def main_fit_single_projections():
# use '0b' for initial geometry
V = load_projection('0b')[0]
T = load_triangles()
lambdas = np.array(
[
1.0, # as-rigid-as-possible
1.0
], # projection
dtype=np.float64)
for index, user_constraints in INPUT_SELECTION:
print 'index:', index
_, C, P = load_projection(user_constraints)
V1 = V.copy()
X = np.zeros_like(V)
status, status_string = solve_single_arap_proj(V, T, X, V1, C, P,
lambdas)
# visualise ?
if False:
vis = visualise.VisualiseMesh(V1, T)
vis.execute()
np.savez_compressed(os.path.join(
OUTPUT_ROOT,
'chihuahua_single_projection_%s.npz' % user_constraints),
V1=V1,
X=X,
lambdas=lambdas)
def main_test_shortest_path():
_, T, C, P = load_model_3()
# requires output from `main_test_problem` under run_test_problem.py
z = np.load('MAIN_TEST_PROBLEM.npz')
V = z['V1'].copy()
lambdas = np.array([1e-3, 1e1, 1e3], dtype=np.float64)
silhouette = load_silhouette_information()
path = test_shortest_path(V,
T,
isCircular=True,
lambdas=lambdas,
**silhouette)
# convert ALL candidates to 3D positions
SilCandAssignedFaces = silhouette['SilCandAssignedFaces']
SilCandU = silhouette['SilCandU']
S = silhouette['S']
SN = silhouette['SN']
Q = np.empty((SilCandAssignedFaces.shape[0], 3), dtype=np.float64)
for i, face_index in enumerate(SilCandAssignedFaces):
Q[i] = bary2pos(V[T[face_index]], make_bary(SilCandU[i]))
# get assigned faces
L = SilCandAssignedFaces[path]
U = SilCandU[path]
np.savez_compressed('MAIN_TEST_SHORTEST_PATH.npz', L=L, U=U, S=S, SN=SN)
vis = visualise.VisualiseMesh(V, T, L)
vis.add_image('Frames/0.png')
vis.add_silhouette(Q, path, [0, S.shape[0] - 1], S)
vis.execute()
def main_test_problem2():
_, T, C, P = load_model_3()
z = np.load('MAIN_TEST_PROBLEM.npz')
V = z['V1'].copy()
lambdas = np.array([1e+0, 1e+2], dtype=np.float64)
status = test_problem2(V, T, C, P, lambdas,
gradientThreshold=1e-6,
maxIterations=5)
vis = visualise.VisualiseMesh(V, T)
vis.add_projection(C, P)
vis.add_image('Frames/0.png')
vis.execute()
def main_test_problem():
V, T, C, P = load_model_3()
V1 = V.copy()
X = np.zeros_like(V)
lambdas = np.array([1e+0, 1e+2], dtype=np.float64)
status = test_problem(V, T, X, V1, C, P, lambdas,
gradientThreshold=1e-6,
maxIterations=200)
print 'Status:', status
vis = visualise.VisualiseMesh(V1, T)
vis.add_projection(C, P)
vis.add_image('Frames/0.png')
vis.execute()
np.savez_compressed('MAIN_TEST_PROBLEM.npz', V1=V1)
def main_test_problem3():
_, T, C, P = load_model_3()
z = np.load('MAIN_TEST_PROBLEM.npz')
V = z['V1'].copy()
z = np.load('MAIN_TEST_SHORTEST_PATH.npz')
S = z['S']
SN = z['SN']
U = z['U']
L = z['L']
lambdas = np.array([1e1, 1e1, 1e3], dtype=np.float64)
preconditioners = np.array([1.0, 200.0], dtype=np.float64)
narrow_band = 2
kwargs = dict(gradientThreshold=1e-6, maxIterations=30, verbosenessLevel=1)
# solve
t1 = clock()
status, status_string = test_problem3(V, T, U, L, S, SN, lambdas,
