def view_hydra_demo_on_rviz (demo_type, demo_name, freq, speed, prompt=False, verbose=False):
"""
Uses hydra_only.data for the segment to quickly visualize the demo.
@demo_type, @demo_name: demo identification.
@freq: basically measure of fine-ness of timesteps.
@speed: how fast to replay demo.
@prompt: does the user hit enter after each time step?
"""
demo_dir = osp.join(demo_files_dir, demo_type, demo_name)
bag_file = osp.join(demo_dir, demo_names.bag_name)
data_file = osp.join(demo_dir, demo_names.hydra_data_name)
calib_file = osp.join(demo_dir, demo_names.calib_name)
with open(osp.join(demo_dir, demo_names.camera_types_name),'r') as fh: cam_types = yaml.load(fh)
if not osp.isfile(data_file):
yellowprint("%s does not exist for this demo. Extracting now."%demo_names.hydra_data_name)
ed.save_hydra_only(demo_type, demo_name)
with open(data_file, 'r') as fh: dat = cp.load(fh)
# get grippers used
grippers = [key for key in dat.keys() if key in 'lr']
# data
rgbd_dirs = {cam:osp.join(demo_dir,demo_names.video_dir%cam) for cam in cam_types if cam_types[cam] == 'rgbd'}
cam_frames = {cam:'/camera%i_rgb_optical_frame'%cam for cam in rgbd_dirs}
tfm_pubs = {}
hydra_dat = {}
pot_dat = {}
_, hydra_dat['l'], pot_dat['l'] = load_data(data_file, 'l', freq, speed, hydra_only=True)
_, hydra_dat['r'], pot_dat['r'] = load_data(data_file, 'r', freq, speed, hydra_only=True)
tmin, _, nsteps = relative_time_streams(hydra_dat.values() + pot_dat.values(), freq, speed)
if rospy.get_name() == "/unnamed":
rospy.init_node("visualize_demo")
## publishers for unfiltered-data:
for lr in grippers:
tfm_pubs[lr] = rospy.Publisher('/%s_hydra_estimate'%(lr), PoseStamped)
## get the point-cloud stream
pc_strms = {cam:streamize_rgbd_pc(rgbd_dirs[cam], cam_frames[cam], freq, tstart=tmin,speed=speed,verbose=verbose) for cam in rgbd_dirs}
pc_pubs = {cam:rospy.Publisher('/point_cloud%i'%cam, PointCloud2) for cam in rgbd_dirs}
cam_tfms = get_cam_transforms (calib_file, len(cam_types))
for cam in rgbd_dirs:
if cam != 1:
publish_static_tfm(cam_frames[1], cam_frames[cam], cam_tfms[cam])
sleeper = rospy.Rate(freq)
T_far = np.eye(4)
T_far[0:3,3] = [10,10,10]
handles = []
dat_snext = {lr:{} for lr in grippers}
for lr in grippers:
dat_snext[lr]['h'] = stream_soft_next(hydra_dat[lr])
dat_snext[lr]['pot'] = stream_soft_next(pot_dat[lr])
prev_ang = {'l': 0, 'r': 0}
for i in xrange(nsteps):
if prompt:
raw_input("Hit enter when ready.")
if verbose:
print "Time stamp: ", tmin+(0.0+i*speed)/freq
## show the point-cloud:
for cam in pc_strms:
try:
pc = pc_strms[cam].next()
if pc is not None:
if verbose:
print "pc%i ts:"%cam, pc.header.stamp.to_sec()
pc.header.stamp = rospy.Time.now()
pc_pubs[cam].publish(pc)
else:
if verbose:
print "pc%i ts:"%cam,None
except StopIteration:
pass
ests = {}
tfms = []
ang_vals = []
for lr in grippers:
ests[lr] = dat_snext[lr]['h']()
ang_val = dat_snext[lr]['pot']()
if ang_val != None and not np.isnan(ang_val):
prev_ang[lr] = ang_val
ang_val = ang_val
else:
ang_val = prev_ang[lr]
ang_val *= 2
if ests[lr] is None:
tfms.append(T_far)
else:
tfms.append(ests[lr])
ang_vals.append(rad_angle(ang_val))
handles = draw_trajectory(cam_frames[1], tfms, color=(1,1,0,1), open_fracs=ang_vals)
for lr in grippers:
if ests[lr] is not None:
tfm_pubs[lr].publish(conversions.pose_to_stamped_pose(conversions.hmat_to_pose(ests[lr]), cam_frames[1]))
sleeper.sleep()
empty_cloud = PointCloud2()
for cam in pc_pubs: pc_pubs[cam].publish(empty_cloud)
def view_demo_on_rviz(demo_type, demo_name, freq, speed=1.0, main='h', prompt=False, verbose=False):
"""
Visualizes recorded demo on rviz (without kalman filter/smoother data).
