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Basically you wouldn't need to build your environment manually. For example, with AWS, if you wanted to run traincascade you would need to setup an instance, configure and install OpenCV and only then run traincascade. I could provide all that ready, you would just need to specify your sample.vec. Plus AWS high performance machines are 32 cores max, mine are 170 ;) there are many more advantages, but this is just a start.

What do you think?

Hello devs,

I've been playing with OpenCV lately and I did notice that a lot of time and resources are spent on things like classifier training, detection, etc. This is especially true when doing calculations on low performance devices. I have some unutilized high performance machines (100+ cpu threads, 40gb memory, etc) that I want to put to a good use, so I was wondering if anyone would be interested in using a service that would allow you to move your OpenCV calculations to 'the cloud'? Kind of like Heroku for OpenCV!

Basic idea is that you would do the calculations remotely either via a webpage or using an API. This would allow you to do things like cascade training in matter of hours instead days/weeks. If this were to gain traction we could expand the idea to cover other things like object detection with custom classifiers and much more, all done somewhere in the cloud without you having to do the coding manually and on the local machine.

Let me know what you think about the idea. Also, if anyone out there is interested in joining forces in such a project, feel free to reach out

Yeah, that helps clear things up, thanks! Just one more thing, regarding:

Is your first task explicitly detecting the bottles location or only verifying that it is on the expected location.

what is the difference?

Thanks Steven. Are there any particular OpenCV tutorials that talk about making a 'smart classifier'? I've seen a few that talk about face recognition, but they seem to be overly specific to faces and not to general objects...

I'm trying to use OpenCV to detect and subsequently distinguish between different types of an object. Say that I want to be able to detect if an object is a bottle and then based on some of its specific characteristics I'd like to be able to distinguish what kind of bottle it is (eg beer bottle, wine bottle, etc). How would I approach this? My idea so far has been to create a separate classifier for each type of the object, and then run comparison on all these classifiers. However this seems inefficient, especially if there are thousands of different types of an object, and I'd like to be able to get a result within few seconds...

I would appreciate if somebody could point me in the right direction here.

On another note as you might imagine I'm completely new to the field of computer vision, so if you can recommend some useful material to learn this from the ground up, I'd be very grateful.