Fix OBB IoU for non-square canvases

[RitwijParmar]

Summary

• fix oriented_box_iou_batch canvas sizing so x coordinates determine width and y coordinates determine height
• add a scale-invariance regression for non-square oriented boxes
• add downstream metric coverage proving perfect non-square OBB predictions score correctly for Precision, Recall, F1, mAP, and mAR

Fixes #2278.

Why this matters

OBB evaluation currently rasterizes polygons onto a canvas with width/height swapped. For non-square extents this can clip the polygons away and return zero IoU, which then makes otherwise correct OBB detections look like complete failures across the metrics stack. This is especially painful for real-world CV datasets where rotated boxes are common for aerial imagery, floorplans, traffic scenes, and industrial inspection.

Tests

• .venv/bin/python -m ruff check src/supervision/detection/utils/iou_and_nms.py tests/detection/utils/test_iou_and_nms.py tests/metrics/test_mean_average_precision.py tests/metrics/test_oriented_bounding_box_metrics.py
• .venv/bin/python -m pytest tests/detection/utils/test_iou_and_nms.py::test_oriented_box_iou_batch_is_invariant_to_non_square_scaling tests/metrics/test_oriented_bounding_box_metrics.py
• .venv/bin/python -m pytest tests/detection/utils/test_iou_and_nms.py tests/metrics/test_oriented_bounding_box_metrics.py tests/metrics/test_mean_average_precision.py tests/metrics/test_mean_average_recall.py tests/metrics/test_precision.py tests/metrics/test_recall.py tests/metrics/test_f1_score.py

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