carla创建地图（四）基于ue4创建地图
对于carla这其实是一种没有完成的方法，因为没有办法产生xodr文件，但是其实可以通过采集的道路经纬度通过脚本生成osm文件（已经补充了相关脚本）。这种方法的优势是自由度高，因为在ue4里其实什么都可以做。我在别的平台实现类似功能的时候使用了这种方法，也可以为carla的创建地图提供一种思路。
一、获取道路位置数据
当时需要为一个比较小的城市创建道路模型，由于是小城市所以osm数据很少无法直接使用。当时尝试了两种方法，一种是基于osm的少量数据通过高德地图的api进行道路点位置数据爬取，另一种是通过一个开源程序手动爬取道路数据。两种方法都是可行的，但是当时需要建的只是那个城市的一部分道路，第一种方法需要去除大量数据反而麻烦，所以主要用的第二种方法。
用的是别人开源的程序取的轨迹经纬度，这个程序也是调用的高德的api
轨迹经纬度抓取
每次获得道路数据后，都进行可视化检查一下，可视化代码如下：
import plotly.graph_objects as go
from transform import LngLatTransfer
#轨迹数据
data = [[116.303849,39.9833],[116.304262,39.983314],[116.304262,39.983314],[116.304262,39.983437],[116.304257,39.983603],[116.304257,39.983684],[116.304268,39.983925],[116.304268,39.983957],[116.303983,39.984027],[116.303468,39.984215],[116.303093,39.984349],[116.302621,39.98458],[116.302621,39.98458],[116.303576,39.984762],[116.303683,39.984778],[116.304515,39.984864],[116.3046,39.984869],[116.30519,39.984901],[116.305501,39.984912],[116.305662,39.984918],[116.306027,39.984939],[116.306376,39.98495],[116.306735,39.984961],[116.308076,39.98502],[116.308286,39.98503],[116.308446,39.985036],[116.309326,39.985052],[116.309664,39.985063],[116.309664,39.985063],[116.310083,39.985207],[116.311295,39.98525],[116.315721,39.98539],[116.316713,39.985422],[116.317646,39.985438],[116.320543,39.985524],[116.323241,39.98561],[116.324475,39.985647],[116.331369,39.985889],[116.337082,39.986151],[116.340531,39.986345],[116.344726,39.986511],[116.345064,39.986521],[116.345214,39.986527],[116.348277,39.986581],[116.350595,39.986661],[116.356399,39.98686],[116.358738,39.98694],[116.363888,39.987106],[116.364526,39.987128],[116.366677,39.987198],[116.367187,39.987214],[116.371559,39.987348],[116.37304,39.987358],[116.37658,39.987476],[116.37739,39.987503],[116.378458,39.987546],[116.379546,39.987584],[116.380346,39.987611],[116.381236,39.987643],[116.383151,39.987691],[116.383994,39.987707],[116.384358,39.987713],[116.384948,39.987729],[116.385463,39.98775],[116.387363,39.987788],[116.387738,39.987798],[116.389143,39.987825],[116.38916,39.987825],[116.389911,39.987863],[116.390211,39.987873],[116.393993,39.987997],[116.397265,39.988083],[116.397437,39.988083],[116.397608,39.988083],[116.397608,39.988083],[116.39771,39.98812],[116.397834,39.988152],[116.398118,39.98819],[116.398724,39.988222],[116.400173,39.98826],[116.400371,39.988281],[116.400527,39.988319],[116.400671,39.988372],[116.400779,39.988426],[116.400897,39.988512],[116.400972,39.988587],[116.401047,39.988678],[116.401085,39.988748],[116.401122,39.988855],[116.401165,39.98908],[116.401117,39.990234],[116.401063,39.9915],[116.401058,39.991698],[116.401053,39.992723],[116.401042,39.993115],[116.401036,39.993485],[116.401036,39.993485],[116.402158,39.993517],[116.402361,39.993522],[116.40344,39.993517],[116.403617,39.993517],[116.404003,39.993506],[116.404309,39.993442],[116.404641,39.993324],[116.404824,39.993233],[116.405065,39.993055],[116.405194,39.992932],[116.405301,39.992798],[116.405387,39.992648],[116.405425,39.992508],[116.405425,39.992235],[116.405425,39.992235],[116.405516,39.992208],[116.406186,39.992208],[116.406186,39.992208],[116.406181,39.992095],[116.406186,39.992047],[116.406186,39.992047],[116.406299,39.992041],[116.40654,39.992036],[116.406953,39.992047]]