preconditioners, narrow_band, **kwargs)
# restart if out of the narrow band
while status == 4:
status, status_string = test_problem3(V, T, U, L, S, SN, lambdas,
preconditioners, narrow_band, **kwargs)
t2 = clock()
print 'Time taken: %.3fs' % (t2 - t1)
Q = np.empty((U.shape[0], 3), dtype=np.float64)
for i, face_index in enumerate(L):
Q[i] = bary2pos(V[T[face_index]], make_bary(U[i]))
print 'Status (%d):' % status, status_string
vis = visualise.VisualiseMesh(V, T, L)
vis.add_image('Frames/0.png')
vis.add_silhouette(Q, np.arange(Q.shape[0]), [0, S.shape[0] - 1], S)
vis.execute()
def main_fit_joint_lap_silhouette():
# use '0b' for initial core geometry
V = load_projection('0b')[0]
T = load_triangles()
# information for silhouette specific only to the model (approx)
silhouette_info = load_silhouette_info()
# weighting lambdas
global_solve_lambdas = np.array(
[
1e-3, # geodesic between preimage
1e0, # silhouette projection
1e3
], # silhouette normal
dtype=np.float64)
lm_lambdas = np.asarray(
np.r_[1.0, # as-rigid-as-possible
global_solve_lambdas[1:], 1.0, # spillage
1e1], # laplacian
dtype=np.float64)
# preconditioning for the joint minimisation
lm_preconditioners = np.array([1.0, 1.0, 100.0], dtype=np.float64)
# other solver options
solver_options = dict(narrowBand=2,
uniformWeights=True,
maxIterations=20,
gradientThreshold=1e-5,
updateThreshold=1e-5,
improvementThreshold=1e-5,
verbosenessLevel=1)
# construct lists for minimisation
(multiX, multiV, multiU, multiL, multiS, multiSN, multiRx,
multiRy) = [list() for i in range(8)]
for index, user_constraints in INPUT_SELECTION:
print 'index:', index
# load geometry from initial projection
z = np.load(
os.path.join(
OUTPUT_ROOT,
'chihuahua_single_projection_%s.npz' % user_constraints))
X = z['X']
V1 = z['V1']
# get the silhouette information for the frame
S, SN = load_silhouette(index)
# get the spillage information for the frame
Rx, Ry = load_spillage(index)
# solve for the initial silhouette positions
U, L = shortest_path_solve(V1,
T,
S,
SN,
lambdas=global_solve_lambdas,
isCircular=False,
**silhouette_info)
multiX.append(X)
multiV.append(V1)
multiU.append(U)
multiL.append(L)
multiS.append(S)
multiSN.append(SN)
multiRx.append(Rx)
multiRy.append(Ry)
# solve_iteration
def solve_iteration():
status = solve_multiview_lap_silhouette(T, V, multiX, multiV, multiU,
multiL, multiS, multiSN,
multiRx, multiRy, lm_lambdas,
lm_preconditioners,
**solver_options)
print 'LM Status (%d): ' % status[0], status[1]
return status
# solve
t1 = clock()
count = 0
status = solve_iteration()
while status[0] in (0, 4):
status = solve_iteration()
count += 1
t2 = clock()
print 'Time taken: %.3fs' % (t2 - t1)
np.savez_compressed(os.path.join(OUTPUT_ROOT,
'chihuahua_lap_silhouette.npz'),
T=T,
V=V,
multiV=multiV,
multiS=multiS,
multiSN=multiSN,
lm_lambdas=lm_lambdas,
lm_preconditioners=lm_preconditioners,
solver_options=solver_options)
