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Using Computer Vision to Win at Duck Hunt

blog.roboflow.com

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Re: Using Computer Vision to Win at Duck Hunt

#11

Post by [deleted] »

cdumler wrote: post #6
burlesona wrote: post #5

Tangential, but fascinating to me: I realized a few years ago that you cannot play duck hunt with an original light gun and original NES connected to a flatscreen TV. I’m sure there are people on HN who can explain this more eloquently than I can, but basically the flatscreen TVs have some latency that the original NES hardware cannot handle. You pull the trigger and the gun looks for the white square immediately, bu…

The fix is actually simple: shoot at a light bulb. It will think you always hit. I figured this out accidentally as a kid one day when I was randomly firing around the room and hit a duck.

[deleted]

Re: Using Computer Vision to Win at Duck Hunt

#12

Post by dmurray »

I'm not sure this solves the hard part of the problem. I was assuming there would be an actual robot arm involved.

This is obviously not a difficult CV problem. I don't want to disparage the post too much, though - the Roboflow tools to make this process accessible for a non-expert look very good, and it's a cool demo of those which is the real point of the blog.

Re: Using Computer Vision to Win at Duck Hunt

#13

Post by NobodyNada »

cdumler wrote: post #6
burlesona wrote: post #5

Tangential, but fascinating to me: I realized a few years ago that you cannot play duck hunt with an original light gun and original NES connected to a flatscreen TV. I’m sure there are people on HN who can explain this more eloquently than I can, but basically the flatscreen TVs have some latency that the original NES hardware cannot handle. You pull the trigger and the gun looks for the white square immediately, bu…

The fix is actually simple: shoot at a light bulb. It will think you always hit. I figured this out accidentally as a kid one day when I was randomly firing around the room and hit a duck.

I haven’t tested this with Duck Hunt, but I believe most NES Zapper games actually detect this exploit by blanking the screen for one frame before drawing the target in white, and checking that the gun saw one dark frame followed by one light frame.

Re: Using Computer Vision to Win at Duck Hunt

#14

Post by d--b »

> I annotated the images with Roboflow and was able to easily export the data

> Roboflow's Dataset Health Check helped me

> I found Roboflow’s Model Library to be the best

> Roboflow would be my go-to platform

> Matt Brems, Growth Manager @ Roboflow

Please call this a demo or “how I use Roboflow to win a duck hunt”

Re: Using Computer Vision to Win at Duck Hunt

#15

Post by [deleted] »

cdumler wrote: post #6
burlesona wrote: post #5

Tangential, but fascinating to me: I realized a few years ago that you cannot play duck hunt with an original light gun and original NES connected to a flatscreen TV. I’m sure there are people on HN who can explain this more eloquently than I can, but basically the flatscreen TVs have some latency that the original NES hardware cannot handle. You pull the trigger and the gun looks for the white square immediately, bu…

The fix is actually simple: shoot at a light bulb. It will think you always hit. I figured this out accidentally as a kid one day when I was randomly firing around the room and hit a duck.

[deleted]

Re: Using Computer Vision to Win at Duck Hunt

#16

Post by jordigh »

I don't understand this example.

Duck Hunt has only a few sprites, maybe four or six. It's really easy to just scan the whole image and match those sprites, exactly pixel-by-pixel. This doesn't really need anything that could reasonably be called machine learning or computer vision. Synthetic 8-bit images can be handled with 8-bit algorithms. We don't need much art and we certainly don't need its state either.

Why pick such an example to advertise roboflow?

The article itself also has pretty much no details on how Roboflow works. Not a single code sample! Just links to Roboflow docs.

This is blogspam.

Re: Using Computer Vision to Win at Duck Hunt

#18

Post by awal2 »

jordigh wrote: post #16

I don't understand this example. Duck Hunt has only a few sprites, maybe four or six. It's really easy to just scan the whole image and match those sprites, exactly pixel-by-pixel. This doesn't really need anything that could reasonably be called machine learning or computer vision. Synthetic 8-bit images can be handled with 8-bit algorithms. We don't need much art and we certainly don't need its state either. Why pi…

When all you have is a hammer

Re: Using Computer Vision to Win at Duck Hunt

#19

Post by jimmies »

jordigh wrote: post #16

I don't understand this example. Duck Hunt has only a few sprites, maybe four or six. It's really easy to just scan the whole image and match those sprites, exactly pixel-by-pixel. This doesn't really need anything that could reasonably be called machine learning or computer vision. Synthetic 8-bit images can be handled with 8-bit algorithms. We don't need much art and we certainly don't need its state either. Why pi…

I agree with you for the most part. This problem definitely doesn't require ML to achieve.

However, if doing it with a classical approach (matching sprites) takes a person half a day, and doing it with the new fangled proprietary ml takes a person 20 minutes, I do see that doing it with the new fangled ml approach does have some merits. People want to get stuff done.

I'm a barely passable programmer, so not exactly a shining benchmark, so maybe my view is distorted. I don't think I can do duck detection flawlessly with classical programming in half a day and it would be a total pain in the ass to do it that way, to be completely honest with you.

Re: Using Computer Vision to Win at Duck Hunt

#20

Post by awal2 »

jimmies wrote: post #19
jordigh wrote: post #16

I don't understand this example. Duck Hunt has only a few sprites, maybe four or six. It's really easy to just scan the whole image and match those sprites, exactly pixel-by-pixel. This doesn't really need anything that could reasonably be called machine learning or computer vision. Synthetic 8-bit images can be handled with 8-bit algorithms. We don't need much art and we certainly don't need its state either. Why pi…

I agree with you for the most part. This problem definitely doesn't require ML to achieve. However, if doing it with a classical approach (matching sprites) takes a person half a day, and doing it with the new fangled proprietary ml takes a person 20 minutes, I do see that doing it with the new fangled ml approach does have some merits. People want to get stuff done. I'm a barely passable programmer, so not exactly a…

You can do sprite matching using new fangled ML tools and save the same amount of developer time.
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