This part expands on that and tackles the issue: ‘How do I know the state of my garage door if I’m not at home?’
Because the code operates no differently than someone pressing a single button on the remote control, you would normally have to look with your own eyes to see if you were closing, stopping, or opening the garage door. This can be an issue when your eyes are nowhere close to it.
I wanted to come up with a solution that didn’t involve running new equipment such as a switch to detect the door’s orientation. I decided to utilize what I already had in the garage: A camera. Namely, this one: Foscam FI8910W
The idea is to use the camera to grab an image, pipe that image into OpenCV to detect known objects, and then declare the door open or closed based off of those results.
I whipped up a couple of shapes in Photoshop to stick on the inside of my door:
I then cropped out the shapes from the above picture to make templates for OpenCV to match.
The basic algorithm is this:
Get the latest image from the camera
Look for our templates with OpenCV
If all objects (templates) were detected, the door is closed – otherwise it’s open
To help make step 3 more accurate, I added a horizontal threshold value which is defined in the configuration file. Basically, we’re using this to make sure we didn’t get a false positive – if the objects we detect are horizontally aligned, we can be pretty certain we have the right ones.
I was happy to find that the shapes worked well in low-light conditions. This may be due to the fact that my garage isn’t very deep so the IR range is sufficient, as well as the high contrast of black shapes on white paper.
Currently I have some experimental code in the project for detecting state changes. This will not only provide more information (e.g. the door is opening because we detect the pentagon has gone up x pixels), but is good for events (e.g. when the alarm system is on, let me know if the door has any state change).
I’ve tested running this on the Raspberry Pi and it works fine, though it can be a good bit slower than a full-blown machine. I have a Raspberry Pi 2 on order and it’ll be interesting to see the difference. Since this code doesn’t need anything specific to the Raspberry Pi, someone may prefer to run it on a faster box to get more info in the short time span it takes for a door to open or close.
I’ve created a video to demo the script in action!
The garage door opener remote was removed from its casing and I located the button used to open the door. My particular model had three buttons.
I soldered a jumper on the button leads to always create a circuit, removing the functionality of the button so it’s “always on” when power is applied. The button was this style – a standard electronics button.
I removed the coin battery and soldered on two leads that will be powered by the Raspberry Pi. When the Raspberry Pi sends power, this will be no different than someone normally pressing the remote button.
I wrote code to control the pin that outputs power.
Garage door openers can vary, but I’m willing to bet a lot (if not most) use a CR2032 battery. The Pi puts out 3.3 volts, (.3 V higher), but I haven’t noticed any issues.
I’ve provided the simplest example here. The next step would be to have the code driven by a web interface that could be loaded on a phone or a web browser.
There will be an exciting part 2 to this article where I take this one step further!
My Dad and I built the alarm system for their house in late 2009. He handled hardware, I did software. From time to time I export the data it collects just for the heck of it, since it’s been running non-stop for years.
The back door at their house has been opened 41,267 since the end of November 2009. That’s kind of surprising to me.
It’s interesting to see some patterns in the graph – it’s how many times the door has been opened per month. You can definitely tell what year I bought my house. 🙂
Clemson and Ole Miss are currently battling it out in a poll on Southern Living’s website. Out of curiosity I wrote some code to grab this data and graph it to help visualize who is catching up or pulling ahead. Check it out here!
With an Arduino Ethernet, Processing, and a Kinect, I was able to easily create this little demo where hand movement can control a servo. This is just a tiny step in my master plan to create a robot clone so that I don’t have to leave my chair.
The following libraries and drivers made this work and also made it super easy for me to create it: