樹(shù)莓派智能小車(chē)結(jié)合攝像頭opencv進(jìn)行物體追蹤的示例分析

樹(shù)莓派智能小車(chē)結(jié)合攝像頭opencv進(jìn)行物體追蹤的示例分析，相信很多沒(méi)有經(jīng)驗(yàn)的人對(duì)此束手無(wú)策，為此本文總結(jié)了問(wèn)題出現(xiàn)的原因和解決方法，通過(guò)這篇文章希望你能解決這個(gè)問(wèn)題。

在幾天的資料整理之后發(fā)現(xiàn)是利用opencv和python實(shí)現(xiàn)的。那么今天告訴大家如何安裝opencv3.0和如何利用它實(shí)現(xiàn)我的小車(chē)追蹤。

之前確實(shí)安裝過(guò)幾次opencv都倒在了cmake編譯的路上，但有問(wèn)題就得解決。翻了好幾個(gè)帖子終于找到了一個(gè)靠譜的。用了一個(gè)下午的時(shí)間終于安裝成功了。安裝的教程篇幅過(guò)長(zhǎng)且容易被頭條認(rèn)為成抄襲所以就在發(fā)到評(píng)論區(qū)吧。然后問(wèn)題來(lái)了，opencv安裝好了，怎么實(shí)現(xiàn)物體追蹤呢。我開(kāi)始在github上找案列，找啊找啊找，輸入關(guān)鍵字 track car raspberry，找到一個(gè)，打開(kāi)看看是樹(shù)莓派加arduino做的。還好arduino只是用來(lái)控制步進(jìn)電機(jī)的。我開(kāi)始把樹(shù)莓派gpio控制電機(jī)的部分移植到這個(gè)項(xiàng)目中。在一天的調(diào)試之后，改造版的樹(shù)莓派物體追蹤小車(chē)出爐了。怎么說(shuō)呢，這只是個(gè)雛形，因?yàn)樾≤?chē)轉(zhuǎn)向不夠靈敏，追蹤的功能需要進(jìn)一步優(yōu)化。個(gè)人水平有限，希望大家一起來(lái)研究。

來(lái)說(shuō)說(shuō)detect.py 小車(chē)物體追蹤的源碼。detect.py中物體追蹤是怎么實(shí)現(xiàn)的呢，首先它需要捕捉一個(gè)frame邊框并確定一個(gè)物體去追蹤。在確定了所要追蹤的物體之后，小車(chē)將保持對(duì)物體的追蹤。源碼中定義了前后左右和停止的動(dòng)作。當(dāng)被鎖定的物體移動(dòng)時(shí)，小車(chē)則根據(jù)物體的位置作出響應(yīng)即追蹤物體前進(jìn)。

附detect.py源碼：

#導(dǎo)入一些必須的包

from picamera.array import PiRGBArray

from picamera import PiCamera

import cv2

import serial

import syslog

import time

import numpy as np

import RPi.GPIO as GPIO

# 定義捕捉的畫(huà)面尺寸

width = 320

height = 240

tracking_width = 40

tracking_height = 40

auto_mode = 0

#如下定義小車(chē)前后左右的功能函數(shù)

def t_stop():

GPIO.output(11, False)

GPIO.output(12, False)

GPIO.output(15, False)

GPIO.output(16, False)

def t_up():

GPIO.output(11, True)

GPIO.output(12, False)

GPIO.output(15, True)

GPIO.output(16, False)

time.sleep(0.05)

GPIO.output(11, False)

GPIO.output(12, False)

GPIO.output(15, False)

GPIO.output(16, False)

time.sleep(0.3)

def t_down():

GPIO.output(11, False)

GPIO.output(12, True)

GPIO.output(15, False)

GPIO.output(16, True)

def t_left():

GPIO.output(11, False)

GPIO.output(12, True)

GPIO.output(15, True)

GPIO.output(16, False)

time.sleep(0.05)

GPIO.output(11, False)

GPIO.output(12, False)

GPIO.output(15, False)

GPIO.output(16, False)

time.sleep(0.3)

def t_right():

GPIO.output(11, True)

GPIO.output(12, False)

GPIO.output(15, False)

GPIO.output(16, True)

time.sleep(0.05)

GPIO.output(11, False)

GPIO.output(12, False)

GPIO.output(15, False)

GPIO.output(16, False)

time.sleep(0.3)

def t_open():

GPIO.setup(22,GPIO.OUT)

GPIO.output(22,GPIO.LOW)

def t_close():

GPIO.setup(22,GPIO.IN)

def check_for_direction(position_x):

GPIO.setmode(GPIO.BOARD)

GPIO.setwarnings(False)

GPIO.setup(11,GPIO.OUT)

GPIO.setup(12,GPIO.OUT)

GPIO.setup(15,GPIO.OUT)

GPIO.setup(16,GPIO.OUT)

GPIO.setup(38,GPIO.OUT)

if position_x == 0 or position_x == width:

print 'out of bound'

t_stop()

if position_x <= ((width-tracking_width)/2 - tracking_width):

print 'move right!'

t_right()

elif position_x >= ((width-tracking_width)/2 + tracking_width):

print 'move left!'

t_left()

else:

# print 'move front'

t_up()

# initialize the camera and grab a reference to the raw camera capture

camera = PiCamera()

圖文無(wú)關(guān)

camera.resolution = (width, height)

camera.framerate = 32

rawCapture = PiRGBArray(camera, size=(width, height))

rawCapture2 = PiRGBArray(camera, size=(width, height))

# allow the camera to warmup

time.sleep(0.1)

# set the ROI (Region of Interest)

c,r,w,h = (width/2 - tracking_width/2), (height/2 - tracking_height/2), tracking_width, tracking_height

track_window = (c,r,w,h)

# capture single frame of tracking image

camera.capture(rawCapture2, format='bgr')

# create mask and normalized histogram

roi = rawCapture2.array[r:r+h, c:c+w]

hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

mask = cv2.inRange(hsv_roi, np.array([0,30,32]), np.array([180,255,255]))

roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0,180])

cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)

term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 80, 1)

# capture frames from the camera

for frame in camera.capture_continuous(rawCapture, format='bgr', use_video_port=True):

# grab the raw NumPy array representing the image, then initialize the timestamp

# and occupied/unoccupied text

image = frame.array

# filtering for tracking algorithm

hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

dst = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1)

ret, track_window = cv2.meanShift(dst, track_window, term_crit)

x,y,w,h = track_window

cv2.rectangle(image, (x,y), (x+w,y+h), 255, 2)

cv2.putText(image, 'Tracked', (x-25, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

# show the frame

cv2.imshow("Raspberry Pi RC Car", image)

key = cv2.waitKey(1) & 0xFF

check_for_direction(x)

time.sleep(0.01)

# clear the stream in preparation for the next frame

rawCapture.truncate(0)

看完上述內(nèi)容，你們掌握樹(shù)莓派智能小車(chē)結(jié)合攝像頭opencv進(jìn)行物體追蹤的示例分析的方法了嗎？如果還想學(xué)到更多技能或想了解更多相關(guān)內(nèi)容，歡迎關(guān)注億速云行業(yè)資訊頻道，感謝各位的閱讀！