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motion_detection_original.py
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# Detects movement from a reference frame
# cv2 chain: gray > gaussianblur >> absdiff > threshold > dilate > findcontours
# base code from pyimagesearch.com tutorials (Adrian Rosebrock)
# https://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/
# USAGE
# python motion_detector.py
# python motion_detector.py --video videos/example_01.mp4
# import the necessary packages
from imutils.video import VideoStream
import argparse
import datetime
import imutils
import time
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
ap.add_argument("-d", "--debug-windows", type=bool, default=True, help="displays debug image windows")
args = vars(ap.parse_args())
# if the video argument is None, then we are reading from webcam
if args.get("video", None) is None:
vs = VideoStream(src=0).start()
time.sleep(0.5)
# otherwise, we are reading from a video file
else:
vs = cv2.VideoCapture(args["video"])
# initialize the first frame in the video stream
firstFrame = None
debugWindows = args["debug_windows"]
displayImageIndex = 0 #debug image
thval = 10 #threshold
# loop over the frames of the video
while True:
# grab the current frame
frame = vs.read()
frame = frame if args.get("video", None) is None else frame[1]
# if the frame could not be grabbed, then we have reached the end of the video
if frame is None:
break
# 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 first frame is None, initialize it
if firstFrame is None:
firstFrame = gray
continue
# compute the absolute difference between the current frame and first frame
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, thval, 255, cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes,
thresh = cv2.dilate(thresh, None, iterations=2)
# then find contours on thresholded image
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
displayImage=firstFrame
if debugWindows:
if displayImageIndex==0:
displayImage = frame
if displayImageIndex==1:
displayImage = thresh
if displayImageIndex==2:
displayImage = frameDelta
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < args["min_area"]:
continue
# compute the bounding box for the contour, draw it on the frame,
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(displayImage, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(displayImage,"threshold: "+str(thval),(10,10),cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), lineType=cv2.LINE_AA)
cv2.imshow("video", displayImage)
key = cv2.waitKey(1) & 0xFF
# keyboard controls
if key == ord("q"):
break
if key == ord("v"):
displayImageIndex+=1
if displayImageIndex>2:
displayImageIndex=0
if key == ord("f"):
firstFrame = None
if key == ord("d"):
debugWindows = not debugWindows
if key == ord("1"):
thval+=10
if thval > 190:
thval=0
# cleanup the camera and close any open windows
vs.stop() if args.get("video", None) is None else vs.release()
cv2.destroyAllWindows()
raise SystemExit()