物體跟蹤效果展示

程序：

一、初始化

二、運動控制函式

三、舵機角度控制

四、攝像頭&&影像處理

1、打開攝像頭

2、把影像轉換為灰度圖

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

物體跟蹤效果展示

程序：

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、運動控制函式

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&&影像處理

# 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，并設定視窗大小，

設定小視窗的原因： 小視窗實時性比較好，

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

2、把影像轉換為灰度圖

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

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)

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)

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)

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

回傳最大輪廓的坐標：

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()

轉載請註明出處，本文鏈接：https://www.uj5u.com/qita/300827.html

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樹莓派視覺小車 -- 物體跟蹤（OpenCV）

物體跟蹤效果展示

程序：

一、初始化

二、運動控制函式

三、舵機角度控制

四、攝像頭&&影像處理

1、打開攝像頭

2、把影像轉換為灰度圖

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

3、高斯濾波（去噪）