using svd to “compress” an image, using opencv and numpy

svd.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# do a lossy compression to an image using Single-value Decomposition (SVD)
# using numpy and opencv (and python)

import numpy as np
import cv2
from sys import argv

# give an image as an argument to this script,
# or put an image named img.jpg in the same folder as the script.

image_file = argv[1] if len(argv) > 1 else 'img.jpg'

# hotkeys:
#    - Esc or q: quit
#    - Space: toggle original/compressed
#    - j: reduce the number of single values used
#    - k: increase the number of single values used

img = cv2.imread(image_file, 0)  # 0 means grayscale

u, s, vh = np.linalg.svd(img)
ns = s.shape[0]  # number of single values

cv2.namedWindow('svd')
cv2.createTrackbar('Original or Truncated', 'svd', 1, 1, lambda *args: None)
cv2.createTrackbar('New Rank', 'svd', 1, ns, lambda *args: None)

while True:
    n = cv2.getTrackbarPos('New Rank', 'svd')
    newimg = np.clip(
                np.dot(u[:, :n] * s[:n], vh[:n, :]),
                0, 255
            ).astype('uint8')
    want_truncated = cv2.getTrackbarPos('Original or Truncated', 'svd')
    if want_truncated:
        cv2.imshow('svd', newimg)
    else:
        cv2.imshow('svd', img)

    key = cv2.waitKey(1) & 0xff
    if key in (27, ord('q')):  # ESC or q
        break
    elif key == ord('j'):
        cv2.setTrackbarPos('New Rank', 'svd', n-1)
    elif key == ord('k'):
        cv2.setTrackbarPos('New Rank', 'svd', n+1)
    elif key == ord(' '):
        cv2.setTrackbarPos('Original or Truncated', 'svd', 1-want_truncated)

cv2.destroyAllWindows()