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 run_mst_generate(args):
"""
generate skeleton in batch
:param args: input folder path and data folder path
"""
test_list = np.loadtxt(os.path.join(args.dataset_folder, 'test_final.txt'),
dtype=np.int)
root_select_model = ROOTNET()
root_select_model.to(device)
root_select_model.eval()
root_checkpoint = torch.load(args.rootnet)
root_select_model.load_state_dict(root_checkpoint['state_dict'])
connectivity_model = PairCls()
connectivity_model.to(device)
connectivity_model.eval()
conn_checkpoint = torch.load(args.bonenet)
connectivity_model.load_state_dict(conn_checkpoint['state_dict'])
for model_id in test_list:
print(model_id)
pred_joints, vox = predict_joints(model_id, args)
mesh_filename = os.path.join(args.dataset_folder,
'obj_remesh/{:d}.obj'.format(model_id))
mesh = o3d.io.read_triangle_mesh(mesh_filename)
surface_geodesic = calc_surface_geodesic(mesh)
data = create_single_data(mesh, vox, surface_geodesic, pred_joints)
root_id = getInitId(data, root_select_model)
with torch.no_grad():
cost_matrix, _ = connectivity_model.forward(data)
connect_prob = torch.sigmoid(cost_matrix)
pair_idx = data.pairs.long().data.cpu().numpy()
cost_matrix = np.zeros((data.num_joint[0], data.num_joint[0]))
cost_matrix[pair_idx[:, 0],
pair_idx[:,
1]] = connect_prob.data.cpu().numpy().squeeze()
cost_matrix = cost_matrix + cost_matrix.transpose()
cost_matrix = -np.log(cost_matrix + 1e-10)
#cost_matrix = flip_cost_matrix(pred_joints, cost_matrix)
cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints,
vox)
skel = Skel()
parent, key, root_id = primMST_symmetry(cost_matrix, root_id,
pred_joints)
for i in range(len(parent)):
if parent[i] == -1:
skel.root = TreeNode('root', tuple(pred_joints[i]))
break
loadSkel_recur(skel.root, i, None, pred_joints, parent)
img = show_obj_skel(mesh_filename, skel.root)
cv2.imwrite(
os.path.join(args.res_folder, '{:d}_skel.jpg'.format(model_id)),
img[:, :, ::-1])
skel.save(
os.path.join(args.res_folder, '{:d}_skel.txt'.format(model_id)))
