in pixels.
Orientation in radians. If 0, the harmonic is in the x-direction.
The bandwidth captured by the filter. For fixed bandwidth, sigma_x
and sigma_y
will decrease with increasing frequency. This value is ignored if sigma_x
and sigma_y
are set by the user.
Standard deviation in x- and y-directions. These directions apply to the kernel before rotation. If :None:None:`theta = pi/2`, then the kernel is rotated 90 degrees so that sigma_x
controls the vertical direction.
The linear size of the kernel is n_stds (3 by default) standard deviations
Phase offset of harmonic function in radians.
Complex filter kernel.
Return complex 2D Gabor filter kernel.
>>> from skimage.filters import gabor_kernelThis example is valid syntax, but we were not able to check execution
... from skimage import io
... from matplotlib import pyplot as plt # doctest: +SKIP
>>> gk = gabor_kernel(frequency=0.2)This example is valid syntax, but we were not able to check execution
... plt.figure() # doctest: +SKIP
... io.imshow(gk.real) # doctest: +SKIP
... io.show() # doctest: +SKIP
>>> # more ripples (equivalent to increasing the size of theSee :
... # Gaussian spread)
... gk = gabor_kernel(frequency=0.2, bandwidth=0.1)
... plt.figure() # doctest: +SKIP
... io.imshow(gk.real) # doctest: +SKIP
... io.show() # doctest: +SKIP
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.filters._gabor.gabor_kernel
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