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物體跟蹤效果展示

OpenCV物體跟蹤樹莓派視覺小車實現過程學習

過程：

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、運動控制函數

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、舵機角度控制

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）：是頂部的舵機（攝像頭上下擺動的那個舵機）

下面的（5）：是底部的舵機（攝像頭左右擺動的那個舵機）

四、攝像頭&&圖像處理

# 1 Image Process
      img, contours = Image_Processing()

width, height = 160, 120
  camera = cv2.VideoCapture(0)
  camera.set(3,width) 
  camera.set(4,height)

1、打開攝像頭

打開攝像頭，并設置窗口大小。

設置小窗口的原因：小窗口實時性比較好。

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

OpenCV物體跟蹤樹莓派視覺小車實現過程學習

2、把圖像轉換為灰度圖

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

OpenCV物體跟蹤樹莓派視覺小車實現過程學習

3、高斯濾波（去噪）

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

4、亮度增強

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

5、轉換為二進制

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

OpenCV物體跟蹤樹莓派視覺小車實現過程學習

6、閉運算處理

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

OpenCV物體跟蹤樹莓派視覺小車實現過程學習

7、獲取輪廓

#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物體跟蹤樹莓派視覺小車實現過程學習

代碼

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、獲取最大輪廓坐標

由于有可能出現多個物體，我們這里只識別最大的物體（深度學習可以搞分類，還沒學到這，學到了再做），得到它的坐標。

# 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

返回最大輪廓的坐標：

OpenCV物體跟蹤樹莓派視覺小車實現過程學習

六、運動

根據反饋回來的坐標，判斷它的位置，進行運動。

# 3 Move
      Move(x,y)

1、沒有識別到輪廓（靜止）

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

2、向前走

識別到物體，且在正中央（中間1/2區域），讓物體向前走。

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

3、向左轉

物體在左邊1/4區域。

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

4、向右轉

物體在右邊1/4區域。

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

代碼

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)

總代碼

#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()

檢測原理是基于最大輪廓的檢測，沒有用深度學習的分類，所以容易受到干擾，后期學完深度學習會繼續優化。有意見或者想法的朋友歡迎交流。

以上就是OpenCV物體跟蹤樹莓派視覺小車實現過程學習的詳細內容，更多關于OpenCV物體跟蹤樹莓派視覺小車的資料請關注服務器之家其它相關文章！

原文鏈接：https://blog.csdn.net/great_yzl/article/details/120338859

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OpenCV物體跟蹤樹莓派視覺小車實現過程學習

物體跟蹤效果展示

一、初始化

二、運動控制函數

三、舵機角度控制

四、攝像頭&&圖像處理

1、打開攝像頭

2、把圖像轉換為灰度圖

3、高斯濾波（去噪）

4、亮度增強

5、轉換為二進制

6、閉運算處理

7、獲取輪廓

代碼

五、獲取最大輪廓坐標

六、運動

1、沒有識別到輪廓（靜止）

2、向前走

3、向左轉

4、向右轉

代碼

總代碼

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OpenCV物體跟蹤樹莓派視覺小車實現過程學習

物體跟蹤效果展示

一、初始化

二、運動控制函數

三、舵機角度控制

四、攝像頭&&圖像處理

1、打開攝像頭

2、把圖像轉換為灰度圖

3、 高斯濾波（去噪）

4、亮度增強

5、轉換為二進制

6、閉運算處理

7、獲取輪廓

代碼

五、獲取最大輪廓坐標

六、運動

1、沒有識別到輪廓（靜止）

2、向前走

3、向左轉

4、向右轉

代碼

總代碼

延伸 · 閱讀

3、高斯濾波（去噪）