103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
# import required libraries
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import cv2
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import time
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import math
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import sys
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import threading
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# import RflySim APIs
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import PX4MavCtrlV4 as PX4MavCtrl
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import VisionCaptureApi
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import UE4CtrlAPI
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ue = UE4CtrlAPI.UE4CtrlAPI()
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vis = VisionCaptureApi.VisionCaptureApi()
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# VisionCaptureApi 中的配置函数
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vis.jsonLoad() # 加载Config.json中的传感器配置文件
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isSuss = vis.sendReqToUE4() # 向RflySim3D发送取图请求，并验证
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if not isSuss: # 如果请求取图失败，则退出
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sys.exit(0)
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vis.startImgCap(True) # 开启取图，并启用共享内存图像转发，转发到填写的目录
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# Send command to UE4 Window 1 to change resolution
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ue.sendUE4Cmd('r.setres 720x405w',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。
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ue.sendUE4Cmd('t.MaxFPS 30',0) # 设置UE4最大刷新频率，同时也是取图频率
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time.sleep(2)
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# Create MAVLink control API instance
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mav = PX4MavCtrl.PX4MavCtrler(1)
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# Init MAVLink data receiving loop
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mav.InitMavLoop()
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# send vehicle position command to create a man, with copterID=100
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# the man is located before the drone, and rotated to 180 degree (face the drone)
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ue.sendUE4Pos(100,30,0,[1,0,-8.086],[0,0,math.pi])
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time.sleep(1)
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# send command to change object with copterID=100 (the man just created) to a walking style
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ue.sendUE4Cmd('RflyChange3DModel 100 16')
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time.sleep(0.5)
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# send command to the first RflySim3D window, to switch to vehicle 1
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ue.sendUE4Cmd('RflyChangeViewKeyCmd B 1',0)
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print("5s, Arm the drone")
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mav.initOffboard()
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time.sleep(0.5)
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mav.SendMavArm(True) # Arm the drone
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print("Arm the drone!, and fly to NED 0,0,-5")
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time.sleep(0.5)
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mav.SendPosNED(0, 0, -1.7, 0) # Fly to target position 0,0，-1.5
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# Process the image in the following timer
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startTime = time.time()
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lastTime = time.time()
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timeInterval = 1/30.0 # time interval of the timer
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face_cascade=cv2.CascadeClassifier(sys.path[0]+'\cascades\haarcascade_frontalface_default.xml')
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num=0
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lastClock=time.time()
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while True:
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#gImgList = sca.getCVImgList(ImgInfoList)
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#img1=sca.getCVImg(ImgInfo1)
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# Get the first camera view and change to gray
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if vis.hasData[0]:
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pic1=cv2.cvtColor(vis.Img[0], cv2.COLOR_BGR2GRAY)
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faces1=face_cascade.detectMultiScale(pic1,1.3,5) # face recognition for the first camera
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for (x,y,w,h) in faces1:
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pic1=cv2.rectangle(pic1,(x,y),(x+w,y+h),(255,0,0),1) # Draw a rectangle to mark the face
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cv2.imshow("pic1",pic1) # Show the processed image
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if vis.hasData[1]:
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#img2=sca.getCVImg(ImgInfo2)
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# Get the second camera view and change to gray
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pic2=cv2.cvtColor(vis.Img[1], cv2.COLOR_BGR2GRAY)
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faces2=face_cascade.detectMultiScale(pic2,1.3,5) # face recognition for the second camera
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for (x,y,w,h) in faces2:
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pic2=cv2.rectangle(pic2,(x,y),(x+w,y+h),(255,0,0),1)
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cv2.imshow("pic2",pic2)
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# add target tracking algorithm here with mav.SendVelNED or SendVelFRD API
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cv2.waitKey(1)
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num=num+1
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if num%100==0:
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tiem=time.time()
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print('MainThreadFPS: '+str(100/(tiem-lastClock)))
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lastClock=tiem
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# The above code will be executed 30Hz (0.033333s)
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lastTime = lastTime + timeInterval
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sleepTime = lastTime - time.time()
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if sleepTime > 0:
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time.sleep(sleepTime)
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else:
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lastTime = time.time()
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