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Legacy Personal Blogs Using an Intel Edison and an old Android phone as a Lightweight Image Processing Platform
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  • Author Author: satyavrat
  • Date Created: 10 Feb 2017 6:35 AM Date Created
  • Views 1219 views
  • Likes 1 like
  • Comments 5 comments
  • image processing
  • python
  • internet of things
  • intel edison
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Using an Intel Edison and an old Android phone as a Lightweight Image Processing Platform

satyavrat
satyavrat
10 Feb 2017

IoT Twitter Sentry Ward Using Intel Edison

 

     One of the first projects I did when I got my Intel Edison in my third year of engineering, was to try and make a home security system. I had seen a lot of people make many varieties in this theme. You had your infra red tripwires, overhead PIR proximity sensors, all which did the job pretty well. What I found a bit lackluster was the actuation bit. An LED, a Buzzer or may be the odd LCD screen with "INTRUDER!" flashing in the ubiquitous black-over-green. Given that I had a lot more horsepower and connectivity options to work with, I tried to make it a bit more "integrated" with the world we live in today. Admittedly, when one builds a project, we don't see it adopted immediately into the desired ecosystem. There's a lot of work and modification involved. However, that doesn't concern makers, because we are pretty happy with prototypes.

     To try and see if the prototype itself could work in the real world, I kept the sensing bit pretty standard, a light sensor to read spikes in ambient light. When it did, however, I decided to click a photo using an old Android phone via remote commands from the Edison, store the photo locally, and then upload it to the user's Twitter handle, which would actually alert the user of a break in, wherever he might be in the world.

     Modifying the Code to the Edison and Testing

     At the time, I thought this was the pinnacle of awesomeness. And for a while it was. It's been a general observation in my local engineering colleges that anything with a display or anything that moves is considered a successful project. So having a computer automatically detect an intruder and upload the photo to your own Twitter handle became the apple of every teacher's eye. Needless to say, all my classmates hated me.

     The process was quite simple. The Android Play Store houses an app called IPCam, which streams the phone's camera output to a local IP on your network. All you have to do is access that stream via Python and grab a snapshot. Once done, upload the snapshot to predefined Twitter handle.

 

     However, it wasn't until I began working at my first internship that I realized that I had missed the true potential of this setup.

     I worked at a company specializing in Image Processing and Embedded Systems for a year, during which time I learned how, with some effort, one can train a computer to intuitively understand what it is seeing through the camera lens. Computer Vision and Image Processing are essentially the back end of how our brains process images as well. (Neural Networks also play a big part here, but let's not get into specifics.) I realized that what I had at home was an ideal platform for some lightweight image processing as well, and how I had severely underutilized its potential in the Twitter project!

     Image processing is rather elegant in its premise, and like most machine learning algorithms, is almost purely arithmetic. A big plus, because then the intuition behind it is much easier to understand. Border detection, histograms and motion sensing. Although these may sound like complex concepts, break them down to their mathematical roots, and all of them are ridiculously simple.

     I plan on making this kind of a rolling blog to document my experiments in image processing. Intermediate proficiency in Python, a few packages, namely OpenCV and Numpy or Pandas, and, if you're planning on integrating that into IoT, maybe urllib or sockets is all you need. Experimenting with Numpy will be a treat. Analyzing pixel values individually may be very slow, but I'm not looking for lightning fast responses. Yet.

Priming the Intel Edison's arsenalimage

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Top Comments

  • DAB
    DAB over 8 years ago +1
    Very interesting project. What types of image processing do you have in mind? DAB
  • clem57
    clem57 over 8 years ago +1
    Very nice blog. One question that comes to mind though, is there anyway to get a cloud to process the image recognition over the internet? Clem
  • satyavrat
    satyavrat over 8 years ago in reply to clem57 +1
    I haven't looked into any available services for cloud based image processing, but I think there might be some options for very niche applications. Personally, I'm most comfortable using Python for image…
  • DAB
    DAB over 8 years ago in reply to satyavrat

    You might want to take a look at Radial Basis Functions.

     

    I was coauthor on a patent a bunch of years back, but we showed in a number of papers that you can get significant signal to noise ratio improvements using that technic to find hidden signals in noisy environments.

     

    Check papers by Dr. Peter Raeth on the subject.

     

    DAB

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  • satyavrat
    satyavrat over 8 years ago in reply to DAB

    As of now, I'll be looking into border detection using something like a Sobel filter. The next step would be shape detection, followed by pattern recognition to identify similar shapes. Later, (and this is a long shot) use neural nets to learn objects and identify them in different images.

     

    I'm interested in machine learning and its applications in embedded systems, and although this seems like a Herculean task at the moment for something like an Edison or a RasPi 3, I'd like to see how it handles the challenge.

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  • satyavrat
    satyavrat over 8 years ago in reply to clem57

    I haven't looked into any available services for cloud based image processing, but I think there might be some options for very niche applications.

     

    Personally, I'm most comfortable using Python for image processing, so if I had to design a cloud based solution for the same, I'd use a cloud based Python application hosting service like Heroku. I don't know if they provide file storage services, if they do, well and good. If they don't I have the option of streaming pixel RGB values to the cloud and/or storing the entire matrix/ matrices of values locally and then sending them as an indexed array. Granted, this implementation would be very rudimentary and would not even hold a candle to the speed of local image processing systems, but it would be a start and grant a lot more flexibility to say remote cameras such as those mounted on drones.

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  • clem57
    clem57 over 8 years ago

    Very nice blog. One question that comes to mind though, is there anyway to get a cloud to process the image recognition over the internet?

    Clem

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  • DAB
    DAB over 8 years ago

    Very interesting project.

     

    What types of image processing do you have in mind?

     

    DAB

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