BASIS_OVERSAMPLING)
# Get sparsity.
sparsity = sketch.computeSparsity(block_coefficients)
print "Sparsity: " + str(sparsity)
# Compute reconstruction for each block.
print "Reconstructing..."
reconstructed_blocks = []
for i, coefficients in enumerate(block_coefficients):
print "Progress: %d / %d" % (i, len(block_coefficients))
reconstructed_blocks.append(bf.rescale((basis *
coefficients).reshape((BLOCK_SIZE,
BLOCK_SIZE))))
# Reassemble.
reconstruction = sketch.assembleBlocks(reconstructed_blocks, img.shape)
visualization = sketch.visualizeBlockwiseSparsity(reconstructed_blocks,
sparsity,
img.shape)
# print estimate of sparsity
#print np.median(np.asarray(coefficients.T))
# Plot.
#max_value = np.absolute(coefficients).max()
plt.figure(1)
plt.subplot(121)
plt.imshow(reconstruction, cmap="gray")
plt.colorbar()
#plt.title("Reconstruction using DCT basis \n %.2f%% sparsity" %
# (100.0-((np.absolute(coefficients) > 0.01*max_value).sum()*100.0/(SIZE[0]*SIZE[1]))))
blocks, RHO, ALPHA, BASIS_OVERSAMPLING)
# Get sparsity.
sparsity = sketch.computeSparsity(block_coefficients)
print "Sparsity: " + str(sparsity)
# Compute reconstruction for each block.
print "Reconstructing..."
reconstructed_blocks = []
for i, coefficients in enumerate(block_coefficients):
print "Progress: %d / %d" % (i, len(block_coefficients))
reconstructed_blocks.append(
bf.rescale((basis * coefficients).reshape(
(BLOCK_SIZE, BLOCK_SIZE))))
# Reassemble.
reconstruction = sketch.assembleBlocks(reconstructed_blocks, img.shape)
visualization = sketch.visualizeBlockwiseSparsity(reconstructed_blocks,
sparsity, img.shape)
# print estimate of sparsity
#print np.median(np.asarray(coefficients.T))
# Plot.
#max_value = np.absolute(coefficients).max()
plt.figure(1)
plt.subplot(121)
plt.imshow(reconstruction, cmap="gray")
plt.colorbar()
#plt.title("Reconstruction using DCT basis \n %.2f%% sparsity" %
# (100.0-((np.absolute(coefficients) > 0.01*max_value).sum()*100.0/(SIZE[0]*SIZE[1]))))
