OpenCV 3 Computer Vision with Python Cookbook

How to do it...

Perform the following steps:

1. Import the packages:

import mathimport cv2import numpy as npimport matplotlib.pyplot as plt

2. Read the test image as grayscale and convert it to np.float32:

image = cv2.imread('../data/Lena.png', 0).astype(np.float32) / 255

3. Construct the real-valued Gabor filter kernel. Normalize the kernel in such a way that it has an L2 unit norm:

kernel = cv2.getGaborKernel((21, 21), 5, 1, 10, 1, 0, cv2.CV_32F)kernel /= math.sqrt((kernel * kernel).sum())

4. Filter the image:

filtered = cv2.filter2D(image, -1, kernel)

5. Visualize the results:

plt.figure(figsize=(8,3))plt.subplot(131)plt.axis('off')plt.title('image')plt.imshow(image, cmap='gray')plt.subplot(132)plt.title('kernel')plt.imshow(kernel, cmap='gray') ...