# visualise ?
if True:
vis = visualise.VisualiseMesh(V, T)
vis.execute()
for i in xrange(len(multiV)):
V1 = multiV[i]
U = multiU[i]
L = multiL[i]
Q = geometry.path2pos(V1, T, L, U)
N = Q.shape[0]
vis = visualise.VisualiseMesh(V1, T, L)
vis.add_silhouette(Q, np.arange(N), [0, N - 1], multiS[i])
vis.add_image(get_frame_path(INPUT_SELECTION[i][0]))
vis.execute()
def main_fit_joint_arap_silhouette():
# use '0b' for initial core geometry
V = load_projection('0b')[0]
T = load_triangles()
# information for silhouette specific only to the model (approx)
silhouette_info = load_silhouette_info()
global_solve_lambdas = np.array(
[
1e-3, # geodesic between preimage
1e0, # silhouette projection
1e3
], # silhouette normal
dtype=np.float64)
lm_lambdas = np.asarray(
np.r_[1.0, # as-rigid-as-possible
global_solve_lambdas[1:]],
dtype=np.float64)
lm_preconditioners = np.array([1.0, 1.0, 100.0], dtype=np.float64)
# construct lists for minimisation
multiX, multiV, multiU, multiL, multiS, multiSN = [
list() for i in range(6)
]
for index, user_constraints in INPUT_SELECTION:
print 'index:', index
# load geometry from initial projection
z = np.load(
os.path.join(
OUTPUT_ROOT,
'chihuahua_single_projection_%s.npz' % user_constraints))
X = z['X']
V1 = z['V1']
# get the silhouette information for the frame
S, SN = load_silhouette(index)
# solve for the initial silhouette positions
U, L = shortest_path_solve(V1,
T,
S,
SN,
lambdas=global_solve_lambdas,
isCircular=False,
**silhouette_info)
multiX.append(X)
multiV.append(V1)
multiU.append(U)
multiL.append(L)
multiS.append(S)
multiSN.append(SN)
# solve_iteration
def solve_iteration():
status = solve_multiview_arap_silhouette(T,
V,
multiX,
multiV,
multiU,
multiL,
multiS,
multiSN,
lm_lambdas,
lm_preconditioners,
narrowBand=2,
maxIterations=50,
gradientThreshold=1e-6,
updateThreshold=1e-6,
improvementThreshold=1e-6)
print 'LM Status (%d): ' % status[0], status[1]
return status
solve_iteration()
# visualise ?
if True:
vis = visualise.VisualiseMesh(V, T)
vis.execute()
for i in xrange(len(multiV)):
V1 = multiV[i]
U = multiU[i]
L = multiL[i]
Q = geometry.path2pos(V1, T, L, U)
N = Q.shape[0]
vis = visualise.VisualiseMesh(V1, T, L)
vis.add_silhouette(Q, np.arange(N), [0, N - 1], multiS[i])
vis.add_image(get_frame_path(INPUT_SELECTION[i][0]))
vis.execute()
def main_fit_single_silhouette():
# use '0b' for initial core geometry
V = load_projection('0b')[0]
T = load_triangles()
# information for silhouette specific only to the model (approx)
silhouette_info = load_silhouette_info()
global_solve_lambdas = np.array(
[
1e-3, # geodesic between preimage
1e0, # silhouette projection
1e3
], # silhouette normal
dtype=np.float64)
lm_lambdas = np.r_[2.0, # laplacian regularisation
global_solve_lambdas[1:], # silhouette
1.0] # spillage
lm_lambdas = np.asarray(lm_lambdas, dtype=np.float64)
lm_preconditioners = np.array([1.0, 5.0], dtype=np.float64)
for i, (index, user_constraints) in enumerate(INPUT_SELECTION):
if i > 0:
break
print 'index:', index
# load geometry from initial projection
z = np.load(
os.path.join(
OUTPUT_ROOT,
'chihuahua_single_projection_%s.npz' % user_constraints))
V = z['V1']
# get the silhouette information for the frame
S, SN = load_silhouette(index)
# get the spillage information for the frame
Rx, Ry = load_spillage(index)
# solve for the initial silhouette positions
U, L = shortest_path_solve(V,
T,
S,
SN,
lambdas=global_solve_lambdas,
isCircular=False,
**silhouette_info)
# fit under laplacian
def solve_iteration():
status = solve_single_lap_silhouette(
V,
T,
U,
L,
S,
SN,
Rx,
Ry,
lm_lambdas,
lm_preconditioners,
narrowBand=3,
maxIterations=20,
gradientThreshold=1e-5,
updateThreshold=1e-5,
improvementThreshold=1e-5,
)
print 'LM Status (%d): ' % status[0], status[1]
return status
status = solve_iteration()
while status[0] in (0, 4):
status = solve_iteration()
print 'Final Status (%d): ' % status[0], status[1]
