Add gabor filter function

skimage/filter/__init__.py

@@ -6,4 +6,5 @@ from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
 from .denoise import tv_denoise, denoise_tv
 from ._denoise import denoise_bilateral
 from ._rank_order import rank_order
+from ._gabor import gabor_kernel, gabor_filter
 from .thresholding import threshold_otsu, threshold_adaptive

skimage/filter/_gabor.py

@@ -0,0 +1,94 @@
+import numpy as np
+from scipy import ndimage
+
+
+def gabor_kernel(sigmax, sigmay, frequency, theta, offset=0):
+    """Build complex 2D Gabor filter kernel.
+
+    Frequency and orientation representations of the Gabor filter are similar to
+    those of the human visual system. It is especially suitable for texture
+    classification using Gabor filter banks.
+
+    Parameters
+    ----------
+    sigmax : float
+        Standard deviation in x-direction.
+    sigmay : float
+        Standard deviation in y-direction.
+    frequency : float
+        Frequency of the harmonic function.
+    theta : float
+        Orientation in radians.
+    offset : float, optional
+        Phase offset of harmonic function in radians.
+
+    Returns
+    -------
+    g : complex array
+        Complex filter kernel.
+
+    References
+    ----------
+    .. [1] http://en.wikipedia.org/wiki/Gabor_filter
+    .. [2] http://mplab.ucsd.edu/tutorials/gabor.pdf
+
+    """
+
+    x0 = np.ceil(max(3 * sigmax, 1))
+    y0 = np.ceil(max(3 * sigmay, 1))
+    y, x = np.mgrid[-x0:x0+1, -y0:y0+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 / sigmax**2 + roty**2 / sigmay**2))
+    g /= 2 * np.pi * sigmax * sigmay
+    g *= np.exp(1j * (2 * np.pi * frequency * rotx + offset))
+
+    return g
+
+
+def gabor_filter(image, sigmax, sigmay, frequency, theta, offset=0,
+                 mode='reflect', cval=0):
+    """Perform Gabor filtering.
+
+    The real and imaginary parts of the Gabor filter kernel are applied to the
+    image.
+
+    Frequency and orientation representations of the Gabor filter are similar to
+    those of the human visual system. It is especially suitable for texture
+    classification using Gabor filter banks.
+
+    Parameters
+    ----------
+    sigmax : float
+        Standard deviation in x-direction.
+    sigmay : float
+        Standard deviation in y-direction.
+    frequency : float
+        Frequency of the harmonic function.
+    theta : float
+        Orientation in radians.
+    offset : float, optional
+        Phase offset of harmonic function in radians.
+
+    Returns
+    -------
+    real, imag : complex arrays
+        Filtered images using the real and imaginary parts of the Gabor filter
+        kernel.
+
+    References
+    ----------
+    .. [1] http://en.wikipedia.org/wiki/Gabor_filter
+    .. [2] http://mplab.ucsd.edu/tutorials/gabor.pdf
+
+    """
+
+    g = gabor_kernel(sigmax, sigmay, frequency, theta, offset=0)
+
+    filtered_real = ndimage.convolve(image, np.real(g), mode=mode, cval=cval)
+    filtered_imag = ndimage.convolve(image, np.imag(g), mode=mode, cval=cval)
+
+    return filtered_real, filtered_imag