2D-DWT.py

import matplotlib.pyplot as plt
import numpy as np

origin_image = [
  [70, 70,  100, 70, 87, 87, 150, 187],
  [85, 100,  96, 79, 87, 154, 87, 113],
  [100, 85, 116, 79, 70, 87, 86, 196],
  [136, 69, 87, 200, 103, 71, 96, 113],
  [161, 70, 87, 200, 113, 113, 85, 161],
  [161, 123, 147, 133, 113, 113, 85, 161],
  [146, 147, 175, 100, 103, 103, 163, 187],
  [156, 146, 189, 70, 113, 161, 163, 197],
]

origin_image = np.array(origin_image, dtype=np.float64)

plt.imshow(origin_image, cmap='gray')
plt.show()

rows = 8
cols = 8
level1_row_haar_transform = np.zeros((rows, cols))

for i in xrange(rows):
  for j in xrange(0, cols, 2):
    average = (origin_image[i][j] + origin_image[i][j+1]) / 2.0
    diff = (origin_image[i][j] - origin_image[i][j+1]) / 2.0
    level1_row_haar_transform[i][j/2] = average
    level1_row_haar_transform[i][j/2 + 4] = diff

# print level1_row_haar_transform
# plt.imshow(level1_row_haar_transform, cmap='gray')
# plt.show()

level1_cols_haar_transform = np.zeros((rows, cols))
for j in xrange(cols):
  for i in xrange(0, rows, 2):
    average = (level1_row_haar_transform[i][j] + level1_row_haar_transform[i+1][j]) / 2.0
    diff = (level1_row_haar_transform[i][j] - level1_row_haar_transform[i+1][j]) / 2.0
    level1_cols_haar_transform[i/2][j] = average
    level1_cols_haar_transform[i/2 +4][j] = diff

print level1_cols_haar_transform

# plt.imshow(level1_cols_haar_transform, cmap='gray')
# plt.show()

rows = 4
cols = 4
level2_rows_t = np.zeros((rows, cols))

for i in xrange(rows):
  for j in xrange(0, cols, 2):
    avg = (level1_cols_haar_transform[i][j] + level1_cols_haar_transform[i][j+1]) / 2.0
    diff = (level1_cols_haar_transform[i][j] - level1_cols_haar_transform[i][j+1]) / 2.0
    level2_rows_t[i][j/2] = avg
    level2_rows_t[i][j/2 + 2] = diff

print level2_rows_t


level2_cols_t = np.zeros((rows, cols))
for j in xrange(cols):
  for i in xrange(0, rows, 2):
    avg = (level2_rows_t[i][j] + level2_rows_t[i+1][j]) / 2.0
    diff = (level2_rows_t[i][j] - level2_rows_t[i+1][j]) / 2.0
    level2_cols_t[i/2][j] = avg
    level2_cols_t[i/2 +2][j] = diff

print level2_cols_t

result = np.copy(level1_cols_haar_transform)
for i in xrange(4):
  for j in xrange(4):
    result[i][j] = level2_cols_t[i][j]

print result

plt.imshow(result, cmap='gray')
plt.show()