def main():
meshes = [
('hand', 5, 'meshes/hand_868.obj', [10, 30], False),
('fertility', 5, 'meshes/fertility/FE_10k.off', [10, 40], False),
('bunny', 1.25, 'meshes/bunny_fixed.obj', [10, 40], False),
]
result_lines = []
for mesh_name, mu, filename, Ks, scaled in meshes:
verts, tris = load_mesh(filename, check=False, normalize=True)
print mesh_name, len(verts), len(tris)
for K in Ks:
t_cmh = time()
Phi_cmh, D_cmh = cmm.compressed_manifold_modes(
verts, tris, K, init='varimax',
mu=mu, scaled=scaled, return_D=True)
t_cmh = time() - t_cmh
t_mh = time()
Phi_mh, D_mh = cmm.manifold_harmonics(
verts, tris, K, return_D=True, scaled=scaled)
t_mh = time() - t_mh
Phi_mh = Phi_mh.astype(np.float64)
Phi_cmh = Phi_cmh.astype(np.float64)
Phi_cmh[np.abs(Phi_cmh) < 1.e-7] = 0
Phi_cmh_sp = sparse.csr_matrix(Phi_cmh, dtype=np.float64)
if scaled:
verts_rec_cmh = np.dot(Phi_cmh, np.dot((D_cmh * Phi_cmh).T, verts))
verts_rec_mh = np.dot(Phi_mh, np.dot((D_mh * Phi_mh).T, verts))
else:
verts_rec_cmh = np.dot(Phi_cmh, np.dot(Phi_cmh.T, verts))
verts_rec_mh = np.dot(Phi_mh, np.dot(Phi_mh.T, verts))
line_format = "{mesh_name} & {num_verts:>8d} & {basis} & {K:d} & {mu:>4} & {error:>6.2f} & {size:>8} & {time:>5.2f}s"
result_lines.append(line_format.format(
mesh_name=mesh_name, basis='MHB', K=K, mu='-',
num_verts=len(verts),
error=np.linalg.norm(verts_rec_mh - verts),
size=sizeof_fmt(Phi_mh.nbytes),
time=t_mh,
))
result_lines.append(line_format.format(
mesh_name=mesh_name, basis='CMB', K=K, mu='%.2f' % mu,
num_verts=len(verts),
error=np.linalg.norm(verts_rec_cmh - verts),
size=sizeof_fmt(Phi_cmh_sp.data.nbytes + Phi_cmh_sp.indptr.nbytes + Phi_cmh_sp.indices.nbytes),
time=t_cmh,
))
print "------------"
print "TABLE START:"
print "------------"
print "mesh & basis & $K$ & $\\mu$ & error & size & time"
for line in result_lines:
print line
def main():
experiments = [
('meshes/fertility/FE_20k.off', 6, [0., 0.25, 0.5, 1.0], 12.5),
('meshes/bimba/bimba_cvd_10k.off', 5, [0, 0.25, 0.5, 1.0], 30),
]
wvs = []
for filename, K, noise_levels, mu in experiments:
verts, tris = load_mesh(filename, check=False)
verts = verts / verts.std()
avg_edge_len = compute_average_edge_length(verts, tris)
# compute cmm for each noise level
vis_meshes = []
for noise_level in noise_levels:
if noise_level <= 0:
verts_noisy = verts
else:
noise_scale = noise_level * avg_edge_len
noise = np.random.normal(scale=noise_scale, size=verts.shape)
verts_noisy = verts + noise
Phi_cmh = cmm.compressed_manifold_modes(
verts_noisy,
tris,
K,
mu=mu,
scaled=True,
init='varimax',
)
if noise_level <= 0:
label = 'no noise'
else:
label = '%.0f%% gaussian noise' % (noise_level * 100)
vis_meshes.append((verts_noisy, tris, Phi_cmh, label))
# initialize the visualization, show later when all experiments done
wvs.append(
WeightsVisualization(vis_meshes,
show_labels=True,
actor_options=dict(interpolation='flat')))
# show visualization windows
for wv in wvs[:-1]:
wv.edit_traits()
wvs[-1].configure_traits()
def main():
experiments = [
('meshes/fertility/FE_20k.off', 6, [0., 0.25, 0.5, 1.0], 12.5),
('meshes/bimba/bimba_cvd_10k.off', 5, [0, 0.25, 0.5, 1.0], 30),
]
wvs = []
for filename, K, noise_levels, mu in experiments:
verts, tris = load_mesh(filename, check=False)
verts = verts / verts.std()
avg_edge_len = compute_average_edge_length(verts, tris)
# compute cmm for each noise level
vis_meshes = []
for noise_level in noise_levels:
if noise_level <= 0:
verts_noisy = verts
else:
noise_scale = noise_level * avg_edge_len
