pywt

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