Things used in this project

Code

pi_surveillance.pyPython
You do not need this code, it is just for reference.
from pyimagesearch.tempimage import TempImage
import dropbox as dbx
from picamera.array import PiRGBArray
from picamera import PiCamera
import warnings
import datetime
import imutils
import json
import time
import cv2

# filter warnings, load the configuration and initialize the Dropbox
# client
warnings.filterwarnings("ignore")
client = None

# Put your token here:
db = dbx.Dropbox("YOUR_TOKEN_HERE")

# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (640,480)
camera.framerate = 16
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

# 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 = 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 %B %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:
				# write the image to temporary file
				t = TempImage()
				cv2.imwrite(t.path, frame)
				print "[UPLOAD] {}".format(ts)
				path = "{base_path}/{timestamp}.jpg".format(base_path="/", timestamp=ts)
				client.put_file(open(t.path, "rb").read(), path)
				t.cleanup()
				
				# update the last uploaded timestamp and reset the motion
				# counter
				lastUploaded = timestamp
				motionCounter = 0

	# otherwise, the room is not occupied
	else:
		motionCounter = 0

	# clear the stream in preparation for the next frame
	rawCapture.truncate(0)

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