I heard you are not having fun enough with d today.

Do you know you can do things like this with dlang now? After some fiddling with it, my last commits made this possible.

how it looks like: https://www.youtube.com/watch?v=ACC_-TDAtqc
source code: https://github.com/aferust/oclcv/tree/main/examples/centroidtracking
DCV: https://github.com/libmir/dcv

Sorry for the potato-quality video. My art director is on vacation.

I am cheating a little with OpenCL since things are not fast enough at the moment.

Hope you like it.

Enjoy!

On Wednesday, 15 February 2023 at 17:32:33 UTC, Ferhat Kurtulmuş wrote:

Nice!

That's pretty cool, thanks for sharing!

On Wednesday, 15 February 2023 at 17:32:33 UTC, Ferhat Kurtulmuş wrote:

Hello everyone,

I was looking for ways to run pre-trained DCNN models (inference) using D. I then ran across onnxruntime, which has a c API. Luckily, it has a bindbc binding readily available. Nowadays, to run inference routines of CNN models, we only need some basic image processing to satisfy the input shape requirements of those models. We have mir.ndslice and dcv, and then we should be able to run, for instance, tinyYOLOv3 with video streams. I believe that such applications will attract more people's attention to d.

Here is how it looks like and the source code:

https://www.youtube.com/watch?v=m3ex9lDELfQ
https://github.com/aferust/dcv-tinyyolov3

On Wednesday, 15 February 2023 at 17:32:33 UTC, Ferhat Kurtulmuş wrote:

Hello everyone,

I was looking for ways to run pre-trained DCNN models (inference) using D. I then ran across onnxruntime, which has a c API. Luckily, it has a bindbc binding readily available. Nowadays, to run inference routines of CNN models, we only need some basic image processing to satisfy the input shape requirements of those models. We have mir.ndslice and dcv, and then we should be able to run, for instance, tinyYOLOv3 with video streams. I believe that such applications will attract more people's attention to d.

Here is how it looks like and the source code:

https://www.youtube.com/watch?v=m3ex9lDELfQ
https://github.com/aferust/dcv-tinyyolov3

On Tuesday, 28 February 2023 at 12:08:14 UTC, Ferhat Kurtulmuş wrote:

Great job. Could we have any comparison in the performance/memory usage versus original solution in Python?

On Tuesday, 28 February 2023 at 12:29:05 UTC, Sergey wrote:

I have not conducted any comparisons yet. There are a lot of factors affecting performance. My old laptop lacks good cuda support, so I disabled the CUDA acceleration. I cannot give you a strongly backed test result, but I can say that preprocessing is not so costly in my example. The FPS drop is primarily due to onnxruntime itself. The preprocessing step only takes 2 or 3 msecs. The newer versions of onnxruntime have various backend options for acceleration, such as CUDA, tensorrt, directML (uses directX).