noise = np.random.normal(scale=noise_scale, size=verts.shape)
verts_noisy = verts + noise
Phi_cmh = cmm.compressed_manifold_modes(
verts_noisy, tris, K,
mu=mu, scaled=True, init='varimax',
)
if noise_level <= 0:
label = 'no noise'
else:
label = '%.0f%% gaussian noise' % (noise_level * 100)
vis_meshes.append((verts_noisy, tris, Phi_cmh, label))
# initialize the visualization, show later when all experiments done
wvs.append(WeightsVisualization(
vis_meshes, show_labels=True,
actor_options=dict(interpolation='flat')))
# show visualization windows
for wv in wvs[:-1]:
wv.edit_traits()
wvs[-1].configure_traits()
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import WeightsVisualization
# load the bumpy meshes in 2 resolutions
verts1, tris1 = load_mesh('meshes/bump512.obj')
verts2, tris2 = load_mesh('meshes/bump32768.obj')
# we need to use a trick here to obtain the mode on the bump itself
# initialize the manifold mode such that it is zero at every vertex,
# but one at the peak of the bump
Phi_init1 = np.zeros((len(verts1), 1))
Phi_init1[verts1[:, 1].argmax()] = 1.0
Phi_init2 = np.zeros((len(verts2), 1))
Phi_init2[verts2[:, 1].argmax()] = 1.0
# set parameters
# NOTE: these might not give exactly the same results as in the paper
# since the code was significantly changed after producing Fig. 2
# the code here still shows the same behavior of the scaling
K = Phi_init1.shape[1]
mu_scaled = 50.
mu_unscaled = 20.0
params = dict(maxiter=5000)
# compute modes
Phi1_scaled = cmm.compressed_manifold_modes(verts1,
tris1,
K,
def main():
experiments = [
('meshes/fertility/FE_20k.off', 6, [0., 500], 12.5),
('meshes/bimba/bimba_cvd_10k.off', 5, [0, 100, 200], 30),
]
wvs = []
for filename, K, all_n_holes, mu in experiments:
verts, tris = load_mesh(filename, check=False)
verts = verts / verts.std()
# compute cmm for each noise level
vis_meshes = []
for n_holes in all_n_holes:
if n_holes <= 0:
tris_holes = tris
verts_holes = verts
else:
while True:
# place holes
hole_centers = np.random.randint(0, len(verts), n_holes)
keep_vertex = np.ones(len(verts), np.bool)
keep_vertex[hole_centers] = False
keep_tri = keep_vertex[tris].all(axis=1)
tris_holes = filter_reindex(keep_vertex, tris[keep_tri])
verts_holes = verts[keep_vertex]
# check if mesh is still a single connected graph
ij = np.r_[np.c_[tris_holes[:, 0], tris_holes[:, 1]],
np.c_[tris_holes[:, 0], tris_holes[:, 2]],
np.c_[tris_holes[:, 1], tris_holes[:, 2]]]
n_keep = verts[keep_vertex].shape[0]
G = csr_matrix(
(np.ones(len(ij)), ij.T),
shape=(n_keep, n_keep))
n_components, labels = connected_components(G, directed=0)
if n_components == 1:
break
else:
# mesh was torn apart by hole creation process
# trying another set of random holes
pass
Phi_cmh = cmm.compressed_manifold_modes(
verts_holes, tris_holes, K,
mu=mu, scaled=True, init='varimax',
)
if n_holes <= 0:
label = 'no holes'
else:
label = '%d holes' % (n_holes)
vis_meshes.append((verts_holes, tris_holes, Phi_cmh, label))
# initialize the visualization, show later when all experiments done
wvs.append(WeightsVisualization(vis_meshes, show_labels=True))
# show visualization windows
for wv in wvs[:-1]:
wv.edit_traits()
wvs[-1].configure_traits()
import sys
from os import path
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_many_weights
scape_dir = path.join('meshes', 'scape')
if not path.exists(scape_dir) or not path.exists(path.join(scape_dir, 'mesh000.off')):
print "SCAPE dataset not found. You need to get it from James Davis."
