def get_dataset_parking_cars(measure_collections, dataset=None):
if dataset is None:
dataset = DataSet(['PARKING_CAR', 'NO_PARKING_CAR'])
for mc in measure_collections:
features = [
mc.avg_distance,
mc.get_length(),
mc.get_duration(),
mc.get_nr_of_measures(),
mc.get_distance_variance(), mc.avg_speed,
mc.get_acceleration(),
mc.first_measure().distance,
mc.measures[len(mc.measures) / 2].distance,
mc.last_measure().distance
]
for interval, surrounding_mc in mc.time_surrounding_mcs.iteritems():
features.append(surrounding_mc.avg_distance)
features.append(surrounding_mc.avg_speed)
features.append(surrounding_mc.length)
features.append(surrounding_mc.get_acceleration())
ground_truth = 'NO_PARKING_CAR'
gt = mc.get_probable_ground_truth()
if GroundTruthClass.is_parking_car(gt):
ground_truth = 'PARKING_CAR'
dataset.append_sample(features, ground_truth)
return dataset
def create_keras_model_dense():
mc = MeasureCollection()
m = Measurement(1, 1111111111, 48, 14, 4,
GroundTruth(1111, GroundTruthClass.FREE_SPACE))
mc.add_measure(m)
dataset = DataSet.get_raw_sensor_dataset([mc])
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=len(dataset.x[0])))
model.add(Dropout(0.1))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(len(dataset.class_labels), activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
def enhance_dataset2(dataset, predictions):
dataset_normal_plus = DataSet(class_labels=dataset.class_labels,
is_softmax_y=dataset.is_softmax_y)
surrounding_mcs = 30
for i in range(0, len(predictions)):
features = [x for x in dataset.x[i]]
features.append(dataset.class_to_index(predictions[i]))
avg_distance_parking_before = 0.0
avg_distance_parking_after = 0.0
avg_distance_overtaken_before = 0.0
avg_distance_overtaken_after = 0.0
j = i - 1
while j >= i - surrounding_mcs:
if j >= 0 and dataset.mcs[j].length_to(dataset.mcs[i]) < 20.0:
if predictions[j] == 'PARKING_CAR':
avg_distance_parking_before = dataset.x[j][0] - dataset.x[
i][0]
break
elif predictions[j] == 'OVERTAKING_SITUATION':
avg_distance_overtaken_before = dataset.x[j][
0] - dataset.x[i][0]
break
j -= 1
j = i + 1
while j <= i + surrounding_mcs:
if j < len(predictions) and dataset.mcs[j].length_to(
dataset.mcs[i]) < 20.0:
if predictions[j] == 'PARKING_CAR':
avg_distance_parking_after = dataset.x[j][0] - dataset.x[
i][0]
break
elif predictions[j] == 'OVERTAKING_SITUATION':
avg_distance_overtaken_after = dataset.x[j][0] - dataset.x[
i][0]
break
j += 1
features.append(avg_distance_parking_before)
features.append(avg_distance_parking_after)
features.append(avg_distance_overtaken_before)
features.append(avg_distance_overtaken_after)
dataset_normal_plus.x.append(features)
dataset_normal_plus.y_true.append(dataset_normal.y_true[i])
return dataset_normal_plus
def enhance_dataset(dataset, predictions, predictions_are_softmax=False):
dataset_normal_plus = DataSet(class_labels=dataset.class_labels,
is_softmax_y=dataset.is_softmax_y)
surrounding_mcs = 20
for i in range(0, len(predictions)):
features = [x for x in dataset.x[i]]
features.append(dataset.class_to_index(predictions[i]))
j = i - 1
while j >= i - surrounding_mcs:
if j >= 0 and predictions[j] == 'PARKING_CAR' and dataset.mcs[
j].length_to(dataset.mcs[i]) < 20.0:
features.append(dataset.class_to_index(predictions[j]))
#features.extend(dataset.x[j])
features.append(dataset.x[j][0])
else:
features.append(-1.0)
features.append(0.0)
#features.extend([0.0 for cnt in range(len(dataset.x[0]))])
j -= 1
j = i + 1
while j <= i + surrounding_mcs:
if j < len(predictions) and predictions[
j] == 'PARKING_CAR' and dataset.mcs[j].length_to(
dataset.mcs[i]) < 20.0:
features.append(dataset.class_to_index(predictions[j]))
