OpenCV物体跟踪树莓派视觉小车实现过程学习

时间:2021-10-12 20:47:10

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.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()

 

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 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.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
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
      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。

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原文链接:https://blog.csdn.net/great_yzl/article/details/120338859