print "Instructions on this website: "
print "http://robotics.stanford.edu/~drago/Projects/scape/scape.html"
sys.exit(1)
meshes = [load_mesh(path.join(scape_dir, 'mesh%03d.off' % i), normalize=True)
for i in [0, 7, 10]]
results = []
for verts, tris in meshes:
Phi_cmh = cmm.compressed_manifold_modes(
verts, tris, 10, mu=12.5, scaled=True, maxiter=2000)
results.append((verts, tris, Phi_cmh))
show_many_weights(results)
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_many_weights
scape_dir = path.join('meshes', 'scape')
if not path.exists(scape_dir) or not path.exists(
path.join(scape_dir, 'mesh000.off')):
print "SCAPE dataset not found. You need to get it from James Davis."
print "Instructions on this website: "
print "http://robotics.stanford.edu/~drago/Projects/scape/scape.html"
sys.exit(1)
meshes = [
load_mesh(path.join(scape_dir, 'mesh%03d.off' % i), normalize=True)
for i in [0, 7, 10]
]
results = []
for verts, tris in meshes:
Phi_cmh = cmm.compressed_manifold_modes(verts,
tris,
10,
mu=12.5,
scaled=True,
maxiter=2000)
results.append((verts, tris, Phi_cmh))
show_many_weights(results)
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_weights
K1 = 8 + 6 + 12
K2 = 4
verts, tris = load_mesh('meshes/bumpy_cube6.obj')
Phi_cpr1 = cmm.compressed_manifold_modes(verts, tris, K1, mu=20., scaled=False)
Phi_cpr2 = cmm.compressed_manifold_modes(verts, tris, K2, mu=20., scaled=False)
Phi_vari1 = cmm.varimax_modes(verts, tris, K1)
Phi_vari2 = cmm.varimax_modes(verts, tris, K2)
# establish some consistent ordering of varimax modes just for visualization
i = (Phi_cpr2[:, 0]**2).argmax() # select one vertex
# permute according to activation strength of that vertex
Phi_vari2 = Phi_vari2[:, (Phi_vari2[i]**2).argsort()[::-1]]
Phi_vari1 = Phi_vari1[:, (Phi_vari1[i]**2).argsort()[::-1]]
Phi_cpr2 = Phi_cpr2[:, (Phi_cpr2[i]**2).argsort()[::-1]]
Phi_cpr1 = Phi_cpr1[:, (Phi_cpr1[i]**2).argsort()[::-1]]
show_weights(verts,
tris, (Phi_cpr1, Phi_cpr2, Phi_vari1, Phi_vari2),
('CMM, K=%d' % K1, 'CMM, K=%d' % K2, 'Variax, K=%d' % K1,
'Varimax, K=%d' % K2),
contours=7,
show_labels=True)
def main():
meshes = [
('hand', 5, 'meshes/hand_868.obj', [10, 30], False),
('fertility', 5, 'meshes/fertility/FE_10k.off', [10, 40], False),
('bunny', 1.25, 'meshes/bunny_fixed.obj', [10, 40], False),
]
result_lines = []
for mesh_name, mu, filename, Ks, scaled in meshes:
verts, tris = load_mesh(filename, check=False, normalize=True)
print mesh_name, len(verts), len(tris)
for K in Ks:
t_cmh = time()
Phi_cmh, D_cmh = cmm.compressed_manifold_modes(verts,
tris,
K,
init='varimax',
mu=mu,
scaled=scaled,
return_D=True)
t_cmh = time() - t_cmh
t_mh = time()
Phi_mh, D_mh = cmm.manifold_harmonics(verts,
tris,
K,
return_D=True,
scaled=scaled)
t_mh = time() - t_mh
Phi_mh = Phi_mh.astype(np.float64)
Phi_cmh = Phi_cmh.astype(np.float64)
Phi_cmh[np.abs(Phi_cmh) < 1.e-7] = 0
Phi_cmh_sp = sparse.csr_matrix(Phi_cmh, dtype=np.float64)
if scaled:
verts_rec_cmh = np.dot(Phi_cmh,
np.dot((D_cmh * Phi_cmh).T, verts))
verts_rec_mh = np.dot(Phi_mh, np.dot((D_mh * Phi_mh).T, verts))
else:
verts_rec_cmh = np.dot(Phi_cmh, np.dot(Phi_cmh.T, verts))
verts_rec_mh = np.dot(Phi_mh, np.dot(Phi_mh.T, verts))
line_format = "{mesh_name} & {num_verts:>8d} & {basis} & {K:d} & {mu:>4} & {error:>6.2f} & {size:>8} & {time:>5.2f}s"
