115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
# import the necessary packages
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import argparse
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import warnings
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import datetime
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import imutils
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import json
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import time
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import cv2
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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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confFile = "./conf.json"
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ap.add_argument("-c", "--conf", required=True, help="path to the JSON configuration file")
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args = vars(ap.parse_args())
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# filter warnings, load the configuration and initialize the Dropbox
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# client
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warnings.filterwarnings("ignore")
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conf = json.load(open(args["conf"]))
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client = None
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# initialize the camera and grab a reference to the raw camera capture
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video_capture = cv2.VideoCapture(0)
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# allow the camera to warmup, then initialize the average frame, last
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# uploaded timestamp, and frame motion counter
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print ("[INFO] warming up...")
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# time.sleep(conf["camera_warmup_time"])
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avg = None
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lastUploaded = datetime.datetime.now()
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motionCounter = 0
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# capture frames from the camera
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while True:
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# grab the raw NumPy array representing the image and initialize
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# the timestamp and occupied/unoccupied text
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ret, frame = video_capture.read()
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timestamp = datetime.datetime.now()
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text = "Unoccupied"
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# resize the frame, convert it to grayscale, and blur it
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frame = imutils.resize(frame, width=500)
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (21, 21), 0)
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# if the average frame is None, initialize it
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if avg is None:
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print ("[INFO] starting background model...")
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avg = gray.copy().astype("float")
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continue
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# accumulate the weighted average between the current frame and
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# previous frames, then compute the difference between the current
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# frame and running average
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cv2.accumulateWeighted(gray, avg, 0.5)
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frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
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# threshold the delta image, dilate the thresholded image to fill
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# in holes, then find contours on thresholded image
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thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,cv2.THRESH_BINARY)[1]
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thresh = cv2.dilate(thresh, None, iterations=2)
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(_,cnts,val) = cv2.findContours(thresh.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
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# loop over the contours
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for c in cnts:
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# if the contour is too small, ignore it
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if cv2.contourArea(c) < conf["min_area"]:
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continue
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# compute the bounding box for the contour, draw it on the frame,
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# and update the text
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(x, y, w, h) = cv2.boundingRect(c)
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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text = "Occupied"
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# draw the text and timestamp on the frame
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ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
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cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
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0.35, (0, 0, 255), 1)
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# check to see if the room is occupied
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if text == "Occupied":
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# check to see if enough time has passed between uploads
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if (timestamp - lastUploaded).seconds >= conf["min_upload_seconds"]:
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# increment the motion counter
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motionCounter += 1
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# check to see if the number of frames with consistent motion is
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# high enough
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if motionCounter >= conf["min_motion_frames"]:
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path = timestamp.strftime("%b-%d_%H_%M_%S" + ".jpg")
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cv2.imwrite(path, frame)
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lastUploaded = timestamp
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motionCounter = 0
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# otherwise, the room is not occupied
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else:
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motionCounter = 0
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# check to see if the frames should be displayed to screen
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if conf["show_video"]:
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print "show video"
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# display the security feed
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cv2.imshow("Security Feed", frame)
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key = cv2.waitKey(1) & 0xFF
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# if the `q` key is pressed, break from the lop
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if key == ord("q"):
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break
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