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OpenCV物体跟踪树莓派视觉小车实现过程学习

目录
  • 物体跟踪效果展示
  • 一、初始化
  • 二、运动控制函数
  • 三、舵机角度控制
  • 四、摄像头&&图像处理
    • 1、打开摄像头
    • 2、把图像转换为灰度图
    • 3、 高斯滤波(去噪)
    • 4、亮度增强
    • 5、转换为二进制
    • 6、闭运算处理
    • 7、获取轮廓
    • 代码
  • 五、获取最大轮廓坐标
    • 六、运动
      • 1、没有识别到轮廓(静止)
      • 2、向前走
      • 3、向左转
      • 4、向右转
      • 代码
    • 总代码

      物体跟踪效果展示

      OpenCV物体跟踪树莓派视觉小车实现过程学习

       

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      过程:

      一、初始化

      def Motor_Init():
          global L_Motor, R_Motor
          L_Motor= GPIO编程客栈.PWM(l_motor,100)
          R_Motor = GPIO.PWM(r_motor,100)
          L_Motor.start(0)
          R_Motor.start(0) 
      def Direction_Init():
          GPIO.setup(left_back,GPIO.OUT)
          GPIO.setup(left_front,GPIO.OUT)
          GPIO.setup(l_motor,GPIO.OUT)
          
          GPIO.setup(right_front,GPIO.OUT)
          GPIO.setup(right_back,GPIO.OUT)
          GPIO.setup(r_motor,GPIO.OUT)  
      def Servo_Init编程客栈():
          global pwm_servo
          pwm_servo=Adafruit_PCA9685.PCA9685()
      def Init():
          GPIO.setwarnings(False) 
          GPIO.setmode(GPIO.BCM)
          Direction_Init()
          Servo_Init()
          Motor_Init()

      二、运动控制函数

      def Front(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,1)   #left_front
          GPIO.output(left_back,0)    #left_back
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,1)  #right_front
          GPIO.output(right_back,0)   #right_back      
      def Back(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,0)   #left_front
          GPIO.output(left_back,1)    #left_back 
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,0)  #right_front
          GPIO.output(right_back,1)   #right_back 
      def Left(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,0)   #left_front
          GPIO.output(left_back,1)    #left_back
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,1)  #right_front
          GPIO.output(right_back,0)   #right_back
      def Right(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,1)   #left_front
          GPIO.output(left_back,0)    #left_back 
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,0)  #right_front
          GPIO.output(right_back,1)   #right_back 
      def Stop():
          L_Motor.ChangeDutyCycle(0)
          GPIO.output(left_front,0)   #left_front
          GPIO.output(left_back,0)    #left_back
          R_Motor.ChangeDutyCycle(0)
          GPIO.output(right_front,0)  #right_front
          GPIO.output(right_back,0)   #right_back

      三、舵机角度控制

      def set_servo_angle(channel,angle):
          angle=4096*((angle*11)+500)/20000
          pwm_servo.set_pwm_freq(50)                #frequency==50Hz (servo)
          pwm_servo.set_pwm(channel,0,int(angle))
      set_servo_angle(4, 110)     #top servo     lengthwise
          #0:back    180:front    
          set_servo_angle(5, 90)     #bottom servo  crosswise
          #0:left    180:right  

      上面的(4):是顶部的舵机(摄像头上下摆动的那个舵机)

      下面的(5):是底部的舵机(摄像头左右摆动的那个舵机)

      四、摄像头&&图像处理

      # 1 Image Process
              img, contours = Image_Processing()
      width, height = 160, 120
          camera = cv2.VideoCapture(0)
          camera.set(3,width) 
          camera.set(4,height) 

      1、打开摄像头

      打开摄像头,并设置窗口大小。

      设置小窗口的原因: 小窗口实时性比较好。

      # Capture the frames
          ret, frame = camera.read()

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      2、把图像转换为灰度图

      # to gray
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
      cv2.imshow('gray',gray)

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      3、 高斯滤波(去噪)

      # Gausi blur
          blur = cv2.GaussianBlur(gray,(5,5),0)

      4、亮度增强

      #brighten
          blur = cv2.convertScaleAbs(blur, None, 1.5, 30)

      5、转换为二进制

      #to binary
          ret,binary = cv2.threshold(blur,150,255,cv2.THRESH_BINARY_INV)
          cv2.imshow('binary',binary)

