Python Image Processing Cookbook

How to do it...

The following are the steps to implement texture classification using the Gabor filter with scikit-learn:

1. First, prepare the Gabor filter bank kernels with the following code snippet:

kernels = []for theta in range(4):    theta = theta / 4. * np.pi    for sigma in (1, 3):        for frequency in (0.05, 0.25):            kernel = np.real(gabor_kernel(frequency, \                        theta=theta, sigma_x=sigma, sigma_y=sigma))            kernels.append(kernel)

2. Define the following function to convolve an input image with the Gabor kernel (with the real and imaginary parts):

def power(image, kernel):    # Normalize images for better comparison.    image = (image - image.mean()) / image.std()    return np.sqrt(ndi.convolve(image, np.real(kernel), \ mode='wrap')**2 + ndi.convolve(image, ...