@demo_type, @demo_name: demo identification.
@freq: basically measure of fine-ness of timesteps.
@speed: how fast to replay demo.
@main: which sensor to display the marker for
@prompt: does the user hit enter after each time step?
"""
demo_dir = osp.join(demo_files_dir, demo_type, demo_name)
bag_file = osp.join(demo_dir, demo_names.bag_name)
data_file = osp.join(demo_dir, demo_names.data_name)
calib_file = osp.join(demo_dir, demo_names.calib_name)
with open(osp.join(demo_dir, demo_names.camera_types_name),'r') as fh: cam_types = yaml.load(fh)
if not osp.isfile(data_file):
yellowprint("%s does not exist for this demo. Extracting now."%demo_names.data_name)
ed.save_observations_rgbd(demo_type, demo_name)
with open(data_file, 'r') as fh: dat = cp.load(fh)
# get grippers used
grippers = [key for key in dat.keys() if key in 'lr']
# data
rgbd_dirs = {cam:osp.join(demo_dir,demo_names.video_dir%cam) for cam in cam_types if cam_types[cam] == 'rgbd'}
cam_frames = {cam:'/camera%i_rgb_optical_frame'%cam for cam in rgbd_dirs}
tfm_pubs = {}
cam_dat = {}
hydra_dat = {}
pot_dat = {}
_, cam_dat['l'], hydra_dat['l'], pot_dat['l'] = load_data(data_file, 'l', freq, speed)
_, cam_dat['r'], hydra_dat['r'], pot_dat['r'] = load_data(data_file, 'r', freq, speed)
all_cam_strms = []
for lr in 'lr':
for cam in cam_dat[lr].keys():
all_cam_strms.append(cam_dat[lr][cam]['stream'])
tmin, _, nsteps = relative_time_streams(hydra_dat.values() + pot_dat.values() + all_cam_strms, freq, speed)
if rospy.get_name() == "/unnamed":
rospy.init_node("visualize_demo")
## publishers for unfiltered-data:
for lr in grippers:
tfm_pubs[lr] = {}
for cam in cam_types:
tfm_pubs[lr][cam] = rospy.Publisher('/%s_ar%i_estimate'%(lr,cam), PoseStamped)
tfm_pubs[lr]['h'] = rospy.Publisher('/%s_hydra_estimate'%(lr), PoseStamped)
## get the point-cloud stream
pc_strms = {cam:streamize_rgbd_pc(rgbd_dirs[cam], cam_frames[cam], freq, tstart=tmin,speed=speed,verbose=verbose) for cam in rgbd_dirs}
pc_pubs = {cam:rospy.Publisher('/point_cloud%i'%cam, PointCloud2) for cam in rgbd_dirs}
# import IPython
# IPython.embed()
cam_tfms = get_cam_transforms (calib_file, len(cam_types))
for cam in rgbd_dirs:
if cam != 1:
publish_static_tfm(cam_frames[1], cam_frames[cam], cam_tfms[cam])
sleeper = rospy.Rate(freq)
T_far = np.eye(4)
T_far[0:3,3] = [10,10,10]
handles = []
prev_ang = {'l': 0, 'r': 0}
dat_snext = {lr:{} for lr in grippers}
for lr in grippers:
dat_snext[lr]['h'] = stream_soft_next(hydra_dat[lr])
dat_snext[lr]['pot'] = stream_soft_next(pot_dat[lr])
for cam in cam_types:
dat_snext[lr][cam] = stream_soft_next(cam_dat[lr][cam]['stream'])
for i in xrange(nsteps):
if prompt:
raw_input("Hit enter when ready.")
