# "track_distance_projected_along_half_angle_vec_exit",
"lane_distance_along_curve_secant_entry", # Distance of lane center line to curve secant ceter point at 0 degree angle
# "lane_distance_along_curve_secant_exit", # Distance of lane center line to curve secant ceter point at 180 degree angle
# "oneway_entry", # Is entry way a oneway street?
# "oneway_exit", # Is exit way a oneway street?
"curvature_entry", # Curvature of entry way over INT_DIST
# "curvature_exit", # Curvature of exit way over INT_DIST
"vehicle_speed_entry", # Measured vehicle speed on entry way at INT_DIST
# "vehicle_speed_exit", # Measured vehicle speed on exit way at INT_DIST
# "lane_count_entry", # Total number of lanes in entry way
# "lane_count_exit", # Total number of lanes in exit way
# "has_right_of_way", # Does the vehicle with the respective manoeuver have right of way at the intersection?
"curve_secant_dist"
]
kitti_samples = automatic_test.load_samples('../data/training_data/samples_kitti/samples.pickle')
darmstadt_samples = automatic_test.load_samples('../data/training_data/samples_darmstadt/samples.pickle')
samples = kitti_samples + darmstadt_samples
select_label_method(samples, 'y_distances')
random.shuffle(samples)
sub_samples, test_samples = automatic_test.get_partitioned_samples(samples, 0.8)
train_samples, validation_samples = automatic_test.get_partitioned_samples(sub_samples, 0.75)
# train_samples, validation_samples = automatic_test.get_cross_validation_samples(sub_samples, 4)
rf_algo = regressors.RandomForestAlgorithm(feature_list, single_target_variable=False, random_state=random, n_estimators=27, max_features=12, max_leaf_nodes=5)
rf_algo2 = regressors.RandomForestAlgorithm(feature_list2, single_target_variable=False, random_state=random, n_estimators=35, max_features=5, max_leaf_nodes=8)
algos = [rf_algo, rf_algo2]
results = automatic_test.test(algos, train_samples, test_samples, cross_validation=False)
# automatic_test.show_intersection_plot(results, test_samples, which_samples="best-worst-case", orientation="curve-secant")
automatic_test.show_intersection_plot(results, validation_samples, which_samples="all", orientation="curve-secant")
#!/usr/bin/python
#coding:utf-8
# Comparing random forest and Extra Trees algorithm
import sys
sys.path.append('../')
import automatic_test
import regressors
import reference_implementations
from extract_features import _feature_types
feature_list = _feature_types
rf_algo = regressors.RandomForestAlgorithm(feature_list)
et_algo = regressors.ExtraTreesAlgorithm(feature_list)
algos = [rf_algo, et_algo]
samples = automatic_test.load_samples('../data/training_data/samples_23_09_15/samples.pickle')
samples = automatic_test.normalize_features(samples)
train_sample_sets, test_sample_sets = automatic_test.get_cross_validation_samples(samples, 0.8, 5)
automatic_test.test(algos, train_sample_sets, test_sample_sets, cross_validation=True)
# results = automatic_test.predict(algos, test_samples)
# automatic_test.show_intersection_plot(results, test_samples, which_samples="best-worst-case")
"maxspeed_entry", # Allowed maximum speed on entry way
"maxspeed_exit", # Allowed maximum speed on exit way
"lane_distance_entry_lane_center", # Distance of lane center line to curve secant ceter point at 0 degree angle
"lane_distance_exit_lane_center", # Distance of lane center line to curve secant ceter point at 180 degree angle
"oneway_entry", # Is entry way a oneway street?
"oneway_exit", # Is exit way a oneway street?
"curvature_entry", # Curvature of entry way over INT_DIST
"curvature_exit", # Curvature of exit way over INT_DIST
"bicycle_designated_entry", # Is there a designated bicycle way in the entry street?
"bicycle_designated_exit", # Is there a designated bicycle way in the exit street?
"lane_count_entry", # Total number of lanes in entry way
"lane_count_exit", # Total number of lanes in exit way
"has_right_of_way", # Does the vehicle with the respective manoeuver have right of way at the intersection?
"curve_secant_dist" # Shortest distance from curve secant to intersection center
]
rf_algo_radii = regressors.RandomForestAlgorithm(feature_list)
rf_algo_distances = regressors.RandomForestAlgorithm(feature_list)
samples_radii = automatic_test.load_samples('../data/training_data/samples.pickle')
# samples_radii = automatic_test.normalize_features(samples)
samples_distances = automatic_test.load_samples('../data/training_data/samples.pickle')
# samples_distances = automatic_test.normalize_features(samples_distances)
select_label_method(samples_distances, 'y_distances')
train_samples_radii, test_samples_radii = automatic_test.get_cross_validation_samples(samples_radii, 0.7, 5)
train_samples_distances, test_samples_distances = automatic_test.get_cross_validation_samples(samples_distances, 0.7, 5)
automatic_test.test([rf_algo_radii], train_samples_radii, test_samples_radii, cross_validation=True)
automatic_test.test([rf_algo_distances], train_samples_distances, test_samples_distances, cross_validation=True)
# automatic_test.train([rf_algo_distances], train_samples_distances)
# results = automatic_test.predict([rf_algo_distances], test_samples_distances)
# automatic_test.show_intersection_plot(results, test_samples_distances, which_samples="all")
import reference_implementations
feature_list = [
"intersection_angle", # Angle between entry and exit way
"maxspeed_entry", # Allowed maximum speed on entry way
"maxspeed_exit", # Allowed maximum speed on exit way
"oneway_entry", # Is entry way a oneway street?
