def __init__(self, source, target, calc_scale=True):
object.__init__(self)
self.source = numpy.asarray(source)
self.target = numpy.asarray(target)
self._calc_scale = calc_scale
self.translation = numpy.zeros(3)
self.scale = 1.0
self.quaternion = Quaternion()
self.matrix_R = self.quaternion.as_matrix3x3()
assert(self.source.shape == self.target.shape)
assert(self.source.shape[1] == 3)
assert(self.source.shape[0] > 0)
self._num_points = self.source.shape[0]
self._compute_affine_transformation()
self.rotation = self.matrix_R
class AffineTransform(object):
'''Compute the affine transform between the source and target point
sets.
Properties:
translation
scale
rotation
'''
def __init__(self, source, target, calc_scale=True):
object.__init__(self)
self.source = numpy.asarray(source)
self.target = numpy.asarray(target)
self._calc_scale = calc_scale
self.translation = numpy.zeros(3)
self.scale = 1.0
self.quaternion = Quaternion()
self.matrix_R = self.quaternion.as_matrix3x3()
assert(self.source.shape == self.target.shape)
assert(self.source.shape[1] == 3)
assert(self.source.shape[0] > 0)
self._num_points = self.source.shape[0]
self._compute_affine_transformation()
self.rotation = self.matrix_R
def _compute_affine_transformation(self):
"""Compute the optimal orientation (scale, rotation, translation)
between two point sets"""
# compute the centriods
self.source_centriod = numpy.sum(self.source, 0) / self._num_points
self.target_centriod = numpy.sum(self.target, 0) / self._num_points
# substract each coordinate by centriod
self.source -= self.source_centriod
self.target -= self.target_centriod
#-----------------------------
# compute the optimal rotation
#-----------------------------
# compute the cross-covariance matrix
cov_matrix = numpy.zeros((3,3))
for source_p, target_p in zip(self.source, self.target):
cov_matrix += source_p[:, numpy.newaxis] * target_p
cov_matrix /= self._num_points
matrix_N = self._compute_matrix_N(cov_matrix)
# compute the eigenvalues of the 4x4 matrix N
eigenvalues, eigen_vectors = numpy.linalg.eig(matrix_N)
# sort eigenvalues: largest -> smallest
sorted_indices = numpy.argsort(eigenvalues)[::-1]
assert(eigenvalues[sorted_indices[0]] > 0)
# the corresponding eigen vector is selected as the optimal quaternion
# refer Horn1987
quaternion = eigen_vectors[:, sorted_indices[0]]
print 'the optimal quaternion:', quaternion
self.quaternion = Quaternion(*quaternion)
#--------------------------
# compute the optimal scale
#--------------------------
if self._calc_scale:
source_d = 0
for source_p in self.source:
source_d += numpy.dot(source_p, source_p)
target_d = 0
for target_p in self.target:
target_d += numpy.dot(target_p, target_p)
self.scale = numpy.sqrt(target_d / source_d)
print "the optimal scale:", self.scale
#--------------------------------
# compute the optimal translation
#--------------------------------
self.matrix_R = self.quaternion.as_matrix3x3()
self.translation = self.target_centriod - \
self.scale * numpy.dot(self.matrix_R, self.source_centriod)
print 'the optimal translation:', self.translation
def _compute_matrix_N(self, cov_matrix):
"""Compute the matrix N (refer to Horn1987, Besl1992)"""
assert(cov_matrix.shape == (3,3))
trace = numpy.trace(cov_matrix)
cov_matrix_t = numpy.transpose(cov_matrix)
matrix_I = numpy.diag(numpy.ones(3))
# anti-symmetric matrix A (Besl1992)
matrix_A = cov_matrix - cov_matrix_t
delta = numpy.array([matrix_A[1,2], matrix_A[2,0], matrix_A[0,1]])
# submatrix of the matrix N
sub_matrix = cov_matrix + cov_matrix_t - trace * matrix_I
# refer to Besl1992
matrix_N = numpy.zeros((4,4))
matrix_N[0, 0] = trace
matrix_N[0, 1:] = delta
matrix_N[1:, 0] = delta
matrix_N[1:, 1:] = sub_matrix
return matrix_N
def transform(self, point):
return self.scale * numpy.dot(self.matrix_R, numpy.asarray(point)) + \
self.translation
def _compute_affine_transformation(self):
"""Compute the optimal orientation (scale, rotation, translation)
between two point sets"""
# compute the centriods
self.source_centriod = numpy.sum(self.source, 0) / self._num_points
self.target_centriod = numpy.sum(self.target, 0) / self._num_points
# substract each coordinate by centriod
self.source -= self.source_centriod
self.target -= self.target_centriod
#-----------------------------
# compute the optimal rotation
#-----------------------------
# compute the cross-covariance matrix
cov_matrix = numpy.zeros((3,3))
for source_p, target_p in zip(self.source, self.target):
cov_matrix += source_p[:, numpy.newaxis] * target_p
cov_matrix /= self._num_points
matrix_N = self._compute_matrix_N(cov_matrix)
# compute the eigenvalues of the 4x4 matrix N
eigenvalues, eigen_vectors = numpy.linalg.eig(matrix_N)
# sort eigenvalues: largest -> smallest
sorted_indices = numpy.argsort(eigenvalues)[::-1]
assert(eigenvalues[sorted_indices[0]] > 0)
# the corresponding eigen vector is selected as the optimal quaternion
# refer Horn1987
quaternion = eigen_vectors[:, sorted_indices[0]]
print 'the optimal quaternion:', quaternion
self.quaternion = Quaternion(*quaternion)
#--------------------------
# compute the optimal scale
#--------------------------
if self._calc_scale:
source_d = 0
for source_p in self.source:
source_d += numpy.dot(source_p, source_p)
target_d = 0
for target_p in self.target:
target_d += numpy.dot(target_p, target_p)
self.scale = numpy.sqrt(target_d / source_d)
print "the optimal scale:", self.scale
#--------------------------------
# compute the optimal translation
#--------------------------------
self.matrix_R = self.quaternion.as_matrix3x3()
self.translation = self.target_centriod - \
self.scale * numpy.dot(self.matrix_R, self.source_centriod)
print 'the optimal translation:', self.translation
