def predict(self, sample):
predicted_line = self.get_interpolating_spline_line(sample)
try:
pred = sample_line_all(predicted_line,
sample['label']['selected_method'],
sample)
return pred
except:
import plot_helper
automatic_test.output_sample_features(sample)
plot_helper.plot_intersection(sample, additional_lines=[predicted_line])
curr_distance = 0.
for i in range(len(track)-1):
this_p = np.array([track[i][0], track[i][1]])
next_p = np.array([track[i+1][0], track[i+1][1]])
this_dist = np.linalg.norm(next_p - this_p)
time_diff = (track[i+1][2] - track[i][2]).total_seconds()
curr_distance += this_dist
velocities.append( this_dist/time_diff*3.6)
# velocities.append( this_dist/time_diff*3.6/1.5)
distance.append(curr_distance)
plt.plot(distance, velocities)
plt.show()
files = ['2010_03_09_drive_0019_1.pickle','2010_03_09_drive_0019_2.pickle']
path = '../data/prepared_data/KITTI_and_Karlsruhe/'
kitti_samples = automatic_test.load_samples('../data/training_data/samples_kitti/samples.pickle')
selected_samples = [s for s in kitti_samples if s['pickled_filename'].split('/')[-1] in files]
for s in selected_samples:
automatic_test.output_sample_features(s)
for fn in files:
fn = path + fn
with open(fn, 'r') as f:
int_sit = pickle.load(f)
int_sit['track'] = extract_features.transform_track_to_cartesian(int_sit['track'])
show_velocity_profile(int_sit)
"curve_secant_dist" # Shortest distance from curve secant to intersection center
]
rf_algo = regressors.RandomForestAlgorithm(feature_list)
is_algo = reference_implementations.InterpolatingSplineAlgorithm()
kitti_samples = automatic_test.load_samples('../data/training_data/samples_15_10_12_rectified/samples.pickle')
darmstadt_samples = automatic_test.load_samples('../data/training_data/samples_15_10_20_darmstadt_rectified/samples.pickle')
select_label_method(kitti_samples, 'y_distances')
select_label_method(darmstadt_samples, 'y_distances')
train_samples_kitti, test_samples_kitti = automatic_test.get_partitioned_samples(kitti_samples, 0.7)
train_samples_darmstadt, test_samples_darmstadt = automatic_test.get_partitioned_samples(darmstadt_samples, 0.7)
train_samples = train_samples_kitti + train_samples_darmstadt
test_samples = test_samples_darmstadt + test_samples_kitti
automatic_test.train([rf_algo], train_samples)
results = automatic_test.predict_all_estimators([rf_algo], test_samples)
is_results = automatic_test.predict([is_algo], test_samples)
for sample, rf_prediction, rf_predictions_all_estimators, is_prediction in zip(test_samples,
results[rf_algo]['predictions'],
results[rf_algo]['predictions_all_estimators'],
is_results[is_algo]['predictions']):
predicted_distances = [pred[0] for pred in rf_predictions_all_estimators]
half_angle_vec = get_half_angle_vec(sample['geometry']['exit_line'], sample['X'][_feature_types.index('intersection_angle')])
heatmap = get_heatmap_from_distances_all_predictors(predicted_distances,
sample['geometry']['entry_line'],
sample['geometry']['exit_line'],
half_angle_vec)
# plot_intersection(sample, predicted_distances, heatmap=heatmap, orientation="curve-secant")
automatic_test.output_sample_features(sample, feature_list)
plot_intersection(sample, [rf_prediction, is_prediction], rgbcolors=['b', 'g'], labels=['RF Algorithm', 'Spline Algorithm'], heatmap=heatmap, orientation="curve-secant")
rf_algo_radii = regressors.RandomForestAlgorithm(feature_list)
samples_radii = automatic_test.load_samples('../data/training_data/samples.pickle')
# samples = automatic_test.normalize_features(samples)
select_label_method(samples_radii, 'y_radii')
train_sample_sets_radii, test_sample_sets_radii = automatic_test.get_cross_validation_samples(samples_radii, 0.8, 5)
for train_samples_distances, test_samples_distances, train_samples_radii, test_samples_radii in zip(train_sample_sets_distances, test_sample_sets_distances, train_sample_sets_radii, test_sample_sets_radii):
automatic_test.train([rf_algo_distances], train_samples_distances)
results_distances = automatic_test.predict_all_estimators([rf_algo_distances], test_samples_distances)
automatic_test.train([rf_algo_radii], train_samples_radii)
results_radii = automatic_test.predict_all_estimators([rf_algo_radii], test_samples_radii)
for sample, prediction, predictions_all_estimators, prediction_radii in zip(test_samples_distances, results_distances[rf_algo_distances]['predictions'], results_distances[rf_algo_distances]['predictions_all_estimators'], results_radii[rf_algo_radii]['predictions']):
automatic_test.output_sample_features(sample, rf_algo_distances.features)
predicted_distances = [pred[0] for pred in predictions_all_estimators]
half_angle_vec = get_half_angle_vec(sample['geometry']['exit_line'], sample['X'][_feature_types.index('intersection_angle')])
heatmap = get_heatmap_from_distances_all_predictors(predicted_distances,
sample['geometry']['entry_line'],
sample['geometry']['exit_line'],
half_angle_vec)
# plot_intersection(sample, predicted_distances, heatmap=heatmap, orientation="curve-secant")
plot_intersection( sample,
[prediction, prediction_radii],
rgbcolors=['b', 'c'],
label_methods=['y_distances', 'y_radii'],
labels=['distances', 'radii'],
title=sample['pickled_filename'],
heatmap=heatmap,
orientation="curve-secant")
