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Detect open door with traincascade?

asked 2015-03-04 14:21:44 -0500

NemoN gravatar image

Hello all,

i'm a opencv beginner. Currently i try to detect a door status (open/closed) under different light conditions (night, day, sunshine, ...). I'm unsure about the best approach. I experimented a little bit with the traincascade but without success. Maybe someone can point me in the right direction.

I attached some example images.

Closed garage door:

image description image description


image description image description

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answered 2015-03-04 16:31:30 -0500

luketheduke gravatar image

There are multiple solutions:

  1. First You could try using a color mask to see if the black door is present.

  2. You could also wire a light connected to a switch which closes when the door is open. That would also use a color mask.

  3. Lastly you could just not use OpenCV at all and use a laser connected to a switch as described above and use a Arduino with a light sensor to see if the door is open.

Hope that helps!

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Using color defined masks won't work since he clearly wants it to work in day and night conditions and also various lighting conditions. Setting a mask based on color will be to fixed to a certain situation. I would skip color information alltogether. The laser approach is better. If you want to use OpenCV, than cascade classifiers are not the way to go. Since your camera is fixed you could try adaptive background subtraction between frames or a classifier based on open and closed doors decribed by descriptors of the door region (which is known due to fixed camera setup)

StevenPuttemans gravatar imageStevenPuttemans ( 2015-03-06 02:42:43 -0500 )edit

answered 2016-01-06 02:13:31 -0500

rbs90 gravatar image

I was also interessted in detecting a door state with OpenCV. My best result was using the floodfill algorithm:

import cv2
from numpy import *

test_imgs = ['night_open.jpg', 'night_closed.jpg', 'day_open.jpg', 'day_closed.jpg']

for imgFile in test_imgs:
    img = cv2.imread(imgFile)
    height, width, channels = img.shape
    mask = zeros((height+2, width+2), uint8)

    #the starting pixel for the floodFill
    start_pixel = (510,110)
    #maximum distance to start pixel:
    diff = (2,2,2)

    retval, rect = cv2.floodFill(img, mask, start_pixel, (0,255,0), diff, diff)

    print retval

    #check the size of the floodfilled area, if its large the door is closed:
    if retval > 10000:
        print imgFile + ": garage door closed"
        print imgFile + ": garage door open"

    cv2.imwrite(imgFile.replace(".jpg", "") + "_result.jpg", img)

Programm output:

night_open.jpg: garage door open
night_closed.jpg: garage door closed
day_open.jpg: garage door open
day_closed.jpg: garage door closed

Result images:

day closed day open night closed night open

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How to decide the retval area threshold for different image size?

pZ gravatar imagepZ ( 2019-05-13 03:17:19 -0500 )edit

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Asked: 2015-03-04 14:21:44 -0500

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Last updated: Mar 04 '15