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      6、闭运算处理

      #Close
          kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17,17))
          close = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
          cv2.imshow('close',close)

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      7、获取轮廓

      #get contours
          binary_c,contours,hierarchy = cv2.findContours(close, 1, cv2.CHAIN_APPROX_NONE)
          cv2.drawContours(image, contours, -1, (255,0,255), 2)
          cv2.imshow('image', image)

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      代码

      def Image_Processing():
          # Capture the frames
          ret, frame = camera.read()
          # Crop the image
          image = frame
          cv2.imshow('frame',frame)
          # to gray
          gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
          cv2.imshow('gray',gray)
          # Gausi blur
          blur = cv2.GaussianBlur(gray,(5,5),0)
          #brighten
          blur = cv2.convertScaleAbs(blur, None, 1.5, 30)
          #to binary
          ret,binary = cv2.threshold(blur,150,255,cv2.THRESH_BINARY_INV)
          cv2.imshow('binary',binary)
          #Close
          kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17,17))
          close = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
          cv2.imshow('close',close)
          #get contours
          binary_c,contours,hierarchy = cv2.findContours(close, 1, cv2.CHAIN_APPROX_NONE)
          cv2.drawContours(image, contours, -1, (255,0,255), 2)
          cv2.imshow('image', image)
          return frame, contours

      五、获取最大轮廓坐标

      由于有可能出现多个物体,我们这里只识别最大的物体(深度学习可以搞分类,还没学到这,学到了再做),得到它的坐标。

      # 2 get coordinates
              x, y = Get_Coord(img, contours)
      def Get_Coord(img, contours):
          image = img.copy()
          try:
              contour = max(contours, key=cv2.contourArea)
              cv2.drawContours(image, contour, -1, (255,0,255), 2)
              cv2.imshow('new_frame', image)
              # get coord
              M = cv2.moments(contour)
              x = int(M['m10']/M['m00'])
              y = int(M['m01']/M['m00'])
              print(x, y) 
              return x,y
              
          except:
              print 'no objects'
              return 0,0

      返回最大轮廓的坐标:

      OpenCV物体跟踪树莓派视觉小车实现过程学习

      六、运动

      根据反馈回来的坐标,判断它的位置,进行运动。

      # 3 Move
              Move(x,y)

      1、没有识别到轮廓(静止)

          if x==0 and y==0:
              Stop()

      2、向前走

      识别到物体,且在正中央(中间1/2区域),让物体向前走。

      #go ahead
          elif width/4 <x and x<(width-width/4):
              Front(70)

      3、向左转

      物体在左边1/4区域。

      #left
          elif x < width/4:
              Left(50)

      4、向右转

      物体在右边1/4区域。

      #Right
          elif x > (width-width/4):
              Right(50)

      代码

      def Move(x,y):
          global second
          #stop
          if x==0 and y==0:
              Stop()
          #go ahead
          elif width/4 <x and x<(width-width/4):
              Front(70)
          #left
          elif x < width/4:
              Left(50)
          #Right
          elif x > (width-width/4):
              Right(50)

