def __init__(self, args):
super(HybridModel, self).__init__()
self.feat_dim = args.embed_dim
if args.cls:
oup_dim = 6890
else:
oup_dim = self.feat_dim
self.fe = PointNet2(inp_dim=0, oup_dim=self.feat_dim)
if args.transf_reg:
if args.RegNet == 'Reg2':
self.reg = Regularization2Module()
else:
if args.RegNet == 'Reg':
self.reg = RegularizationModule(init=args.init)
if args.animals:
gt_feats = torch.Tensor(
helper.loadSMALDescriptors(args.desc)[:, :self.feat_dim])
gt_points = torch.Tensor(
np.array(helper.loadSMALModels()['cat'].vertices))
else:
gt_feats = torch.Tensor(
helper.loadSMPLDescriptors(args.desc)[:, :self.feat_dim])
gt_points = torch.Tensor(helper.loadSMPLModels()[0].verts)
self.register_buffer('gt_feats', gt_feats)
self.register_buffer('gt_points', gt_points)
self.transf_reg = args.transf_reg
def __init__(self,
descriptor_dim,
sampler=None,
split='train',
transform=DefaultTransform,
cls=False,
build_graph=False):
super(SurrealFEPts5k).__init__()
self.name = 'SurrealFEPts5k'
self.split = split
if self.split == 'train':
self.IDlist = IDlist[:, :-(num_test * num_views)].reshape(-1)
elif self.split == 'test':
self.IDlist = IDlist[:, -(num_test * num_views):].reshape(-1)
elif self.split == 'val':
self.IDlist = IDlist[:, :num_views].reshape(-1)
self.file_path = '{}/scans/{{0:06d}}/{{1:03d}}.mat'.format(
PATH_TO_SURREAL)
self.template_feats = helper.loadSMPLDescriptors()[:, :descriptor_dim]
self.template_points = helper.loadSMPLModels()[0].verts
self.cls = cls
if build_graph:
self.transform = T.Compose(
[transform, T.KNNGraph(k=6),
T.ToDense(5000)])
else:
self.transform = T.Compose([transform, T.ToDense(5000)])
def __init__(self, descriptor_dim, split='train', desc='Laplacian_n',
transform='default', cls=False):
super(SurrealFEPts).__init__()
self.name = 'SurrealFEPts'
if (split == 'train') or (split == 'val'):
self.num_views = 20
else:
self.num_views = 100
self.IDlist = np.stack([np.arange(100000*self.num_views),
np.arange(115000*self.num_views,
215000*self.num_views)],
axis=0)
self.split = split
if self.split == 'train':
self.IDlist = self.IDlist[:, :-(num_test*self.num_views)].reshape(-1)
self.file_path = '{}/scans/{{0:06d}}/{{1:03d}}.mat'.format(hc.PATH_TO_SURREAL)
elif self.split == 'test':
self.IDlist = self.IDlist[:, -(num_test*self.num_views):].reshape(-1)
self.file_path = '{}/scans/{{0:06d}}/{{1:03d}}.mat'.format(hc.PATH_TO_SURREAL_TEST)
elif self.split == 'val':
self.IDlist = self.IDlist[:, :(5*self.num_views)].reshape(-1)
self.file_path = '{}/scans/{{0:06d}}/{{1:03d}}.mat'.format(hc.PATH_TO_SURREAL)
#ll = [20,24,25,26,28,35,36,37,39,52,53,106,128,152,160,178,187,191]
#self.IDlist = np.array([self.IDlist[l] for l in ll])
self.result_path = '{}/result/SURREAL/'.format(hc.PATH_TO_DATA)
self.template_feats = helper.loadSMPLDescriptors(desc)[:, :descriptor_dim]
self.template_points = helper.loadSMPLModels()[0].verts
self.cls = cls
if transform == 'default':
if self.split == 'train':
self.transform = TrainTransform
else:
self.transform = TestTransform
else:
self.transform = transform
def __init__(self,
descriptor_dim,
split='train',
desc='Laplacian_n',
transform='default',
cls=False):
super(Shrec19FEPts).__init__()
self.name = 'Shrec19FEPts'
