def extract_mlp_features(filename):
features = load_label_feature(filename)
mlp_features = None
for feature in features:
if not feature.HasField('lane') or \
not feature.lane.HasField('lane_graph'):
continue
lane_seq_sz = len(feature.lane.lane_graph.lane_sequence)
for i in range(lane_seq_sz):
lane_seq = feature.lane.lane_graph.lane_sequence[i]
if len(lane_seq.features.mlp_features) != mlp_feature_size:
continue
mlp_feature = []
for i in range(mlp_feature_size):
mlp_feature.append(lane_seq.features.mlp_features[i])
mlp_feature.append(lane_seq.label)
mlp_feature_np = np.array(mlp_feature)
if mlp_features is None:
mlp_features = mlp_feature_np
else:
mlp_features = np.concatenate((mlp_features, mlp_feature_np),
axis=0)
if (mlp_features is None) or (np.size(mlp_features) == 0):
return
mlp_features = mlp_features.reshape(
(np.shape(mlp_features)[0] / (mlp_feature_size + 1),
(mlp_feature_size + 1)))
return mlp_features
def extract_mlp_features(filename):
features = load_label_feature(filename)
mlp_features = None
for feature in features:
if not feature.HasField('lane') or \
not feature.lane.HasField('lane_graph'):
continue
lane_seq_sz = len(feature.lane.lane_graph.lane_sequence)
for i in range(lane_seq_sz):
lane_seq = feature.lane.lane_graph.lane_sequence[i]
if len(lane_seq.features.mlp_features) != mlp_feature_size:
continue
mlp_feature = []
for i in range(mlp_feature_size):
mlp_feature.append(lane_seq.features.mlp_features[i])
mlp_feature.append(lane_seq.label)
mlp_feature_np = np.array(mlp_feature)
if mlp_features is None:
mlp_features = mlp_feature_np
else:
mlp_features = np.concatenate(
(mlp_features, mlp_feature_np), axis=0)
if (mlp_features is None) or (np.size(mlp_features) == 0):
return
mlp_features = mlp_features.reshape(
(np.shape(mlp_features)[0] / (mlp_feature_size + 1),
(mlp_feature_size + 1)))
return mlp_features
def extract_mlp_features(filename):
features = load_label_feature(filename)
mlp_features = None
for feature in features:
if not feature.HasField('lane') or \
not feature.lane.HasField('lane_graph'):
continue
lane_seq_sz = len(feature.lane.lane_graph.lane_sequence)
for i in range(lane_seq_sz):
lane_seq = feature.lane.lane_graph.lane_sequence[i]
if len(lane_seq.features.mlp_features) != mlp_feature_size:
continue
mlp_feature = []
for j in range(mlp_feature_size):
mlp_feature.append(lane_seq.features.mlp_features[j])
mlp_feature.append(lane_seq.label)
mlp_feature.append(lane_seq.time_to_lane_edge)
mlp_feature.append(lane_seq.time_to_lane_center)
mlp_feature_np = np.array(mlp_feature)
if mlp_features is None:
mlp_features = mlp_feature_np.reshape(
1, mlp_feature_size + num_output)
else:
mlp_features = np.concatenate(
(mlp_features,
mlp_feature_np.reshape(1, mlp_feature_size + num_output)),
axis=0)
if (mlp_features is None) or (np.size(mlp_features) == 0):
return None
return mlp_features
def extract_mlp_features(filename):
features = load_label_feature(filename)
mlp_features = None
for fea in features:
if not fea.HasField('junction_feature'):
continue
if len(fea.junction_feature.junction_mlp_feature) != mlp_feature_size or \
len(fea.junction_feature.junction_mlp_label) != mlp_label_size:
continue
mlp_feature = []
for i in range(mlp_feature_size):
mlp_feature.append(fea.junction_feature.junction_mlp_feature[i])
for i in range(mlp_label_size):
mlp_feature.append(fea.junction_feature.junction_mlp_label[i])
mlp_feature_np = np.array(mlp_feature)
if mlp_features is None:
mlp_features = mlp_feature_np.reshape(
1, mlp_feature_size+mlp_label_size)
else:
mlp_features = np.concatenate(
(mlp_features, mlp_feature_np.reshape(1, mlp_feature_size+mlp_label_size)), axis=0)
if (mlp_features is None) or (np.size(mlp_features) == 0):
return
#mlp_features = mlp_features.reshape(
# (np.shape(mlp_features)[0] / (mlp_feature_size + 2),
# (mlp_feature_size + 2)))
return mlp_features