if verbose:
print "Time stamp: ", tmin+(0.0+i*speed)/freq
## show the point-cloud:
found_pc = False
for cam in pc_strms:
try:
pc = pc_strms[cam].next()
if pc is not None:
if verbose:
print "pc%i ts:"%cam, pc.header.stamp.to_sec()
pc.header.stamp = rospy.Time.now()
pc_pubs[cam].publish(pc)
found_pc = True
else:
if verbose:
print "pc%i ts:"%cam,None
except StopIteration:
pass
next_est = {lr:{} for lr in grippers}
tfms = []
ang_vals = []
if main != 'h': main = int(main)
for lr in grippers:
next_est[lr]['h'] = dat_snext[lr]['h']()
for cam in cam_types:
next_est[lr][cam] = dat_snext[lr][cam]()
ang_val = dat_snext[lr]['pot']()
if ang_val != None and not np.isnan(ang_val):
prev_ang[lr] = ang_val
ang_val = ang_val
else:
ang_val = prev_ang[lr]
ang_val *= 2
tfm = next_est[lr][main]
if tfm is None:
tfms.append(T_far)
else:
tfms.append(tfm)
ang_vals.append(rad_angle(ang_val))
handles = draw_trajectory(cam_frames[1], tfms, color=(1,1,0,1), open_fracs=ang_vals)
for lr in grippers:
for m,est in next_est[lr].items():
if est != None:
tfm_pubs[lr][m].publish(conversions.pose_to_stamped_pose(conversions.hmat_to_pose(est), cam_frames[1]))
else:
tfm_pubs[lr][m].publish(conversions.pose_to_stamped_pose(conversions.hmat_to_pose(T_far), cam_frames[1]))
sleeper.sleep()
def view_tracking_on_rviz(demo_type, demo_name, tps_model_fname, freq=30.0, speed=1.0, use_smoother=True, prompt=False, verbose=False):
"""
Visualizes demo after kalman tracking/smoothing on rviz.
@demo_type, @demo_name: demo identification.
@freq: basically measure of fine-ness of timesteps.
@speed: how fast to replay demo.
@main: which sensor to display the marker for
@prompt: does the user hit enter after each time step?
"""
demo_dir = osp.join(demo_files_dir, demo_type, demo_name)
bag_file = osp.join(demo_dir, demo_names.bag_name)
traj_file = osp.join(demo_dir, demo_names.traj_name)
calib_file = osp.join(demo_dir, demo_names.calib_name)
with open(osp.join(demo_dir, demo_names.camera_types_name),'r') as fh: cam_types = yaml.load(fh)
if not osp.isfile(traj_file):
yellowprint("%s does not exist for this demo. Running kalman filter/smoother now with default args."%demo_names.traj_name)
data_file = osp.join(demo_dir, demo_names.data_name)
if not osp.isfile(data_file):
yellowprint("%s does not exist for this demo. Extracting now."%demo_names.data_name)
ed.save_observations_rgbd(demo_type, demo_name)
filter_traj(demo_dir, tps_model_fname=tps_model_fname, save_tps=True, do_smooth=True, plot='', block=False)
with open(traj_file, 'r') as fh: traj = cp.load(fh)
# get grippers used
grippers = traj.keys()
if rospy.get_name() == "/unnamed":
rospy.init_node("visualize_demo")
# data
rgbd_dirs = {cam:osp.join(demo_dir,demo_names.video_dir%cam) for cam in cam_types if cam_types[cam] == 'rgbd'}
pc_pubs = {cam:rospy.Publisher('/point_cloud%i'%cam, PointCloud2) for cam in rgbd_dirs}
cam_frames = {cam:'/camera%i_rgb_optical_frame'%cam for cam in rgbd_dirs}
cam_tfms = get_cam_transforms (calib_file, len(cam_types))
for cam in rgbd_dirs:
if cam != 1:
publish_static_tfm(cam_frames[1], cam_frames[cam], cam_tfms[cam])
# Remove segment "done", it is just a single frame
segs = sorted(traj[grippers[0]].keys())
if 'done' in segs: segs.remove('done')
sleeper = rospy.Rate(freq)
T_far = np.eye(4)
T_far[0:3,3] = [10,10,10]
for seg in segs:
if prompt:
raw_input("Press enter for segment %s."%seg)
else:
yellowprint("Segment %s beginning."%seg)
time.sleep(1)
# Initializing data streams:
traj_strms = {}
pot_strms = {}
tfms_key = 'tfms_s' if use_smoother else 'tfms'
for lr in grippers:
traj_strms[lr] = streamize(traj[lr][seg][tfms_key], traj[lr][seg]['stamps'], freq, avg_transform, speed=speed)
# HACK
pot_strms[lr] = streamize(traj[lr][seg]['pot_angles'][:len(traj[lr][seg]['stamps'])], traj[lr][seg]['stamps'], freq, np.mean, speed=speed)
tmin, tmax, nsteps = relative_time_streams(traj_strms.values() + pot_strms.values(), freq, speed)
pc_strms = {cam:streamize_rgbd_pc(rgbd_dirs[cam], cam_frames[cam], freq, tstart=tmin, tend=tmax,speed=speed,verbose=verbose) for cam in rgbd_dirs}
prev_ang = {'l': 0, 'r': 0}
dat_snext = {lr:{} for lr in grippers}
for lr in grippers:
dat_snext[lr]['traj'] = stream_soft_next(traj_strms[lr])
dat_snext[lr]['pot'] = stream_soft_next(pot_strms[lr])
for i in xrange(nsteps):
if prompt:
raw_input("Hit enter when ready.")
