gabor_filter
-
skimage.filters.gabor_filter(*args, **kwargs)
[source] -
Deprecated function. Use
skimage.filters.gabor
instead.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
andsigma_y
will decrease with increasing frequency. This value is ignored ifsigma_x
andsigma_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 thatsigma_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 tondi.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
[R188] http://en.wikipedia.org/wiki/Gabor_filter [R189] http://mplab.ucsd.edu/tutorials/gabor.pdf Examples
>>> from skimage.filters import gabor >>> from skimage import data, io >>> from matplotlib import pyplot as plt
>>> image = data.coins() >>> # detecting edges in a coin image >>> filt_real, filt_imag = gabor(image, frequency=0.6) >>> plt.figure() >>> io.imshow(filt_real) >>> io.show()
>>> # less sensitivity to finer details with the lower frequency kernel >>> filt_real, filt_imag = gabor(image, frequency=0.1) >>> plt.figure() >>> io.imshow(filt_real) >>> io.show()
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