OpenCV 3 Computer Vision with Python Cookbook

How to do it...

You need to complete the following steps:

1. Import the modules:

import cv2import numpy as np

2. Generate a random matrix:

A = np.random.randn(10, 10)

3. Compute the SVD:

w, u, v_t = cv2.SVDecomp(A)

4. Compute the rank-constrained matrix approximation:

RANK = 5w[RANK:,0] = 0B = u @ np.diag(w[:,0]) @ v_t

5. Check the results:

print('Rank before:', np.linalg.matrix_rank(A))print('Rank after:', np.linalg.matrix_rank(B))print('Norm before:', cv2.norm(A))print('Norm after:', cv2.norm(B))