def rebuild_centerline(self):
'''rebuild centerline and save data'''
save_path_shapely = self.save_path + '/' + self.city + '_shapely'
save_path_array = self.save_path + '/' + self.city + '_array'
try:
f = open(save_path_shapely, 'rb')
line_set_shapely = pickle.load(f)
f.close()
f = open(save_path_array, 'rb')
line_set_array = pickle.load(f)
f.close()
except:
am = ArgoverseMap()
line_set_shapely = {}
line_set_array = {}
centerline_ind = am.build_centerline_index()[
self.city] # 生成全部centerline
for ctlID, ctlinfo in centerline_ind.items():
line_set_shapely[ctlID] = LineString(ctlinfo.centerline)
line_set_array[ctlID] = ctlinfo.centerline
# 保存
f = open(save_path_shapely, 'wb')
pickle.dump(line_set_shapely, f)
f.close()
f = open(save_path_array, 'wb')
pickle.dump(line_set_array, f)
f.close()
self.line_set_shapely = line_set_shapely
self.line_set_array = line_set_array
def build_city_lane_graphs(
am: ArgoverseMap) -> Mapping[str, Mapping[int, List[int]]]:
"""
Args:
- am
Returns:
- city_graph_dict
"""
city_lane_centerlines_dict = am.build_centerline_index()
city_graph_dict = {}
for city_name in ["MIA", "PIT"]:
city_graph = {}
for lane_id, segment in city_lane_centerlines_dict[city_name].items():
# allow left/right lane changes
if segment.l_neighbor_id:
if lanes_travel_same_direction(lane_id, segment.l_neighbor_id,
am, city_name):
city_graph.setdefault(str(lane_id), []).append(
str(segment.l_neighbor_id))
if segment.r_neighbor_id:
if lanes_travel_same_direction(lane_id, segment.r_neighbor_id,
am, city_name):
city_graph.setdefault(str(lane_id), []).append(
str(segment.r_neighbor_id))
if segment.predecessors:
for pred_id in segment.predecessors:
city_graph.setdefault(str(pred_id),
[]).append(str(lane_id))
if segment.successors:
for succ_id in segment.successors:
city_graph.setdefault(str(lane_id),
[]).append(str(succ_id))
for k, v in city_graph.items():
city_graph[k] = list(set(v))
city_graph[k].sort()
city_graph_dict[city_name] = city_graph
return city_graph_dict
from argoverse.map_representation.map_api import ArgoverseMap
import pandas as pd
import numpy as np
from tqdm import tqdm
avm = ArgoverseMap()
a = avm.build_centerline_index()
'''
AV - Autonomous Vehicle
AGENT - The object with most interesting trajectory or track
OTHERS - Include all the other objects in the scene for which tracks are recorded
'''
X_ID = 3
Y_ID = 4
def vecLink(a, polyID, AVTIME):
a = np.array(a)
ans = []
type = 0 if a[0, 2] == 'AGENT' else 1
for i in range(a.shape[0] - 1):
l, r = a[i], a[i + 1]
# if type == 1 and (l[0] > AVTIME or r[0] > AVTIME):
# break
now = [l[X_ID], l[Y_ID], r[X_ID], r[Y_ID], type,
l[0], # time stamp
r[0],
np.sqrt(np.square(l[X_ID]-r[X_ID])+np.square(l[Y_ID]-r[Y_ID])) / (r[0]-l[0]), # moved l2 distance normalized with time difference