result_lines.append(
line_format.format(
mesh_name=mesh_name,
basis='MHB',
K=K,
mu='-',
num_verts=len(verts),
error=np.linalg.norm(verts_rec_mh - verts),
size=sizeof_fmt(Phi_mh.nbytes),
time=t_mh,
))
result_lines.append(
line_format.format(
mesh_name=mesh_name,
basis='CMB',
K=K,
mu='%.2f' % mu,
num_verts=len(verts),
error=np.linalg.norm(verts_rec_cmh - verts),
size=sizeof_fmt(Phi_cmh_sp.data.nbytes +
Phi_cmh_sp.indptr.nbytes +
Phi_cmh_sp.indices.nbytes),
time=t_cmh,
))
print "------------"
print "TABLE START:"
print "------------"
print "mesh & basis & $K$ & $\\mu$ & error & size & time"
for line in result_lines:
print line
from itertools import product
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_weights
K = 1
num_inits = 6
at_iters = [0, 2, 10, 50, 200, 1000]
verts, tris = load_mesh('meshes/bunny_fixed.obj')
_, _, color1 = load_mesh('meshes/bunny_fixed_ear.off', no_colors=False)
_, _, color2 = load_mesh('meshes/bunny_fixed_sgp.off', no_colors=False)
all_Phis = []
for init in xrange(num_inits):
if init == 0:
Phi_init = -1 * (color1[:, 0] > 200).astype(np.float).reshape(len(verts), K)
elif init == 1:
Phi_init = 1 * (color2[:, 0] > 200).astype(np.float).reshape(len(verts), K)
else:
Phi_init = np.random.uniform(-1, 1, (len(verts), K))
Phis = [Phi_init]
def callback(H, mu, Phi, E, S, **kwargs):
global i
i += 1
if i in at_iters:
Phis.append(Phi)
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_weights
K1 = 8 + 6 + 12
K2 = 4
verts, tris = load_mesh('meshes/bumpy_cube6.obj')
Phi_cpr1 = cmm.compressed_manifold_modes(verts, tris, K1, mu=20., scaled=False)
Phi_cpr2 = cmm.compressed_manifold_modes(verts, tris, K2, mu=20., scaled=False)
Phi_vari1 = cmm.varimax_modes(verts, tris, K1)
Phi_vari2 = cmm.varimax_modes(verts, tris, K2)
# establish some consistent ordering of varimax modes just for visualization
i = (Phi_cpr2[:, 0]**2).argmax() # select one vertex
# permute according to activation strength of that vertex
Phi_vari2 = Phi_vari2[:, (Phi_vari2[i]**2).argsort()[::-1]]
Phi_vari1 = Phi_vari1[:, (Phi_vari1[i]**2).argsort()[::-1]]
Phi_cpr2 = Phi_cpr2[:, (Phi_cpr2[i]**2).argsort()[::-1]]
Phi_cpr1 = Phi_cpr1[:, (Phi_cpr1[i]**2).argsort()[::-1]]
show_weights(
verts, tris,
(Phi_cpr1, Phi_cpr2, Phi_vari1, Phi_vari2),
('CMM, K=%d' % K1, 'CMM, K=%d' % K2, 'Variax, K=%d' % K1, 'Varimax, K=%d' % K2),
contours=7, show_labels=True)
def main():
# prepare data
verts, tris = load_mesh('meshes/bumpy_cube6.obj')
K = 8 + 6 + 12
mu = 20.0
maxiter = 500
num_experiments = 1
# prepare data structures
ResultData = namedtuple('ResultData', 'Phi info logger')
data_per_algo = defaultdict(list)
# parameters passed to algorithms - to be fair,
# turn off automatic penalty adjustment in our method
# set check_interval = 1 to force callback to be called in every iteration
algorithms = [
('osher', cmm.solve_compressed_osher,
dict()),
('ours', cmm.solve_compressed_splitorth,
dict(auto_adjust_penalty=False, check_interval=1)),
]
# run the algorithm with same input mesh and
# different random initializations
print "will now run the method with %d different initializations" % num_experiments
print "the primal residual with our method should consistently be lower compared to Oshers et al."
