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>
- Remove unused compatibility operations and use focused Pillow and NumPy paths to reduce maintained fallback code.
- Preserve numerical decisions and hot-path performance with exact regression coverage and bounded algorithms.
- Preserve INTER_LINEAR uint8 reductions within one LSB while retaining the resize performance budget and numeric RGBA handling.
- Restore repeated-endpoint contour anchors and bound cross-platform chamfer coefficient drift in regression tests.
---------
Co-authored-by: Codex <codex@openai.com>
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_get_text_size approximated thickness-to-stroke padding with
thickness // 2 formulas that diverge from the thickness - 1
stroke_width _put_text actually renders with. Past thickness 2 the
padding grows too slowly, so heavy-stroke descender pixels can fall
outside the reported box, breaking the documented enclosure
guarantee. Both functions now derive stroke_width from one shared
helper.
---
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Text fallback now renders through Pillow with the DejaVu Sans face
resolved via matplotlib font_manager, replacing the Hershey stroke-font
reader; getTextSize metrics derive from the same font and differ from
OpenCV within the documented visual-divergence tier.
Remove the packaged Hershey glyph data (hershey_fonts.json, provenance,
license) and its _cv2/data package-data entry.
Delete unused fallbacks: _geometry _fill_poly and _point_in_polygon
(live fillPoly is the Pillow one in _drawing) and _common _unavailable.
Replace test_hershey with Pillow-oriented test_text, drop test_common,
and point test_contours/test_geometry at _drawing._fill_poly. Document
the fallback text-backend change in the changelog.
---------
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Add the PyAV-backed file-video and audio fallback to the compatibility layer.
- Declare PyAV alongside OpenCV until the final dependency-removal integration.
- _VideoWriter now rejects is_color=False (NotImplementedError) instead of
silently dropping it, since the PyAV fallback only encodes 3-channel frames.
- _mux_audio cleanup (container closes, temp-file removal) is now best-effort
so a failing close/remove in finally can no longer mask the primary result
or the original exception.
- The subprocess used to validate the cv2-free fallback had no timeout;
a hang (import deadlock, codec probe stall) could block the whole CI
run. Added a 60s timeout so a hang fails fast with a clear traceback
instead of an opaque suite-wide stall.
- process_video(preserve_audio=True) docstring still described the old
ffmpeg-based muxing; audio remuxing was reimplemented with PyAV and no
longer requires an external ffmpeg executable.
- get_video_frames_generator's documented webcam fallback
(`_cv2.VideoCapture(0)`) silently fails under the PyAV backend: the
BackendUnavailableError raised for integer sources was swallowed with no
logging, so isOpened() just returns False with zero diagnostic signal.
Doc note now states the limitation explicitly and the capture logs a
warning instead of failing silently.
---------
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- Added `sv.ImageWindow`, a Tkinter/Pillow-based desktop image viewer with BGR, grayscale, and BGRA support, keyboard polling, left-click callbacks, context-manager usage, window-state checks, and clean close handling
- Added responsive image resizing with optional aspect-ratio preservation and correctly mapped mouse coordinates after scaling or letterboxing
- Updated compatible runnable examples to use `sv.ImageWindow`, while retaining OpenCV display APIs for worker-thread streaming examples that are incompatible with Tkinter
- Improved `sv.cv2_to_pillow` to support grayscale and BGRA images
- Updated webcam guidance to clarify capture ownership and explicit `VideoCapture` cleanup
- Fixed image-window event handling to prevent stale keypresses, ghost windows, close-time races, and blocked waits after the window closes
---------
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`hex_to_rgba` previously stripped every leading `#`, so invalid inputs such as `##000000` were accepted despite `is_valid_hex` rejecting them.
Remove only one optional prefix and add regression coverage for the minimized failing input.
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
---------
Co-authored-by: Codex <codex@openai.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Extend active deprecation removals to 0.31.0 and align deprecated API docs, changelog, and warnings.
- Add missing reference docs for VLM, conversion helpers, geometry, metrics extras, and tracker deprecation notices.
- Raise when ImageSink cannot write an image and cover the failure path with a regression test.
- Correct conversion and deprecated docs to match exported names and restore KeyPoints.confidence.
- Add regression coverage for SUPERVISION_DEPRECATION_WARNING precedence and document ImageSink.save_image() failure behavior.
* test: add validation and behavior tests for Color, Position, and polygon approximation adjustments
---------
Co-authored-by: Codex <codex@openai.com>
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- Reinstated NumPy-safe `Classifications` equality and ordered class-list comparisons in dataset equality.
