def main():
# Parse command-line arguments.
args = parse_args()
Hfile = os.path.join(args.datadir, "H.txt")
obsfile = os.path.join(args.datadir, "obsmat.txt")
# Parse homography matrix.
H = np.loadtxt(Hfile)
Hinv = np.linalg.inv(H)
# Parse pedestrian annotations.
frames, timeframes, timesteps, agents = ewap.parse_annotations(
Hinv, obsfile)
# Agents 319-338
timeline = range(11205, 11554)
print 'Running experiment...'
util.reset_timer()
num_samples = 100
variances = np.array([0.1, 0.3, 1, 3, 10, 30, 100])
entropies = np.zeros_like(variances)
for i, sigma2 in enumerate(variances):
M = np.zeros((2, 2))
total_samples = 0
for frame in timeline:
t = frames[frame]
if t == -1: continue
T = len(timeline)
print '{:.1%} complete'.format(
(T * i + frame - timeline[0]) / (T * len(variances)))
for _ in range(num_samples):
# predictions = util.make_predictions(t, timesteps, agents)
predictions = util.fake_predictions(t, timesteps, agents,
sigma2)
for a, plan in enumerate(predictions.plan):
if plan.shape[0] > 1:
error = predictions.true_paths[a][1, 0:2] - plan[1,
0:2]
M += np.outer(error, error)
total_samples += 1
M /= total_samples
entropies[i] = 0.5 * np.log((2 * np.pi * np.e)**2 * np.linalg.det(M))
# print 'entropy is', entropy
# print 'variance:'
# print M
print 'EXPERIMENT COMPLETE.'
util.report_time()
results = np.column_stack((variances, entropies))
print results
np.savetxt('experiments/entropy_vs_variance.txt', results)
def main():
# Parse command-line arguments.
args = parse_args()
Hfile = os.path.join(args.datadir, "H.txt")
obsfile = os.path.join(args.datadir, "obsmat.txt")
# Parse homography matrix.
H = np.loadtxt(Hfile)
Hinv = np.linalg.inv(H)
# Parse pedestrian annotations.
frames, timeframes, timesteps, agents = ewap.parse_annotations(Hinv, obsfile)
# Agents 319-338
timeline = range(11205, 11554)
print 'Running experiment...'
util.reset_timer()
num_samples = 100
variances = np.array([0.1, 0.3, 1, 3, 10, 30, 100])
entropies = np.zeros_like(variances)
for i, sigma2 in enumerate(variances):
M = np.zeros((2,2))
total_samples = 0
for frame in timeline:
t = frames[frame]
if t == -1: continue
T = len(timeline)
print '{:.1%} complete'.format((T*i+frame-timeline[0])/(T*len(variances)))
for _ in range(num_samples):
# predictions = util.make_predictions(t, timesteps, agents)
predictions = util.fake_predictions(t, timesteps, agents, sigma2)
for a,plan in enumerate(predictions.plan):
if plan.shape[0] > 1:
error = predictions.true_paths[a][1,0:2] - plan[1,0:2]
M += np.outer(error, error)
total_samples += 1
M /= total_samples
entropies[i] = 0.5*np.log((2*np.pi*np.e)**2 * np.linalg.det(M))
# print 'entropy is', entropy
# print 'variance:'
# print M
print 'EXPERIMENT COMPLETE.'
util.report_time()
results = np.column_stack((variances, entropies))
print results
np.savetxt('experiments/entropy_vs_variance.txt', results)
def main():
# Parse command-line arguments.
args = parse_args()
Hfile = os.path.join(args.datadir, "H.txt")
mapfile = os.path.join(args.datadir, "map.png")
obsfile = os.path.join(args.datadir, "obsmat.txt")
destfile = os.path.join(args.datadir, "destinations.txt")
