def find_labels(x2ds, model, x2ds_indices, model_indices, threshold,
labels_out):
A, B = fit_points(x2ds[x2ds_indices], model[model_indices])
print 'cf', np.dot(x2ds[x2ds_indices], A) + B - model[model_indices]
cloud = ISCV.HashCloud2D(model, threshold)
L = np.dot(x2ds, A) + B
scores, matches, matches_splits = cloud.score(L)
sc = ISCV.min_assignment_sparse(scores, matches, matches_splits,
threshold**2, labels_out)
ms = np.sum(labels_out != -1)
return ((sc - (len(x2ds) - len(model)) * (threshold**2)) /
len(model))**0.5, ms
def getMapping(hi_geo, triangles, lo_geo, threshold=20.0):
'''given a hi-res geometry and topology, and a lo-res geometry, find the triangles and barycentric weights that
when applied to the hi-res geometry, best fit the lo-res geometry.
The mapping is returned as a list of weight triples and a list of index triples, per vertex.
The output vertex is the weighted sum of the extracted indicated source vertices.'''
is3D = (hi_geo.shape[1] == 3)
lunterp = lunterp3D if is3D else lunterp2D
numVertsHi = hi_geo.shape[0]
numVertsLo = lo_geo.shape[0]
weights = np.zeros((numVertsLo, 3), dtype=np.float32)
indices = -np.ones((numVertsLo, 3), dtype=np.int32)
import ISCV
cloud = ISCV.HashCloud3D(hi_geo, threshold) if is3D else ISCV.HashCloud2D(
hi_geo, threshold)
scores, matches, matches_splits = cloud.score(lo_geo.copy())
# the indices of the closest 3 hi verts to each lo vert
D = [
matches[m0 + np.argsort(scores[m0:m1])[:3]] if m0 != m1 else []
for m0, m1 in zip(matches_splits[:-1], matches_splits[1:])
]
# for speed-up, compute all the triangles involving each hi vertex.
T = [[] for x in xrange(numVertsHi)]
for ti, tri in enumerate(triangles):
for tj in tri:
T[tj].append(tri)
bads = []
for vi, (lo_x, nearIndices, ws,
xis) in enumerate(zip(lo_geo, D, weights, indices)):
best = -10
for ni in nearIndices:
for tri in T[ni]:
xws = lunterp(hi_geo[tri], lo_x)
sc = np.min(xws)
if sc > best: # pick the best triangle (the one that it's closest to being inside)
best = sc
xis[:] = tri
ws[:] = xws
if best >= 0: break
if best >= 0: break
# the vertex *might* not be inside any of these triangles
if best < -0.1:
bads.append(vi)
ws[:] = 0.0 # ensure there's no weight
xis[:] = -1 # and no label
if len(bads):
print 'vertices outside', len(bads)
print bads[:10], '...'
which = np.where(indices[:, 0] != -1)[0]
print len(which), 'vertices inside'
return which, weights[which], indices[which]