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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pywt
import pywt.data
# Load image
original = pywt.data.camera()
# Wavelet transform of image, and plot approximation and details
titles = ['Approximation', ' Horizontal detail',
'Vertical detail', 'Diagonal detail']
coeffs2 = pywt.dwt2(original, 'bior1.3')
LL, (LH, HL, HH) = coeffs2
fig = plt.figure()
for i, a in enumerate([LL, LH, HL, HH]):
ax = fig.add_subplot(2, 2, i + 1)
ax.imshow(a, interpolation="nearest", cmap=plt.cm.gray)
ax.set_title(titles[i], fontsize=12)
ax.set_xticks([])
ax.set_yticks([])
fig.suptitle("dwt2 coefficients", fontsize=14)
# Now reconstruct and plot the original image
reconstructed = pywt.idwt2(coeffs2, 'bior1.3')
fig = plt.figure()
plt.imshow(reconstructed, interpolation="nearest", cmap=plt.cm.gray)
# Check that reconstructed image is close to the original
np.testing.assert_allclose(original, reconstructed, atol=1e-13, rtol=1e-13)
# Now do the same with dwtn/idwtn, to show the difference in their signatures
coeffsn = pywt.dwtn(original, 'bior1.3')
fig = plt.figure()
for i, key in enumerate(['aa', 'ad', 'da', 'dd']):
ax = fig.add_subplot(2, 2, i + 1)
ax.imshow(coeffsn[key], interpolation="nearest", cmap=plt.cm.gray)
ax.set_title(titles[i], fontsize=12)
ax.set_xticks([])
ax.set_yticks([])
fig.suptitle("dwtn coefficients", fontsize=14)
# Now reconstruct and plot the original image
reconstructed = pywt.idwtn(coeffsn, 'bior1.3')
fig = plt.figure()
plt.imshow(reconstructed, interpolation="nearest", cmap=plt.cm.gray)
# Check that reconstructed image is close to the original
np.testing.assert_allclose(original, reconstructed, atol=1e-13, rtol=1e-13)
plt.show()
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