New evaluator integration

The evaluator must extend the abstract class Evaluator which is described in core/evaluator.py. Specifically, the function run(tracker_state) must be implemented for the evaluation to be done.

## EvaluatorBase extension

The tracker_state object is the data structure that aggregates all the information and results related to the tracking. It contains in particular the predicitions, the metadata related to the dataset image_metadas and video_metadas but also the ground truths detections_gt, which were created during the dataset initialization. All these attributes are pd.DataFrame, which makes sorting for evaluation much easier.

You can find an example of an evaluator for the MOT challenge in the file wrappers/eval/mot/mot20_evaluator.py. You will then have to add the new object evaluator in the __init__.py file.

Config file

Our framework works with the [Hydra](https://hydra.cc/) configuration system which takes advantage of a hierarchical configuration via files. This is very convenient to easily change modules.

You will have to create a new .yaml configuration file, add the required arguments in the cfg field and link the _target_ to the new object for the instantiation. The completed example is available in configs/eval/mot20. yaml.

You will also have to change in the main config file configs/config.yaml the field eval: mot20 to evaluate with your new evaluator.