refactor(metrics): update MeanAveragePrecisionResult to use float32 precision (#2169)

* refactor(metrics): update MeanAveragePrecisionResult to use float32 precision

This change reduces memory usage and improves computation speed while maintaining acceptable precision for most computer vision tasks.

- Changed mAP_scores, ap_per_class, and iou_thresholds from float64 to float32
- Updated EPS constant to use float32 precision
- Modified COCOEvaluator parameters and computations to use float32
- Added type annotations for variables in filter_segments_by_distance function
- Changed precision and recall calculations to use float32
- Updated average precision computation to handle float32 values
- Modified summarize_predictions to return float32 array

* update type annotation for `keep_labels` to use `npt.NDArray[np.bool_]`
* update EPS constant and mAP calculations for precision consistency
* Apply suggestions from code review

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Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>