Addition of new tests for detection and tracking
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Katarzyna Kaczyńska
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# USAGE
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# python motion_detector.py
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# python motion_detector.py --video videos/example_01.mp4
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from imutils.video import VideoStream
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import argparse
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import datetime
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import imutils
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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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ap.add_argument("-v", "--video", help="recordings/test1.webm")
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ap.add_argument("-a", "--min-area", type=int, default=500, help="50px")
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args = vars(ap.parse_args())
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# if the video argument is None, then we are reading from webcam
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if args.get("video", None) is None:
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vs = VideoStream(src=0).start()
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time.sleep(2.0)
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# otherwise, we are reading from a video file
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else:
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vs = cv2.VideoCapture(args["video"])
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# initialize the first frame in the video stream
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firstFrame = True
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# loop over the frames of the video
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while True:
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# grab the current frame and initialize the occupied/unoccupied
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# text
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frame = vs.read()
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frame = frame if args.get("video", None) is None else frame[1]
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text = "Unoccupied"
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# if the frame could not be grabbed, then we have reached the end
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# of the video
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if frame is None:
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break
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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 first frame is None, initialize it
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if firstFrame is None:
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firstFrame = gray
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continue
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# compute the absolute difference between the current frame and
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# first frame
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frameDelta = cv2.absdiff(firstFrame, gray)
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thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
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# dilate the thresholded image to fill in holes, then find contours
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# on thresholded image
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thresh = cv2.dilate(thresh, None, iterations=2)
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cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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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) < args["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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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, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
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(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
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# show the frame and record if the user presses a key
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cv2.imshow("Security Feed", frame)
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cv2.imshow("Thresh", thresh)
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cv2.imshow("Frame Delta", frameDelta)
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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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# cleanup the camera and close any open windows
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vs.stop() if args.get("video", None) is None else vs.release()
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cv2.destroyAllWindows()
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62
project.py
62
project.py
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@ -1,52 +1,36 @@
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from pyimagesearch.centroidtracker import CentroidTracker
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from pyimagesearch.trackableobject import TrackableObject
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from imutils.video import VideoStream
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from imutils.video import FPS
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import numpy as np
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import argparse
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import imutils
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import time
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import dlib
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import cv2
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cap = cv2.VideoCapture('recordings/test1.webm')
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ret, frame1 = cap.read()
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ret, frame2 = cap.read()
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while(cap.isOpened()):
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diff = cv2.absdiff(frame1, frame2)
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gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
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blur = cv2.GaussianBlur(gray, (5,5), 0)
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_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
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dilated = cv2.dilate(thresh, None, iterations=3)
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contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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for contour in contours:
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(x, y, w, h) = cv2.boundingRect(contour)
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if cv2.contourArea(contour) < 9000:
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continue
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cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
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cv2.putText(frame1, "Status: {}".format('Movement'), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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# import numpy as np
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# import cv2
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cv2.imshow("feed", frame1)
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frame1 = frame2
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ret, frame2 = cap.read()
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# cap = cv2.VideoCapture('recordings/test1.webm')
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if cv2.waitKey(25) & 0xFF == ord('q'):
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break
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# ret, frame1 = cap.read()
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# ret, frame2 = cap.read()
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# while(cap.isOpened()):
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# diff = cv2.absdiff(frame1, frame2)
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# gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
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# blur = cv2.GaussianBlur(gray, (5,5), 0)
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# _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
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# dilated = cv2.dilate(thresh, None, iterations=3)
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# contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# for contour in contours:
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# (x, y, w, h) = cv2.boundingRect(contour)
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# if cv2.contourArea(contour) < 9000:
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# continue
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# cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
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# cv2.putText(frame1, "Status: {}".format('Movement'), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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# cv2.imshow("feed", frame1)
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# frame1 = frame2
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# ret, frame2 = cap.read()
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# if cv2.waitKey(25) & 0xFF == ord('q'):
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# break
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# cap.release()
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# cv2.destroyAllWindows()
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cap.release()
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cv2.destroyAllWindows()
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@ -1,10 +0,0 @@
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class TrackableObject:
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def __init__(self, objectID, centroid):
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# store the object ID, then initialize a list of centroids
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# using the current centroid
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self.objectID = objectID
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self.centroids = [centroid]
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# initialize a boolean used to indicate if the object has
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# already been counted or not
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self.counted = False
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