for random_seed in range(num_experiments):
np.random.seed(random_seed)
Phi_init = np.random.random((len(verts), K))
print "--"
print "running experiment %d" % random_seed
for name, method, options in algorithms:
Phi, info, logger = test_convergence(
verts, tris, K, mu,
Phi_init, method, maxiter, **options)
print name, info['r_primal']
data_per_algo[name].append(ResultData(Phi, info, logger))
# visualize results
import matplotlib
if len(sys.argv) > 1:
matplotlib.use('pgf') # TODO PGF
else:
matplotlib.use('WxAgg')
import pylab as pl
pl.rc('text', usetex=True)
pl.rc('font', size=6)
pl.figure(figsize=(0.9, 1.1))
plot_fun = pl.semilogy
plot_fun(data_per_algo['osher'][0].logger.r_primals, c='r',
label=r'osher')
plot_fun(data_per_algo['ours'][0].logger.r_primals, c='g',
label=r'ours')
pl.legend(loc='center right').draggable()
pl.locator_params(nbins=4, axis='x')
pl.xlabel('iteration')
pl.ylabel(r'$ \left\Vert r \right\Vert_2$')
pl.gca().yaxis.set_major_locator(matplotlib.ticker.LogLocator(numticks=4))
if len(sys.argv) > 2:
pl.savefig(sys.argv[1]) #, bbox_inches='tight')
else:
pl.ion()
pl.show()
# let us first visualize the modes where things go wrong
# which are most probably those where the primal residual is very high
Q = data_per_algo['osher'][0].info['Q']
P = data_per_algo['osher'][0].info['P']
worst_first = ((Q - P)**2).sum(axis=0).argsort()[::-1]
show_weights(
verts, tris,
(data_per_algo['osher'][0].Phi[:, worst_first],
data_per_algo['ours'][0].Phi[:, worst_first]),
('osher', 'ours')
)
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_many_weights
K = 6
verts, tris = load_mesh('meshes/hand_4054.obj')
# compute bases
Phi_cmh = cmm.compressed_manifold_modes(verts, tris, K, mu=2, scaled=False)
Phi_mh = cmm.manifold_harmonics(verts, tris, K, scaled=False)
# reconstruct from bases
verts_rec_cmh = np.dot(Phi_cmh, np.dot(Phi_cmh.T, verts))
verts_rec_mh = np.dot(Phi_mh, np.dot(Phi_mh.T, verts))
show_many_weights(
((verts, tris, None, 'Input'),
(verts_rec_mh, tris, None, 'MH reconstr.'),
(verts_rec_cmh, tris, None, 'CMM reconstr.')),
show_labels=True,
actor_options=dict(edge_visibility=True, line_width=1.0))
def main(input_filename, K, mu, output_dir=None, visualize=False, scaled=False,
maxiter=None, ply=False, off=False):
if (off or ply) and not output_dir:
print "please specify an output directory"
return 1
if output_dir and not path.exists(output_dir):
print "%s does not exist" % output_dir
return 2
verts, tris = load_mesh(input_filename, normalize=True)
print "%d vertices, %d faces" % (len(verts), len(tris))
Phi_cpr, D = cmm.compressed_manifold_modes(
verts, tris, K, mu=mu, scaled=scaled,
maxiter=maxiter, verbose=100, return_D=True)
if D is None:
D_diag = np.ones(len(verts))
D = sparse.eye(len(verts))
else:
D_diag = D.data
if visualize:
from cmmlib.vis.weights import show_weights
show_weights(verts, tris, Phi_cpr)
if output_dir:
# save in simple text format
np.savetxt(path.join(output_dir, 'phi.txt'), Phi_cpr, fmt='%f')
np.savetxt(path.join(output_dir, 'D_diag.txt'), D_diag, fmt='%f')
# save in matlab format
savemat(path.join(output_dir, 'phi.mat'),
dict(verts=verts, tris=tris+1, phi=Phi_cpr, D=D))
# save HDF5 format if possible
try:
import h5py
except ImportError:
print "Cannot save as HDF5, please install the h5py module"
else:
with h5py.File(path.join(output_dir, 'phi.h5'), 'w') as f:
f['verts'] = verts
f['tris'] = tris
f['phi'] = Phi_cpr
f['d_diag'] = D_diag
# save NPY format
np.save(path.join(output_dir, 'phi.npy'), Phi_cpr)
np.save(path.join(output_dir, 'D_diag.npy'), Phi_cpr)
