* feat: add resize() method and benchmark stage * perf: optimise resize() — vectorised coords, L3 direct RLE * refactor: split _rle_resize and extract _resize_crop * test: expand resize() tests for scaling and edge cases * refactor: switch resize helpers to F-order (column-major) RLE * fix: merge True/True RLE junctions in _rle_join_cols * perf: vectorize _rle_scale_col RLE re-encoding * test: add density-dispatch and parallel-path resize tests * refactor: harden resize() threading and RLE invariants * fix: accurate resize timing and exact nearest-neighbour parity * type: add explicit numpy typing to ndarray declarations --------- Co-authored-by: Claude Code <noreply@anthropic.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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|---|---|---|
| .. | ||
| compact_mask | ||
| count_people_in_zone | ||
| heatmap_and_track | ||
| speed_estimation | ||
| time_in_zone | ||
| tracking | ||
| traffic_analysis | ||
| README.md | ||
README.md
Examples
Here, you'll find end-to-end examples that show how to solve common computer vision problems using Supervision.
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