- `detections_from_xml_obj` now builds `np.empty((0, H, W))` for a background image under `force_masks=True` instead of letting `np.array([])` collapse to shape `(0,)`, which failed `Detections` mask validation
- document the forced `class_id` `dtype=int` with an inline comment and state the integer-dtype guarantee in the `detections_from_xml_obj` docstring Returns section
- add background-image coverage: force_masks empty 3D mask, all-background dataset, background-first ordering, and save-then-load round-trip
- add changelog entry for the `force_masks=True` background-image mask fix
---
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: OpenAI Codex <codex@openai.com>
detections_from_xml_obj built class_id with np.array(...) over a list of
indices. For an annotation file with no object elements that list is empty,
so NumPy inferred float64 and DetectionDataset validation rejected the
resulting Detections, making any Pascal VOC dataset that contains an
unannotated image impossible to load.
save_coco_annotations iterated the dataset, cv2-decoding every image only
to read its shape — even for labels-only exports. Sizes now come from the
in-memory array when present, else a lazy PIL header read, the same
optimization from_yolo uses (#1636).
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Re-verify remaining supervision review backlog against develop HEAD; most items were already resolved by an intervening commit, only genuinely-open gaps got new fixes.
Fix float32 precision loss in box_iou_batch for large coordinates (GeoTIFF-scale) by accumulating in float64.
Raise ValueError instead of a strippable assert in EvaluationDataset.load_predictions for unknown image ids.
Add HeatMapAnnotator.reset() to clear accumulated heat for annotator reuse.
Add missing coverage: labelme export basename collisions, _greedy_match matcher, metrics.core ABC/enum contracts, metrics.utils.utils pandas guard; remove a global RNG-seed pollution site in a metrics test.
Document the last two undocumented public exports (calculate_masks_centroids, is_compressed_rle) and add usage examples to 17 previously-example-less public functions/classes (NMS/NMM helpers, draw utils, PolygonZoneAnnotator, mask/polygon converters).
* tests: load_predictions ValueError branch + empty-dataset coverage
* fix: box_iou_batch int-dtype overflow, narrow float32 precision claim
* feat: add reset() to TraceAnnotator/DetectionsSmoother, fix docstrings
* docs: fix temp file leak in coco.py docstring, rename misnamed test
---------
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- Added `sv.mask_to_roi` as an explicit migration path for exclusive mask bounds
- Fixed COCO, CreateML, and Pascal VOC export validation to reject ambiguous or colliding dataset paths before writing
- Fixed in-memory `DetectionDataset` split and merge behavior
- Fixed `supervision` imports to avoid loading ByteTrack until it is used
- Fixed detection conversion helpers to support coordinate-convention migration while preserving legacy inclusive defaults
- Fixed Azure tag mapping, anchor rounding, and line-zone smoothing to avoid incorrect or ghost detections
- Fixed video processing shutdown handling for timeout and full-queue cases
- Improved downloader, validator, documentation, and regression coverage for the shipped dataset, detection, annotator, image, and video behavior
---------
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- Avoid mutating caller-owned Detections during dataset construction and reject invalid class ids with clear ValueErrors.
- Make COCO loading/export tolerant of missing optional metadata, add from_coco(use_iscrowd), and export mask pixel area when needed.
- Let folder-structure and YOLO loading skip common clutter and accept PIL-readable image modes with regression coverage.
- Preserve from_coco positional show_progress compatibility while keeping use_iscrowd keyword-only.
- Filter class-folder loading to image files and export missing COCO mask area from decoded masks.
- Add regression coverage, changelog updates, and types-tqdm for mypy.
---------
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- Fixed crop annotation so overlapping detections sample from the original scene
- Fixed dataset exports to reject basename collisions, including case-insensitive collisions
- Fixed LMM connector mapping to support mirror enum aliases without a hand-maintained dispatch table
- Updated benchmark documentation to install the released inference package with metrics support
---------
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- `train_test_split` seeded the global `random` module and shuffled the caller's list in place, so `DetectionDataset.split()` reordered its own `image_paths` and polluted process-wide randomness; use a local `random.Random` and shuffle a copy
- Pascal VOC class ids were assigned in `set`-iteration and filesystem-glob order, so the same dataset produced different `class_id` values across runs; sort class names and the loaded file list
- dataset exports keyed output files on basename, silently overwriting when two entries shared a name across directories (common after `merge()`); detect basename collisions and raise
- add regression tests for split determinism, VOC id stability, and export collisions
* fix(dataset): add LabelMe collision guard, hoist pre-flight checks, make guard private
* test(dataset): add collision guard tests for as_yolo, as_pascal_voc, and boundary cases
* docs(dataset): document ValueError raises, fix stale docstrings, add non-mutation guarantee
---------
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Add postponed annotations to test modules and modernize one test helper annotation for Python 3.9-compatible collection.
