feat: add segmentation mask quality utilities

[Aniketiitk21]

Description

Adds small, composable segmentation mask quality utilities for comparing a predicted binary mask with a target mask.

Type of Change

• New feature (non-breaking change which adds functionality)

Motivation and Context

Supervision already provides mask_iou_batch and strong segmentation support through Detections.mask and CompactMask. This PR adds single-mask quality primitives that are easy to reuse in segmentation model debugging, annotation QA, dataset inspection, and contour-sensitive evaluation workflows.

Changes Made

• Added mask_iou, dice_coefficient, boundary_iou, and boundary_f_score
• Added tests for overlap math, boundary tolerance behavior, empty-mask semantics, dtype handling, validation, and CompactMask parity
• Added docs/examples and a docs navigation entry for the new utilities

Testing

• I have tested this code locally
• I have added unit tests that prove my feature works
• All new tests pass locally

Commands run:

python -m venv .venv
./.venv/Scripts/python.exe -m pip install -e . pytest ruff mypy
./.venv/Scripts/python.exe -m pip install mypy==1.15.0
./.venv/Scripts/python.exe -m ruff check src/supervision/detection/utils/mask_metrics.py tests/detection/utils/test_mask_metrics.py src/supervision/__init__.py
./.venv/Scripts/python.exe -m mypy src/supervision/detection/utils/mask_metrics.py
./.venv/Scripts/python.exe -m pytest tests/detection/utils/test_mask_metrics.py -q
./.venv/Scripts/python.exe -m pytest src/supervision/detection/utils/mask_metrics.py -q

Google Colab

Colab link: N/A

Screenshots/Videos

N/A

Additional Notes

Semantics are explicit and documented:

• both masks empty -> 1.0
• one mask empty -> 0.0
• tolerance is an integer pixel distance for boundary matching

This PR is intentionally focused as PR 1 of a broader segmentation quality toolkit roadmap and does not include reports, error maps, or visualization helpers yet.

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