Raspberry Pi - Image Recognition Initial Setup

The Raspberry Pi system provides a low power solution with enough CPU to perform realtime image recognition capabilities. For this I used a creative camera with UVC support so that I didn’t have to worry about compiling any special drivers into Raspbian.
On the software side, some starting steps to take may include installing a video capture software such as mjpg_streamer. First, grab some of the dependent utility and image processing libraries.

 sudo apt-get update
 sudo apt-get install libjpeg8-dev imagemagick
 sudo apt-get install subversion

You will also need to download the source and compile it for mjpg_streamer. cd ~ mkdir tmp cd tmp svn co https://svn.code.sf.net/p/mjpg-streamer/code cd code/mjpg-streamer make sudo make install After mjpg_streamer is built, then you can run it using a command similar to the following: ./mjpg_streamer -i “./input_uvc.so -n -f 15 -r 240x160” -o “./output_http.so -n -w ./www” The above command assumes that you have changed to the directory containing mjpg_streamer and the relevant *.so files.

The resolution can be changed from 240x160 to higher values depending on what your camera supports. I found that the lower resolution images were much more responsive in terms of any analysis that I needed to do on video. These numbers may need to vary depending on the work load placed on your Raspberry Pi and network interface bandwidth.

To automatically run mjpg_streamer on system startup, you can type ‘sudo nano /etc/rc.local’ and add something similar to: /usr/local/bin/mjpg_streamer -i “/usr/local/lib/input_uvc.so -n -f 15 -r 240x160” -o “/usr/local/lib/output_http.so -n -w /usr/local/www” &

One can then use the default or create a custom page to display the video. I used the following which I placed at /usr/local/www/index.html:

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
    "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<title>Live Feed</title>
<style>
body {
    background-color: #ccc;
}
img {
    border: solid 1px #000;
}
</style>
</head>
<script type="text/javascript">

var imageNr = 0; // Serial number of current image
var finished = new Array(); // References to img objects which have finished downloading
var paused = false;

function createImageLayer() {
  var img = new Image();
  img.style.position = "absolute";
  img.style.zIndex = -1;
  img.onload = imageOnload;
  img.onclick = imageOnclick;
  img.style.width='500px';
  img.src = "/?action=snapshot&n=" + (++imageNr);
  var webcam = document.getElementById("webcam");
  webcam.insertBefore(img, webcam.firstChild);
}

// Two layers are always present (except at the very beginning), to avoid flicker
function imageOnload() {
  this.style.zIndex = imageNr; // Image finished, bring to front!
  while (1 < finished.length) {
    var del = finished.shift(); // Delete old image(s) from document
    del.parentNode.removeChild(del);
  }
  finished.push(this);
  if (!paused) createImageLayer();
  finished.push(this);
  if (!paused) createImageLayer();
}

function imageOnclick() { // Clicking on the image will pause the stream
  paused = !paused;
  if (!paused) createImageLayer();
}

</script>
<body onload="createImageLayer();">
<div id="webcam"><noscript><img src="/?action=snapshot" /></noscript></div>

</body>
</html>

One can then use a computer vision analysis package such as OpenCV from another computer and process images streamed from the Raspberry Pi by connecting to the Raspberry Pi’s assigned IP address.

Raspberry Pi Image Recognition Initial Setup