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motion-detector.py
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motion-detector.py
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# import the necessary packages
import argparse
import warnings
import datetime
import imutils
import json
import time
import cv2
import numpy as np
# Take args from command
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True,
help="path to the JSON configuration file")
args = vars(ap.parse_args())
warnings.filterwarnings("ignore")
conf = json.load(open(args["conf"]))
client = None
# initialize the camera
video_capture = cv2.VideoCapture(0)
# uploaded timestamp, and frame motion counter
print "[INFO] Starting..."
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0
out = None
path = None
fourcc = cv2.VideoWriter_fourcc(*'XVID')
# capture frames from the camera
while True:
# grab the raw NumPy array representing the image and initialize
# the timestamp and occupied/unoccupied text
ret, frame = video_capture.read()
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 model..."
avg = gray.copy().astype("float")
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, 0.1)
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)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < conf["min_area"]:
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":
newPath = timestamp.strftime("%d-%m-%y" + ".avi")
if (path == None) | (newPath != path):
path = newPath
width = np.size(frame, 1)
height = np.size(frame, 0)
out = cv2.VideoWriter(
filename=newPath, fourcc=fourcc, fps=15, frameSize=(width, height))
out.write(frame)
lastUploaded = timestamp
# otherwise, the room is not occupied
else:
# Min record time
if (timestamp - lastUploaded).seconds <= 5:
newPath = timestamp.strftime("%d-%m-%y" + ".avi")
# If new day started then change change the storage location
if (path == None) | (newPath != path):
path = newPath
width = np.size(frame, 1)
height = np.size(frame, 0)
out = cv2.VideoWriter(
filename=newPath, fourcc=fourcc, fps=15, frameSize=(width, height))
abc = out.write(frame)
# 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