#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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Currently a detection counts as 'in the zone' only when every anchor in
triggering_anchors is inside. For boxes that straddle the zone boundary
this means a detection with many anchors (e.g. the four corners) is often
under-counted unless the user shrinks triggering_anchors to a single point.
Add require_all_anchors: bool = True so callers can opt into 'any anchor
inside is enough'. Default preserves current behaviour.
* test: strengthen PolygonZone require_all_anchors coverage
* docs: clarify require_all_anchors anchor-based semantics
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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).
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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.
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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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- Organize facade constants and implementations into thematic private modules.
- Add NumPy/Pillow/SciPy fallbacks with OpenCV parity coverage.
- Mirror package modules in cv2 tests and verify blocked imports.
- Split compound operation tests into isolated cases.
- Parameterize color parity and fallback bindings for targeted failures.
- Inline color conversion cases at their only use site.
- Inline fallback binding cases while preserving reusable manifests.
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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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