Commit Graph

8 Commits

Author SHA1 Message Date
Jirka Borovec bcbe8de1e4
chore(pre-commit): remove unnecessary exclude rule for changelog... (#2452)
* chore(pre-commit): remove unnecessary exclude rule for changelog and deprecated docs
* docs(changelog): reformat code blocks for consistency and clarity
* docs(changelog): reformat and align code blocks for consistent indentation and readability
* chore(pre-commit): update mdformat hooks to include gfm and frontmatter extensions
* chore(pre-commit): split mdformat hook into gfm and mkdocs variants
* docs(changelog): fix nested code fences breaking mdformat-mkdocs
* fix(pre_commit): 🎨 auto format pre-commit hooks

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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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2026-07-21 19:33:35 +02:00
Jirka Borovec 3ecd5d0744
Optimize mask annotation ROI blending (#2368)
- Improved MaskAnnotator performance by blending mask overlays only within the affected ROI while preserving dense mask and CompactMask rendering behavior
- Fixed all-false masks to skip unnecessary ROI blending
- Updated compact-mask benchmark output to clarify annotation speedup reporting

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Co-authored-by: Codex <codex@openai.com>
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
2026-07-01 15:55:21 +02:00
Jirka Borovec beb047095f
feat: add compact RLE mask ingestion (#2367)
- Added compact COCO RLE mask ingestion with a `CompactMask` representation and optional compact mask parsing during inference for substantially lower memory usage on sparse segmentation results.
- Added `Detections.to_compact_masks()` to convert existing dense masks into compact masks while preserving detection and collection metadata.
- Improved compact mask decoding performance with cropped RLE processing, batched decoding on the fast path, vectorized decoding for small images, optimized RLE traversal, and faster delta decoding.
- Improved mask metrics to operate directly on `CompactMask` instances, preserving the compact representation while producing results equivalent to dense masks.
- Fixed mixed-modality inference handling by keeping detections and masks aligned, isolating malformed RLE failures to individual predictions where possible, and falling back safely when decoding cannot be completed.
- Fixed compact mask conversion and parsing to preserve dense-mask pixel content across public parsing and slicing paths, while correctly documenting and applying the intended bbox-cropping behavior for compact COCO RLE masks.
- Improved COCO RLE validation with checks for malformed payloads, invalid dimensions, count overflows, image size limits, count-sum mismatches, bounding-box mismatches, and safe fallback behavior for incompatible mask sizes.
- Added inference benchmarks and documentation demonstrating the memory and inference-time characteristics of compact masks, including guidance on their performance tradeoffs and behavior.

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Co-authored-by: Codex <codex@openai.com>
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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2026-07-01 12:50:40 +02:00
Jirka Borovec 0a95bae8a8
chore: bump minimum Python to 3.10 (#2260)
- Drop Python 3.9 from CI test matrix
- requires-python = ">=3.10" in pyproject.toml
- ruff target-version py39 → py310
- mypy python_version 3.9 → 3.10
- Remove Python 3.9 classifier

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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: Codex <codex@openai.com>
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2026-06-29 14:45:30 +02:00
Jirka Borovec 09b21992c5
chore: port example typing refinements (#2358)
Co-authored-by: Codex <codex@openai.com>
2026-06-27 09:08:54 +02:00
Jirka Borovec 153cde5854
feat(compact_mask): add `resize()` method and benchmark stage (#2227)
* 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

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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>
2026-04-22 15:34:12 +02:00
Jirka Borovec 5a59dcdec6
refactor(masks): switch CompactMask RLE to F-order (#2228)
* refactor(masks): switch CompactMask RLE to F-order
* docs(masks): update _rle_area docstring example to F-order RLE
* test(masks): verify _rle_encode produces COCO-compatible F-order RLE

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Co-authored-by: Claude Code <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
2026-04-22 08:12:00 +02:00
Jirka Borovec 9b7099d3fc
feat: add CompactMask for memory-efficient crop-RLE mask storage (#2159)
Dense (N, H, W) bool masks cause OOM for aerial imagery (1000 objects x
4K image ~ 8.3 GB). CompactMask encodes each mask as a run-length
sequence of its bounding-box crop, reducing typical usage to ~2 MB.

* fix: correct bounding box coordinates in CompactMask doctests
* feat: implement memory-efficient IoU and NMS with CompactMask integration
* test: add extensive tests for CompactMask IoU, NMS, and InferenceSlicer integration
* feat(examples): add CompactMask demo and benchmark
* feat(examples): expand CompactMask benchmark with new stages and metrics
* feat(tests): add detailed CompactMask tests for NMM, centroids, holes, and segments
* feat(compact_mask): add repack(), fix merge perf, and add parity tests
* fix(masks): handle empty crops by defaulting centroid to (0, 0)
* feat(compact_mask): add `bbox_xyxy` property and improve type annotations
* feat(compact_mask): enhance `with_offset` for clipping and add tests
* docs(compact_mask): unwrap prose and add per-operation speedup analysis
* perf(compact_mask): fast path in with_offset avoids decode/re-encode
* fix(benchmark): count NMS mismatches and explain exact-vs-resize difference
* test(compact_mask): add 121 parametrised random-scenario parity tests
* refactor: rename single-char variables to descriptive names
* fix(nms): remove resize-to-640 approximation from mask_non_max_suppression
* docs(compact_mask): update README with fresh benchmark results
* refactor(benchmark): improve summary table logic, add CSV export
* docs(compact_mask): update README with revised benchmark speedups and cleanup

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Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Codex <codex@openai.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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2026-04-13 11:10:12 +02:00