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motiondetect.py
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motiondetect.py
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from pyimagesearch import *
from pyimagesearch.tempimage import TempImage
from picamera.array import PiRGBArray
from picamera import PiCamera
import warnings
import datetime
import imutils
import json
import time
import cv2
import os
import numpy as np
import glob
import dropbox as dbx
import RPi.GPIO as GPIO1
import sys
def motionmain():
pin_num = 22
# filter warnings, load the configuration and initialize the Dropbox
warnings.filterwarnings("ignore")
#setup gpio
GPIO1.setmode(GPIO1.BCM)
# GPIO 23 & 17 set up as inputs, pulled up to avoid false detection.
# Both ports are wired to connect to GND on button press.
# So we'll be setting up falling edge detection for both
GPIO1.setup(pin_num, GPIO1.IN, pull_up_down=GPIO1.PUD_UP)
#dropbox:
with open("/home/pi/Desktop/pisecuritysystem/permissions.json") as f:
data = json.load(f)
client = dbx.Dropbox(data['db-token'])
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
#default 640x480 - decrease to go faster
#motion-detect camera resolution
camera.resolution = (640,480)
rawCapture = PiRGBArray(camera, size=(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
text = ""
name = ""
# 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()
# resize the frame, convert it to grayscale, and blur it
#frame=500 default, decrease it to go faster
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)
# 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 = "!"
# draw the text and timestamp on the frame
ts = timestamp.strftime("%A_%d_m_%Y_%I:%M:%S%p")
cv2.putText(frame, "{}".format(ts), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# check to see if the room is occupied
if text == "!":
# 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: #originally 8
print("Capturing image.")
t = TempImage()
cv2.imwrite(t.path, frame)
name = "{base}{timestamp}".format(base="", timestamp=ts)
os.rename(t.path[3:], "{new}.jpg".format(new=name))
print("[UPLOAD] {}".format(ts))
with open("/home/pi/Desktop/pisecuritysystem/{name}.jpg".format(name=name), "rb") as f:
client.files_upload(f.read(), "/{name}.jpg".format(name=name), mute = True)
os.remove("{name}.jpg".format(name=name))
# update the last uploaded timestamp and reset the motion
# counter
lastUploaded = timestamp
motionCounter = 0
text=""
# otherwise, the room is not occupied
else:
motionCounter = 0
text=""
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
if GPIO1.input(pin_num) == False:
print("button pressed")
print("exit now")
break
GPIO1.cleanup()
time.sleep(.25) #pause for .25 seconds
camera.close()
print("camera closed")
time.sleep(.25)
if __name__ == '__main__':
motionmain()