self.num_views = 100
self.result_path = '{}/result/SHREC19/'.format(hc.PATH_TO_DATA)
self.IDlist = np.arange(1 * num_views, 45 * num_views)
self.split = split
if self.split == 'train':
raise RuntimeError("This dataset is Test Only")
elif self.split == 'test':
self.IDlist = self.IDlist
elif self.split == 'val':
self.IDlist = self.IDlist[:num_views]
self.file_path = '{}/scans/{{}}/{{:03d}}.mat'.format(
hc.PATH_TO_SHREC19)
self.template_feats = helper.loadSMPLDescriptors(
desc)[:, :descriptor_dim]
self.template_points = helper.loadSMPLModels()[0].verts
self.cls = cls
if transform == 'default':
if self.split == 'train':
self.transform = TestTransform
else:
self.transform = TrainTransform
else:
self.transform = transform
def __init__(self,
descriptor_dim,
split='train',
desc='Laplacian_n',
transform='default',
cls=False):
super(DGFSurrealFEPts).__init__()
self.name = 'DGFSurrealFEPts'
self.num_views = 20
self.split = split
self.result_path = '{}/result/DGFSURREAL/'.format(hc.PATH_TO_DATA)
if self.split == 'train':
self.IDlist = IDlist.reshape(-1)
elif self.split == 'test':
self.IDlist = IDlist[-(num_test * num_views):].reshape(-1)
elif self.split == 'val':
self.IDlist = IDlist[:num_views * 5].reshape(-1)
#ll = [20,24,25,26,28,35,36,37,39,52,53,106,128,152,160,178,187,191]
#self.IDlist = np.array([self.IDlist[l] for l in ll])
self.file_path = '{}/scans/{{0:06d}}/{{1:03d}}.mat'.format(
hc.PATH_TO_DGFSURREAL)
self.template_feats = helper.loadSMPLDescriptors(
desc)[:, :descriptor_dim]
self.template_points = helper.loadSMPLModels()[0].verts
self.cls = cls
if transform == 'default':
if self.split == 'train':
self.transform = TrainTransform
else:
self.transform = TestTransform
else:
self.transform = transform
def __init__(self,
descriptor_dim,
split='train',
desc='Laplacian_n',
transform='default',
cls=False):
super(FaustTestFEPts).__init__()
self.name = 'FaustTestFEPts'
self.num_views = 200
self.result_path = '{}/result/FAUST-Test/'.format(hc.PATH_TO_DATA)
self.IDlist = np.arange(20000)
self.split = split
if self.split == 'train':
raise RuntimeError("This dataset is Test Only")
elif self.split == 'test':
self.IDlist = self.IDlist
elif self.split == 'val':
self.IDlist = self.IDlist[:40]
self.file_path = '{}/scans/{{0:03d}}_{{1:03d}}.mat'.format(
hc.PATH_TO_FAUST_TEST_SCANS)
self.template_feats = helper.loadSMPLDescriptors(
desc)[:, :descriptor_dim]
self.template_points = helper.loadSMPLModels()[0].verts
#self.cls = cls
if transform == 'default':
if self.split == 'train':
self.transform = TrainTransform
else:
self.transform = TestTransform
else:
self.transform = transform
def __init__(self, num_points, descriptor_dim, split='train'):
super(SurrealFEDepthImgs).__init__()
self.name = 'SurrealFEDepthImgs'
self.split = split
self.num_sample_points = num_points
if self.split == 'train':
self.IDlist = IDlist[:, :-(num_test * num_views)].reshape(-1)
elif self.split == 'test':
self.IDlist = IDlist[:, -(num_test * num_views):].reshape(-1)
elif self.split == 'val':
self.IDlist = IDlist[:, :num_views].reshape(-1)
#self.file_path = '{}/scans/%d_%d.mat'.format(PATH_TO_SURREAL)
self.file_path = '{}/scans/{{0:06d}}/{{1:03d}}.mat'.format(
PATH_TO_SURREAL)
