Swap Ultralytics YOLO references for RFDETRSmall (rfdetr package) and
rfdetr-small (Roboflow Inference) to showcase RF-DETR as the primary
model in the quickstart and connector examples.
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* fix: prevent single object from appearing in multiple polygon zones
when checking if a detection is inside a polygon zone, the previous implementation
would clip the bounding box to fit within each ROI's dimensions before calculating
anchor points. This caused the same detection to produce different anchor points
for different ROIs, allowing it to be counted as present in multiple zones.
* Add regression test for PolygonZone trigger issue #1987 and remove unused `frame_resolution_wh` attribute
* refactor(polygon_zone): vectorize trigger() and strengthen tests
Replace the O(n×m) Python double-loop in PolygonZone.trigger() with
vectorized NumPy. Semantics are identical: compute a (num_anchors,
num_detections) in_bounds mask, use np.clip solely for safe fancy-index
access, then AND with the polygon mask and reduce with np.all(axis=0).
Also removes the now-unused `from dataclasses import replace` import and
a latent np.all(axis=1) call on a 1D array.
Test improvements:
- Group into TestPolygonZoneInit / TestPolygonZoneTrigger classes
- Replace the trivially-passing regression (sum=0 on both old and new
code) with adjacent zones + straddling detection that gives sum=2 on
the old clip_boxes implementation and sum=1 on the fix
- Rename tests to describe behaviour, not issue numbers
- Add test_out_of_bounds_anchor_excluded and
test_anchor_on_polygon_boundary_included edge cases
* test(polygon_zone): verify current_count updates with expected results during trigger
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* refactor docstrings in `src/supervision/detection/tools`
* Refine type annotations and docstrings in `transformers.py` to include `TensorLike` protocol and enhance segmentation handling
* Enhance type safety in `transformers.py` by introducing `_is_tensor_like` with `TypeGuard` and refining segmentation result handling
* Refine `CSVSink` type annotations by introducing `WriterProtocol` for improved type safety
* Remove `_is_tensor_like`, `TensorLike` protocol, and `TypeGuard` usage from `transformers.py`, streamline segmentation handling, and fix typos in `CSVSink` and `JSONSink` docstrings
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* 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
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* refactor docstrings in draw, classification, and key_points
* Refactor type annotations, logging, and empty output handling across key modules
* Refactor type annotations in `core.py` to include conditional `TYPE_CHECKING` for `torch` imports
* Apply suggestions from code review
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* refactor(metrics): update MeanAveragePrecisionResult to use float32 precision
This change reduces memory usage and improves computation speed while maintaining acceptable precision for most computer vision tasks.
- Changed mAP_scores, ap_per_class, and iou_thresholds from float64 to float32
- Updated EPS constant to use float32 precision
- Modified COCOEvaluator parameters and computations to use float32
- Added type annotations for variables in filter_segments_by_distance function
- Changed precision and recall calculations to use float32
- Updated average precision computation to handle float32 values
- Modified summarize_predictions to return float32 array
* update type annotation for `keep_labels` to use `npt.NDArray[np.bool_]`
* update EPS constant and mAP calculations for precision consistency
* Apply suggestions from code review
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DetectionDataset.__init__ now maps class_id to class names using
CLASS_NAME_DATA_FIELD so that LabelAnnotator displays human-readable
labels instead of raw integer IDs.
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* 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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* Added color utility functions: hex_to_rgba, rgba_to_hex, is_valid_hex with tests
* Parametrize hex and RGBA utility tests in `test_utils.py` for improved clarity and coverage
* Enhance hex and RGBA utilities: improve test coverage, validations, and docstrings; refactor shared logic.
* Refactor color input handling with `_normalize_color_input` utility; add hex string support across annotators and enhance test coverage
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* 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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* fix exteranl track ids getting assigned too early
* Fixed docstring to correct minimum_matching_threshold explaination
* Change overlap value for duplicate tracks to be 0.05
* Add test to ensure ByteTracker handles short-lived tracks without consuming external IDs
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* Update lychee configuration to accept HTTP status code 401
* remove status code 401 and add note for URL with inconsistent responses
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* Refactor Copilot and CONTRIBUTING guidelines for clarity and conciseness
* Streamline and condense PR review guidelines in CONTRIBUTING.md for improved clarity and usability.
* Apply suggestions from code review
* fix(pre_commit): 🎨 auto format pre-commit hooks
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* Add ImageAssets for download
* Update directory for consistency with video assets directory and add soccer image
* Fix unaligned table
* Add support for `ImageAssets` and extend `download_assets` functionality to handle both image and video assets.
* Refactor `Assets` enum initialization and improve `download_assets` logic with enhanced file checks and re-download handling. Add new tests for invalid asset cases and update existing test assertions.
* Simplify `download_assets` type handling and refactor `Assets` enum with additional attributes for filename and md5_hash.
---------
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* Supersedes #1967
* fix: COCO-compliant mAR calculation
* Add complex test of mAP
* fix(metrics): cast optional detections fields in mAR metric for mypy
* Add `create_yolo_dataset` utility and refactor tests with fixtures for reusable scenarios
* Refine docstrings for clarity and consistency, adding inline formatting and fixing typos in test helpers and metrics.
---------
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* Added support for creating Detections instances from SAM3 output - both from `inference` and from RF hosted server (dict)
* added tests, addressed pr comments
* fix(pre_commit): 🎨 auto format pre-commit hooks
* Apply suggestions from code review
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* Add `AGENTS.md` with detailed guidelines for Copilot task execution
* Simplify `AGENTS.md` with concise AI agent guidelines and enhanced structure
* fix(pre_commit): 🎨 auto format pre-commit hooks
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* refactor(tracker): improve typing and docstrings
* refactor(tracker): improve typing for track objects
* refactor(tracker): improve typing in matching and filters
* Apply suggestions from review
* Refactor: enhance type annotations, assertions, and docstrings across `byte_tracker` for clarity and type safety.
* Refactor: replace "Parameters" with "Args" in docstrings for consistency with style guide
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dependabot[bot]