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

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.tup(left_back,gpio.out)    gpio.tup(left_front,gpio.out)    gpio.tup(l_motor,gpio.out)        gpio.tup(right_front,gpio.out)    gpio.tup(right_back,gpio.out)    gpio.tup(r_motor,gpio.out)  def rvo_init():    global pwm_rvo    pwm_rvo=adafruit_pca9685.pca9685()def init():    gpio.twarnings(fal)     gpio.tmode(gpio.bcm)    direction_init()    rvo_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_backdef right(speed):    l_motor.changedutycycle(speed)    gpio.output(left_front,1)   #left_front    gin前缀pio.output(left_back,0) 反比例   #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 t_rvo_angle(channel,angle):    angle=4096*((angle*11)+500)/20000    pwm_rvo.t_pwm_freq(50)                #frequency==50hz (rvo)    pwm_rvo.t_pwm(channel,0,int(angle))

t_rvo_angle(4, 110)     #top rvo     lengthwi    #0:back    180:front        t_rvo_angle(5, 90)     #bottom rvo  crosswi    #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.t(3,width)     camera.t(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 graygray = cv2.cvtcolor(frame, cv2.color_bgr2gray)cv2.imshow('gray',gray)

3、 外套英文高斯滤波（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

#clo    kernel = cv2.getstructuringelement(cv2.morph_rect, (17,17))    clo = cv2.morphologyex(binary, cv2.morph_clo, kernel)    cv2.imshow('clo',clo)

7、获取轮廓

#get contours    binary_c,contours,hierarchy = cv2.findcontours(clo, 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)    #clo    kernel = cv2.getstructuringelement(cv2.morph_rect, (17,17))    clo = cv2.morphologyex(binary, cv2.morph_clo, kernel)    cv2.imshow('clo',clo)    #get contours    binary_c,contours,hierarchy = cv2.findcontours(clo, 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 cond    #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 trackingimport  rpi.gpio as gpioimport timeimport adafruit_pca9685import numpy as npimport cv2cond = 0 width, height = 160, 120camera = cv2.videocapture(0)camera.t(3,width) camera.t(4,height) l_motor = 18left_front   =  22left_back   =  27r_motor = 23right_front   = 25right_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.tup(left_back,gpio.out)    gpio.tup(left_front,gpio.out)    gpio.tup(l_motor,gpio.out)        gpio.tup(right_front,gpio.out)    gpio.tup(right_back,gpio.out)    gpio.tup(r_motor,gpio.out) def rvo_init():    global pwm_rvo    pwm_rvo=adafruit_pca9685.pca9685()def init():    gpio.twarnings(fal)     gpio.tmode(gpio.bcm)    direction_init()    rvo_init()    motor_init()def front(speed):    l_motor.changedutycycle(speed)    gpio.output(l入党鉴定书eft_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_虱子和跳蚤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_backdef 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_backdef t_rvo_angle(channel,angle):    angle=4096*((angle*11)+500)/20000    pwm_rvo.t_pwm_freq(50)                #frequency==50hz (rvo)    pwm_rvo.t_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)    #clo    kernel = cv2.getstructuringelement(cv2.morph_rect, (17,17))    clo = cv2.morphologyex(binary, cv2.morph_clo, kernel)    cv2.imshow('clo',clo)    #get contours    binary_c,contours,hierarchy = cv2.findcontours(clo, 1, cv2.chain_approx_none)    cv2.drawcontours(image, contours, -1, (255,0,255), 2)    cv2.imshow('image', image)    return frame, contoursdef 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 cond    #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()        t_rvo_angle(4, 110)     #top rvo     lengthwi    #0:back    180:front        t_rvo_angle(5, 90)     #bottom rvo  crosswi    #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(otherwi 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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