      总代码

      #Object Tracking
      import  RPi.GPIO as GPIO
      import time
      import Adafruit_PCA9685
      import numpy as np
      import cv2
      second = 0 
      width, height = 160, 120
      camera = cv2.VideoCapture(0)
      camera.set(3,width) 
      camera.set(4,height) 
      l_motor = 18
      left_front   =  22
      left_back   =  27
      r_motor = 23
      right_front   = 25
      right_back  =  24 
      def Motor_Init():
          global L_Motor, R_Motor
          L_Motor= GPIO.PWM(l_motor,100)
          R_Motor = GPIO.PWM(r_motor,100)
          L_Motor.start(0)
          R_Motor.start(0) 
       def Direction_Init():
          GPIO.setup(left_back,GPIO.OUT)
          GPIO.setup(left_front,GPIO.OUT)
          GPIO.setup(l_motor,GPIO.OUT)    
          GPIO.setup(right_front,GPIO.OUT)
          GPIO.setup(right_back,GPIO.OUT)
          GPIO.setup(r_motor,GPIO.OUT) 
      def Servo_Init():
          global pwm_servo
          pwm_servo=Adafruit_PCA9685.PCA9685()
      def Init():
          GPIO.setwarnings(False) 
          GPIO.setmode(GPIO.BCM)
          Direction_Init()
          Servo_Init()
          Motor_Init()
      def Front(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,1)   #left_front
          GPIO.output(left_back,0)    #left_back
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,1)  #right_front
          GPIO.output(right_back,0)   #right_back   
      def Back(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,0)   #left_front
          GPIO.output(left_back,1)    #left_back 
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,0)  #right_front
          GPIO.output(right_back,1)   #right_back 
      def Left(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,0)   #left_front
          GPIO.output(left_back,1)    #left_back 
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,1)  #right_front
          GPIO.output(right_back,0)   #right_back  
      def 编程客栈Right(speed):
          L_Motor.ChangeDutyCycle(speed)
          GPIO.output(left_front,1)   #left_front
          GPIO.output(left_back,0)    #left_back 
          R_Motor.ChangeDutyCycle(speed)
          GPIO.output(right_front,0)  #right_front
          GPIO.output(right_back,1)   #right_back
      def Stop():
          L_Motor.ChangeDutyCycle(0)
          GPIO.output(left_front,0)   #left_front
          GPIO.output(left_back,0)    #left_back 
          R_Motor.ChangeDutyCycle(0)
          GPIO.output(right_front,0)  #right_front
          GPIO.output(right_back,0)   #right_back
      def set_servo_angle(channel,angle):
          angle=4096*((angle*11)+500)/20000
          pwm_servo.set_pwm_freq(50)                #frequency==50Hz (servo)
          pwm_servo.set_pwm(channel,0,int(angle)) 
      def Image_Processing():
          # Capture the frames
          ret, frame = camera.read()
          # Crop the image
          image = frame
          cv2.imshow('frame',frame)
          # to gray
          gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
          cv2.imshow('gray',gray)
          # Gausi blur
          blur = cv2.GaussianBlur(gray,(5,5),0)
          #brighten
          blur = cv2.convertScaleAbs(blur, None, 1.5, 30)
          #to binary
          ret,binary = cv2.threshold(blur,150,255,cv2.THRESH_BINARY_INV)
          cv2.imshow('binary',binary)
          #Close
          kernel = cv2.gewww.cppcns.comtStructuringElement(cv2.MORPH_RECT, (17,17))
          close = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
          cv2.imshow('close',close)
          #get contours
          binary_c,contours,hierarchy = cv2.findContours(close, 1, cv2.CHAIN_APPROX_NONE)
          cv2.drawContours(image, contours, -1, (255,0,255), 2)
          cv2.imshow('image', image)
          return frame, contours
      def Get_Coord(img, contours):
          image = img.copy()
          try:
              contour = max(contours, key=cv2.contourArea)
              cv2.drawContours(image, contour, -1, (255,0,255), 2)
              cv2.imshow('new_frame', image)
              # get coord
              M = cv2.moments(contour)
              x = int(M['m10']/M['m00'])
              y = int(M['m01']/M['m00'])
              print(x, y) 
              return x,y        
          except:
              print 'no objects'
              return 0,0    
      def Move(x,y):
          global second
          #stop
          if x==0 and y==0:
              Stop()
          #go ahead
          elif width/4 <x and x<(width-width/4):
              Front(70)
          #left
          elif x < width/4:
              Left(50)
          #Right
          elif x > (width-width/4):
              Right(50)   
      if __name__ == '__main__':
          Init()    
          set_servo_angle(4, 110)     #top servo     lengthwise
          #0:back    180:front    
          set_servo_angle(5, 90)     #bottom servo  crosswise
          #0:left    180:right      
          while 1:
              # 1 Image Process
        http://www.cppcns.com      img, contours = Image_Processing() 
              # 2 get coordinates
              x, y = Get_Coord(img, contours)
              # 3 Move
              Move(x,y)       
              # must include this codes(otherwise you can't open camera successfully)
              if cv2.waitKey(1) & 0xFF == ord('q'):
                  Stop()
                  GPIO.cleanup()    
                  break    
          #Front(50)
          #Back(50)
          #$Left(50)
          #Right(50)
          #time.sleep(1)
          #Stop()
       

      检测原理是基于最大轮廓的检测,没有用深度学习的分类,所以容易受到干扰,后期学完深度学习会继续优化。有意见或者想法的朋友欢迎交流。

      以上就是OpenCV物体跟踪树莓派视觉小车实现过程学习的详细内容,更多关于OpenCV物体跟踪树莓派视觉小车的资料请关注我们其它相关文章!

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