- Restored greedy matching plus size-bucket scoring for Precision, Recall, F1, and MeanAverageRecall, with regression coverage for the medium-object boundary case.
- Filter size-bucket precision, recall, and F1 against target boxes so predictions no longer claim the bucket.
- Preserve confidence order for bucketed mAR@K scoring and return zero when a bucket has no support.
- Add regression coverage for bucket matching, empty-support mAR, top-K limits, and missing-mask errors.
---------
Co-authored-by: Codex <codex@openai.com>
- Convert timm classification logits with softmax so confidence values match the normalized scale used by other classification adapters.
- Verify asset MD5 hashes after fresh downloads and retry once when a payload is corrupted.
- Add focused regressions for timm confidence scaling and asset download integrity paths.
- Convert from_timm outputs to probabilities before applying thresholds and document that existing thresholds may need retuning.
- Add downloader regression coverage for repeated MD5 mismatches so exhausted retries now raise ValueError.
---------
Co-authored-by: Codex <codex@openai.com>
- Use COCO 101-point AP averaging in the legacy mAP path so perfect and imperfect curves score consistently.
- Validate confusion-matrix class ids before indexing and preserve target ignore flags in the COCO-style evaluator.
- Keep mAR per-class recall for each max-detection cutoff and cover the scoring fixes with focused regressions.
- Return empty mAR scores with the same max-detection axis as non-empty results.
- Add an empty-input regression covering recall score and per-class result shapes.
- Update the public mAR docstring to describe per-image detection limits.
---------
Co-authored-by: Codex <codex@openai.com>
- 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.
---------
Co-authored-by: Codex <codex@openai.com>
- Added deterministic color lookup with flexible palette resolution and clear errors for empty palettes
- Improved annotator and utility handling for warning formatting, plotting imports, and icon caching
- Added validation for keypoint edges, MediaPipe inputs, and VideoSink state
---------
Co-authored-by: Codex <codex@openai.com>
- Fixed detection medium findings across adapters, mask non-max merge, sinks, segmentation parsing, LineZone history, and mask ROI handling
- Fixed mask non-max merge deprecation warnings to honor the standard warning opt-out and include version context
- Fixed mask non-max merge validation for invalid IoU thresholds
- Fixed CompactMask non-max merge grouping to update merged mask candidates correctly
- Fixed selected and compacted detections to copy arrays and metadata, preventing mutations from leaking back to source detections
- Fixed LineZone crossing history eviction to tolerate short tracking gaps and evict stale state per tracker/class key
- Fixed semantic segmentation handling to preserve class ID 0
- Improved mask ROI conversion performance by avoiding unnecessary full-frame copies and repeated scans
- Updated JSONSink changelog/docs to document native bool/int/float output while leaving CSVSink unchanged
- Updated detection docstrings for mask parsing, selection copy semantics, validation errors, and argument readability guidance
---------
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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>
* fix(annotators): clip BackgroundOverlayAnnotator boxes to the scene before restoring detection regions
* fix(annotators): use explicit np.int32 cast in BackgroundOverlayAnnotator
* test(annotators): strengthen BackgroundOverlayAnnotator test coverage
* docs(changelog): add Unreleased entry for BackgroundOverlayAnnotator fix
---------
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Fixed annotators to avoid internal deprecation warnings from image overlay usage while preserving the public deprecated wrapper
- Fixed CropAnnotator crashes for partially out-of-frame detections by clipping crops to scene bounds and skipping degenerate boxes
- Fixed HeatMapAnnotator heat disappearing after 256 accumulated frames
- Fixed video frame generation to release the capture when iteration ends early
- Updated documentation for overlay deprecation, crop clipping behavior, and video capture release guarantees
---------
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Fixed in-memory dict-form `DetectionDataset` image access, iteration, equality, and merge behavior, with deprecation messaging retained
- Fixed mAP to honor `metric_target` for mask and oriented-bounding-box evaluation, including correct IoU routing, area handling, crowd semantics, and missing-content errors
- Fixed `ConfusionMatrix.plot()` when plotting raw counts with default normalization disabled
- Improved mask mAP crowd handling performance and memory usage
- Updated the count-in-zone guide to use current APIs
---------
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Fixed `get_anchors_coordinates` to compute anchor positions from oriented bounding boxes when OBB geometry is available, ensuring anchor-based operations (such as zone counting and annotators) align with the rotated object instead of its axis-aligned bounding box.
