Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
93.6k
if recording_name not in ground_pene_dist_list.keys():
ground_pene_dist_list[recording_name] = []
ground_pene_freq_list[recording_name] = []
ground_pene_dist_list[recording_name].append(pene_dist)
ground_pene_freq_list[recording_name].append(pene_freq)
else:
ground_pene_dist_list[recording_name].append(pene_dist)
ground_pene_freq_list[recording_name].append(pene_freq)
if args.visualize or args.render:
############ get smplx vertices
smpl_verts_rec_list = []
joints_rec_list = []
smpl_verts_input_list = []
joints_input_list = []
with torch.no_grad():
for idx in tqdm(range(n_seq)):
cur_total_dim = 0
repr_dict_rec = {}
repr_dict_input = {}
for repr_name in repr_name_list:
repr_dict_rec[repr_name] = motion_repr_rec_list[idx:(idx + 1), ..., cur_total_dim:(cur_total_dim + repr_dim_dict[repr_name])]
repr_dict_rec[repr_name] = torch.from_numpy(repr_dict_rec[repr_name]).to(dist_util.dev())
repr_dict_input[repr_name] = motion_repr_noisy_list[idx:(idx + 1), ..., cur_total_dim:(cur_total_dim + repr_dim_dict[repr_name])]
repr_dict_input[repr_name] = torch.from_numpy(repr_dict_input[repr_name]).to(dist_util.dev())
cur_total_dim += repr_dim_dict[repr_name]
joints_rec, smpl_verts_rec = recover_from_repr_smpl(repr_dict_rec, recover_mode='smplx_params', smplx_model=smplx_neutral, return_verts=True, return_full_joints=True)
joints_input, smpl_verts_input = recover_from_repr_smpl(repr_dict_input, recover_mode='smplx_params', smplx_model=smplx_neutral, return_verts=True, return_full_joints=True)
smpl_verts_rec_list.append(smpl_verts_rec.detach().cpu().numpy())
joints_rec_list.append(joints_rec.detach().cpu().numpy())
smpl_verts_input_list.append(smpl_verts_input.detach().cpu().numpy())
joints_input_list.append(joints_input.detach().cpu().numpy())
smpl_verts_rec_list = np.concatenate(smpl_verts_rec_list, axis=0)
joints_rec_list = np.concatenate(joints_rec_list, axis=0)
smpl_verts_input_list = np.concatenate(smpl_verts_input_list, axis=0)
joints_input_list = np.concatenate(joints_input_list, axis=0)
########### transform back to scene coord
for seq_idx in range(n_seq):
cur_verts_scene_coord = points_coord_trans(smpl_verts_rec_list[seq_idx].reshape(-1, 3), np.linalg.inv(trans_scene2cano_list[seq_idx]))
smpl_verts_rec_list[seq_idx] = cur_verts_scene_coord.reshape(clip_len_rec, -1, 3)
cur_joints_scene_coord = points_coord_trans(joints_rec_list[seq_idx].reshape(-1, 3), np.linalg.inv(trans_scene2cano_list[seq_idx]))
joints_rec_list[seq_idx] = cur_joints_scene_coord.reshape(clip_len_rec, -1, 3)
cur_verts_scene_coord = points_coord_trans(smpl_verts_input_list[seq_idx].reshape(-1, 3), np.linalg.inv(trans_scene2cano_list[seq_idx]))
smpl_verts_input_list[seq_idx] = cur_verts_scene_coord.reshape(clip_len_rec, -1, 3)
cur_joints_scene_coord = points_coord_trans(joints_input_list[seq_idx].reshape(-1, 3), np.linalg.inv(trans_scene2cano_list[seq_idx]))
joints_input_list[seq_idx] = cur_joints_scene_coord.reshape(clip_len_rec, -1, 3)
####################################### visualization #############################
if args.visualize:
for bs in range(0, n_seq, 1):
if bs % args.vis_interval == 0:
for t in range(0, clip_len_rec, 1):
################################# body skeletons
cur_joint_mask_vis = mask_joint_vis_list[bs, t] # [22]
cur_mask_joint_id = np.where(cur_joint_mask_vis == 0)[0].tolist()
skeleton_input_list = vis_skeleton(joints=joints_input_list[bs, t], limbs=LIMBS_BODY_SMPL,
mask_scheme='video', cur_mask_joint_id=cur_mask_joint_id,
color_occ=[0, 128 / 255, 0], color_vis=[0, 128 / 255, 0])
skeleton_rec_list = vis_skeleton(joints=joints_rec_list[bs, t], limbs=LIMBS_BODY_SMPL,
mask_scheme='video', cur_mask_joint_id=cur_mask_joint_id)
################################# foot contact labels
foot_sphere_rec_list = vis_foot_contact(joints=joints_rec_list[bs, t], contact_lbl=contact_lbl_rec_list[bs, t])
################################# body mesh
body_mesh_rec = o3d.geometry.TriangleMesh()
body_mesh_rec.vertices = o3d.utility.Vector3dVector(smpl_verts_rec_list[bs, t])
body_mesh_rec.triangles = o3d.utility.Vector3iVector(smplx_neutral.faces)
body_mesh_rec.compute_vertex_normals()
body_mesh_rec.paint_uniform_color(COLOR_VIS_O3D)
body_mesh_input = o3d.geometry.TriangleMesh()
body_mesh_input.vertices = o3d.utility.Vector3dVector(smpl_verts_input_list[bs, t])
body_mesh_input.triangles = o3d.utility.Vector3iVector(smplx_neutral.faces)
body_mesh_input.compute_vertex_normals()
body_mesh_input.paint_uniform_color([0, 128 / 255, 0])
if args.vis_option == 'mesh':
vis.add_geometry(body_mesh_rec)
vis.add_geometry(body_mesh_input)
if args.vis_option == 'skeleton':
for arrow in skeleton_rec_list:
vis.add_geometry(arrow)
for arrow in skeleton_input_list:
vis.add_geometry(arrow)
for sphere in foot_sphere_rec_list:
vis.add_geometry(sphere)
ctr = vis.get_view_control()
cam_param = ctr.convert_to_pinhole_camera_parameters()
cam_param = update_cam(cam_param, cam2world)
ctr.convert_from_pinhole_camera_parameters(cam_param)
vis.poll_events()
vis.update_renderer()
# time.sleep(0.03)
if args.vis_option == 'mesh':
vis.remove_geometry(body_mesh_rec)
vis.remove_geometry(body_mesh_input)
if args.vis_option == 'skeleton':