[FEATURE]: Batching .extract_faces

[galthran-wq]

Description

Related:
#1433
#1101

I think it would be a good idea to be able to batch input images on detection.
Many currently existing detectors are natively able to to perform batched inference (to name a few, YOLO, retinaface, MTCNN)
The hypothesis is that batching would improve the performance significantly.
Partially validated by serengil/retinaface#116.
Since retinaface is not going to support batching, we could at least have YOLO, MTCNN and some others

@serengil what do you think?

Additional Info

No response

[NatLee]

Hello, I have a question regarding .extract_faces. If it supports batch input, would the subsequent models, such as emotion or age detection, also need to be adjusted based on the output of .extract_faces?

For instance, if I have three input images, each will produce face detection results. How should the subsequent analysis define multiple faces across these three images? Perhaps using the input order to each face could be a solution?

[galthran-wq]

hi. I do not think they would have to be adjusted, because the function is supposed to work exactly the same for a single image input and all the susequent models are using it currently with a single image.

Now, if we were to adjust the usage in subsequent models to batched inputs, then you indeed raise a good point on how to recover which detection results correspond to which images in the input. And I think the solution here is this. The output type of the .extract_faces should be Union[List[Dict[str, Any]], List[List[Dict[str, Any]]. So, it is List[Dict[str, Any]] for a single image input (just like before), and List[List[Dict[str, Any] if a list of images is passed -- for each input face, a list of detected faces.

I'm planing to incorporate this change in the PR

[serengil]

@galthran-wq so, if you feed 3 images to input as list or numpy and the 2nd one has 5 faces, then function will still return a list of 3. The second item in the response will have 5 items. Liked that design.