pi_surveillance.py

# USAGE
# python pi_surveillance.py --conf conf.json

# import the necessary packages
from __future__ import print_function
from picamera.array import PiRGBArray
from picamera import PiCamera
import argparse
import warnings
import datetime
import imutils
import json
import time
import cv2
import RPi.GPIO as GPIO
from imutils.object_detection import non_max_suppression
import numpy as np

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True,
    help="path to the JSON configuration file")
args = vars(ap.parse_args())

# filter warnings, load the configuration
warnings.filterwarnings("ignore")
conf = json.load(open(args["conf"]))
client = None

# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = tuple(conf["resolution"])
camera.framerate = conf["fps"]
rawCapture = PiRGBArray(camera, size=tuple(conf["resolution"]))

# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

#Setup Pins
pin = 18
GPIO.setmode(GPIO.BCM)
GPIO.setup(pin, GPIO.OUT, initial=0)

# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print("[INFO] warming up...")
time.sleep(conf["camera_warmup_time"])
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
    frame = imutils.resize(frame, width=500)
    orig = frame.copy()

    # detect people in the image
    (rects, weights) = hog.detectMultiScale(frame, winStride=(4, 4),
        padding=(8, 8), scale=1.05)

    # convert image to grayscale and blur it
    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, conf["delta_thresh"], 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 = cnts[0] if imutils.is_cv2() else cnts[1]

    # loop over the contours
    for c in cnts:
        # if the contour is too small, ignore it
        if cv2.contourArea(c) < conf["min_area"]:
            continue

        # draw the original bounding boxes
        for (x, y, w, h) in rects:
            cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
 
        # apply non-maxima suppression to the bounding boxes using a
        # fairly large overlap threshold to try to maintain overlapping
        # boxes that are still people
        rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
        pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
 
        # draw the final bounding boxes
        for (xA, yA, xB, yB) in pick:
            cv2.rectangle(frame, (xA, yA), (xB, yB), (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":
        
        #Activate pin
        GPIO.output(pin, GPIO.HIGH)
        
        # check to see if enough time has passed between uploads
        if (timestamp - lastUploaded).seconds >= conf["min_upload_seconds"]:
            # increment the motion counter
            motionCounter += 1

            # check to see if the number of frames with consistent motion is
            # high enough
            if motionCounter >= conf["min_motion_frames"]:

                # update the last uploaded timestamp and reset the motion
                # counter
                lastUploaded = timestamp
                motionCounter = 0

    # otherwise, the room is not occupied
    else:
        motionCounter = 0
        
        #Deactivate Pin
        GPIO.output(pin, GPIO.LOW)
        

    # check to see if the frames should be displayed to screen
    if conf["show_video"]:
        # display the security feed
        cv2.imshow("Security Feed", frame)
        key = cv2.waitKey(1) & 0xFF

        # if the `q` key is pressed, break from the lop
        if key == ord("q"):
            break

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

GPIO.cleanup()