OpenCV物體跟蹤樹莓派視覺小車實現(xiàn)過程學習
更新時間：2021年10月16日 14:41:02 作者：_睿智_
這篇文章主要介紹了OpenCV物體跟蹤樹莓派視覺小車的實現(xiàn)過程學習，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進步
物體跟蹤效果展示
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(shù)
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()
2、把圖像轉(zhuǎn)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(zhuǎn)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(xiàn)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：
六、運動
根據(jù)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(qū)域），讓物體向前走。
#go ahead
elif width/4 <x and x<(width-width/4):
Front(70)
3、向左轉(zhuǎn)
物體在左邊1/4區(qū)域。
#left
elif x < width/4:
Left(50)
4、向右轉(zhuǎn)
物體在右邊1/4區(qū)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(xù)優(yōu)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(xiàn)過程學習的詳細內(nèi)容，更多關于OpenCV物體跟蹤樹莓派視覺小車的資料請關注腳本之家其它相關文章！
相關文章
這篇文章主要介紹了利用python計算windows全盤文件md5值的腳本,文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值,需要的朋友可以參考下2019-07-07
這篇文章主要介紹了Django 多語言教程的實現(xiàn)(i18n)，小編覺得挺不錯的，現(xiàn)在分享給大家，也給大家做個參考。一起跟隨小編過來看看吧2018-07-07
這篇文章主要介紹了python實現(xiàn)json文件的增刪改操作,本文通過示例代碼給大家介紹的非常詳細，對大家的學習或工作具有一定的參考借鑒價值，需要的朋友可以參考下2023-06-06
python?Sweetviz探索性數(shù)據(jù)可視化分析庫使用特征詳解
這篇文章主要為大家介紹了python?Sweetviz探索性數(shù)據(jù)可視化分析庫特征使用詳解,有需要的朋友可以借鑒參考下,希望能夠有所幫助,祝大家多多進步,早日升職加薪2024-01-01
這篇文章主要介紹了Python高并發(fā)和多線程有什么關系,文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值,需要的朋友可以參考下2020-11-11
PDF文件合并工具是非常好用可以把多個pdf文件合并成一個，本文將利用Python中的PyQT5模塊，制作一個簡易的PDF文件合并器，感興趣的可以了解一下2022-03-03
JSON(JavaScript Object Notation) 是一種輕量級的數(shù)據(jù)交換格式。接下來通過本文給大家介紹python解析json文件相關知識，對python解析json文件相關知識感興趣的朋友一起學習吧2016-03-03
解決python文件字符串轉(zhuǎn)列表時遇到空行的問題
下面小編就為大家?guī)硪黄鉀Qpython文件字符串轉(zhuǎn)列表時遇到空行的問題。小編覺得挺不錯的，現(xiàn)在就分享給大家，也給大家做個參考。一起跟隨小編過來看看吧2017-07-07
這篇文章主要介紹了Django組件之cookie與session的使用方法，小編覺得挺不錯的，現(xiàn)在分享給大家，也給大家做個參考。一起跟隨小編過來看看吧2019-01-01
這篇文章主要介紹了Python IO口多路復用的的相關資料，文中講解的非常細致，幫助大家更好的理解和學習，感興趣的朋友可以參考下2020-06-06