if verbose:
print "Time stamp: ", tmin+(0.0+i*speed)/freq
## show the point-cloud:
found_pc = False
for cam in pc_strms:
try:
pc = pc_strms[cam].next()
if pc is not None:
if verbose:
print "pc%i ts:"%cam, pc.header.stamp.to_sec()
pc.header.stamp = rospy.Time.now()
pc_pubs[cam].publish(pc)
found_pc = True
else:
if verbose:
print "pc%i ts:"%cam,None
except StopIteration:
pass
tfms = []
ang_vals = []
for lr in grippers:
if lr == 'r':
continue
tfm = dat_snext[lr]['traj']()
ang_val = dat_snext[lr]['pot']()
if ang_val != None and not np.isnan(ang_val):
prev_ang[lr] = ang_val
ang_val = ang_val
else:
ang_val = prev_ang[lr]
ang_val *= 2
if tfm is None:
tfms.append(T_far)
else:
tfms.append(tfm)
ang_vals.append(rad_angle(ang_val))
handles = draw_trajectory(cam_frames[cam], tfms, color=(1,0,0,1), open_fracs=ang_vals)
time.sleep(1.0/freq)
def rviz_kalman(demo_dir, bag_file, data_file, calib_file, freq, use_rgbd, use_smoother, use_spline, customized_shift, single_camera):
'''
For rgbd, data_file and bag_file are redundant
Otherwise, demo_dir is redundant
'''
if use_rgbd:
bag_file = osp.join(demo_dir, 'demo.bag')
rgbd1_dir = osp.join(demo_dir, 'camera_#1')
rgbd2_dir = osp.join(demo_dir, 'camera_#2')
data_file = osp.join(demo_dir, 'demo.data')
bag = rosbag.Bag(bag_file)
else:
bag = rosbag.Bag(bag_file)
dat = cp.load(open(data_file))
grippers = dat.keys()
pub = rospy.Publisher('/point_cloud1', PointCloud2)
pub2= rospy.Publisher('/point_cloud2', PointCloud2)
c1_tfm_pub = {}
c2_tfm_pub = {}
hydra_tfm_pub = {}
T_filt = {}
ang_strm = {}
ar1_strm = {}
ar2_strm = {}
hy_strm = {}
smooth_hy = {}
## publishers for unfiltered-data:
for lr in grippers:
c1_tfm_pub[lr] = rospy.Publisher('/%s_ar1_estimate'%(lr), PoseStamped)
c2_tfm_pub[lr] = rospy.Publisher('/%s_ar2_estimate'%(lr), PoseStamped)
hydra_tfm_pub[lr] = rospy.Publisher('/%s_hydra_estimate'%(lr), PoseStamped)
_, _, _, ar1_strm[lr], ar2_strm[lr], hy_strm = relative_time_streams(data_file, lr, freq, single_camera)
## run the kalman filter:
nsteps, tmin, F_means, S,A,R = run_kalman_filter(data_file, lr, freq, use_spline, True, single_camera)
## run the kalman smoother:
S_means, _ = smoother(A, R, F_means, S)
X_kf = np.array(F_means)
X_kf = np.reshape(X_kf, (X_kf.shape[0], X_kf.shape[1])).T
X_ks = np.array(S_means)
X_ks = np.reshape(X_ks, (X_ks.shape[0], X_ks.shape[1])).T
# Shifting between filter and smoother:
if customized_shift != None:
shift = customized_shift
else:
shift = correlation_shift(X_kf, X_ks)
X_ks = np.roll(X_ks,shift,axis=1)
X_ks[:,:shift] = X_ks[:,shift][:,None]
if use_smoother:
T_filt[lr] = state_to_hmat(list(X_ks.T))
else:
T_filt[lr] = state_to_hmat(list(X_kf.T))
## load the potentiometer-angle stream:
pot_data = cp.load(open(data_file))[lr]['pot_angles']
ang_ts = np.array([tt[1] for tt in pot_data]) ## time-stamps
ang_vals = [tt[0] for tt in pot_data] ## angles