---------
Co-authored-by: Codex <codex@openai.com>
- Merge test_polygon_shapes_require_image_dims and test_force_masks_requires_image_dims into test_requires_image_dims_when_mask_needed with polygon-shape and force-masks-rectangle ids
---
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Added LabelMe import and export support for DetectionDataset, including per-image JSON loading/saving alongside existing dataset formats
- Added LabelMe rectangle-to-box and polygon-to-mask conversion, with rectangle masks available when mask output is requested or polygon annotations are present
- Added LabelMe path-safety protections by resolving image paths by basename and rejecting unsafe or ambiguous image references
- Added validation for duplicate image basenames, malformed shape points, missing imagePath values, and invalid class IDs during LabelMe load/export
- Improved LabelMe handling by warning and skipping unsupported shape types
- Updated documentation and changelog with LabelMe workflow examples and supported-format references
---------
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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: Codex <codex@openai.com>
object_to_pascal_voc applied the 1-index offset in place (xyxy += 1).
Because Detections.__iter__ yields each row of xyxy as a view sharing
memory with detections.xyxy, detections_to_pascal_voc wrote the +1 shift
straight back into the caller's array. A single export shifted every box
by +1px; a second export compounded it, producing wrong XML. A single
export-then-reload happened to round-trip because from_pascal_voc
subtracts 1, which is why no test caught it.
Rebind to a new array (xyxy = xyxy + 1) instead of mutating in place.
On-disk output is unchanged; the source detections are left intact.
Add regression tests asserting object_to_pascal_voc does not mutate its
inputs and that two consecutive exports are identical and leave xyxy
unchanged.
---------
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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
DetectionDataset.from_yolo accepts is_obb=True and stores the four
corners in detections.data["xyxyxyxy"], but DetectionDataset.as_yolo
has no matching option and only reads xyxy/mask. The standard
from_yolo -> split -> as_yolo flow silently writes 5-token
axis-aligned lines, and re-loading the saved file with is_obb=True
crashes the validator because it expects 9 tokens.
Add is_obb to as_yolo, save_yolo_annotations, and
detections_to_yolo_annotations. When True, the four corners from
data["xyxyxyxy"] are serialized via the existing object_to_yolo
polygon path. Masks are ignored, mirroring from_yolo(is_obb=True)
semantics. A missing xyxyxyxy raises ValueError early.
- Add UserWarning in as_yolo when area/approx params passed with is_obb=True (silently ignored)
- Add UserWarning in detections_to_yolo_annotations when mask present + is_obb=True
- Update ValueError message to include expected shape (N, 4, 2) for manual callers
- Add Google-style docstrings to detections_to_yolo_annotations and save_yolo_annotations
- Add test: N>1 OBB detections per image (corner indexing via data-dict slicing)
- Add test: dataset round-trip with background-only (no label file) image
- Add test: as_yolo() without is_obb=True on OBB-loaded dataset emits 5-token lines
- Replace all tempfile.TemporaryDirectory / os.path.join / os.makedirs
with pytest tmp_path and pathlib Path
- Merge 3 load-mask tests into parametrized test_load_yolo_annotations_mask_behaviour
(obb-no-mask, obb-force_masks-ignored, segmentation-produces-mask)
- Merge token-count tests into parametrized test_dataset_as_yolo_obb_output_token_count
(obb-save-nine-tokens, default-save-five-tokens)
- Split corner accuracy into dedicated test_dataset_as_yolo_obb_round_trip_corner_accuracy
- Drop import os and import tempfile
---------
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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
* fix(dataset): make COCO annotation/image ids chainable across splits (#768)
Exporting train/valid/test splits with DetectionDataset.as_coco
previously restarted image_id and annotation_id at 1 for every split,
producing three JSON files whose ids collided and could not be safely
merged into a single COCO collection.
Adds optional starting_image_id and starting_annotation_id parameters
to save_coco_annotations and DetectionDataset.as_coco (default 1 to
preserve existing behavior) and returns a (next_image_id,
next_annotation_id) tuple so callers can feed the result of one
export straight into the next:
next_image, next_ann = train.as_coco(annotations_path="train.json")
next_image, next_ann = valid.as_coco(
annotations_path="valid.json",
starting_image_id=next_image,
starting_annotation_id=next_ann,
)
test.as_coco(
annotations_path="test.json",
starting_image_id=next_image,
starting_annotation_id=next_ann,
)
The images-only branch of as_coco (annotations_path=None) round-trips
the starting ids unchanged so chaining still works there.
Adds 4 regression tests covering defaults, custom starting ids,
end-to-end three-split chaining with global uniqueness assertions,
and the images-only round-trip.
