diff --git a/docs/changelog.md b/docs/changelog.md index 47d160aa..024edd1a 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,3 +1,83 @@ +### 0.21.0 Jun 5, 2024 + +- Added [#500](https://github.com/roboflow/supervision/pull/500): [`sv.Detections.with_nmm`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.with_nmm) to perform non-maximum merging on the current set of object detections. + +- Added [#1221](https://github.com/roboflow/supervision/pull/1221): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.from_lmm) allowing to parse Large Multimodal Model (LMM) text result into [`sv.Detections`](https://supervision.roboflow.com/develop/detection/core/) object. For now `from_lmm` supports only [PaliGemma](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-paligemma-on-detection-dataset.ipynb) result parsing. + +```python +import supervision as sv + +paligemma_result = " cat" +detections = sv.Detections.from_lmm( + sv.LMM.PALIGEMMA, + paligemma_result, + resolution_wh=(1000, 1000), + classes=['cat', 'dog'] +) +detections.xyxy +# array([[250., 250., 750., 750.]]) + +detections.class_id +# array([0]) +``` + +- Added [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.VertexLabelAnnotator`](https://supervision.roboflow.com/develop/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator.annotate) allowing to annotate every vertex of a keypoint skeleton with custom text and color. + +```python +import supervision as sv + +image = ... +key_points = sv.KeyPoints(...) + +edge_annotator = sv.EdgeAnnotator( + color=sv.Color.GREEN, + thickness=5 +) +annotated_frame = edge_annotator.annotate( + scene=image.copy(), + key_points=key_points +) +``` + +- Added [#1147](https://github.com/roboflow/supervision/pull/1147): [`sv.KeyPoints.from_inference`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints.from_inference) allowing to create [`sv.KeyPoints`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints) from [Inference](https://github.com/roboflow/inference) result. + +- Added [#1138](https://github.com/roboflow/supervision/pull/1138): [`sv.KeyPoints.from_yolo_nas`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints.from_yolo_nas) allowing to create [`sv.KeyPoints`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints) from [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) result. + +- Added [#1163](https://github.com/roboflow/supervision/pull/1163): [`sv.mask_to_rle`](https://supervision.roboflow.com/develop/datasets/utils/#supervision.dataset.utils.rle_to_mask) and [`sv.rle_to_mask`](https://supervision.roboflow.com/develop/datasets/utils/#supervision.dataset.utils.rle_to_mask) allowing for easy conversion between mask and rle formats. + +- Changed [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.InferenceSlicer`](https://supervision.roboflow.com/develop/detection/tools/inference_slicer/) allowing to select overlap filtering strategy (`NONE`, `NON_MAX_SUPPRESSION` and `NON_MAX_MERGE`). + +- Changed [#1178](https://github.com/roboflow/supervision/pull/1178): [`sv.InferenceSlicer`](https://supervision.roboflow.com/develop/detection/tools/inference_slicer/) adding instance segmentation model support. + +```python +import cv2 +import numpy as np +import supervision as sv +from inference import get_model + +model = get_model(model_id="yolov8x-seg-640") +image = cv2.imread() + +def callback(image_slice: np.ndarray) -> sv.Detections: + results = model.infer(image_slice)[0] + return sv.Detections.from_inference(results) + +slicer = sv.InferenceSlicer(callback = callback) +detections = slicer(image) + +mask_annotator = sv.MaskAnnotator() +label_annotator = sv.LabelAnnotator() + +annotated_image = mask_annotator.annotate( + scene=image, detections=detections) +annotated_image = label_annotator.annotate( + scene=annotated_image, detections=detections) +``` + +- Changed [#1228](https://github.com/roboflow/supervision/pull/1228): [`sv.LineZone`](https://supervision.roboflow.com/develop/detection/tools/line_zone/) making it 10-20 times faster, depending on the use case. + +- Changed [#1163](https://github.com/roboflow/supervision/pull/1163): [`sv.DetectionDataset.from_coco`](https://supervision.roboflow.com/develop/datasets/core/#supervision.dataset.core.DetectionDataset.from_coco) and [`sv.DetectionDataset.as_coco`](https://supervision.roboflow.com/develop/datasets/core/#supervision.dataset.core.DetectionDataset.as_coco) adding support for run-length encoding (RLE) mask format. + ### 0.20.0 April 24, 2024 - Added [#1128](https://github.com/roboflow/supervision/pull/1128): [`sv.KeyPoints`](/0.20.0/keypoint/core/#supervision.keypoint.core.KeyPoints) to