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 run_experiment(cap, disp, timeframes, timesteps, agents):
# We're going to compare ourselves to the agents with the following IDs.
print 'Running experiment...'
util.reset_timer()
agents_to_test = range(319, 331)
# agents_to_test = [48, 49]
true_paths = []
planned_paths = []
one_step_paths = []
# IGP_scores = np.zeros((len(agents_to_test), 2))
# ped_scores = np.zeros((len(agents_to_test), 2))
display.plot_prediction_metrics([], [], [])
for i, agent in enumerate(agents_to_test):
print 'Agent ', agent
ped_path = agents[agent]
path_length = ped_path.shape[0]
start = 3 # Initializes path with start+1 points.
final_path = np.zeros((path_length, 3))
final_path[0:start+1, :] = ped_path[0:start+1,1:4]
one_step_final_path = final_path.copy()
# Run IGP through the path sequence.
for t in range(start, path_length-1):
frame_num = timeframes[int(ped_path[t,0])]
print 'doing frame', frame_num
disp.set_frame(frame_num)
final_path[t+1], one_step_final_path[t+1] = disp.do_frame(agent, final_path[:t+1], False, True)
if cv2.waitKey(1) != -1:
print 'Canceled!'
return
# Compute the final score for both IGP and pedestrian ground truth.
print 'Agent', agent, 'done.'
# start_time = int(ped_path[0,0])
# other_peds = [agents[a] for a in timesteps[start_time] if a != agent]
# other_paths = [util.get_path_at_time(start_time, fullpath)[1][:,1:4] for fullpath in other_peds]
# IGP_scores[i] = util.length_and_safety(final_path, other_paths)
# ped_scores[i] = util.length_and_safety(ped_path[:,1:4], other_paths)
true_paths.append(ped_path[:,1:4])
planned_paths.append(final_path)
one_step_paths.append(one_step_final_path)
pred_errs = util.calc_pred_scores(true_paths, planned_paths, util.prediction_errors)
path_errs = util.calc_pred_scores(true_paths, planned_paths, util.path_errors)
display.plot_prediction_metrics(pred_errs, path_errs, agents_to_test[0:i+1])
one_step_errors = util.calc_pred_scores(true_paths, one_step_paths, util.prediction_errors)
pl.figure(2)
pl.clf()
display.plot_prediction_error('One-Step Prediction Errors', one_step_errors, agents_to_test[0:i+1])
pl.draw()
# results = np.column_stack((agents_to_test, ped_scores, IGP_scores))
pred_results = np.column_stack((agents_to_test, pred_errs.T))
path_results = np.column_stack((agents_to_test, path_errs.T))
# np.savetxt('experiment.txt', results)
np.savetxt('prediction_errors.txt', pred_results)
np.savetxt('path_errors.txt', path_results)
print 'EXPERIMENT COMPLETE.'
util.report_time()
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 run_experiment(cap, disp, timeframes, timesteps, agents):
# We're going to compare ourselves to the agents with the following IDs.
print 'Running experiment...'
util.reset_timer()
agents_to_test = range(319, 331)
# agents_to_test = [48, 49]
true_paths = []
planned_paths = []
one_step_paths = []
# IGP_scores = np.zeros((len(agents_to_test), 2))
# ped_scores = np.zeros((len(agents_to_test), 2))
display.plot_prediction_metrics([], [], [])
for i, agent in enumerate(agents_to_test):
print 'Agent ', agent
ped_path = agents[agent]
path_length = ped_path.shape[0]
start = 3 # Initializes path with start+1 points.
final_path = np.zeros((path_length, 3))
final_path[0:start + 1, :] = ped_path[0:start + 1, 1:4]
one_step_final_path = final_path.copy()
# Run IGP through the path sequence.
for t in range(start, path_length - 1):
frame_num = timeframes[int(ped_path[t, 0])]
print 'doing frame', frame_num
disp.set_frame(frame_num)
final_path[t + 1], one_step_final_path[t + 1] = disp.do_frame(
agent, final_path[:t + 1], False, True)
if cv2.waitKey(1) != -1:
print 'Canceled!'
return
# Compute the final score for both IGP and pedestrian ground truth.
print 'Agent', agent, 'done.'
# start_time = int(ped_path[0,0])
# other_peds = [agents[a] for a in timesteps[start_time] if a != agent]
# other_paths = [util.get_path_at_time(start_time, fullpath)[1][:,1:4] for fullpath in other_peds]
# IGP_scores[i] = util.length_and_safety(final_path, other_paths)
# ped_scores[i] = util.length_and_safety(ped_path[:,1:4], other_paths)
true_paths.append(ped_path[:, 1:4])
planned_paths.append(final_path)
one_step_paths.append(one_step_final_path)
pred_errs = util.calc_pred_scores(true_paths, planned_paths,
util.prediction_errors)
path_errs = util.calc_pred_scores(true_paths, planned_paths,
util.path_errors)
display.plot_prediction_metrics(pred_errs, path_errs,
agents_to_test[0:i + 1])
one_step_errors = util.calc_pred_scores(true_paths, one_step_paths,
util.prediction_errors)
pl.figure(2)
pl.clf()
display.plot_prediction_error('One-Step Prediction Errors',
one_step_errors, agents_to_test[0:i + 1])
pl.draw()
# results = np.column_stack((agents_to_test, ped_scores, IGP_scores))
pred_results = np.column_stack((agents_to_test, pred_errs.T))
path_results = np.column_stack((agents_to_test, path_errs.T))
# np.savetxt('experiment.txt', results)
np.savetxt('prediction_errors.txt', pred_results)
np.savetxt('path_errors.txt', path_results)
print 'EXPERIMENT COMPLETE.'
util.report_time()
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)