d.display_start()
for it in range(iter):
img_index = np.random.randint(0, sh.shape[-1])
d.solve(sh[..., [img_index]])
d.display_end()
D1 = d.getdict()
print("OnlineConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
"""
Display initial and final dictionaries.
"""
D1 = D1.squeeze()
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), title='D0', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1), title='D1', fig=fig)
fig.show()
"""
Get iterations statistics from solver object and plot functional value.
"""
its = d.getitstat()
fig = plot.figure(figsize=(7, 7))
plot.plot(np.vstack((its.DeltaD, its.Eta)).T, xlbl='Iterations',
lgnd=('Delta D', 'Eta'), fig=fig)
"""
Plot comparison of reference and recovered representations.
"""
plot.plot(np.hstack((x0, x)), title='Sparse representation',
lgnd=['Reference', 'Reconstructed'])
"""
Plot lmbda error curve, functional value, residuals, and rho
"""
its = b.getitstat()
fig = plot.figure(figsize=(15, 10))
plot.subplot(2, 2, 1)
plot.plot(fvmx, x=lrng, ptyp='semilogx', xlbl='$\lambda$',
ylbl='Error', fig=fig)
plot.subplot(2, 2, 2)
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)
plot.subplot(2, 2, 3)
plot.plot(np.vstack((its.PrimalRsdl, its.DualRsdl)).T,
ptyp='semilogy', xlbl='Iterations', ylbl='Residual',
lgnd=['Primal', 'Dual'], fig=fig)
plot.subplot(2, 2, 4)
plot.plot(its.Rho, xlbl='Iterations', ylbl='Penalty Parameter', fig=fig)
fig.show()
fig, ax = plot.subplots(nrows=1, ncols=3, figsize=(21, 7))
fig.suptitle('ConvProdDictL1L1GrdJoint Results (false colour, '
'bands 10, 20, 30)')
plot.imview(img[..., 10:40:10], title='Reference', ax=ax[0], fig=fig)
plot.imview(imgn[..., 10:40:10], title='Noisy', ax=ax[1], fig=fig)
plot.imview(imgd[..., 10:40:10], title='Denoised', ax=ax[2], fig=fig)
fig.show()
"""
Get iterations statistics from solver object and plot functional value, ADMM primary and dual residuals, and automatically adjusted ADMM penalty parameter against the iteration number.
"""
its = b.getitstat()
fig = plot.figure(figsize=(20, 5))
plot.subplot(1, 3, 1)
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)
plot.subplot(1, 3, 2)
plot.plot(np.vstack((its.PrimalRsdl, its.DualRsdl)).T,
ptyp='semilogy', xlbl='Iterations', ylbl='Residual',
lgnd=['Primal', 'Dual'], fig=fig)
plot.subplot(1, 3, 3)
plot.plot(its.Rho, xlbl='Iterations', ylbl='Penalty Parameter', fig=fig)
fig.show()
# Wait for enter on keyboard
input()
"""
Reconstruct image from sparse representation.
"""
shr = b.reconstruct().squeeze()
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % sm.psnr(img, imgr))
"""
Display low pass component and sum of absolute values of coefficient maps of highpass component.
"""
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(sl, title='Lowpass component', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(np.sum(abs(X), axis=b.cri.axisM).squeeze(), cmap=plot.cm.Blues,
title='Sparse representation', fig=fig)
fig.show()
"""
Display original and reconstructed images.
"""
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(img, title='Original', fig=fig)
print('Solve time: %.2f s' % t)
"""
Reconstruct the image from the sparse representation.
"""
imgr = np.sum(spl.fftconv(D, X), axis=2)
print("Reconstruction PSNR: %.2fdB\n" % spm.psnr(img, imgr))
"""
Display representation and reconstructed image.
"""
fig = plot.figure(figsize=(14, 14))
plot.subplot(2, 2, 1)
plot.imview(X[..., 0].squeeze(), title='Lowpass component', fig=fig)
plot.subplot(2, 2, 2)
plot.imview(np.sum(abs(X[..., 1:]), axis=2).squeeze(),
cmap=plot.cm.Blues, title='Main representation', fig=fig)
plot.subplot(2, 2, 3)
plot.imview(imgr, title='Reconstructed image', fig=fig)
plot.subplot(2, 2, 4)
plot.imview(imgr - img, fltscl=True, title='Reconstruction difference',
fig=fig)
fig.show()
# Wait for enter on keyboard
input()
"""
Display solve time and denoising performance.