if off or ply:
# map phi scalars to colors
from mayavi.core.lut_manager import LUTManager
from cmmlib.vis.weights import _centered
lut = LUTManager(lut_mode='RdBu').lut.table.to_array()[:, :3]
colors = [
lut[(_centered(Phi_cpr[:, k]) * (lut.shape[0]-1)).astype(int)]
for k in xrange(K)]
# save in a single scene as a collage
w = int(np.ceil(np.sqrt(K))) if K > 6 else K
spacing = 1.2 * verts.ptp(axis=0)
all_verts = [verts + spacing * (1.5, 0, 0)]
all_tris = [tris]
all_color = [np.zeros(verts.shape, np.int) + 127]
for k in xrange(K):
all_verts.append(verts + spacing * (-(k % w), 0, int(k / w)))
all_tris.append(tris + len(verts) * (k+1))
all_color.append(colors[k])
if off:
save_coff(path.join(output_dir, 'input.off'),
verts.astype(np.float32), tris)
for k in xrange(K):
save_coff(path.join(output_dir, 'cmh_%03d.off' % k),
verts.astype(np.float32), tris, colors[k])
save_coff(path.join(output_dir, 'all.off'),
np.vstack(all_verts), np.vstack(all_tris),
np.vstack(all_color))
if ply:
from tvtk.api import tvtk
pd = tvtk.PolyData(
points=np.vstack(all_verts).astype(np.float32),
polys=np.vstack(all_tris).astype(np.uint32))
pd.point_data.scalars = np.vstack(all_color).astype(np.uint8)
pd.point_data.scalars.name = 'colors'
ply = tvtk.PLYWriter(
file_name=path.join(output_dir, 'all.ply'),
input=pd, color=(1, 1, 1))
ply.array_name = 'colors'
ply.write()
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import WeightsVisualization
# load the bumpy meshes in 2 resolutions
verts1, tris1 = load_mesh('meshes/bump512.obj')
verts2, tris2 = load_mesh('meshes/bump32768.obj')
# we need to use a trick here to obtain the mode on the bump itself
# initialize the manifold mode such that it is zero at every vertex,
# but one at the peak of the bump
Phi_init1 = np.zeros((len(verts1), 1))
Phi_init1[verts1[:, 1].argmax()] = 1.0
Phi_init2 = np.zeros((len(verts2), 1))
Phi_init2[verts2[:, 1].argmax()] = 1.0
# set parameters
# NOTE: these might not give exactly the same results as in the paper
# since the code was significantly changed after producing Fig. 2
# the code here still shows the same behavior of the scaling
K = Phi_init1.shape[1]
mu_scaled = 50.
mu_unscaled = 20.0
params = dict(maxiter=5000)
# compute modes
Phi1_scaled = cmm.compressed_manifold_modes(
verts1, tris1, K, mu_scaled,
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_weights
K = 8 + 6 + 12
mu = 20.0
filename = 'meshes/bumpy_cube6.obj'
verts, tris = load_mesh(filename)
Phi_cpr = cmm.compressed_manifold_modes(verts, tris, K, mu=mu)
Phi_dense = cmm.manifold_harmonics(verts, tris, K)
Phi_vari = cmm.varimax_modes(verts, tris, K)
show_weights(verts,
tris, (Phi_cpr, Phi_dense, Phi_vari), ('CMM', 'MH', 'Varimax'),
show_labels=True)
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_many_weights
mu = 2.0
K = 6
vms = []
for i, fn in enumerate(['hand_868', 'hand_868_holes2']):
verts, tris = load_mesh('meshes/%s.obj' % fn)
Phi_cpr = cmm.compressed_manifold_modes(verts, tris, K, mu=mu)
Phi_dense = cmm.manifold_harmonics(verts, tris, K)
vms += [(verts, tris, Phi_cpr, 'CMM #%d' % i),
(verts, tris, Phi_dense, 'MH #%d' % i)]
show_many_weights(vms, show_labels=True)
def main(input_filename, K, mu, output_dir=None, visualize=False, scaled=False,
maxiter=None, ply=False, off=False):
if (off or ply) and not output_dir:
print ("please specify an output directory")
return 1
if output_dir and not path.exists(output_dir):
print("%s does not exist" % output_dir)
return 2
verts, tris = load_mesh(input_filename, normalize=True)
print ("%d vertices, %d faces" % (len(verts), len(tris)))
