def __init__(self, source, target):
super(CachedPWATransform, self).__init__(source, target)
# make sure the source and target satisfy the c requirements
source_c = np.require(self.source.points, dtype=np.float64,
requirements=['C'])
trilist_c = np.require(self.trilist, dtype=np.uint32,
requirements=['C'])
# build the cython wrapped C object and store it locally
self._fastpwa = CLookupPWA(source_c, trilist_c)
self.ti, self.tij, self.tik = None, None, None
self._rebuild_target_vectors()
class CachedPWATransform(AbstractPWATransform):
r"""
A piecewise affine transformation.
The apply method in this case involves dotting the triangle vectors with
the values of alpha and beta found. The calculation of alpha and beta is
done in C, and a hash map is used to cache lookup values.
Parameters
----------
source : :class:`menpo.shape.PointCloud` or :class:`menpo.shape.TriMesh`
The source points. If a TriMesh is provided, the triangulation on
the TriMesh is used. If a :class:`menpo.shape.PointCloud`
is provided, a Delaunay triangulation of the source is performed
automatically.
target : :class:`PointCloud`
The target points. Note that the trilist is entirely decided by
the source.
Raises
------
ValueError
Source and target must both be 2D.
TriangleContainmentError
All points to apply must be contained in a source triangle. Check
``error.points_outside_source_domain`` to handle this case.
"""
def __init__(self, source, target):
super(CachedPWATransform, self).__init__(source, target)
# make sure the source and target satisfy the c requirements
source_c = np.require(self.source.points, dtype=np.float64,
requirements=['C'])
trilist_c = np.require(self.trilist, dtype=np.uint32,
requirements=['C'])
# build the cython wrapped C object and store it locally
self._fastpwa = CLookupPWA(source_c, trilist_c)
self.ti, self.tij, self.tik = None, None, None
self._rebuild_target_vectors()
def _rebuild_target_vectors(self):
r"""
Rebuild the vectors that are used in the apply method. This needs to
be called whenever the target is changed.
"""
t = self.target.points[self.trilist]
# get vectors ij ik for the target
self.tij, self.tik = t[:, 1] - t[:, 0], t[:, 2] - t[:, 0]
# target i'th vertex positions
self.ti = t[:, 0]
def index_alpha_beta(self, points):
points_c = np.require(points, dtype=np.float64, requirements=['C'])
index, alpha, beta = self._fastpwa.index_alpha_beta(points_c)
if np.any(index < 0):
raise TriangleContainmentError(index < 0)
else:
return index, alpha, beta
def _apply(self, x, **kwargs):
"""
Applies this transform to a new set of vectors.
Parameters
----------
x : (K, 2) ndarray
Points to apply this transform to.
Returns
-------
transformed : (K, 2) ndarray
The transformed array.
"""
tri_index, alpha, beta = self.index_alpha_beta(x)
return (self.ti[tri_index] +
alpha[:, None] * self.tij[tri_index] +
beta[:, None] * self.tik[tri_index])
def _target_setter(self, new_target):
r"""
CachedPWATransform is particularly efficient to update
from target - we don't have to do much at all, just rebuild the target
vectors.
"""
self._target = new_target
self._rebuild_target_vectors()