provide initial support for pose estimation and broader keypoint detection models. diff --git a/mkdocs.yml b/mkdocs.yml index 19d6a4fd..96728971 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -35,15 +35,15 @@ extra_css: nav: - - Home: index.md - - How to: + - Supervision: index.md + - Learn: - Detect and Annotate: how_to/detect_and_annotate.md - Save Detections: how_to/save_detections.md - Filter Detections: how_to/filter_detections.md - Detect Small Objects: how_to/detect_small_objects.md - Track Objects on Video: how_to/track_objects.md - - API: + - Reference - Code API: - Detection and Segmentation: - Core: detection/core.md - Annotators: detection/annotators.md @@ -79,7 +79,7 @@ nav: - Contributing: contributing.md - Code of Conduct: code_of_conduct.md - License: license.md - - Changelog: + - Release Notes: - Changelog: changelog.md - Deprecated: deprecated.md diff --git a/pyproject.toml b/pyproject.toml index 640d462e..59d4176d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "supervision" -version = "0.21.0rc5" +version = "0.21.0" description = "A set of easy-to-use utils that will come in handy in any Computer Vision project" authors = ["Piotr Skalski "] maintainers = ["Piotr Skalski "] diff --git a/supervision/detection/core.py b/supervision/detection/core.py index 7b2e8ba9..37dde153 100644 --- a/supervision/detection/core.py +++ b/supervision/detection/core.py @@ -1239,7 +1239,9 @@ class Detections: Raises: AssertionError: If `confidence` is None or `class_id` is None and class_agnostic is False. - """ + + ![non-max-merging](https://media.roboflow.com/supervision-docs/non-max-merging.png){ align=center width="800" } + """ # noqa: E501 // docs if len(self) == 0: return self diff --git a/supervision/detection/lmm.py b/supervision/detection/lmm.py index 0278fc00..5f61db0a 100644 --- a/supervision/detection/lmm.py +++ b/supervision/detection/lmm.py @@ -41,7 +41,7 @@ def from_paligemma( ) -> Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]: w, h = resolution_wh pattern = re.compile( - r"(?) ([\w\s]+)" + r"(?) ([\w\s\-]+)" ) matches = pattern.findall(result) matches = np.array(matches) if matches else np.empty((0, 5)) diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py index 2b58655e..5b92aedd 100644 --- a/supervision/detection/utils.py +++ b/supervision/detection/utils.py @@ -155,6 +155,25 @@ def clip_boxes(xyxy: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray: np.ndarray: A numpy array of shape `(N, 4)` where each row corresponds to a bounding box with coordinates clipped to fit within the frame resolution. + + Examples: + ```python + import numpy as np + import supervision as sv + + xyxy = np.array([ + [10, 20, 300, 200], + [15, 25, 350, 450], + [-10, -20, 30, 40] + ]) + + sv.clip_boxes(xyxy=xyxy, resolution_wh=(320, 240)) + # array([ + # [ 10, 20, 300, 200], + # [ 15, 25, 320, 240], + # [ 0, 0, 30, 40] + # ]) + ``` """ result = np.copy(xyxy) width, height = resolution_wh @@ -181,6 +200,23 @@ def pad_boxes(xyxy: np.ndarray, px: int, py: Optional[int] = None) -> np.ndarray np.ndarray: A numpy array of shape `(N, 4)` where each row corresponds to a bounding box with coordinates padded according to the provided padding values. + + Examples: + ```python + import numpy as np + import supervision as sv + + xyxy = np.array([ + [10, 20, 30, 40], + [15, 25, 35, 45] + ]) + + sv.pad_boxes(xyxy=xyxy, px=5, py=10) + # array([ + # [ 5, 10, 35, 50], + # [10, 15, 40, 55] + # ]) + ``` """ if py is None: py = px @@ -553,7 +589,7 @@ def scale_boxes( [30, 30, 40, 40] ]) - scaled_bb = sv.scale_boxes(xyxy=xyxy, factor=1.5) + sv.scale_boxes(xyxy=xyxy, factor=1.5) # array([ # [ 7.5, 7.5, 22.5, 22.5], # [27.5, 27.5, 42.5, 42.5] diff --git a/test/detection/test_lmm.py b/test/detection/test_lmm.py index 129aa44b..4448d8db 100644 --- a/test/detection/test_lmm.py +++ b/test/detection/test_lmm.py @@ -76,7 +76,27 @@ from supervision.detection.lmm import from_paligemma None, np.array(["black cat"]).astype(np.dtype("U")), ), - ), # correct response; no classes + ), # correct response; class name with space; no classes + ( + " black-cat", + (1000, 1000), + None, + ( + np.array([[250.0, 250.0, 750.0, 750.0]]), + None, + np.array(["black-cat"]).astype(np.dtype("U")), + ), + ), # correct response; class name with hyphen; no classes + ( + " black_cat", + (1000, 1000), + None, + ( + np.array([[250.0, 250.0, 750.0, 750.0]]), + None, + np.array(["black_cat"]).astype(np.dtype("U")), + ), + ), # correct response; class name with underscore; no classes ( " cat ;", (1000, 1000),