Phi_cpr, D = cmm.compressed_manifold_modes(
verts, tris, K, mu=mu, scaled=scaled,
maxiter=maxiter, verbose=100, return_D=True)
if D is None:
D_diag = np.ones(len(verts))
D = sparse.eye(len(verts))
else:
D_diag = D.data
if visualize:
from cmmlib.vis.weights import show_weights
show_weights(verts, tris, Phi_cpr)
if output_dir:
# save in simple text format
np.savetxt(path.join(output_dir, 'phi.txt'), Phi_cpr, fmt='%f')
np.savetxt(path.join(output_dir, 'D_diag.txt'), D_diag, fmt='%f')
# save in matlab format
savemat(path.join(output_dir, 'phi.mat'),
dict(verts=verts, tris=tris+1, phi=Phi_cpr, D=D))
# save HDF5 format if possible
try:
import h5py
except ImportError:
print ("Cannot save as HDF5, please install the h5py module")
else:
with h5py.File(path.join(output_dir, 'phi.h5'), 'w') as f:
f['verts'] = verts
f['tris'] = tris
f['phi'] = Phi_cpr
f['d_diag'] = D_diag
# save NPY format
np.save(path.join(output_dir, 'phi.npy'), Phi_cpr)
np.save(path.join(output_dir, 'D_diag.npy'), Phi_cpr)
if off or ply:
# map phi scalars to colors
from mayavi.core.lut_manager import LUTManager
from cmmlib.vis.weights import _centered
lut = LUTManager(lut_mode='RdBu').lut.table.to_array()[:, :3]
colors = [
lut[(_centered(Phi_cpr[:, k]) * (lut.shape[0]-1)).astype(int)]
for k in range(K)]
# save in a single scene as a collage
w = int(np.ceil(np.sqrt(K))) if K > 6 else K
spacing = 1.2 * verts.ptp(axis=0)
all_verts = [verts + spacing * (1.5, 0, 0)]
all_tris = [tris]
all_color = [np.zeros(verts.shape, np.int) + 127]
for k in range(K):
all_verts.append(verts + spacing * (-(k % w), 0, int(k / w)))
all_tris.append(tris + len(verts) * (k+1))
all_color.append(colors[k])
if off:
save_coff(path.join(output_dir, 'input.off'),
verts.astype(np.float32), tris)
for k in range(K):
save_coff(path.join(output_dir, 'cmh_%03d.off' % k),
verts.astype(np.float32), tris, colors[k])
save_coff(path.join(output_dir, 'all.off'),
np.vstack(all_verts), np.vstack(all_tris),
np.vstack(all_color))
if ply:
from tvtk.api import tvtk
pd = tvtk.PolyData(
points=np.vstack(all_verts).astype(np.float32),
polys=np.vstack(all_tris).astype(np.uint32))
pd.point_data.scalars = np.vstack(all_color).astype(np.uint8)
pd.point_data.scalars.name = 'colors'
ply = tvtk.PLYWriter(
file_name=path.join(output_dir, 'all.ply'),
input=pd, color=(1, 1, 1))
ply.array_name = 'colors'
ply.write()
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_weights
K = 8 + 6 + 12
mu = 20.0
filename = 'meshes/bumpy_cube6.obj'
verts, tris = load_mesh(filename)
Phi_cpr = cmm.compressed_manifold_modes(verts, tris, K, mu=mu)
Phi_dense = cmm.manifold_harmonics(verts, tris, K)
Phi_vari = cmm.varimax_modes(verts, tris, K)
show_weights(verts, tris, (Phi_cpr, Phi_dense, Phi_vari),
('CMM', 'MH', 'Varimax'), show_labels=True)
from itertools import product
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_weights
K = 1
num_inits = 6
at_iters = [0, 2, 10, 50, 200, 1000]
verts, tris = load_mesh('meshes/bunny_fixed.obj')
_, _, color1 = load_mesh('meshes/bunny_fixed_ear.off', no_colors=False)
_, _, color2 = load_mesh('meshes/bunny_fixed_sgp.off', no_colors=False)
all_Phis = []
for init in xrange(num_inits):
if init == 0:
Phi_init = -1 * (color1[:, 0] > 200).astype(np.float).reshape(
len(verts), K)
elif init == 1:
Phi_init = 1 * (color2[:, 0] > 200).astype(np.float).reshape(
len(verts), K)
else:
Phi_init = np.random.uniform(-1, 1, (len(verts), K))
Phis = [Phi_init]
def callback(H, mu, Phi, E, S, **kwargs):
global i
i += 1
if i in at_iters:
Phis.append(Phi)
def main():