# Parse homography matrix.
H = np.loadtxt(Hfile)
Hinv = np.linalg.inv(H)
# Parse obstacle map.
obs_map = ewap.create_obstacle_map(mapfile)
# Parse pedestrian annotations.
frames, timeframes, timesteps, agents = ewap.parse_annotations(
Hinv, obsfile)
# Parse destinations.
destinations = np.loadtxt(destfile)
# Play the video
seqname = os.path.basename(args.datadir)
cap = cv2.VideoCapture(os.path.join(args.datadir, seqname + ".avi"))
disp = display.Display(cap, Hinv, obs_map, frames, timesteps, agents,
destinations)
disp.model = models.model21
# We can set the exact frame we want to look at:
# disp.set_frame(780) # ETH sequence, beginning
# disp.set_frame(2238) # ETH sequence, agent #48 (1 v 1)
disp.set_frame(11301) # ETH sequence, big crowds both ways
# disp.set_frame(8289) # ETH sequence, agent #175 (1 v crowd)
# disp.set_frame(9867) # ETH sequence, agent #236 (1 v crowd)
# disp.set_frame(8859) # ETH sequence, agent #194 (1 v 1, choosing non-optimal avoidance)
# disp.set_frame(9261) # Hotel sequence, agent #175
# We can also go by minutes & seconds:
# seekpos = 7.5 * 60 * 1000 # About 7 mins 30 secs
# endpos = 8.7 * 60 * 1000 # About 8 mins 40 secs
# cap.set(POS_MSEC, seekpos)
# Alternatively, we can search for a particular agent we're interested in:
# agent_to_search = 194
# ped_path = agents[agent_to_search]
# frame_num = timeframes[int(ped_path[0,0])]
# disp.set_frame(frame_num)
# disp.agent_num = agent_to_search
# disp.do_predictions = False
pl.ion()
display.plot_prediction_metrics([], [], [])
while cap.isOpened():
disp.do_frame()
key = cv2.waitKey(0) & 0xFF
if key == ord('e'):
run_experiment(cap, disp, timeframes, timesteps, agents)
elif key == ord('q') or key == ESC:
break
elif key == ord('r'):
disp.redo_prediction()
elif key == ord(' '):
disp.toggle_prediction()
elif key == LEFT:
disp.back_one_frame()
elif key == UP:
if disp.draw_all_agents: disp.next_sample()
else: disp.next_agent()
elif key == DOWN:
if disp.draw_all_agents: disp.prev_sample()
else: disp.prev_agent()
elif key == ord('a'):
disp.draw_all_agents = not disp.draw_all_agents
disp.draw_all_samples = not disp.draw_all_samples
disp.reset_frame()
elif key == ord('t'):
disp.draw_truth = not disp.draw_truth
disp.reset_frame()
elif key == ord('f'):
disp.draw_plan = not disp.draw_plan
disp.reset_frame()
elif key == ord('p'):
disp.draw_past = not disp.draw_past
disp.reset_frame()
elif key == ord('s'):
disp.draw_samples = (disp.draw_samples +
1) % display.SAMPLE_CHOICES
disp.reset_frame()
cap.release()
cv2.destroyAllWindows()
pl.close('all')
def main():
# Parse command-line arguments.
args = parse_args()
Hfile = os.path.join(args.datadir, "H.txt")
obsfile = os.path.join(args.datadir, "obsmat.txt")
# Parse homography matrix.
H = np.loadtxt(Hfile)
Hinv = np.linalg.inv(H)
# Parse pedestrian annotations.
frames, timeframes, timesteps, agents = ewap.parse_annotations(Hinv, obsfile)
test_sequences = np.array([
range(2112, 2395),
range(3648, 3769),
range(4163, 4374),
range(6797, 7164),
range(7301, 7608),
range(8373, 8620),
range(8859, 9544),
range(9603, 10096),
range(10101, 10528),
range(11205, 11500),
range(11835, 12172),
range(12201, 12382),
])
total_t = 0
for s in test_sequences: total_t += len(s)
model = models.model16
print 'Running experiment...'
