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.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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