experiments = [
('meshes/fertility/FE_20k.off', 6, [0., 500], 12.5),
('meshes/bimba/bimba_cvd_10k.off', 5, [0, 100, 200], 30),
]
wvs = []
for filename, K, all_n_holes, mu in experiments:
verts, tris = load_mesh(filename, check=False)
verts = verts / verts.std()
# compute cmm for each noise level
vis_meshes = []
for n_holes in all_n_holes:
if n_holes <= 0:
tris_holes = tris
verts_holes = verts
else:
while True:
# place holes
hole_centers = np.random.randint(0, len(verts), n_holes)
keep_vertex = np.ones(len(verts), np.bool)
keep_vertex[hole_centers] = False
keep_tri = keep_vertex[tris].all(axis=1)
tris_holes = filter_reindex(keep_vertex, tris[keep_tri])
verts_holes = verts[keep_vertex]
# check if mesh is still a single connected graph
ij = np.r_[np.c_[tris_holes[:, 0], tris_holes[:, 1]],
np.c_[tris_holes[:, 0], tris_holes[:, 2]],
np.c_[tris_holes[:, 1], tris_holes[:, 2]]]
n_keep = verts[keep_vertex].shape[0]
G = csr_matrix((np.ones(len(ij)), ij.T),
shape=(n_keep, n_keep))
n_components, labels = connected_components(G, directed=0)
if n_components == 1:
break
else:
# mesh was torn apart by hole creation process
# trying another set of random holes
pass
Phi_cmh = cmm.compressed_manifold_modes(
verts_holes,
tris_holes,
K,
mu=mu,
scaled=True,
init='varimax',
)
if n_holes <= 0:
label = 'no holes'
else:
label = '%d holes' % (n_holes)
vis_meshes.append((verts_holes, tris_holes, Phi_cmh, label))
# initialize the visualization, show later when all experiments done
wvs.append(WeightsVisualization(vis_meshes, show_labels=True))
# show visualization windows
for wv in wvs[:-1]:
wv.edit_traits()
wvs[-1].configure_traits()
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_many_weights
mu = 2.0
K = 6
vms = []
for i, fn in enumerate(['hand_868', 'hand_868_holes2']):
verts, tris = load_mesh('meshes/%s.obj' % fn)
Phi_cpr = cmm.compressed_manifold_modes(verts, tris, K, mu=mu)
Phi_dense = cmm.manifold_harmonics(verts, tris, K)
vms += [(verts, tris, Phi_cpr, 'CMM #%d' % i),
(verts, tris, Phi_dense, 'MH #%d' % i)]
show_many_weights(vms, show_labels=True)
import numpy as np
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_many_weights
K = 6
verts, tris = load_mesh('meshes/hand_4054.obj')
# compute bases
Phi_cmh = cmm.compressed_manifold_modes(verts, tris, K, mu=2, scaled=False)
Phi_mh = cmm.manifold_harmonics(verts, tris, K, scaled=False)
# reconstruct from bases
verts_rec_cmh = np.dot(Phi_cmh, np.dot(Phi_cmh.T, verts))
verts_rec_mh = np.dot(Phi_mh, np.dot(Phi_mh.T, verts))
show_many_weights(
((verts, tris, None, 'Input'), (verts_rec_mh, tris, None, 'MH reconstr.'),
(verts_rec_cmh, tris, None, 'CMM reconstr.')),
show_labels=True,
actor_options=dict(edge_visibility=True, line_width=1.0))
from mayavi.core.lut_manager import LUTManager
from cmmlib import cmm
from cmmlib.inout import load_mesh
from cmmlib.vis.weights import show_many_weights
experiments = [
('meshes/armadillo_61372.obj', [3, 7], 20.),
('meshes/elk.off', [3, 7], 20.),
]
results = []
for filename, Ks, mu in experiments:
verts, tris = load_mesh(filename, normalize=True)
# for different K, compute the CMMs
Phis = []
for K in Ks:
Phi_cmh = cmm.compressed_manifold_modes(
verts, tris, K, mu, scaled=True, init='varimax')
Phis.append(Phi_cmh)
# color the mesh according to the CMMs
lut = LUTManager(lut_mode='hsv').lut.table.to_array()
colors = []
for K, Phi in zip(Ks, Phis):
# pass Phi through the lookup table depending on it's strength
ti = np.linspace(0, 1, K, endpoint=False) * (lut.shape[0]-1)
lut_rgb = lut[ti.astype(np.int), :3].astype(np.float)
