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()
