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OpenCV物体跟踪树莓派视觉小车实现过程学习

2024-04-02 19:55

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物体跟踪效果展示

 

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()

2、把图像转换为灰度图


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

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)

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)

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)

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：

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 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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