def test_vector_to_array(self):
""" Test the vector to array coordinate system.
"""
for pos, val in zip(self.positions, self.values):
self.array[pos] = val
vector_index = array_to_vector_index(pos, self.array)
self.assertEqual(self.array.flatten()[vector_index], val)
array_index = vector_to_array_index(vector_index, self.array)
self.assertEqual(tuple(array_index), pos)
def denoise(self):
""" Method that computes the denoised image using NLM algorithm.
Returns
-------
denoise_array: array
the denoised image.
"""
# Information message
logger.info("Compute the denoised image using NLM algorithm.")
# Allocate the denoised output image and the weight image
denoise_array = numpy.zeros(self.shape, dtype=numpy.single)
weights = numpy.zeros(self.shape, dtype=numpy.single)
# Go through all the voxels and compute the denoise image
logger.info("Running denoising...")
for vector_index in range(self.to_denoise_array.size):
# Convert vector index to array index
index = vector_to_array_index(vector_index, self.to_denoise_array)
# Falst blockwise non overlap speed up
if (self.blockwise_strategy != "fastblockwise" or
(numpy.mod(index, self.half_patch_size + 1) == 0).all()):
# Check if have to compute this voxel
if self.mask_array[tuple(index)] > 0:
# Compute the denoised patch
patch, weight = self._get_denoised_patch(index)
# Apply the denoised patch
self._apply_patch(index, patch, denoise_array, weights,
weight=1.)
# Apply the weightings to the denoised image
weights[numpy.where(weights == 0)] = 1.
denoise_array /= weights
return denoise_array
def test_speed(self):
""" Test the patch creation speed.
"""
for vector_index in range(self.array.size):
index = vector_to_array_index(vector_index, self.array)
patch = c_get_patch(index, self.array, self.full_patch_size)
def test_speed(self):
""" Test the patch creation speed.
"""
for vector_index in range(self.array.size):
index = vector_to_array_index(vector_index, self.array)
patch = c_get_patch(index, self.array, self.full_patch_size)