# visualise ?
if True:
Q = geometry.path2pos(V, T, L, U)
N = Q.shape[0]
vis = visualise.VisualiseMesh(V, T, L)
vis.add_silhouette(Q, np.arange(N), [0, N - 1], S)
vis.add_image(get_frame_path(index))
vis.execute()
def main():
T = wsp.load_triangles()
#V, C, P = wsp.load_projection('0f')
V, C, P = wsp.load_projection('0b')
single_arap_lambdas = np.array(
[
1.0, # as-rigid-as-possible
1e1
], # projection
dtype=np.float64)
# solve for projection
X = np.zeros_like(V)
V1 = V.copy()
print 'solve_single_arap_proj'
status = solve_single_arap_proj(V,
T,
X,
V1,
C,
P,
single_arap_lambdas,
maxIterations=20,
gradientThreshold=1e-6,
updateThreshold=1e-6,
improvementThreshold=1e-6)
print 'Status (%d): ' % status[0], status[1]
# show
vis = visualise.VisualiseMesh(V1, T)
vis.add_image(wsp.get_frame_path(0))
vis.add_projection(C, P)
vis.execute()
# solve for Laplacian
global_sil_lambdas = np.array(
[
1e0, # geodesic between preimage points
1e0, # silhouette projection
1e3
], # silhouette normal
dtype=np.float64)
#lm_lambdas = np.r_[1e1, 0.0, global_sil_lambdas[1:]]
lm_lambdas = np.r_[1e1, global_sil_lambdas[1:]]
lm_lambdas = np.asarray(lm_lambdas, dtype=np.float64)
print 'lm_lambdas:', lm_lambdas
lm_preconditioners = np.array([1.0, 100.0], dtype=np.float64)
print 'lm_preconditioners :', lm_preconditioners
# use as initialisation for next stage
V = V1.copy()
# solve for silhouette
silhouette_info = wsp.load_silhouette_info()
S, SN = wsp.get_silhouette(0)
print 'shortest_path_solve'
U, L = shortest_path_solve(V,
T,
S,
SN,
lambdas=global_sil_lambdas,
isCircular=True,
**silhouette_info)
# show
vis = visualise.VisualiseMesh(V, T, L)
vis.add_image(wsp.get_frame_path(0))
Q = geometry.path2pos(V, T, L, U)
N = Q.shape[0]
vis.add_silhouette(Q, np.arange(N), [0, N - 1], S)
vis.add_projection(C, P)
vis.execute()
# fit under laplacian
def solve_iteration():
status = solve_single_lap_silhouette(
V,
T,
U,
L,
S,
SN,
lm_lambdas,
lm_preconditioners,
narrowBand=3,
maxIterations=50,
gradientThreshold=1e-6,
updateThreshold=1e-6,
improvementThreshold=1e-6,
verbosenessLevel=1,
useAsymmetricLambda=True,
)
print 'LM Status (%d): ' % status[0], status[1]
#status = solve_single_lap_proj_silhouette(V, T, U, L, C, P, S, SN,
# lm_lambdas,
# lm_preconditioners,
# narrowBand=2,
# maxIterations=50,
# gradientThreshold=1e-6,
# updateThreshold=1e-6,
# improvementThreshold=1e-6,
# verbosenessLevel=1,
# useAsymmetricLambda=True,
# )
#print 'LM Status (%d): ' % status[0], status[1]
return status
#for i in xrange(10):
# status = solve_iteration()
count = 0
while status[0] in (0, 4) and count < 5:
status = solve_iteration()
count += 1
# show
vis = visualise.VisualiseMesh(V, T, L)
vis.add_image(wsp.get_frame_path(0))
Q = geometry.path2pos(V, T, L, U)
N = Q.shape[0]
vis.add_silhouette(Q, np.arange(N), [0, N - 1], S)
vis.add_projection(C, P)
vis.execute()