I understand an issue recently discussed with embodiment concerns methods for processing visual input. It's well known that at this time sending raw video into atomspace is a bad idea and that humans have built in visual processors that assist our conscious minds in understanding what our eyes see. (Obvious simple example being that the image is preflipped).
I understand opencog has (in some form) a python api which leads me to think using the visual processing engine OpenCV may not be a bad idea. It has a fantastic python api, allows for exporting specific data from raw video such as "33% of the screen is red", or there are 2 lines in the field of view." it also has a PHENOMINAL foreground/background separation engine that allows only a processing of new or moving objects in the field of view.
While a more mature opencog engine may prefer a more "raw" processor, I see OpenCV as a great place to start for getting useful information into atomspace quickly.
I have yet to start work on this, heck, I have yet to fully learn the ropes of the current opencog system, but I wanted to at least drop the info here in case anyone else had comments or wanted to get a head-start on me.
Best regards my friends.
Noah B.
PS: My personal experience with OpenCV was specifically dealing with automated turrets. There are great YouTube examples of using OpenCV for face-tracking webcams attached to servos, and blob isolating security cameras if you wanted specific examples to look up.
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Thank you for the info Linas,
I'll look at the current code and see if I can get a more complete implementation of OpenCV started. You mentioned another dev's overly simple integration which, while better than nothing, hardly fulfills our goal or utilizes the full potential of OpenCV.
With luck maybe I can get the visual end of opencog a bit more useful than a glorified motion detector. :P
Thanks again! I'll report back any major advances, otherwise check the pull requests and maybe my branch of you get curious.
As a side, if I am not mistaken, atomspace does most of its storage in sql right? Perhaps I could see about offloading visual processing to a dedicated machine along with whatever camera/sensor is being used, and get that set up with an "atomspace client" that could dump pre-formatted atoms straight into the db.
If there aren't any logistical restrictions to this method, it could provide a more modular design to opencog and also reduce unnecessary primary server strain.
Noah B.
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Thank you for the info Linas,
I'll look at the current code and see if I can get a more complete implementation of OpenCV started. You mentioned another dev's overly simple integration which, while better than nothing, hardly fulfills our goal or utilizes the full potential of OpenCV.
With luck maybe I can get the visual end of opencog a bit more useful than a glorified motion detector. :P
Thanks again! I'll report back any major advances, otherwise check the pull requests and maybe my branch of you get curious.
As a side, if I am not mistaken, atomspace does most of its storage in sql right?
Perhaps I could see about offloading visual processing to a dedicated machine along with whatever camera/sensor is being used, and get that set up with an "atomspace client" that could dump pre-formatted atoms straight into the db.
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The concept of building a 3d map of the environment in atomspace certainly would be a better goal of there had to be a choice between the two. I'll definitely read up on kinfu before starting any sort of work.
A few simple examples of information gained through a more mature OpenCV implementation could consist of the following:
A simple place to start that would have little overhead and export atoms easily used could be:
Knowing environment. Consistent items not the focus.
Blob statistics, delimited by motion
Size of blob
Color of blob
Location on fov
Speed and direction
Acceleration
Advanced sampling:
Division of blob into sections, quarters horizontally,
Shape/size/color/edge flat or rounded statistics of that quadrant
Vertical division by the same standards.
Obviously this would not be the end. We could divide a blob into more slices, account for variation in background, etc. We would need a more advanced way to get more information from a visual feed to get it anywhere near human-like. But we could at least get more useful data than we currently are.
This kind of implementation could potentially augment other more "logical" representations of the environment by providing a more analog "eye-like" processing system. It also has the advantage of being potentially faster to implement and scale.
I don't see this implementation ever being a replacement for any sort of 3d map formation, but rather a low-overhead way of quickly making sense of raw visual input without pumping raw video into atomspace.
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Ah, OK, I get it.... Yeah, having openCog know the size and color and
direction of blobs would be nice... I'm not quite sure what to do with
it immediately though...
> Blob statistics, delimited by motion
> Size of blob
> Color of blob
> Location on fov
> Speed and direction
> Acceleration
>
> Advanced sampling:
> Division of blob into sections, quarters horizontally,
> Shape/size/color/edge flat or rounded statistics of that quadrant
> Vertical division by the same standards.
>
> This kind of implementation could potentially augment other more "logical"
> representations of the environment by providing a more analog "eye-like"
> processing system. It also has the advantage of being potentially faster to
> implement and scale.
>
> I don't see this implementation ever being a replacement for any sort of 3d
> map formation, but rather a low-overhead way of quickly making sense of raw
> visual input without pumping raw video into atomspace.
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