- 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>
- 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
---------
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>
- 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>
- `from_tensorflow` scaled boxes in place on the array returned by `.numpy()`, which can share memory with the source tensor — corrupting caller data and double-scaling on a repeat call; copy before scaling
- `from_lmm` raised a bare `KeyError` for `MOONDREAM` and `QWEN_3_VL`, which the enum and docstring advertise; map both to their `VLM` members
- `from_deepseek_vl_2` returned a `(0,)`-shaped `xyxy` on empty output, so a zero-detection response crashed the `Detections` constructor; return `(0, 4)` like the other parsers
- `extract_ultralytics_masks` binarized bilinear-resized masks with `> 0`, dilating every mask at object boundaries; threshold at 0.5 to match Ultralytics
- add connector coverage: fake-result shims and round-trip tests (N>1, N=1, empty) for the nine previously untested `from_*` connectors and the `detection/tools/transformers.py` processors; one empty-`segments_info` panoptic case is xfail-marked pending a separate fix
* test(ci-fix): drop deprecated Pillow mode arg from panoptic helpers
* test(coverage): add from_qwen_3_vl end-to-end parametrized tests
* test(quality): harden test isolation, xfail strictness, and kwarg forwarding
* fix(detection): fix class_name empty dtype; annotate mask threshold asymmetry
* chore: ruff-format cleanup (blank lines)
---------
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
`Detections.from_tensorflow` scaled the normalized box coordinates by the
wrong image dimensions: the y coordinates (ymin/ymax, columns 0 and 2) were
multiplied by width and the x coordinates (xmin/xmax, columns 1 and 3) by
height. Tensorflow Hub object-detection models emit `detection_boxes` as
normalized `[ymin, xmin, ymax, xmax]`, so y must scale by height and x by
width.
The bug is masked on square images (width == height) but corrupts every
coordinate on the common non-square case — e.g. a box normalized to
`[0.1, 0.2, 0.5, 0.6]` on a 1000x500 image came out as
`[100, 100, 300, 500]` instead of the correct `[200, 50, 600, 250]`.
Swap the two multipliers so y scales by `resolution_wh[1]` (height) and x by
`resolution_wh[0]` (width). Adds a non-square regression test (the connector
was previously untested).
- Expand tensorflow_results arg to document required dict keys and tensor
shapes so callers know what to pass before getting a KeyError
- Add Note: section documenting the [ymin, xmin, ymax, xmax] normalized
box format; the inline comment was only visible to code readers
- Fix SOURCE_IMAGE_PATH undefined identifier → "<SOURCE_IMAGE_PATH>"
string placeholder (consistent with other connector examples in file)
---------
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: claude[bot] <209825114+claude[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
Ruben