OpenCV 3 Computer Vision with Python Cookbook

How to do it

You need to complete the following steps:

1. Import the necessary modules:

import mathimport cv2import numpy as np

2. Load the test images:

img = cv2.imread('../data/Lena.png')

3. Prepare the per-pixel transformation maps:

xmap = np.zeros((img.shape[1], img.shape[0]), np.float32)ymap = np.zeros((img.shape[1], img.shape[0]), np.float32)for y in range(img.shape[0]):    for x in range(img.shape[1]):        xmap[y,x] = x + 30 * math.cos(20 * x / img.shape[0])        ymap[y,x] = y + 30 * math.sin(20 * y / img.shape[1])

4. Remap the source image:

remapped_img = cv2.remap(img, xmap, ymap, cv2.INTER_LINEAR, None, cv2.BORDER_REPLICATE)

5. Visualize the results:

plt.figure(0)plt.axis('off')plt.imshow(remapped_img[:,:,[2,1,0]])plt.show()