树莓派+摄像头实现对移动物体的检测

Z时代
2024-01-10
分类：综合

在上一篇文章中实现了树莓派下对摄像头的调用，有兴趣的可以看一下：python+opencv实现摄像头调用的方法

接下来，我们将使用python+opencv实现对移动物体的检测

一、环境变量的配置

我们可以参照上一篇文章对我们的树莓派进行环境的配置

当我们将cv2的库安装之后，就可以实现对摄像头的操作

二、摄像头的连接

在此实验中，我使用的为usb摄像头

当我们连接摄像头之后，终端输入

ls /dev/video*

如果终端提示如下：

则表示摄像头连接成功

三、编码实现对移动物体的检测

使用python编写程序，实现对移动物体的检测，代码如下


#encoding=utf-8
import RPi.GPIO as GPIO
import cv2
import time
import os
GPIO.setmode(GPIO.BCM)
GPIO.setup(18,GPIO.OUT)
camera = cv2.VideoCapture(0)
if camera is None:
 print('please connect the camera')
 exit()
fps = 30 
pre_frame = None
led = False
while True:
 start = time.time()
 res, cur_frame = camera.read()
 if res != True:
 break
 end = time.time()
 seconds = end - start
 if seconds < 1.0/fps:
 time.sleep(1.0/fps - seconds)
 cv2.namedWindow('img',0);
 #cv2.imshow('img', cur_frame)
 key = cv2.waitKey(30) & 0xff
 if key == 27:
 break
 gray_img = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY)
 gray_img = cv2.resize(gray_img, (500, 500))
 gray_img = cv2.GaussianBlur(gray_img, (21, 21), 0)
 if pre_frame is None:
 pre_frame = gray_img
 else:
 img_delta = cv2.absdiff(pre_frame, gray_img)
 thresh = cv2.threshold(img_delta, 25, 255, cv2.THRESH_BINARY)[1]
 thresh = cv2.dilate(thresh, None, iterations=2)
 contours, hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
 for c in contours:
  if cv2.contourArea(c) < 1000:
  continue
  else:
 (x,y,w,h) = cv2.boundingRect(c)
 cv2.rectangle(cur_frame,(x,y),(x+w,y+h),(0,255,0),2)
 print("something is moving!!!")
 led = True
 if led == True:
 for i in range(30):
 GPIO.output(18,GPIO.HIGH)
 time.sleep(0.03)
 GPIO.output(18,GPIO.LOW)
 time.sleep(0.03)
  break
 cv2.imshow('img', cur_frame) 
 pre_frame = gray_img
camera.release()
cv2.destroyAllWindows()