util.reset_timer()
num_samples = 5
iters = 0
total_samples = 0
entropies = np.zeros(test_sequences.shape[0])
for i,timeline in enumerate(test_sequences):
M = np.zeros((2,2))
for frame in timeline:
iters += 1
t = frames[frame]
if t == -1: continue
for _ in range(num_samples):
predictions = util.make_predictions(t, timesteps, agents, model)
for a,plan in enumerate(predictions.plan):
if plan.shape[0] > 1:
error = predictions.true_paths[a][1,0:2] - plan[1,0:2]
M += np.outer(error, error)
total_samples += 1
print 'frame {}: {:.1%} complete'.format(frame, iters/total_t)
M /= total_samples
entropies[i] = 0.5*np.log((2*np.pi*np.e)**2 * np.linalg.det(M))
# print 'entropy is', entropy
# print 'variance:'
# print M
print 'EXPERIMENT COMPLETE.'
util.report_time()
np.savetxt('experiments/hyperparam-test-194-202-joint-2.txt', entropies)
def main():
# Parse command-line arguments.
args = parse_args()
Hfile = os.path.join(args.datadir, "H.txt")
mapfile = os.path.join(args.datadir, "map.png")
obsfile = os.path.join(args.datadir, "obsmat.txt")
destfile = os.path.join(args.datadir, "destinations.txt")
# Parse homography matrix.
H = np.loadtxt(Hfile)
Hinv = np.linalg.inv(H)
# Parse obstacle map.
obs_map = ewap.create_obstacle_map(mapfile)
# Parse pedestrian annotations.
frames, timeframes, timesteps, agents = ewap.parse_annotations(Hinv, obsfile)
# Parse destinations.
destinations = np.loadtxt(destfile)
# Play the video
seqname = os.path.basename(args.datadir)
cap = cv2.VideoCapture(os.path.join(args.datadir, seqname+".avi"))
disp = display.Display(cap, Hinv, obs_map, frames, timesteps, agents, destinations)
disp.model = models.model21
# We can set the exact frame we want to look at:
# disp.set_frame(780) # ETH sequence, beginning
# disp.set_frame(2238) # ETH sequence, agent #48 (1 v 1)
disp.set_frame(11301) # ETH sequence, big crowds both ways
# disp.set_frame(8289) # ETH sequence, agent #175 (1 v crowd)
# disp.set_frame(9867) # ETH sequence, agent #236 (1 v crowd)
# disp.set_frame(8859) # ETH sequence, agent #194 (1 v 1, choosing non-optimal avoidance)
# disp.set_frame(9261) # Hotel sequence, agent #175
# We can also go by minutes & seconds:
# seekpos = 7.5 * 60 * 1000 # About 7 mins 30 secs
# endpos = 8.7 * 60 * 1000 # About 8 mins 40 secs
# cap.set(POS_MSEC, seekpos)
# Alternatively, we can search for a particular agent we're interested in:
# agent_to_search = 194
# ped_path = agents[agent_to_search]
# frame_num = timeframes[int(ped_path[0,0])]
# disp.set_frame(frame_num)
# disp.agent_num = agent_to_search
# disp.do_predictions = False
pl.ion()
display.plot_prediction_metrics([], [], [])
while cap.isOpened():
disp.do_frame()
key = cv2.waitKey(0) & 0xFF
if key == ord('e'):
run_experiment(cap, disp, timeframes, timesteps, agents)
elif key == ord('q') or key == ESC:
break
elif key == ord('r'):
disp.redo_prediction()
elif key == ord(' '):
disp.toggle_prediction()
elif key == LEFT:
disp.back_one_frame()
elif key == UP:
if disp.draw_all_agents: disp.next_sample()
else: disp.next_agent()
elif key == DOWN:
if disp.draw_all_agents: disp.prev_sample()
else: disp.prev_agent()
elif key == ord('a'):
disp.draw_all_agents = not disp.draw_all_agents
disp.draw_all_samples = not disp.draw_all_samples
disp.reset_frame()
elif key == ord('t'):
disp.draw_truth = not disp.draw_truth
disp.reset_frame()
elif key == ord('f'):
disp.draw_plan = not disp.draw_plan
disp.reset_frame()
elif key == ord('p'):
disp.draw_past = not disp.draw_past
disp.reset_frame()
elif key == ord('s'):
disp.draw_samples = (disp.draw_samples + 1) % display.SAMPLE_CHOICES
disp.reset_frame()
cap.release()
cv2.destroyAllWindows()
pl.close('all')