# "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")
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
"lane_distance_entry_exact", # Distance of track line to curve secant center point at 0 degree angle
"lane_distance_exit_exact", # Distance of track line to curve secant center 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" # Shortest distance from curve secant to intersection center
]
samples = automatic_test.load_samples('../data/training_data/samples.pickle')
samples = automatic_test.normalize_features(samples)
train_samples, test_samples = automatic_test.get_partitioned_samples(samples, 0.7)
automatic_test.test_feature_permutations(
regressors.RandomForestAlgorithm,
train_samples,
test_samples,
feature_list,
min_num_features=10,
raise_feature_num=False,
cross_validation=False)
import reference_implementations
from extract_features import _feature_types, select_label_method, get_half_angle_vec
import extract_features
from plot_helper import plot_intersection, get_heatmap_from_distances_all_predictors, get_heatmap_from_polar_all_predictors
import numpy.random as random
feature_list = _feature_types
rf_algo = regressors.RandomForestAlgorithm(feature_list)
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')
extract_features.select_label_method(kitti_samples, 'y_radii')
extract_features.select_label_method(darmstadt_samples, 'y_radii')
random.shuffle(kitti_samples)
random.shuffle(darmstadt_samples)
kitti_train_samples, kitti_test_samples = automatic_test.get_partitioned_samples(kitti_samples, 0.7)
darmstadt_train_samples, darmstadt_test_samples = automatic_test.get_partitioned_samples(darmstadt_samples, 0.7)
train_samples = kitti_train_samples + darmstadt_train_samples
test_samples = kitti_test_samples + darmstadt_test_samples
# samples = automatic_test.load_samples('../data/training_data/samples.pickle')
# # samples = automatic_test.normalize_features(samples)
# select_label_method(samples, 'y_distances')
# train_samples, test_samples = automatic_test.get_partitioned_samples(samples, 0.8)
# #automatic_test.test([rf_algo], train_samples, test_samples, cross_validation=False)
automatic_test.train([rf_algo], train_samples)
results = automatic_test.predict_all_estimators([rf_algo], test_samples)
for sample, prediction, predictions_all_estimators in zip(test_samples, results[rf_algo]['predictions'], results[rf_algo]['predictions_all_estimators']):
predicted_distances = [pred[0] for pred in predictions_all_estimators]
sys.path.append('../')
import automatic_test
import regressors
import reference_implementations
import numpy as np
import matplotlib.pyplot as plt
feature_list = [
"lane_distance_entry_exact",
"curve_secant_dist",
"lane_distance_exit_exact",
"maxspeed_entry",
"vehicle_speed_entry",
"vehicle_speed_exit",
"curvature_exit",
"oneway_exit",
"lane_count_entry",
"has_right_of_way",
"curvature_entry"
]
kitti_samples = automatic_test.load_samples('../data/training_data/samples_23_09_15/samples.pickle')
kitti_samples = automatic_test.normalize_features(kitti_samples)
cmu_samples = automatic_test.load_samples('../data/training_data/samples_CMU/samples.pickle')
cmu_samples = automatic_test.normalize_features(cmu_samples)
kitti_train_samples, kitti_test_samples = automatic_test.get_partitioned_samples(kitti_samples, 0.8)
rf_algo = regressors.RandomForestAlgorithm(feature_list)
automatic_test.test([rf_algo], kitti_train_samples, kitti_test_samples, cross_validation=False)
automatic_test.test([rf_algo], kitti_train_samples, cmu_samples, cross_validation=False)
"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)
print "###### Rectified data ######"
kitti_samples_rectified = automatic_test.load_samples("../data/training_data/samples_15_10_08_rectified/samples.pickle")
darmstadt_samples_rectified = automatic_test.load_samples(
"../data/training_data/samples_15_10_20_darmstadt_rectified/samples.pickle"
"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']):
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'],
