first of all, thank you for reading this Since I just discovered this place and therefore not helped anybody so far I feel a bit bad to ask straight forward …
I managed to compile the c++ application for local inference on windows.
Now, the next step is to not provide the raw features in an array but to hand over an image.
The goal is to make the methods and classes inside the example kinda ‘public’ and then access them from C# .NET
If anybody has suggestions or ideas, I would greatly appreciate them Stay healthy!
well, sorry for replying that late - I have been very busy over the last days …
Anyways, thank you a lot for those really detailed answers - they already helped
That being said, since I’m a developer these kind of things aren’t my really big issues. I’m fairly new to the whole AI stuff and I really being overwhelmed by how good your service actually works
In the last weeks I tinkered a lot with ml.net, darknet and so on.
To the point where there were examples; just put an image into it and see how objects are being detected.
Just like it works on your webapp - after an object detection model has been trained you can run images against it. That’s the kind of thing I want the c++ library to do. But I don’t really get it
Using the example standalone inferencing app, you need to feed in the raw_features vector with your image pixels in an RGB format. To test it quickly, just use the “Live Classifier” from your Edge Impulse project and copy/paste the raw features in a txt file as shown here:
Your image needs to have the resolution defined in your impulse (usually 320x320 for object detection).
You can then call the standalone application and check the predictions: ./edge-impulse-standalone features.txt
Once this is working, you can work on retrieving raw pixels from images directly in your C++ application, there should be some existing libraries for it.
Quick note on object detection: our Linux SDK supports full hardware acceleration in case you can switch your OS from Windows to Linux.
Thank you a lot for all of your replies
Okay, so I guess my last - and biggest - obstacle is effective yet fast feature extraction. Since I’m fairly new I actually even don’t know what these features actually are?
Are those just the extracted pixels or are they mixed with some algorithms - how do you guys do it