fix：新增例程

人工智能/e3.双目视觉人脸识别实验/ManDetect3.py

@@ -0,0 +1,102 @@
+# import required libraries
+import cv2
+import time
+import math
+import sys
+import threading
+
+# import RflySim APIs
+import PX4MavCtrlV4 as PX4MavCtrl
+
+import VisionCaptureApi
+import UE4CtrlAPI
+ue = UE4CtrlAPI.UE4CtrlAPI()
+
+vis = VisionCaptureApi.VisionCaptureApi()
+
+# VisionCaptureApi 中的配置函数
+vis.jsonLoad() # 加载Config.json中的传感器配置文件
+
+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(True) # 开启取图，并启用共享内存图像转发，转发到填写的目录
+
+# Send command to UE4 Window 1 to change resolution 
+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。
+ue.sendUE4Cmd('t.MaxFPS 30',0) # 设置UE4最大刷新频率，同时也是取图频率
+time.sleep(2)    
+
+# Create MAVLink control API instance
+mav = PX4MavCtrl.PX4MavCtrler(1)
+# Init MAVLink data receiving loop
+mav.InitMavLoop()
+
+
+# send vehicle position command to create a man, with copterID=100 
+# the man is located before the drone, and rotated to 180 degree (face the drone)
+ue.sendUE4Pos(100,30,0,[1,0,-8.086],[0,0,math.pi])
+time.sleep(1)
+
+# send command to change object with copterID=100 (the man just created) to a walking style
+ue.sendUE4Cmd('RflyChange3DModel 100 16') 
+time.sleep(0.5)
+
+# send command to the first RflySim3D window, to switch to vehicle 1
+ue.sendUE4Cmd('RflyChangeViewKeyCmd B 1',0)
+
+print("5s, Arm the drone")
+mav.initOffboard()
+time.sleep(0.5)
+mav.SendMavArm(True) # Arm the drone
+print("Arm the drone!, and fly to NED 0,0,-5")
+time.sleep(0.5)
+mav.SendPosNED(0, 0, -1.7, 0) # Fly to target position 0,0，-1.5
+
+# Process the image in the following timer
+startTime = time.time()
+lastTime = time.time()
+timeInterval = 1/30.0 # time interval of the timer
+face_cascade=cv2.CascadeClassifier(sys.path[0]+'\cascades\haarcascade_frontalface_default.xml')
+
+num=0
+lastClock=time.time()
+while True:
+    #gImgList = sca.getCVImgList(ImgInfoList)
+    #img1=sca.getCVImg(ImgInfo1)
+    # Get the first camera view and change to gray
+    
+    if vis.hasData[0]: 
+        pic1=cv2.cvtColor(vis.Img[0], cv2.COLOR_BGR2GRAY)
+        faces1=face_cascade.detectMultiScale(pic1,1.3,5) # face recognition for the first camera
+        for (x,y,w,h) in faces1:
+            pic1=cv2.rectangle(pic1,(x,y),(x+w,y+h),(255,0,0),1) # Draw a rectangle to mark the face
+        cv2.imshow("pic1",pic1) # Show the processed image 
+        
+    if vis.hasData[1]:         
+        #img2=sca.getCVImg(ImgInfo2)
+        # Get the second camera view and change to gray
+        pic2=cv2.cvtColor(vis.Img[1], cv2.COLOR_BGR2GRAY)
+        faces2=face_cascade.detectMultiScale(pic2,1.3,5) # face recognition for the second camera
+        for (x,y,w,h) in faces2:
+            pic2=cv2.rectangle(pic2,(x,y),(x+w,y+h),(255,0,0),1)
+        cv2.imshow("pic2",pic2)
+    
+    
+    # add target tracking algorithm here with mav.SendVelNED or SendVelFRD API
+
+    cv2.waitKey(1)
+    
+    num=num+1
+    if num%100==0:
+        tiem=time.time()
+        print('MainThreadFPS: '+str(100/(tiem-lastClock)))
+        lastClock=tiem   
+
+    # The above code will be executed 30Hz (0.033333s)    
+    lastTime = lastTime + timeInterval
+    sleepTime = lastTime - time.time()
+    if sleepTime > 0:
+        time.sleep(sleepTime)
+    else:
+        lastTime = time.time()