ECE497 Project Set Playing Beagle - Revision history

Yoder at 21:03, 11 November 2011

2011-11-11T21:03:07Z

2011-05-17T03:29:38Z

2011-05-17T03:28:53Z

2011-05-17T02:56:51Z

2011-05-17T02:56:26Z

2011-05-17T02:54:23Z

2011-05-17T02:53:48Z

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2011-05-17T02:38:44Z

← Older revision		Revision as of 21:03, 11 November 2011
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	* Dr. Zach Dodd at Harvey Mudd University for providing starting image recognition code.		* Dr. Zach Dodd at Harvey Mudd University for providing starting image recognition code.

−	[[Category:~~ECE497~~]]	+	[[Category:ECE497Spring2011]]

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 * The SPEd image comes with OpenCV already installed, but if you do not have it, you will need it.
 <pre> $ opkg install opencv </pre>
-* Install python-opencv
+* Install python-opencv, and espeak
 <pre> $ opkg install python-opencv </pre>
 * You will also need a webcam and a way to mount it. We used a Sony Playstation Eye, but any webcam with at least 640x480 resolution should work. Our mount held the camera about 7 inches above the playing surface, and had it pointing straight down.
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 {| border="1"
 |-
-|Sam Allen || Did some work on getting the camera values set correctly so that the image was more usable.
+|Sam Allen || Did some work on getting the camera values set correctly so that the image was more usable. Espeak integration.
 |-
 |Julian Ametsitsi || Tested using TI Pico Projector to project indication of set of card. Developed algorithm with Stephen to identify individual cards in a single image.

@@ Line 80: / Line 80: @@
 | Better image classification || Stephen - if it gets done, it will be done during 10th week
 |-
-| Espeak integration  || Sam - if it gets done, it will be done during 10th week
+| Faster camera refresh rates || Sam, Stephen - if it gets done, it will be done during 10th week
 |}

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 There is a trade-off between camera quality and image recognition success. In the first stages of our image recognition process, we worked with images of much higher resolution, and found that we had much better success. On the other hand, we could have used higher powered classifiers (Support Vector Machines, for example) to classify the images, instead of our rather fragile manual tweaked approach.
-=== Thanks ===
+=== Thanks To ===
-Thanks to
 * Mark Crosby and Gary Meyer for making us a camera mount.
 * Dr. Rob Bunch for providing advice and lenses for the projector

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 |Julian Ametsitsi || Tested using TI Pico Projector to project indication of set of card. Developed algorithm with Stephen to identify individual cards in a single image.
 |-
-|Stephen Mayhew ||
+|Stephen Mayhew || Tweaking and customization of image recognition code. Software integration with hardware.
 |}

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 You can quit the program either by pressing Enter while the program is in the ordinary recognizing loop, or by doing Ctrl-C in the terminal.
 == Highlights ==
 The interest of our project is in how it demonstrates the use of OpenCV on the Beagleboard. It shows that OpenCV functionality on the Beagleboard is very similar to what one would find on any other computer, if a little slower.
 == Theory of Operation ==

← Older revision		Revision as of 02:53, 17 May 2011
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	\|}		\|}

−	~~Also list here what doesn't work yet and when you think it will be finished and who is finishing it.~~

	== Conclusions ==		== Conclusions ==

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 == Work Breakdown ==
 {| border="1"
 |-
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 |}
-List the major tasks in your project and who did what.
+To be finished:
 Also list here what doesn't work yet and when you think it will be finished and who is finishing it.

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 == Conclusions ==
-We were pleased with how well the Beagleboard runs OpenCV.
+We were pleased with how well the Beagleboard runs OpenCV. It was not at all difficult to install and run. While it is noticeably slower than on larger machines, it is not prohibitively so.
 Originally, it was in the scope to have a projector project indication of sets back onto the actual cards. We had planned to use a DLP Pico projector for this, but we found early on that the Pico projector projects at a very small resolution, and we would have had to mount it significantly higher than the camera if it was to cover the whole playing area. We experimented with putting lenses in front of the projector to magnify it, but this turned out to be much more difficult than we imagined, so we focused our efforts on the image recognition, and on making the system coherent and smooth.