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RflySimhighschool/基础智能/e10.距离传感器数据获取实验/RangingImage.py

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2025-07-25 17:54:28 +08:00
import time
import sys
import VisionCaptureApi
import PX4MavCtrlV4 as PX4MavCtrl
import cv2
import UE4CtrlAPI
import json
import numpy as np
import os
# xxx
ue = UE4CtrlAPI.UE4CtrlAPI()
#Create a new MAVLink communication instance, UDP sending port (CopterSims receving port) is 20100
mav = PX4MavCtrl.PX4MavCtrler(1)
# The IP should be specified by the other computer
vis = VisionCaptureApi.VisionCaptureApi()
# Send command to UE4 Window 1 to change resolution
ue.sendUE4Cmd('r.setres 1280x720w',0) # 设置UE4窗口分辨率注意本窗口仅限于显示取图分辨率在json中配置本窗口设置越小资源需求越少。
ue.sendUE4Cmd('t.MaxFPS 30',0) # 设置UE4最大刷新频率同时也是取图频率
ue.sendUE4Cmd('')
time.sleep(2)
# VisionCaptureApi 中的配置函数
vis.jsonLoad() # 加载Config.json中的传感器配置文件
# vis.RemotSendIP = '192.168.3.80'
# 注意手动修改RemotSendIP的值可以将图片发送到本地址
# 如果不修改这个值那么发送的IP地址为json文件中SendProtocol[1:4]定义的IP
# 图片的发送端口为json中SendProtocol[5]定义好的。
isSuss = vis.sendReqToUE4() # 向RflySim3D发送取图请求并验证
if not isSuss: # 如果请求取图失败,则退出
sys.exit(0)
vis.startImgCap() # 开启取图,并启用共享内存图像转发,转发到填写的目录
#mav.InitMavLoop(UDPMode), where UDPMode=0,1,2,3,4
# Use MAVLink_Full Mode to control PX4
# In this mode, this script will send MAVLinkOffboard message to PX4 directly
# and receive MAVLink data from PX4
# Obviously, MAVLink_Full mode is slower than UDP mode, but the functions and data are more comprehensive
mav.InitMavLoop() # the same as mav.InitMavLoop() in other PythonVision demos
time.sleep(1)
print('Start Offboard Send!')
mav.initOffboard()
time.sleep(1)
# Check if the PX4'EKF has correctlly initialized, which is necessary for offboard control
if not mav.isPX4Ekf3DFixed:
print('CopterSim/PX4 still not 3DFxied, please wait and try again.')
sys.exit(0)
else:
print('CopterSim/PX4 3D Fixed, ready to fly.')
mav.SendMavArm(True)
print('Fly to pos 0, 0, -2!')
mav.SendPosNED(0, 0, -2, 0)
lastTime = time.time()
# 获取当前文件所在目录
current_directory = os.path.dirname(os.path.abspath(__file__))
# 构建完整的文件路径
config_file_path = os.path.join(current_directory, 'Config.json')
# 加载测距传感器配置文件
with open(config_file_path, 'r', encoding='utf-8') as config_file:
config = json.load(config_file)
sensors = config['VisionSensors']
sensor = sensors[0]
sensor_distance = sensor['SensorPosXYZ'][0]
while True:
if vis.hasData[0] and vis.hasData[1]: # 是否成功取图和获取测距传感器
img = vis.Img[0] # 获取0号传感器图像数据指针格式为opencv图像格式
obj_distance = vis.Img[1] # 获取1号传感器距离数据指针格式见VisionCaptureApi.DistanceSensor()
# 获取图像尺寸
height, width, _ = img.shape
# 计算中心坐标
center_x = width // 2
center_y = height // 2
crosshair_length = 20
crosshair_thickness = 2
cv2.line(img, (center_x - crosshair_length, center_y), (center_x + crosshair_length, center_y), (0, 0, 255), crosshair_thickness)
cv2.line(img, (center_x, center_y - crosshair_length), (center_x, center_y + crosshair_length), (0, 0, 255), crosshair_thickness)
# 绘制距离信息
distance_text = f"Distance: {obj_distance.Distance} m"
cv2.putText(img, distance_text, (center_x - 50, center_y + crosshair_length + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# 显示图像
cv2.imshow('Image', img)
cv2.waitKey(1)
time.sleep(0.01)
# 注意:距离传感器的数据定义如下
# class DistanceSensor:
# ## @brief DistanceSensor的构造函数
# # @param 初始化类属性
# def __init__(self):
# ## @var DistanceSensor.TimeStamp
# # @brief 这是当前消息的时间戳,初始化为 0
# self.TimeStamp = 0
# ## @var DistanceSensor.Distance
# # @brief 这是距离传感器测量到的距离,初始化为 0
# self.Distance = 0
# ## @var DistanceSensor.CopterID
# # @brief 用于标识直升机的ID初始化为 0
# self.CopterID = 0
# ## @var DistanceSensor.RayStart
# # @brief 这是射线起点的坐标,初始化为[0,0,0]
# self.RayStart = [0,0,0]
# ## @var DistanceSensor.AngEular
# # @brief 这是传感器的欧拉角Euler Angles初始化为[0,0,0]
# self.AngEular = [0,0,0]
# ## @var DistanceSensor.ImpactPoint
# # @brief 这是碰撞点的坐标,初始化为[0,0,0]
# self.ImpactPoint = [0,0,0]
# ## @var DistanceSensor.BoxOri
# # @brief 这是盒子的原点或参考点,初始化为[0,0,0]
# self.BoxOri = [0,0,0]