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pimotion.py
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pimotion.py
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from picamera.array import PiRGBArray
from picamera import PiCamera
import warnings
import datetime
import imutils
import time
import cv2
def playSound():
import pygame
pygame.mixer.init()
pygame.mixer.music.load("buzz.wav")
pygame.mixer.music.play()
while pygame.mixer.music.get_busy() == True:
continue
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = tuple([640, 480])
camera.framerate = 16
rawCapture = PiRGBArray(camera, size=tuple([640, 480]))
# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print ("[INFO] warming up...")
time.sleep(2.5)
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0
# capture frames from the camera
for f in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
# grab the raw NumPy array representing the image and initialize
# the timestamp and occupied/unoccupied text
frame = f.array
timestamp = datetime.datetime.now()
text = "Unoccupied"
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the average frame is None, initialize it
if avg is None:
print ("[INFO] starting background model...")
avg = gray.copy().astype("float")
rawCapture.truncate(0)
continue
# accumulate the weighted average between the current frame and
# previous frames, then compute the difference between the current
# frame and running average
cv2.accumulateWeighted(gray, avg, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
# threshold the delta image, dilate the thresholded image to fill
# in holes, then find contours on thresholded image
thresh = cv2.threshold(frameDelta, 5, 255,
cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
#(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
_, cnts, _= cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < 5000:
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
# draw the text and timestamp on the frame
ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
# check to see if the room is occupied
if text == "Occupied":
# check to see if enough time has passed between uploads
if (timestamp - lastUploaded).seconds >= 3.0:
# increment the motion counter
motionCounter += 1
# check to see if the number of frames with consistent motion is
# high enough
if motionCounter >= 8:
# update the last uploaded timestamp and reset the motion
# counter
lastUploaded = timestamp
motionCounter = 0
# otherwise, the room is not occupied
else:
motionCounter = 0
# check to see if the frames should be displayed to screen
# clear the stream in preparation for the next frame
if text == 'Occupied':
playSound()
print (text)
rawCapture.truncate(0)