# plt.plot(ang_vals)
# plt.show()
ang_vals = [0*open_frac(x) for x in ang_vals]
ang_strm[lr] = streamize(ang_vals, ang_ts, freq, lambda x : x[-1], tmin)
if use_spline:
smooth_hy[lr] = (t for t in fit_spline_to_stream(hy_strm, nsteps))
else:
smooth_hy[lr] = hy_strm
## get the point-cloud stream
cam1_frame_id = '/camera1_rgb_optical_frame'
cam2_frame_id = '/camera2_rgb_optical_frame'
if use_rgbd:
pc1_strm = streamize_rgbd_pc(rgbd1_dir, cam1_frame_id, freq, tmin)
if single_camera:
pc2_strm = streamize_rgbd_pc(None, cam2_frame_id, freq, tmin)
else:
pc2_strm = streamize_rgbd_pc(rgbd2_dir, cam2_frame_id, freq, tmin)
else:
pc1_strm = streamize_pc(bag, '/camera1/depth_registered/points', freq, tmin)
if single_camera:
pc2_strm = streamize_pc(bag, None, freq, tmin)
else:
pc2_strm = streamize_pc(bag, '/camera2/depth_registered/points', freq, tmin)
## get the relative-transforms between the cameras:
cam_tfm = get_cam_transform(calib_file)
publish_static_tfm(cam1_frame_id, cam2_frame_id, cam_tfm)
## frame of the filter estimate:
sleeper = rospy.Rate(freq)
T_far = np.eye(4)
T_far[0:3,3] = [10,10,10]
handles = []
prev_ang = {'l': 0, 'r': 0}
for i in xrange(nsteps):
#raw_input("Hit next when ready.")
print "Kalman ts: ", tmin+(0.0+i)/freq
## show the point-cloud:
found_pc = False
try:
pc = pc1_strm.next()
if pc is not None:
print "pc1 ts:", pc.header.stamp.to_sec()
pc.header.stamp = rospy.Time.now()
pub.publish(pc)
found_pc = True
else:
print "pc1 ts:",None
except StopIteration:
print "pc1 ts: finished"
#print "no more point-clouds"
pass
try:
pc2 = pc2_strm.next()
if pc2 is not None:
#print "pc2 not none"
print "pc2 ts:", pc2.header.stamp.to_sec()
pc2.header.stamp = rospy.Time.now()
pub2.publish(pc2)
found_pc = True
else:
print "pc2 ts:", None
except StopIteration:
print "pc2 ts: finished"
pass
ang_vals = []
T_filt_lr = []
for lr in grippers:
ang_val = soft_next(ang_strm[lr])
if ang_val != None:
prev_ang[lr] = ang_val
ang_val = ang_val
else:
ang_val = prev_ang[lr]
ang_vals.append(ang_val)
T_filt_lr.append(T_filt[lr][i])
handles = draw_trajectory(cam1_frame_id, T_filt_lr, color=(1,1,0,1), open_fracs=ang_vals)
# draw un-filtered estimates:
for lr in grippers:
ar1_est = soft_next(ar1_strm[lr])
if ar1_est != None:
c1_tfm_pub[lr].publish(pose_to_stamped_pose(hmat_to_pose(ar1_est), cam1_frame_id))
else:
c1_tfm_pub[lr].publish(pose_to_stamped_pose(hmat_to_pose(T_far), cam1_frame_id))
ar2_est = soft_next(ar2_strm[lr])
if ar2_est != None:
c2_tfm_pub[lr].publish(pose_to_stamped_pose(hmat_to_pose(ar2_est), cam1_frame_id))
else:
c2_tfm_pub[lr].publish(pose_to_stamped_pose(hmat_to_pose(T_far), cam1_frame_id))
hy_est = soft_next(smooth_hy[lr])
if hy_est != None:
hydra_tfm_pub[lr].publish(pose_to_stamped_pose(hmat_to_pose(hy_est), cam1_frame_id))
else:
hydra_tfm_pub[lr].publish(pose_to_stamped_pose(hmat_to_pose(T_far), cam1_frame_id))
sleeper.sleep()