- Preserved existing behavior for axis-aligned boxes, while continuing to use mask centroids for `CENTER_OF_MASS` anchors when masks are available.
- Improved the `get_anchors_coordinates` documentation with the updated anchor selection order, OBB usage examples, and notes describing OBB winding-order requirements and anchor tie-breaking behavior.
---------
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Improved `Detections.merge()` to preserve `CompactMask` output when merging dense and compact masks by converting dense masks to compact form, avoiding unnecessary full-mask materialization while keeping all-dense and all-compact behavior unchanged.
- Added validation to mixed-mask merging that raises `ValueError` when compact masks have inconsistent image shapes or dense mask dimensions do not match the compact mask image size.
- Added the public `CompactMask.image_shape` property for safe access to compact mask dimensions.
- Updated `Detections.merge()` documentation to describe mixed-mask merge behavior, output types, validation errors, the lossy dense-to-compact conversion outside detection bounding boxes, and that NMS/NMM pairwise operations do not preserve `CompactMask`.
- Added a comprehensive "Use Compact Masks" how-to guide covering compact mask ingestion, inference, annotator mask requirements, and mixed-mask merging, and integrated it into the documentation navigation.
---------
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- Added a `requires_mask` flag to annotators so integrations can determine whether masks must be materialized before annotation.
- Updated mask-only annotators to declare `requires_mask=True`, while mask-optional annotators explicitly declare `requires_mask=False`, including compatibility support for `ComparisonAnnotator`.
---------
Co-authored-by: Codex <codex@openai.com>
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- 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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process_roboflow_result appended tracker_id only for predictions that carried
one, while xyxy/confidence/class_id were appended for every prediction. A
result where some predictions are tracked and others are not produced a
tracker_id array shorter than the boxes, so Detections.from_inference raised
"tracker_id must be a 1D np.ndarray with shape (N,)".
Collect tracker_id for every prediction (None when absent) and build the array
only when all detections carry one, otherwise leave it None. Fully-tracked and
untracked results are unchanged.
---------
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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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InferenceSlicer can now accept an open rasterio-style dataset and read each tile via a windowed read instead of loading the whole image into memory, enabling tiled inference on multi-GB aerial/drone GeoTIFFs. Detection is duck-typed so rasterio stays an optional dependency (supervision[geotiff]) and the library imports no rasterio symbols. Adds CRS projected validation and tests. Closes#2027.
- Add threading.Lock around raster.read() in _run_callback to prevent
data race when thread_workers > 1 shares a DatasetReader (GDAL releases
GIL inside GDALRasterIO — reads are genuinely concurrent C code)
- Return TypeGuard[WindowedRasterDataset] from _is_windowed_raster;
TYPE_CHECKING guard imports typing_extensions for Python 3.9 compat
- Add @runtime_checkable to WindowedRasterDataset Protocol; crs typed
as object|None; guard .is_projected via getattr(..., True)
- Extract _get_resolution_wh and _apply_overlap_filter helpers from
__call__ to bring cyclomatic complexity under PLR0912 limit (16 → ~4)
- Widen callback type to Callable[[NDArray[Any]], Detections] to accept
any dtype (uint16 raster tiles are not NDArray[uint8])
- Add Raises section to __call__ docstring for geographic CRS ValueError
- Add one-line summary to move_detections docstring
- Export WindowedRasterDataset from sv.__init__
- Move changelog entry from 0.29.1 (released) to UnReleased
- Add comment explaining rasterio>=1.3 lower bound in pyproject.toml
- Restructure tests: class grouping, parametrize CRS cases, add
docstrings; add compact_masks, thread_workers>1, single-band,
single-tile test cases
---------
Co-authored-by: madhavcodez <madhavcodez@users.noreply.github.com>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
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Co-authored-by: Codex <codex@openai.com>
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Two ways the VLM parsers crashed on adversarial model output instead of
degrading gracefully (the contract they already honor for invalid JSON):
1. Gemini 2.5: a mask value that is not a 'data:image/png;base64,' string
appended an empty mask and then 'continue'd, skipping the confidence
handler at the bottom of the loop. The item's box was recorded but its
confidence was not, so the confidence array ended up shorter than xyxy
and Detections.from_vlm raised a shape ValueError. Replaced the
'continue' with an if/else so the confidence handler always runs.
