def plot_sample_intersection_curvature(samples, title="Sample curvature over intersection coordinates", ax=None, color=None):
"""Plot each sample's curvature relative to the intersection distances coordinate system"""
print "Curvature calculation..."
sample_steps = 100
curvatures = np.zeros((len(samples), sample_steps))
line_dists = np.array(curvatures)
for i, s in enumerate(samples):
track_line = s['geometry']['track_line']
entry_line = s['geometry']['entry_line']
exit_line = s['geometry']['exit_line']
try:
half_angle_vec = extract_features.get_half_angle_vec(exit_line, s['X'][_feature_types.index('intersection_angle')])
# Limit path to a set s_di interval at intersection
# _, track_line = split_path_at_line_dist(track_line, entry_line, half_angle_vec, entry_line.length-36.0)
# track_line, _ = split_path_at_line_dist(track_line, exit_line, half_angle_vec, 36.0)
curvature_sample_coords = [track_line.interpolate(dist).coords[0] for dist in np.linspace(0, track_line.length, sample_steps)]
X, Y = zip(*curvature_sample_coords)
way_line, dists = extract_features.set_up_way_line_and_distances(entry_line, exit_line)
way_line = extract_features.extend_line(way_line, 1000.0, direction="both") # Make sure the way_line is not too short to cover the whole track
LineDistances, _ = extract_features.get_distances_from_cartesian(X, Y, way_line, half_angle_vec)
line_dists[i] = LineDistances - 1000.0 - INT_DIST # Shift to the actual coordinate system
curvatures[i] = extract_features.get_line_curvature(track_line, sample_steps)
except extract_features.NoIntersectionError as e:
#plot_helper.plot_intersection(s, additional_lines=[way_line])
print e
continue
# fig = plt.figure()
# sns.plt.hold(True)
for i in range(curvatures.shape[0]):
handle, = ax.plot(line_dists[i], np.degrees(curvatures[i]), color=color, linestyle='-')
return handle # Only need one
"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")
