os.path.join(SYNTH_DIR, "fl",
r"\1.avi")]
)
def fl_to_sim(positions_file, (out_pickle, out_avi)):
print "THIS IS", positions_file
positions = np.load(positions_file)
# frames thing
directory = positions_file[:-len('positions.npy')]
cf = pickle.load(open(os.path.join(directory, "config.pickle")))
start_f = cf['start_f']
# open the frame tarball
tf = tarfile.open(os.path.join(directory, "%08d.tar.gz" % start_f),
"r:gz")
positions_interp, missing = measure.interpolate(positions)
pos_derived = measure.compute_derived(positions_interp)
N = len(positions)
state = np.zeros(N, dtype=util.DTYPE_STATE)
state['x'] = positions_interp['x']
state['y'] = positions_interp['y']
state['phi'] = pos_derived['phi']
state['theta'] = np.pi/2.0
env = util.Environmentz((1.5, 2), (240, 320))
images = simulate.render(env, state[:100])
NOISE = 0
regex(r".+/(.+)/positions.npy$"), [
os.path.join(SYNTH_DIR, "fl", r"\1.pickle"),
os.path.join(SYNTH_DIR, "fl", r"\1.avi")
])
def fl_to_sim(positions_file, (out_pickle, out_avi)):
print "THIS IS", positions_file
positions = np.load(positions_file)
# frames thing
directory = positions_file[:-len('positions.npy')]
cf = pickle.load(open(os.path.join(directory, "config.pickle")))
start_f = cf['start_f']
# open the frame tarball
tf = tarfile.open(os.path.join(directory, "%08d.tar.gz" % start_f), "r:gz")
positions_interp, missing = measure.interpolate(positions)
pos_derived = measure.compute_derived(positions_interp)
N = len(positions)
state = np.zeros(N, dtype=util.DTYPE_STATE)
state['x'] = positions_interp['x']
state['y'] = positions_interp['y']
state['phi'] = pos_derived['phi']
state['theta'] = np.pi / 2.0
env = util.Environmentz((1.5, 2), (240, 320))
images = simulate.render(env, state[:100])
NOISE = 0
new_images = simulate.add_noise_background(images, NOISE, NOISE, [])
np.random.seed(0)
cf = pickle.load(open(epoch_config_filename, 'r'))
region = pickle.load(open(region_filename, 'r'))
env = util.Environmentz(cf['field_dim_m'],
cf['frame_dim_pix'])
led_params = pickle.load(open(led_params_filename, 'r'))
eo_params = measure.led_params_to_EO(cf, led_params)
if frame_end > cf['end_f']:
frame_end = cf['end_f']
truth = np.load(os.path.join(epoch_dir, 'positions.npy'))
truth_interp, missing = measure.interpolate(truth)
derived_truth = measure.compute_derived(truth_interp,
T_DELTA)
frame_pos = np.arange(frame_start, frame_end)
# load frames
frames = organizedata.get_frames(epoch_dir, frame_pos)
FRAMEN = len(frames)
coordinates = []
regions = np.zeros((FRAMEN, frames[0].shape[0], frames[0].shape[1]),
dtype=np.uint8)
point_est_track_data = []
for fi, frame in enumerate(frames):
# convert types
vals = dict([(x, []) for x in STATEVARS])
for p in particles:
for v in STATEVARS:
vals[v].append([s[v] for s in p])
for v in STATEVARS:
if v == 'phi':
vals[v] = np.array(vals[v]) % (2*np.pi)
else:
vals[v] = np.array(vals[v])
vals_dict = {}
# build up the dictionary of true values
derived_truth = measure.compute_derived(truth_interp,
T_DELTA,
led_sep_m)
truth_interp_dict = {'x' : truth_interp['x'],
'y' : truth_interp['y'],
'xdot' : derived_truth['xdot'],
'ydot' : derived_truth['ydot'],
'phi' : derived_truth['phi'] % (2*np.pi),
'theta' : np.abs(derived_truth['theta'] - np.pi/2)}
results = {}
pylab.figure(figsize=(8, 10))
for vi, v in enumerate(STATEVARS):
v_bar = np.average(vals[v], axis=1, weights=weights)
if v == "theta" :
v_bar = np.abs(v_bar - np.pi / 2)
STATEVARS = ['x', 'y', 'xdot', 'ydot', 'phi', 'theta']
# convert types
vals = dict([(x, []) for x in STATEVARS])
for p in particles:
for v in STATEVARS:
vals[v].append([s[v] for s in p])
for v in STATEVARS:
if v == 'phi':
vals[v] = np.array(vals[v]) % (2 * np.pi)
else:
vals[v] = np.array(vals[v])
vals_dict = {}
# build up the dictionary of true values
derived_truth = measure.compute_derived(truth_interp, T_DELTA, led_sep_m)
truth_interp_dict = {
'x': truth_interp['x'],
'y': truth_interp['y'],
'xdot': derived_truth['xdot'],
'ydot': derived_truth['ydot'],
'phi': derived_truth['phi'] % (2 * np.pi),
'theta': np.abs(derived_truth['theta'] - np.pi / 2)
}
results = {}
pylab.figure(figsize=(8, 10))
for vi, v in enumerate(STATEVARS):
v_bar = np.average(vals[v], axis=1, weights=weights)
if v == "theta":
v_bar = np.abs(v_bar - np.pi / 2)
led_params_filename), outfile, epoch, frame_start, frame_end):
np.random.seed(0)
cf = pickle.load(open(epoch_config_filename, 'r'))
region = pickle.load(open(region_filename, 'r'))
env = util.Environmentz(cf['field_dim_m'], cf['frame_dim_pix'])
led_params = pickle.load(open(led_params_filename, 'r'))
eo_params = measure.led_params_to_EO(cf, led_params)
if frame_end > cf['end_f']:
frame_end = cf['end_f']
truth = np.load(os.path.join(epoch_dir, 'positions.npy'))
truth_interp, missing = measure.interpolate(truth)
derived_truth = measure.compute_derived(truth_interp, T_DELTA)
frame_pos = np.arange(frame_start, frame_end)
# load frames
frames = organizedata.get_frames(epoch_dir, frame_pos)
FRAMEN = len(frames)
coordinates = []
regions = np.zeros((FRAMEN, frames[0].shape[0], frames[0].shape[1]),
dtype=np.uint8)
point_est_track_data = []
for fi, frame in enumerate(frames):
abs_frame_index = frame_pos[fi]