self.template_feats = helper.loadSMPLDescriptors()[:, :descriptor_dim]
self.template_points = helper.loadSMPLModels()[0].verts
#if not os.path.exists('{}/{}.txt'.format(rotation_path, n_views-1)):
# thetas = np.linspace(0, np.pi*2, n_views)
# rotations = [linalg.rodriguez(np.random.randn(3)) for i in range(n_views)]
# #rotations = [linalg.rodriguez(np.array([1.,0.,0.])*thetas[i] + np.random.randn(3)*0.2) for i in range(n_views)]
# for i, rotation in enumerate(rotations):
# np.savetxt('{}/{}.txt'.format(rotation_path, i), rotation)
#else:
# rotations = [np.loadtxt('{}/{}.txt'.format(rotation_path, i)).reshape((3, 3)) for i in range(n_views)]
render_path = '{}/surreal-test/scans'.format(PATH_TO_DATA)
MAT_PATH = '{}/{{0:06d}}'.format(render_path)
MAT = '{}/{{0:06d}}/{{1:03d}}.mat'.format(render_path)
OBJ = '{}/{{0:06d}}/{{1:03d}}.obj'.format(render_path)
os.system('mkdir -p %s' % render_path)
models = helper.loadSMPLModels()
gt_dsc = helper.loadSMPLDescriptors(desc='Laplacian_n')
edges = computeGraph(6890, models[0].faces, knn=args.knn)
for mesh_id in range(offset, offset+length):
os.system('mkdir -p %s' % MAT_PATH.format(mesh_id))
params = np.array(smpl_params[mesh_id, :])
params[11:14] = 0.0
gender = int(params[0])
model = models[gender]
zero_params = np.zeros(85)
model.update_params(zero_params)
rest_mesh = vis.getTriangleMesh(model.verts, model.faces)
params = np.concatenate([np.zeros(3), params[1:]], axis=0)
model.update_params(params)
mesh = vis.getTriangleMesh(model.verts, model.faces)
point_rotations, point_translations = helper.computeLocalRotations(np.array(mesh.vertices), np.array(mesh.triangles), np.array(rest_mesh.vertices), np.arange(np.array(mesh.vertices).shape[0]), edges=edges) # [N, 3]
if __name__ == '__main__':
SCAN = '%s/MPI-FAUST/training/scans/tr_scan_{0:03d}.ply' % (PATH_TO_DATA)
SCAN_CORRES = '%s/MPI-FAUST/training/scans/tr_scan_{0:03d}.corres' % (PATH_TO_DATA)
REG = '%s/MPI-FAUST/training/registrations/tr_reg_{0:03d}.ply' % (PATH_TO_DATA)
FAUST_DESC = '%s/faust/faust_descriptors/{0:03d}.mat' % (PATH_TO_DATA)
rotation_path = '%s/faust/render_rotations' % (PATH_TO_DATA)
render_path = '%s/faust/scans' % (PATH_TO_DATA)
OBJ = '%s/{0:03d}_{1:03d}.obj' % (render_path)
CORRES = '%s/{0:03d}_{1:03d}.corres' % (render_path)
MAT = '%s/{0:03d}_{1:03d}.mat' % (render_path)
#camera = PinholeCamera()
#rest_mesh = helper.loadSMPLModels()[0]
#edges = computeGraph(6890, rest_mesh.faces, knn=7)
#rest_mesh = vis.getTriangleMesh(rest_mesh.verts, rest_mesh.faces)
gt_descriptors = helper.loadSMPLDescriptors('Laplacian_n')
dsc_tree = NN(n_neighbors=1, n_jobs=10).fit(gt_descriptors)
for scan_id in range(100):
print(scan_id)
""" Correspondence translation """
raw_mesh = o3d.io.read_triangle_mesh(SCAN.format(scan_id)) # raw mesh
reg_mesh = o3d.io.read_triangle_mesh(REG.format(scan_id)) # registration mesh
tree = NN(n_neighbors=1, n_jobs=10).fit(np.array(reg_mesh.vertices))
dists, indices = tree.kneighbors(np.array(raw_mesh.vertices))
scan2reg = indices[:, 0]
Nraw = np.array(raw_mesh.vertices).shape[0]
Nreg = np.array(reg_mesh.vertices).shape[0]
mesh = o3d.io.read_triangle_mesh(SCAN.format(scan_id))
dsc_file = FAUST_DESC.format(scan_id)
gt_dsc = sio.loadmat(dsc_file)['dsc'] # [6890, 128]
