def predict_skeleton(input_data, vox, root_pred_net, bone_pred_net, mesh_filename):
"""
Predict skeleton structure based on joints
:param input_data: wrapped data
:param vox: voxelized mesh
:param root_pred_net: network to predict root
:param bone_pred_net: network to predict pairwise connectivity cost
:param mesh_filename: meshfilename for debugging
:return: predicted skeleton structure
"""
root_id = getInitId(input_data, root_pred_net)
pred_joints = input_data.y[:input_data.num_joint[0]].data.cpu().numpy()
with torch.no_grad():
connect_prob, _ = bone_pred_net(input_data)
connect_prob = torch.sigmoid(connect_prob)
pair_idx = input_data.pairs.long().data.cpu().numpy()
prob_matrix = np.zeros((data.num_joint[0], data.num_joint[0]))
prob_matrix[pair_idx[:, 0], pair_idx[:, 1]] = connect_prob.data.cpu().numpy().squeeze()
prob_matrix = prob_matrix + prob_matrix.transpose()
cost_matrix = -np.log(prob_matrix + 1e-10)
cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints, vox)
pred_skel = Info()
parent, key = primMST_symmetry(cost_matrix, root_id, pred_joints)
for i in range(len(parent)):
if parent[i] == -1:
pred_skel.root = TreeNode('root', tuple(pred_joints[i]))
break
loadSkel_recur(pred_skel.root, i, None, pred_joints, parent)
pred_skel.joint_pos = pred_skel.get_joint_dict()
#show_mesh_vox(mesh_filename, vox, pred_skel.root)
img = show_obj_skel(mesh_filename, pred_skel.root)
return pred_skel
def genDataset(process_id):
global dataset_folder
print("process ID {:d}".format(process_id))
if process_id < 6:
model_list = np.loadtxt(os.path.join(dataset_folder,
'train_final.txt'),
dtype=int)
model_list = model_list[365 * process_id:365 * (process_id + 1)]
split_name = 'train'
elif process_id == 6:
model_list = np.loadtxt(os.path.join(dataset_folder, 'val_final.txt'),
dtype=int)
split_name = 'val'
elif process_id == 7:
model_list = np.loadtxt(os.path.join(dataset_folder, 'test_final.txt'),
dtype=int)
split_name = 'test'
mkdir_p(os.path.join(dataset_folder, split_name))
for model_id in model_list:
remeshed_obj_filename = os.path.join(
dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id))
info_filename = os.path.join(
dataset_folder, 'rig_info_remesh/{:d}.txt'.format(model_id))
remeshed_obj = o3d.io.read_triangle_mesh(remeshed_obj_filename)
remesh_obj_v = np.asarray(remeshed_obj.vertices)
remesh_obj_vn = np.asarray(remeshed_obj.vertex_normals)
remesh_obj_f = np.asarray(remeshed_obj.triangles)
rig_info = Info(info_filename)
#vertices
vert_filename = os.path.join(
dataset_folder, '{:s}/{:d}_v.txt'.format(split_name, model_id))
input_feature = np.concatenate((remesh_obj_v, remesh_obj_vn), axis=1)
np.savetxt(vert_filename, input_feature, fmt='%.6f')
#topology edges
edge_index = get_tpl_edges(remesh_obj_v, remesh_obj_f)
graph_filename = os.path.join(
dataset_folder, '{:s}/{:d}_tpl_e.txt'.format(split_name, model_id))
np.savetxt(graph_filename, edge_index, fmt='%d')
# geodesic_edges
surface_geodesic = calc_surface_geodesic(remeshed_obj)
edge_index = get_geo_edges(surface_geodesic, remesh_obj_v)
graph_filename = os.path.join(
dataset_folder, '{:s}/{:d}_geo_e.txt'.format(split_name, model_id))
np.savetxt(graph_filename, edge_index, fmt='%d')
# joints
joint_pos = rig_info.get_joint_dict()
joint_name_list = list(joint_pos.keys())
joint_pos_list = list(joint_pos.values())
joint_pos_list = [np.array(i) for i in joint_pos_list]
adjacent_matrix = rig_info.adjacent_matrix()
joint_filename = os.path.join(
dataset_folder, '{:s}/{:d}_j.txt'.format(split_name, model_id))
adj_filename = os.path.join(
dataset_folder, '{:s}/{:d}_adj.txt'.format(split_name, model_id))
np.savetxt(adj_filename, adjacent_matrix, fmt='%d')
np.savetxt(joint_filename, np.array(joint_pos_list), fmt='%.6f')
# pre_trained attn
shutil.copyfile(
os.path.join(dataset_folder,
'pretrain_attention/{:d}.txt'.format(model_id)),
os.path.join(dataset_folder,
'{:s}/{:d}_attn.txt'.format(split_name, model_id)))
# voxel
shutil.copyfile(
os.path.join(dataset_folder, 'vox/{:d}.binvox'.format(model_id)),
os.path.join(dataset_folder,
'{:s}/{:d}.binvox'.format(split_name, model_id)))
#skinning information
num_nearest_bone = 5
geo_dist = np.load(
os.path.join(
dataset_folder,
"volumetric_geodesic/{:d}_volumetric_geo.npy".format(
model_id)))
bone_pos, bone_names, bone_isleaf = get_bones(rig_info)
input_samples = [] # mesh_vertex_id, (bone_id, 1 / D_g, is_leaf) * N
ground_truth_labels = [] # w_1, w_2, ..., w_N
for vert_remesh_id in range(len(remesh_obj_v)):
this_sample = [vert_remesh_id]
this_label = []
skin = rig_info.joint_skin[vert_remesh_id]
skin_w = {}
for i in np.arange(1, len(skin), 2):
skin_w[skin[i]] = float(skin[i + 1])
bone_id_near_to_far = np.argsort(geo_dist[vert_remesh_id, :])
for i in range(num_nearest_bone):
if i >= len(bone_id_near_to_far):
this_sample += [-1, 0, 0]
this_label.append(0.0)
continue
bone_id = bone_id_near_to_far[i]
this_sample.append(bone_id)
this_sample.append(1.0 /
(geo_dist[vert_remesh_id, bone_id] + 1e-10))
this_sample.append(bone_isleaf[bone_id])
start_joint_name = bone_names[bone_id][0]
if start_joint_name in skin_w:
this_label.append(skin_w[start_joint_name])
del skin_w[start_joint_name]
else:
this_label.append(0.0)
input_samples.append(this_sample)
ground_truth_labels.append(this_label)
with open(
os.path.join(dataset_folder, '{:s}/{:d}_skin.txt').format(
split_name, model_id), 'w') as fout:
for i in range(len(bone_pos)):
fout.write('bones {:s} {:s} {:.6f} {:.6f} {:.6f} '
'{:.6f} {:.6f} {:.6f}\n'.format(
bone_names[i][0], bone_names[i][1],
bone_pos[i, 0], bone_pos[i, 1], bone_pos[i, 2],
bone_pos[i, 3], bone_pos[i, 4], bone_pos[i, 5]))
for i in range(len(input_samples)):
fout.write('bind {:d} '.format(input_samples[i][0]))
for j in np.arange(1, len(input_samples[i]), 3):
fout.write('{:d} {:.6f} {:d} '.format(
input_samples[i][j], input_samples[i][j + 1],
input_samples[i][j + 2]))
fout.write('\n')
for i in range(len(ground_truth_labels)):
fout.write('influence ')
for j in range(len(ground_truth_labels[i])):
fout.write('{:.3f} '.format(ground_truth_labels[i][j]))
fout.write('\n')
