Skip to content
Permalink
Branch: master
Find file Copy path
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
176 lines (146 sloc) 6.85 KB
import numpy as np
from scipy import ndimage as ndi
from .._shared.utils import assert_nD
__all__ = ['gabor_kernel', 'gabor']
def _sigma_prefactor(bandwidth):
b = bandwidth
# See http://www.cs.rug.nl/~imaging/simplecell.html
return 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * \
(2.0 ** b + 1) / (2.0 ** b - 1)
def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None,
n_stds=3, offset=0):
"""Return complex 2D Gabor filter kernel.
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.
Parameters
----------
frequency : float
Spatial frequency of the harmonic function. Specified in pixels.
theta : float, optional
Orientation in radians. If 0, the harmonic is in the x-direction.
bandwidth : float, optional
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.
sigma_x, sigma_y : float, optional
Standard deviation in x- and y-directions. These directions apply to
the kernel *before* rotation. If `theta = pi/2`, then the kernel is
rotated 90 degrees so that `sigma_x` controls the *vertical* direction.
n_stds : scalar, optional
The linear size of the kernel is n_stds (3 by default) standard
deviations
offset : float, optional
Phase offset of harmonic function in radians.
Returns
-------
g : complex array
Complex filter kernel.
References
----------
.. [1] https://en.wikipedia.org/wiki/Gabor_filter
.. [2] https://web.archive.org/web/20180127125930/http://mplab.ucsd.edu/tutorials/gabor.pdf
Examples
--------
>>> from skimage.filters import gabor_kernel
>>> from skimage import io
>>> from matplotlib import pyplot as plt # doctest: +SKIP
>>> gk = gabor_kernel(frequency=0.2)
>>> plt.figure() # doctest: +SKIP
>>> io.imshow(gk.real) # doctest: +SKIP
>>> io.show() # doctest: +SKIP
>>> # more ripples (equivalent to increasing the size of the
>>> # 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
"""
if sigma_x is None:
sigma_x = _sigma_prefactor(bandwidth) / frequency
if sigma_y is None:
sigma_y = _sigma_prefactor(bandwidth) / frequency
x0 = np.ceil(max(np.abs(n_stds * sigma_x * np.cos(theta)),
np.abs(n_stds * sigma_y * np.sin(theta)), 1))
y0 = np.ceil(max(np.abs(n_stds * sigma_y * np.cos(theta)),
np.abs(n_stds * sigma_x * np.sin(theta)), 1))
y, x = np.mgrid[-y0:y0 + 1, -x0:x0 + 1]
rotx = x * np.cos(theta) + y * np.sin(theta)
roty = -x * np.sin(theta) + y * np.cos(theta)
g = np.zeros(y.shape, dtype=np.complex)
g[:] = np.exp(-0.5 * (rotx ** 2 / sigma_x ** 2 + roty ** 2 / sigma_y ** 2))
g /= 2 * np.pi * sigma_x * sigma_y
g *= np.exp(1j * (2 * np.pi * frequency * rotx + offset))
return g
def gabor(image, frequency, theta=0, bandwidth=1, sigma_x=None,
sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0):
"""Return real and imaginary responses to Gabor filter.
The real and imaginary parts of the Gabor filter kernel are applied to the
image and the response is returned as a pair of arrays.
Gabor filter is a linear filter with a Gaussian kernel which is modulated
by a sinusoidal plane wave. Frequency and orientation representations of
the Gabor filter are similar to those of the human visual system.
Gabor filter banks are commonly used in computer vision and image
processing. They are especially suitable for edge detection and texture
classification.
Parameters
----------
image : 2-D array
Input image.
frequency : float
Spatial frequency of the harmonic function. Specified in pixels.
theta : float, optional
Orientation in radians. If 0, the harmonic is in the x-direction.
bandwidth : float, optional
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.
sigma_x, sigma_y : float, optional
Standard deviation in x- and y-directions. These directions apply to
the kernel *before* rotation. If `theta = pi/2`, then the kernel is
rotated 90 degrees so that `sigma_x` controls the *vertical* direction.
n_stds : scalar, optional
The linear size of the kernel is n_stds (3 by default) standard
deviations.
offset : float, optional
Phase offset of harmonic function in radians.
mode : {'constant', 'nearest', 'reflect', 'mirror', 'wrap'}, optional
Mode used to convolve image with a kernel, passed to `ndi.convolve`
cval : scalar, optional
Value to fill past edges of input if `mode` of convolution is
'constant'. The parameter is passed to `ndi.convolve`.
Returns
-------
real, imag : arrays
Filtered images using the real and imaginary parts of the Gabor filter
kernel. Images are of the same dimensions as the input one.
References
----------
.. [1] https://en.wikipedia.org/wiki/Gabor_filter
.. [2] https://web.archive.org/web/20180127125930/http://mplab.ucsd.edu/tutorials/gabor.pdf
Examples
--------
>>> from skimage.filters import gabor
>>> from skimage import data, io
>>> from matplotlib import pyplot as plt # doctest: +SKIP
>>> image = data.coins()
>>> # detecting edges in a coin image
>>> filt_real, filt_imag = gabor(image, frequency=0.6)
>>> plt.figure() # doctest: +SKIP
>>> io.imshow(filt_real) # doctest: +SKIP
>>> io.show() # doctest: +SKIP
>>> # less sensitivity to finer details with the lower frequency kernel
>>> filt_real, filt_imag = gabor(image, frequency=0.1)
>>> plt.figure() # doctest: +SKIP
>>> io.imshow(filt_real) # doctest: +SKIP
>>> io.show() # doctest: +SKIP
"""
assert_nD(image, 2)
g = gabor_kernel(frequency, theta, bandwidth, sigma_x, sigma_y, n_stds,
offset)
filtered_real = ndi.convolve(image, np.real(g), mode=mode, cval=cval)
filtered_imag = ndi.convolve(image, np.imag(g), mode=mode, cval=cval)
return filtered_real, filtered_imag
You can’t perform that action at this time.