# 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((basis * coefficients).reshape(
(blocks[0].shape[0], blocks[0].shape[1])))
# Reassemble.
reconstruction = sketch.assembleBlocks_withOverlap(reconstructed_blocks,
BLOCK_SIZE, img.shape,
OVERLAP_PERCENT)
# Note this may not work because the reconstructed_blocks are bigger than expected
# because of the overlap
# 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)
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((basis * coefficients).reshape((blocks[0].shape[0],
blocks[0].shape[1])))
# Reassemble.
reconstruction = sketch.assembleBlocks_withOverlap(reconstructed_blocks,
BLOCK_SIZE, img.shape, OVERLAP_PERCENT)
# Note this may not work because the reconstructed_blocks are bigger than expected
# because of the overlap
# 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)