"""
print("ConvBPDN solve time: %5.2f s" % b.timer.elapsed('solve'))
print("Noisy image PSNR: %5.2f dB" % sm.psnr(img, imgn))
print("Denoised image PSNR: %5.2f dB" % sm.psnr(img, imgd))
"""
Display the reference, noisy, and denoised images.
"""
fig = plot.figure(figsize=(21, 7))
plot.subplot(1, 3, 1)
plot.imview(img, title='Reference', fig=fig)
plot.subplot(1, 3, 2)
plot.imview(imgn, title='Noisy', fig=fig)
plot.subplot(1, 3, 3)
plot.imview(imgd, title='CSC Result', fig=fig)
fig.show()
"""
Plot functional evolution during ADMM iterations.
"""
its = b.getitstat()
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional')
"""
Report performances of different methods of solving the same problem.
"""
print("Corrupted image PSNR: %5.2f dB" % metric.psnr(img, imgw))
print("Serial Reconstruction PSNR: %5.2f dB" % metric.psnr(img, imgr))
print("Parallel Reconstruction PSNR: %5.2f dB\n" % metric.psnr(img, imgr_par))
"""
Display reference, test, and reconstructed images
"""
fig = plot.figure(figsize=(14, 14))
plot.subplot(2, 2, 1)
plot.imview(img, fig=fig, title='Reference Image')
plot.subplot(2, 2, 2)
plot.imview(imgw, fig=fig, title=('Corrupted Image PSNR: %5.2f dB' %
metric.psnr(img, imgw)))
plot.subplot(2, 2, 3)
plot.imview(imgr, fig=fig, title=('Serial reconstruction PSNR: %5.2f dB' %
metric.psnr(img, imgr)))
plot.subplot(2, 2, 4)
plot.imview(imgr_par, fig=fig, title=('Parallel reconstruction PSNR: %5.2f dB' %
metric.psnr(img, imgr_par)))
fig.show()
"""
"""
Plot the three functions on the same axes.
"""
plot.plot(np.stack((y1, y2, y3)).T, x, xlbl='x', ylbl='y',
title='Plot Example', lgnd=('$|x|$', '$|x|^{(3/2)}$', '$x^2$'),
lglc='upper center')
"""
We can also create a plot and then add to it. In this case we need to create the figure object separately and pass it as argument to the :func:`.plot.plot` function so that it doesn't automatically call `fig.show` after the first plot call.
"""
fig = plot.figure()
plot.plot(np.stack((y1, y2, y3)).T, x, xlbl='x', ylbl='y',
title='Plot Example', lgnd=('$|x|$', '$|x|^{(3/2)}$', '$x^2$'),
lglc='upper center', fig=fig)
plot.plot(y1[::5], x[::5], lw=0, ms=8.0, marker='o', fig=fig)
fig.show()
"""
Define `x` and `y` arrays and a 2-d surface on `x`, `y`.
"""
x = np.linspace(0, 2, 50)[np.newaxis, :]
y = np.linspace(-1, 1, 51)[:, np.newaxis]
z = np.sin(y) * np.cos(2*x*y)
print("BPDN standard FISTA backtracking solve time: %.2fs" %
b.timer.elapsed('solve'))
print("")
"""
Plot comparison of reference and recovered representations.
"""
plot.plot(np.hstack((x0, x)),
title='Sparse representation (standard FISTA)',
lgnd=['Reference', 'Reconstructed'])
"""
Plot lmbda error curve, functional value, residuals, and rho
"""
its = b.getitstat()
fig = plot.figure(figsize=(21, 7))
plot.subplot(1, 3, 1)
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)
plot.subplot(1, 3, 2)
plot.plot(its.Rsdl,
ptyp='semilogy',
xlbl='Iterations',
ylbl='Residual',
fig=fig)
plot.subplot(1, 3, 3)
plot.plot(its.L,
xlbl='Iterations',
ylbl='Inverse of Step Size (Standard FISTA)',
fig=fig)
fig.show()
"""
"""
Display solve time and denoising performance.
"""
print("TVL1Denoise solve time: %5.2f s" % b.timer.elapsed('solve'))
print("Noisy image PSNR: %5.2f dB" % metric.psnr(img, imgn))
print("Denoised image PSNR: %5.2f dB" % metric.psnr(img, imgr))
"""
Display reference, corrupted, and denoised images.