lon_list = []
lat_list = []
for i in range(len(data)):
lon_ = data[i][0]
lat_ = data[i][1]
lon,lat = LngLatTransfer().GCJ02_to_WGS84(lon_,lat_) #高德经纬度转wgs84经纬度
lon_list.append(lon)
lat_list.append(lat)
scatter = go.Scattermapbox(lon=lon_list #使用Scattermapbox函数画散点图
,lat=lat_list
,name='位置数据'
,mode = "markers+lines"
)
fig = go.Figure(data=scatter) #将散点图导入画布
fig.update_layout(mapbox_style='stamen-terrain') #将地图设置为画布
#可以使用的免费地图："open-street-map", "carto-positron", "carto-darkmatter", "stamen-terrain", "stamen-toner" or "stamen-watercolor"
fig.show()
二、使用ue4道路生成插件 SnappyRoads
这个是一个ue4蓝图类，可以通过拖拽创建道路，淘宝上大概一两块钱吧。也可以自己写一个类似的插件，这个插件就是根据ue4的spline组件开发的，如果只是简单使用就没必要了。
可以改变spline起点和终点的位置和切线进而改变形状，按住alt拖动样条点可以增加新的样条点。
三、ue4的python API接口
第一步获得了道路位置数据，第二步实现了在ue4里创建道路，将二者结合就可以根据实际道路在ue4里创建道路模型了。但是，这里面还有两个问题坐标转换和脚本自动化实现创建道路。二者都将通过python程序实现。
1、坐标转换
坐标转换是指需要把采集到的高德经纬坐标（火星坐标）转换成墨卡托坐标系，上面的可视化程序也用到了高德坐标转换wgs84坐标。
坐标转换程序可以参考：Python坐标转换并保存文件
2、使用python在ue4中创建道路
def spaw_road_all(x_list,y_list,frame_list):
#1、生成道路actor
road_class = unreal.EditorAssetLibrary.load_blueprint_class("/Game/SnappyRoads/Blueprints/BP_SnappyRoad")
road = unreal.EditorLevelLibrary.spawn_actor_from_object(road_class,[0,0,0])
road.set_actor_label('road_test1')
#2、获得道路实例的spline组件
print(road.root_component.get_children_components(True)[0])
spline_component = road.root_component.get_children_components(True)[0]
#3、添加样条点
for i in range(len(frame_list)):
point = unreal.SplinePoint(position = unreal.Vector(x_list[i],y_list[i],0),input_key = float(int(i)+2),scale=[1,1,1])
spline_component.add_point(point)
print('生成点：',x_list[i],y_list[i],frame_list[i])
关于ue4的python使用，可以参考：Python 开发学习笔记
整体代码如下：
import unreal
import csv
def spaw_road_all(x_list,y_list,frame_list):
#1、生成道路actor
road_class = unreal.EditorAssetLibrary.load_blueprint_class("/Game/SnappyRoads/Blueprints/BP_SnappyRoad")
road = unreal.EditorLevelLibrary.spawn_actor_from_object(road_class,[0,0,0])
road.set_actor_label('road_test1')
#2、获得道路实例的spline组件
print(road.root_component.get_children_components(True)[0])
spline_component = road.root_component.get_children_components(True)[0]
#3、添加样条点
for i in range(len(frame_list)):
point = unreal.SplinePoint(position = unreal.Vector(x_list[i],y_list[i],0),input_key = float(int(i)+2),scale=[1,1,1])
spline_component.add_point(point)
print('生成点：',x_list[i],y_list[i],frame_list[i])
def main():
#1、数据读取
x_list= []
y_list= []
frame_list= []
with open(r'D:\desktop\unreal\send_road.csv',encoding='UTF8') as f: #这里读取的数据已经经过坐标转换
f_csv = csv.reader(f)
for row in f_csv:
x_list.append(float(row[1]))
y_list.append(-float(row[2]))
frame_list.append(float(row[0]))
#2、创建道路
spaw_road_all(x_list,y_list,frame_list)
main()
效果如下：
四、位置点数据转osm
1、原始位置点数据
2、位置点转osm数据
import pandas as pd
import numpy as np
#1、获取原始点数据