* docs: address review polish on COCO id-chaining
* fix(dataset): align save_coco_annotations approximation_percentage default to 0.0
* docs(dataset): add one-line summary to save_coco_annotations docstring
* docs: add changelog entry for COCO id chaining (PR #2267)
* docs(dataset): document file_name uniqueness limitation in save_coco_annotations
* feat(dataset): validate starting_image_id and starting_annotation_id >= 1
* docs(dataset): add Example section to save_coco_annotations docstring
* docs(dataset): unpack final as_coco return value in chaining example
* test(dataset): add COCO chaining tests and fix test helper for zero detections
---------
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Co-authored-by: Claude Code <noreply@anthropic.com>
* Detections.from_inference works on RLE-encoded masks
* Apply suggestions from code review
* fix: harden RLE handling in from_inference and decoder
* lint: fix cv2.fillPoly color type in polygon_to_mask
* fix: resize RLE mask to image dims when size mismatches
* fix: pass RLE counts directly in coco_annotations_to_masks
* test: document mixed RLE + box-only batch misalignment
* test: add compressed RLE iscrowd case to coco_annotations_to_detections
* fix: cast polygon mask to bool in process_roboflow_result
* fix: log warning when RLE decode fails in process_roboflow_result
* fix: replace assert with ValueError in rle_to_mask
* test: add bytes invalid UTF-8 case to rle_to_mask tests
* docs: note rle_to_mask dtype change from uint8 to bool in changelog
* refactor: tighten rle_to_mask NDArray input type to np.integer[Any]
* refactor: drop mask_to_rle overloads; cast at call site
* docs: clarify COCO column-major RLE order in rle_to_mask/mask_to_rle
* refactor: update @deprecated annotations and docstrings for mask_to_rle/rle_to_mask; add pydeprecate dependency
* refactor: replace mask_to_rle body with `void` function to suppress unused argument warnings
---------
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix: preserve area and iscrowd from detection data in COCO export
* fix: use np.asarray().item() to satisfy mypy in iscrowd/area extraction
* test: shorten test name to fix ruff E501 line-length violation
* test: verify data["area"] overrides bbox area when mask is present
* test: stricter iscrowd type check (bool subclass fix)
* test: stricter iscrowd type check in preserves_iscrowd_from_data
* test: stricter iscrowd type check in iscrowd_is_int_when_mask_provided
* fix: prefer data["iscrowd"] over geometry when mask is present
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Borda <6035284+Borda@users.noreply.github.com>
Co-authored-by: Claude Code <noreply@anthropic.com>
* refactor docstrings in `scr/supervision/detection`
* Enhance docstrings across multiple modules: clarify attributes/args, improve formatting, and update logic for handling sentinel values in metrics calculation.
* Ensure consistent handling of `class_id` as integer across YOLO and Pascal VOC formats, fix NoneType handling in line zone logic, and add test coverage for multiclass annotator with None `class_id`.
* Enforce `class_id` as integer in YOLO export, update line zone class count docstrings, and add test for non-integer `class_id`.
* Apply suggestions from code review
---------
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Fix `detections_to_coco_annotations` function for empty polygons.
* Add `segmentation` empty field for bboxes coco format
* update coco.py
Always include box, area, and segmentation in the result COCO JSON.
* `as_coco()` : Add COCO format disjoint masks support
* Fix `force_masks` parameter on `from_coco()` function
Allows reconstruction of disjointed masks
* Ensures the mask is binary
* Refactor `coco_annotations_to_masks` to handle disjoint polygon segmentation and missing segmentation gracefully. Add corresponding unit tests.
* Refactor `coco_annotations_to_masks` for improved type annotations and cleaner formatting
* Add warnings for handling empty polygons during COCO segmentation and related unit tests
---------
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* Fix mask_annotate for int dtypes
* Add depreciation warning
* Add dtype=bool to test masks
* Remove ValueError (testing)
* Ensure boolean masks are consistently used in `Detections` and update validations, tests, and warnings for stricter type handling.
* 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>
* make force_masks consistent with yolo and voc
* Refactor mask detection logic across dataset formats to improve consistency and clarity. Rename helper functions for better readability. Add parametrized test cases with descriptive IDs.
* Refactor PASCAL VOC and COCO mask handling to improve consistency, add tests for mixed annotations and mask inference logic.
* Apply suggestions from code review
---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* refactor: reorganize project structure and update module imports
* configure setuptools to use 'src' layout and adjust package discovery
* refactor: move tests to `tests` directory and update imports accordingly
* update lint and mypy configurations to align with `src` and `tests` structure
* update test imports to use corrected `tests.helpers` module path
* fix(pre_commit): 🎨 auto format pre-commit hooks
Jirka Borovec
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Ruben
Abdelrahman Gomaa
Tamil Adhavan S K
Agis Kounelis
Andrew Barnes
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Junghwan
Ishaan
Omkar Kabde
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