features.append(dataset.x[j][0])
#features.extend(dataset.x[j])
else:
features.append(-1.0)
features.append(0.0)
#features.extend([0.0 for cnt in range(len(dataset.x[0]))])
j += 1
dataset_normal_plus.x.append(features)
dataset_normal_plus.mcs.append(dataset.mcs[i])
dataset_normal_plus.y_true.append(dataset.y_true[i])
return dataset_normal_plus
def get_overtaking_situation_dataset(measure_collections, dataset=None):
if dataset is None:
dataset = DataSet(['NO_OVERTAKING_SITUATION', 'OVERTAKING_SITUATION'])
for mc in measure_collections:
if mc.get_length() > 1.0:
features = [
mc.avg_distance,
mc.get_length(),
mc.get_duration(),
mc.get_nr_of_measures(),
mc.get_distance_variance(), mc.avg_speed,
mc.get_acceleration(),
mc.first_measure().distance,
mc.measures[len(mc.measures) / 2].distance,
mc.last_measure().distance
]
for interval, surrounding_mc in mc.time_surrounding_mcs.iteritems(
):
features.append(surrounding_mc.avg_distance)
features.append(surrounding_mc.avg_speed)
features.append(surrounding_mc.length)
features.append(surrounding_mc.get_acceleration())
ground_truth = 'NO_OVERTAKING_SITUATION'
gt = mc.get_probable_ground_truth()
if GroundTruthClass.is_overtaking_situation(gt):
ground_truth = 'OVERTAKING_SITUATION'
# undersampling
if not GroundTruthClass.is_overtaking_situation(
gt) and random.randint(0, 10) < 10:
dataset.append_sample(features, ground_truth)
elif GroundTruthClass.is_overtaking_situation(gt):
i = 0
while i < 3:
dataset.append_sample(features, ground_truth)
i += 1
return dataset
'mc_min_measure_count': 2,
# 'mc_surrounding_times_s': [2.0, 5.0],
'outlier_threshold_distance': 1.0,
'outlier_threshold_diff': 0.5,
# 'replacement_values': {0.01: 10.01},
'min_measurement_value': 0.06,
}
dataset = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items():
print(file_name)
dataset = DataSet.get_raw_sensor_dataset_per_10cm(measure_collections,
dataset=dataset,
is_softmax_y=True)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
# Generate dummy data
x_train = [x_t for i, x_t in enumerate(dataset.x) if i < len(dataset.x) * 0.8]
y_train = [
x_t for i, x_t in enumerate(dataset.y_true) if i < len(dataset.x) * 0.8
]
x_test = [x_t for i, x_t in enumerate(dataset.x) if i >= len(dataset.x) * 0.8]
y_test = [
x_t for i, x_t in enumerate(dataset.y_true) if i >= len(dataset.x) * 0.8
]
print('x_train[0]', x_train[0])
def create_staged_forest(model, dataset, x_train, y_train, x_test, y_test,
y_pred):
derived_dataset = DataSet(dataset.class_labels, dataset.is_softmax_y)
'mc_min_measure_count': 2,
# 'mc_surrounding_times_s': [2.0, 5.0],
'outlier_threshold_distance': 1.0,
'outlier_threshold_diff': 0.5,
# 'replacement_values': {0.01: 10.01},
'min_measurement_value': 0.06,
}
dataset = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items():
print(file_name)
dataset = DataSet.get_dataset(measure_collections,
dataset=dataset,
use_floats=True)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
# keras.utils.to_categorical(
x_train = np.array(
[x_t for i, x_t in enumerate(dataset.x) if i < len(dataset.x) * 0.8])
y_train = np.array(
[x_t for i, x_t in enumerate(dataset.y_true) if i < len(dataset.x) * 0.8])
#num_classes=len(dataset.class_labels))
x_test = np.array(
[x_t for i, x_t in enumerate(dataset.x) if i >= len(dataset.x) * 0.8])
y_test = np.array(
[x_t for i, x_t in enumerate(dataset.y_true) if i >= len(dataset.x) * 0.8])
#num_classes=len(dataset.class_labels))
dataset_softmax_10cm = None
dataset_normal = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
parking_space_map_clusters, _ = create_parking_space_map(
measure_collections_files_dir)