"""
fig = plot.figure(figsize=(20, 5))
plot.subplot(1, 3, 1)
plot.imview(img, title='Reference', fig=fig)
plot.subplot(1, 3, 2)
plot.imview(imgn, title='Corrupted', fig=fig)
plot.subplot(1, 3, 3)
plot.imview(imgr, title=r'Restored ($\ell_1$-TV)', fig=fig)
fig.show()
"""
Get iterations statistics from solver object and plot functional value, ADMM primary and dual residuals, and automatically adjusted ADMM penalty parameter against the iteration number.
"""
its = b.getitstat()
fig = plot.figure(figsize=(20, 5))
# Initialise and run ConvBPDN object
b = cbpdn.ConvBPDN(D, sh, lmbda, opt)
X = b.solve()
print("ConvBPDN solve time: %.2fs" % b.timer.elapsed('solve'))
# Reconstruct representation
shr = b.reconstruct().squeeze()
imgr = sl + shr
print("reconstruction PSNR: %.2fdB\n" % sm.psnr(img, imgr))
# Display representation and reconstructed image
fig1 = plot.figure(1, figsize=(14,14))
plot.subplot(2,2,1)
plot.imview(sl, fgrf=fig1, title='Lowpass component')
plot.subplot(2,2,2)
plot.imview(np.sum(abs(X), axis=b.cri.axisM).squeeze(), fgrf=fig1,
cmap=plot.cm.Blues, title='Main representation')
plot.subplot(2,2,3)
plot.imview(imgr, fgrf=fig1, title='Reconstructed image')
plot.subplot(2,2,4)
plot.imview(imgr - img, fgrf=fig1, fltscl=True,
title='Reconstruction difference')
fig1.show()
# Plot functional value, residuals, and rho
its = b.getitstat()
c = cbpdn.ConvBPDN(D, sh, lmbda, opt)
X = c.solve().squeeze()
t = c.timer.elapsed('solve')
print('Solve time: %.2f s' % t)
"""
Reconstruct the image from the sparse representation.
"""
shr = np.sum(spl.fftconv(D, X), axis=2)
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % spm.psnr(img, imgr))
"""
Display representation and reconstructed image.
"""
fig = plot.figure(figsize=(14, 14))
plot.subplot(2, 2, 1)
plot.imview(sl, fgrf=fig, title='Lowpass component')
plot.subplot(2, 2, 2)
plot.imview(np.sum(abs(X), axis=2).squeeze(),
fgrf=fig,
cmap=plot.cm.Blues,
title='Main representation')
plot.subplot(2, 2, 3)
plot.imview(imgr, fgrf=fig, title='Reconstructed image')
plot.subplot(2, 2, 4)
plot.imview(imgr - img,
fgrf=fig,
fltscl=True,
title='Reconstruction difference')
fig.show()
def test_02(self):
x = np.linspace(-1, 1, 20)
y = x**2
fig = plot.figure()
plot.plot(y, x=x, title='Plot Test', xlbl='x', ylbl='y', fgrf=fig)
plot.close()
}
})
"""
Create solver object and solve.
"""
d = parcnsdl.ConvBPDNDictLearn_Consensus(D0, sh, lmbda, opt)
D1 = d.solve()
print("ConvBPDNDictLearn_Consensus solve time: %.2fs" %
d.timer.elapsed('solve'))
"""
Display initial and final dictionaries.
"""
D1 = D1.squeeze()
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), title='D0', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1), title='D1', fig=fig)
fig.show()
"""
Get iterations statistics from solver object and plot functional value
"""
its = d.getitstat()
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional')
# Wait for enter on keyboard
input()
"""
Plot comparison of reference and recovered representations.
"""
plot.plot(np.hstack((x0, x)), title='Sparse representation',
lgnd=['Reference', 'Reconstructed'])
"""
Plot lmbda error curve, functional value, residuals, and rho
"""
its = b.getitstat()
fig = plot.figure(figsize=(15, 10))
plot.subplot(2, 2, 1)
plot.plot(fvmx, x=lrng, ptyp='semilogx', xlbl='$\lambda$',
ylbl='Error', fgrf=fig)
plot.subplot(2, 2, 2)
plot.plot(its.ObjFun, fgrf=fig, xlbl='Iterations', ylbl='Functional')
plot.subplot(2, 2, 3)
plot.plot(np.vstack((its.PrimalRsdl, its.DualRsdl)).T, fgrf=fig,
ptyp='semilogy', xlbl='Iterations', ylbl='Residual',
lgnd=['Primal', 'Dual'])
plot.subplot(2, 2, 4)
plot.plot(its.Rho, fgrf=fig, xlbl='Iterations', ylbl='Penalty Parameter')
fig.show()