#2331 made Precision and F1Score include classes that appear only in
predictions, and added regression tests to both. Recall was not touched, so
the line #2331 replaced is still there and the three metrics disagree about
which classes exist for identical input:
precision.matched_classes -> [0 1] precision_per_class (2, 10)
recall.matched_classes -> [0] recall_per_class (1, 10)
f1.matched_classes -> [0 1]
These read as parallel outputs, so zipping them silently truncates rather
than raising.
Recall for a class with no ground-truth instances is 0.0 rather than
undefined, which is what sklearn reports (it infers labels from the union of
y_true and y_pred) and what #2331 cited as its own standard. MICRO is
unchanged because an absent class contributes no false negatives, and
WEIGHTED is unchanged because its ground-truth support is zero. MACRO does
change, and the changelog says so.
Also of note: recall.py already carried #2331's WEIGHTED zero-support guard,
whose comment refers to 'only false-positive classes'. That state could not
arise in recall.py, because unique_classes came from ground truth alone. The
guard was propagated; the union that gives it meaning was not.
Addresses the review on #2468. Building the class union inside
_compute_recall_for_classes only covers samples that reach it, and samples with
predictions but no targets are skipped earlier in _compute. So matched_classes
could still disagree with Precision and F1Score for list inputs containing a
background image, which is the exact invariant the new test asserts.
Before, for one normal sample plus one background image predicting class 2:
precision.matched_classes -> [0 2]
recall.matched_classes -> [0]
Recall now handles len(targets) == 0 and len(predictions) > 0 the way Precision
does. No recall value changes, since a background image produces no false
negatives; only the tracked class set does.
* test: cover Recall bg-image size-bucket, dup & non-contiguous ids
* docs: strengthen Recall changelog migration note
* docs+perf: Recall doctest example; dedupe-then-union micro-opt
---------
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- `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
---
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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.
- Added image loading from HTTP and HTTPS URLs with descriptive URL validation errors
- Added optional caching for image URL loads using the shared Supervision cache
- Improved URL downloads with atomic file replacement and shared download behavior across image loading and asset downloads
- Updated image decoding compatibility with Pillow fallbacks when OpenCV decoding or encoding is unavailable
---------
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Add VLM.GOOGLE_GEMINI_3_5 enum and from_google_gemini_3_5 connector reusing the 2.5 parser, wire it into Detections.from_vlm, and salvage valid entries from partially malformed Gemini JSON arrays. Includes tests and changelog.
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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>
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- 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.
---------
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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.
---------
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- 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
---------
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- 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
---------
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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.
---------
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- 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.
---------
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- 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.
---------
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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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- 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
---------
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- 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
---------
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* 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
---------
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- 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
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- 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
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- 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.
---------
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- 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`.
---------
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- 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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- 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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Jirka Borovec
Arthi Arumugam
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Erik
Andrew Barnes
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Piotr Skalski
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