measure_collections_files_dir = filter_parking_space_map_mcs(
measure_collections_files_dir, parking_space_map_clusters)
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items(
):
print(file_name)
dataset_softmax_10cm = DataSet.get_raw_sensor_dataset_per_10cm(
measure_collections,
dataset=dataset_softmax_10cm,
is_softmax_y=True)
dataset_normal = DataSet.get_dataset(measure_collections,
dataset=dataset_normal)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
start = time.time()
# confusion_m_simp = evaluate_model(simple_dense_model, dataset)
evaluator = DriveByEvaluation()
confusion_m_lstm, predictions = evaluator.evaluate(create_random_forest,
predict,
dataset_normal,
number_of_splits=10,
shuffle=True)
'max_measure_value': 10.0,
# 'replacement_values': {0.01: 10.01},
'min_measurement_value': 0.06
}
camera_folder = scenario_path + '_images_Camera\\'
ground_truth_file = [
os.path.join(camera_folder, f) for f in os.listdir(camera_folder) if
os.path.isfile(os.path.join(camera_folder, f)) and f.startswith('00gt')
][0]
measurements_scenario = Measurement.read(scenario_path,
ground_truth_file,
options=options)
measure_collections_scenario = MeasureCollection.create_measure_collections(
measurements_scenario, options=options)
dataset_scenario = DataSet.get_dataset(measure_collections_scenario)
options['exclude_mcs'] = measure_collections_scenario
dataset = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
for file_name, measure_collections in measure_collections_files_dir.items(
):
dataset = DataSet.get_dataset(measure_collections, dataset=dataset)
clf = RandomForestClassifier(n_estimators=1000, n_jobs=-1, random_state=42)
clf.fit(dataset.x, dataset.y_true)
predictions = clf.predict(np.array(dataset_scenario.x)).reshape(1, -1)[0]
'mc_min_measure_count': 2,
# 'mc_surrounding_times_s': [2.0, 5.0],
'outlier_threshold_distance': 1.0,
'outlier_threshold_diff': 0.5,
# 'replacement_values': {0.01: 10.01},
'min_measurement_value': 0.06,
}
dataset = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items(
):
print(file_name)
dataset = DataSet.get_dataset(measure_collections, dataset=dataset)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
classifiers = {
'DecisionTree_GINI': DecisionTreeClassifier(max_depth=3),
}
for name, clf in classifiers.items():
clf.fit(dataset.x, dataset.y_true)
import pydot
from io import StringIO
#dot_data = StringIO()
tree.export_graphviz(clf, out_file='tree_pruned_2.dot')
measure_collections_files_dir)
measure_collections_files_dir = filter_parking_space_map_mcs(
measure_collections_files_dir, parking_space_map_clusters)
print(MeasureCollection.get_size(measure_collections_files_dir))
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items(
):
print(file_name)
#print(len(measure_collection))
#measure_collection = filter_acceleration_situations(measure_collection)
#print('filtered', len(measure_collection))
#MeasureCollection.write_arff_file(measure_collections1, ml_file_path)
#measure_collection = [mc for mc in measure_collection if mc.length > 0.5]
dataset_raw = DataSet.get_raw_sensor_dataset(measure_collections,
dataset=dataset_raw,
is_softmax_y=True)
dataset_10cm = DataSet.get_raw_sensor_dataset_per_10cm(
measure_collections, dataset=dataset_10cm, is_softmax_y=False)
dataset_10cm_surrounding = DataSet.get_raw_sensor_dataset_per_10cm_p_surroundings(
measure_collections,
dataset=dataset_10cm_surrounding,
is_softmax_y=False)
dataset_parking = DataSet.get_raw_sensor_dataset_parking_space_detection(
measure_collections, dataset=dataset_parking)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
datasets = {
#'dataset_raw': dataset_raw,
'dataset_raw_10cm': dataset_10cm,