pi_surveillance.py

# https://www.hackster.io/brendan-lewis/detect-motion-with-opencv-no-pir-sensor-needed-bbeacf

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)