2. Gemini 2.0 / Gemini 2.5 / Qwen 2.5: valid JSON whose top level is not a
list, or whose elements are not dicts (e.g. '[1, 2, 3]'), raised
TypeError from the 'key not in item' membership test. Added a top-level
list guard (Gemini 2.0/2.5; Qwen already had one) and a per-element
dict guard so wrong-shaped JSON degrades to empty Detections.
Add regression tests for the mask/confidence alignment and for graceful
degradation across all three parsers.
---------
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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>
- Added an end-to-end cookbook focused on oriented bounding boxes (OBB), demonstrating how OBB detections differ from axis-aligned boxes, why oriented overlap and NMS matter, how to use footprint-based filtering with `Detections.area`, and how to export annotations in YOLO OBB format.
- Added visual examples that clearly compare axis-aligned and oriented boxes, including a close-up showing how axis-aligned envelopes can significantly overestimate object footprints for angled objects.
- Improved the cookbook narrative to center on the practical consequences of using oriented versus axis-aligned boxes, including tighter localization and more appropriate NMS behavior for densely packed, rotated objects.
- Updated cookbook references, naming, dependency versions, image attribution, and changelog links to align with the released 0.29.0 documentation.
- Fixed the 0.29.0 changelog by removing a duplicate `Detections.area` entry and keeping the more accurate correctness-fix classification.
---------
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* feat: add with_nms() method to KeyPoints class Derive axis-aligned bounding boxes from valid keypoints and delegate to box_non_max_suppression for filtering. Requires detection_confidence; supports class-aware and class-agnostic modes.
- Add overlap_metric: OverlapMetric = OverlapMetric.IOU param to KeyPoints.with_nms() for API parity with Detections.with_nms()
- Integrate self.visible into keypoint validity: valid = valid & self.visible when visible is not None
- Pass overlap_metric through to box_non_max_suppression
- Fix docstring: add Defaults to for threshold/class_agnostic, threshold range constraint, overlap_metric arg
- Add UnReleased changelog entry
- Add 5 new test cases: all-zero-skeleton-passes-through, visible-mask-excludes-keypoints-from-bbox, single-valid-keypoint-zero-area-bbox, threshold boundary 0.0/1.0
- Add missing raises test: no-detection-confidence-class-agnostic
- Update `with_nms` method to raise `ValueError` instead of `AssertionError` for missing required fields (`detection_confidence`, `class_id` when `class_agnostic=False`).
- Adjust corresponding test to check for `ValueError` with match argument.
---------
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- Fixed `JSONSink` to correctly serialize NumPy scalar values stored in `custom_data`
- Added JSON serialization support for NumPy arrays by converting them to standard JSON-compatible lists
- Prevented buffered export failures caused by non-serializable NumPy values during `json.dump`
- Added regression coverage for multiple NumPy scalar types, NumPy arrays, and unsupported-object error handling
- Updated documentation and changelog to describe NumPy serialization behavior in `JSONSink`
---------
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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Fixed `DetectionsSmoother` to work with detections that have no confidence scores
- Changed confidence aggregation to average only the confidence values that are present, leaving confidence as `None` when no values exist
- Fixed smoothing of mixed-confidence tracks (some frames with confidence, some without) while preserving available confidence information
- Fixed crashes when merging smoothed tracks that disagree on confidence availability by normalizing confidence fields before merge
- Added regression coverage for no-confidence, mixed-confidence, multi-track, full-window, and tracker-id-missing scenarios
- Updated documentation and changelog to reflect the new confidence-handling behavior
---------
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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
- Fixed Precision and F1Score metrics to correctly count false positives on images with no ground-truth objects
- Fixed Precision and F1Score metrics to include prediction-only classes when computing class statistics and confusion-matrix aggregates
- Corrected MICRO and MACRO averaging so false positives from absent classes affect the score as expected
- Preserved WEIGHTED averaging behavior by weighting only classes with ground-truth support
- Added safeguards for all-background evaluation batches, returning stable zero-valued weighted scores when no support exists
- Fixed handling of predictions with `class_id=None` in background-only evaluation paths
- Added regression coverage for background-image false positives, absent-class predictions, averaging modes, and zero-support edge cases
- Updated metric documentation and result metadata to reflect that tracked classes now include prediction-only classes
---------
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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Lee Clement
Jirka Borovec
Shehzad Waseem
Abhijith Neil Abraham
Nick Herrig
Teïlo M
Agis Kounelis
Saif Khan
Madhav-C
Dylan Parsons
Ruben
Piotr Skalski