"oneway_exit", # Is exit way a oneway street?
"vehicle_speed_entry", # Measured vehicle speed on entry way at INT_DIST
"vehicle_speed_exit", # Measured vehicle speed on exit way at INT_DIST
"curvature_entry",
"curvature_exit",
"curve_secant_dist" # Shortest distance from curve secant to intersection center
]
sample_files = [
"samples_10.pickle",
"samples_20.pickle",
"samples_30.pickle",
"samples_40.pickle",
"samples_50.pickle"
]
for fn in sample_files:
rf_algo = regressors.RandomForestAlgorithm(feature_list)
samples = automatic_test.load_samples('../data/training_data/samples_INT_DIST/'+fn)
samples = automatic_test.normalize_features(samples)
train_sample_sets, test_sample_sets = automatic_test.get_cross_validation_samples(samples, 0.8, 5)
automatic_test.test([rf_algo], train_sample_sets, test_sample_sets, cross_validation=True)
# results = automatic_test.predict(algos, test_samples)
# automatic_test.show_intersection_plot(results, test_samples, which_samples="best-worst-case")
# 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
# plt.title(title)
# sns.plt.show()
def show_bar_plot():
pass
if __name__ == "__main__":
dataset_samples = [("KITTI + Karlsruhe", automatic_test.load_samples("data/training_data/samples_analysis/samples_kitti.pickle")),
("Darmstadt", automatic_test.load_samples("data/training_data/samples_analysis/samples_darmstadt.pickle"))]
sns.set_style("whitegrid", {"legend.frameon":True})
# Intersection angles
figure1, axes1 = sns.plt.subplots(1, 2, sharey=True)
for i, (name, samples) in enumerate(dataset_samples):
ax = axes1[i]
# sns.set_style("whitegrid")
intersection_angles = list(get_array_from_feature(samples, 'intersection_angle')/(np.pi)*180.0)
left_turn_count = len([ia for ia in intersection_angles if ia >= 0.])
right_turn_count = len([ia for ia in intersection_angles if ia < 0.])
print "%s: links: %d/%d rechts: %d/%d" % (name, left_turn_count, len(intersection_angles), right_turn_count, len(intersection_angles))
sns.distplot(intersection_angles, bins=20, kde=False, rug=True, ax=ax)
# sns.plt.bar(intersection_angles, bins=20)
# ax.set_xlabel(u"Kreuzungswinkel [°]")
"maxspeed_exit", # Allowed maximum speed on exit way
"lane_distance_entry_projected_normal",
"lane_distance_exit_projected_normal",
"oneway_entry", # Is entry way a oneway street?
"oneway_exit", # Is exit way a oneway street?
"curvature_entry", # Curvature of entry way over INT_DIST
"curvature_exit", # Curvature of exit way over INT_DIST
"bicycle_designated_entry", # Is there a designated bicycle way in the entry street?
"bicycle_designated_exit", # Is there a designated bicycle way in the exit street?
"lane_count_entry", # Total number of lanes in entry way
"lane_count_exit", # Total number of lanes in exit way
"has_right_of_way", # Does the vehicle with the respective manoeuver have right of way at the intersection?
"curve_secant_dist", # Shortest distance from curve secant to intersection center
]
kitti_samples = automatic_test.load_samples("../data/training_data/samples_15_10_08/samples.pickle")
darmstadt_samples = automatic_test.load_samples("../data/training_data/samples_15_10_20_darmstadt/samples.pickle")
extract_features.select_label_method(kitti_samples, "y_distances")
extract_features.select_label_method(darmstadt_samples, "y_distances")
train_kitti, test_kitti = automatic_test.get_partitioned_samples(kitti_samples, 0.7)
train_darmstadt, test_darmstadt = automatic_test.get_partitioned_samples(darmstadt_samples, 0.7)
train_samples = train_kitti + train_darmstadt
test_samples = test_kitti + test_darmstadt
rf_algo = regressors.RandomForestAlgorithm(feature_list)
print "###### Non-rectified data ######"
print "------ Only KITTI ------"
automatic_test.test([rf_algo], train_kitti, test_kitti, cross_validation=False)
print "------ KITTI + Darmstadt ------"
automatic_test.test([rf_algo], train_samples, test_samples, cross_validation=False)
"lane_distance_exit_projected_normal",
"oneway_entry", # Is entry way a oneway street?
"oneway_exit", # Is exit way a oneway street?
"curvature_entry", # Curvature of entry way over INT_DIST
"curvature_exit", # Curvature of exit way over INT_DIST
"bicycle_designated_entry", # Is there a designated bicycle way in the entry street?
"bicycle_designated_exit", # Is there a designated bicycle way in the exit street?
"lane_count_entry", # Total number of lanes in entry way
"lane_count_exit", # Total number of lanes in exit way
"has_right_of_way", # Does the vehicle with the respective manoeuver have right of way at the intersection?
"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']):