def read_data(path,start,end,dtype):
# 读取表头
head_row = pd.read_csv(path, nrows=0)
# 表头列转为 list
head_row_list = list(head_row)
# 读取
csv_result = pd.read_csv(path,usecols=head_row_list,dtype=dtype)#{'lon':np.float64,'lat':np.float64}
# row_list = csv_result.values.tolist()
csv_result = csv_result[start:end]
return csv_result
#2、定义函数
#生成点函数
def creat_node(id,lon,lat):
lon = lon - 112.1851171
lat = lat - 30.91744581
content = ' +str(id)+'" '+'version = "3" '+'lat='+'"'+str(lat)+'" '+'lon='+'"'+str(lon)+'"/>'
return content
#生成单位函数
def creat_way(id,node_list):
head = ' + str(id)+'" version = "3" >'
end = ' '
content = []
for node_id in node_list:
content.append(' + str(node_id)+'"/>')
return head , content , end
#3、生成文件
def main():
node_list = []
way_list = []
#基础格式
osm_head1 = ''
osm_head2 = ''
osm_end = ''
#读取数据
data = read_data('data.csv',0,100,{'lon':np.float64,'lat':np.float64})
print('读取数据完成')
#osm数据元素生成
for i in range(len(data)):
id = data['id'].loc[i]
lon = data['lon'].loc[i]
lat = data['lat'].loc[i]
node_content = creat_node(id,lon,lat)
node_list.append(node_content)
group_id_save = []
way_list_ = {}
for i in range(len(data)):
id = data['id'].loc[i]
group_id = data['group_id'].loc[i]
if(group_id in group_id_save):
way_list_[group_id].append(id)
else:
group_id_save.append(group_id)
way_list_[group_id] = [id]
for id in way_list_:
head , content , end = creat_way(id,way_list_[id])
way_list.append([head,content,end])
print('数据处理完成')
#osm存储
Note=open('x3.txt',mode='w')
Note.write(osm_head1+'\n')
Note.write(osm_head2+'\n')
for row in node_list:
Note.write(row+'\n')
for row in way_list:
Note.write(row[0]+'\n')
for nd in row[1]:
Note.write(nd+'\n')
Note.write(' 生成结果
<?xml version="1.0" encoding="UTF-8"?>
<osm version="0.6" >
<node id="1" version = "3" lat="0.0" lon="0.0"/>
<node id="2" version = "3" lat="0.00019585000000077457" lon="-1.810000000546097e-05"/>
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<node id="5" version = "3" lat="0.0031980000000011444" lon="-7.750000000328328e-05"/>
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<nd ref="48"/>
<nd ref="49"/>
<nd ref="50"/>
<nd ref="51"/>
<nd ref="52"/>
<nd ref="53"/>
<nd ref="54"/>
<nd ref="55"/>
<nd ref="56"/>
<nd ref="57"/>
<nd ref="58"/>
<nd ref="59"/>
<nd ref="60"/>
<nd ref="61"/>
<nd ref="62"/>
<nd ref="63"/>
<nd ref="64"/>
<nd ref="65"/>
<nd ref="66"/>
<nd ref="67"/>
<nd ref="68"/>
<nd ref="69"/>
<nd ref="70"/>
<nd ref="71"/>
<nd ref="72"/>
<nd ref="73"/>
<nd ref="74"/>
<nd ref="75"/>
<nd ref="76"/>
<nd ref="77"/>
<nd ref="78"/>
<nd ref="79"/>
<nd ref="80"/>
<nd ref="81"/>
<nd ref="82"/>
<nd ref="83"/>
<nd ref="84"/>
<nd ref="85"/>
<nd ref="86"/>
<nd ref="87"/>
<nd ref="88"/>
<nd ref="89"/>
<nd ref="90"/>
<nd ref="91"/>
<nd ref="92"/>
<nd ref="93"/>
<nd ref="94"/>
<nd ref="95"/>
<nd ref="96"/>
<nd ref="97"/>
<nd ref="98"/>
<nd ref="99"/>
<nd ref="100"/>
<tag k="highway" v="tertiary"/>
</way>
</osm>
3、osm数据转xodr数据
这里直接使用carla自带的osm转xodr程序 PythonAPI\util\osm_to_xodr.py
python osm_to_xodr.py -i x3.osm -o x3.xodr
