Merge branch 'roboflow:develop' into feat/oriented_box_iou_batch
This commit is contained in:
Zeel B Patel
GitHub
commit
887bb7c2cb
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@ -32,7 +32,7 @@ repos:
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additional_dependencies: ["bandit[toml]"]
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.6.3
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rev: v0.6.5
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hooks:
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- id: ruff
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args: [--fix, --exit-non-zero-on-fix]
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@ -6,7 +6,7 @@ We are actively improving this library to reduce the amount of work you need to
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## Code of Conduct
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Please read and adhere to our [Code of Conduct](CODE_OF_CONDUCT.md). This document outlines the expected behavior for all participants in our project.
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Please read and adhere to our [Code of Conduct](https://supervision.roboflow.com/latest/code_of_conduct/). This document outlines the expected behavior for all participants in our project.
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|
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## Table of Contents
|
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|
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@ -86,6 +86,7 @@ Use conventional commit messages to clearly describe your changes. The format is
|
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<type>[optional scope]: <description>
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Common types include:
|
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|
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- feat: A new feature
|
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- fix: A bug fix
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- docs: Documentation only changes
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@ -128,13 +129,16 @@ PRs must pass all tests and linting requirements before they can be merged.
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Before starting your work on the project, set up your development environment:
|
||||
|
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1. Clone your fork of the project:
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|
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```bash
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git clone https://github.com/YOUR_USERNAME/supervision.git
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cd supervision
|
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```
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Replace `YOUR_USERNAME` with your GitHub username.
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|
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2. Create and activate a virtual environment:
|
||||
|
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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|
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@ -143,17 +147,20 @@ Before starting your work on the project, set up your development environment:
|
|||
3. Install Poetry:
|
||||
|
||||
Using pip:
|
||||
|
||||
```bash
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||||
pip install -U pip setuptools
|
||||
pip install poetry
|
||||
```
|
||||
|
||||
Or using pipx (recommended for global installation):
|
||||
|
||||
```bash
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pipx install poetry
|
||||
```
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||||
|
||||
4. Install project dependencies:
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||||
|
||||
```bash
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poetry install
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```
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|
|
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@ -1357,7 +1357,7 @@
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}
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],
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"source": [
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"IMAGE_NAME = list(ds.images.keys())[0]\n",
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"IMAGE_NAME = next(iter(ds.images.keys()))\n",
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"\n",
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"image = ds.images[IMAGE_NAME]\n",
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"annotations = ds.annotations[IMAGE_NAME]\n",
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@ -1,6 +1,6 @@
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### 0.23.0 <small>Aug 28, 2024</small>
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- Added [#930](https://github.com/roboflow/supervision/pull/930): `IconAnnotator`, a [new annotator](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.IconAnnotator) that allows drawing icons on each detection. Useful if you want to draw a specific icon for each class.
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- Added [#930](https://github.com/roboflow/supervision/pull/930): `IconAnnotator`, a [new annotator](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.IconAnnotator) that allows drawing icons on each detection. Useful if you want to draw a specific icon for each class.
|
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|
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```python
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import supervision as sv
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@ -31,7 +31,7 @@ annotated_frame = icon_annotator.annotate(
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)
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```
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- Added [#1385](https://github.com/roboflow/supervision/pull/1385): [`BackgroundColorAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BackgroundColorAnnotator), that draws an overlay on the background images of the detections.
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- Added [#1385](https://github.com/roboflow/supervision/pull/1385): [`BackgroundColorAnnotator`](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.BackgroundColorAnnotator), that draws an overlay on the background images of the detections.
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|
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```python
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import supervision as sv
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@ -50,7 +50,7 @@ annotated_frame = background_overlay_annotator.annotate(
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)
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```
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- Added [#1386](https://github.com/roboflow/supervision/pull/1386): Support for Transformers v5 functions in [`sv.Detections.from_transformers`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_transformers). This includes the `DetrImageProcessor` methods `post_process_object_detection`, `post_process_panoptic_segmentation`, `post_process_semantic_segmentation`, and `post_process_instance_segmentation`.
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- Added [#1386](https://github.com/roboflow/supervision/pull/1386): Support for Transformers v5 functions in [`sv.Detections.from_transformers`](https://supervision.roboflow.com/0.23.0/detection/core/#supervision.detection.core.Detections.from_transformers). This includes the `DetrImageProcessor` methods `post_process_object_detection`, `post_process_panoptic_segmentation`, `post_process_semantic_segmentation`, and `post_process_instance_segmentation`.
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|
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```python
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import torch
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@ -76,7 +76,7 @@ detections = sv.Detections.from_transformers(
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id2label=model.config.id2label)
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```
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|
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- Added [#1354](https://github.com/roboflow/supervision/pull/1354): Ultralytics SAM (Segment Anything Model) support in [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics). [SAM2](https://sam2.metademolab.com/) was released during this update, and is already supported via [`sv.Detections.from_sam`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_sam).
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- Added [#1354](https://github.com/roboflow/supervision/pull/1354): Ultralytics SAM (Segment Anything Model) support in [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/0.23.0/detection/core/#supervision.detection.core.Detections.from_ultralytics). [SAM2](https://sam2.metademolab.com/) was released during this update, and is already supported via [`sv.Detections.from_sam`](https://supervision.roboflow.com/0.23.0/detection/core/#supervision.detection.core.Detections.from_sam).
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|
||||
```python
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import supervision as sv
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@ -92,15 +92,15 @@ sam_result = mask_generator.generate(IMAGE)
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detections = sv.Detections.from_sam(sam_result=sam_result)
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```
|
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|
||||
- Added [#1458](https://github.com/roboflow/supervision/pull/1458): `outline_color` options for [`TriangleAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.TriangleAnnotator) and [`DotAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.DotAnnotator).
|
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- Added [#1458](https://github.com/roboflow/supervision/pull/1458): `outline_color` options for [`TriangleAnnotator`](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.TriangleAnnotator) and [`DotAnnotator`](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.DotAnnotator).
|
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|
||||
- Added [#1409](https://github.com/roboflow/supervision/pull/1409): `text_color` option for [`VertexLabelAnnotator`](https://supervision.roboflow.com/latest/keypoint/annotators/#supervision.keypoint.annotators.VertexLabelAnnotator) keypoint annotator.
|
||||
- Added [#1409](https://github.com/roboflow/supervision/pull/1409): `text_color` option for [`VertexLabelAnnotator`](https://supervision.roboflow.com/0.23.0/keypoint/annotators/#supervision.keypoint.annotators.VertexLabelAnnotator) keypoint annotator.
|
||||
|
||||
- Changed [#1434](https://github.com/roboflow/supervision/pull/1434): [`InferenceSlicer`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/) now features an `overlap_ratio_wh` parameter, making it easier to compute slice sizes when handling overlapping slices.
|
||||
- Changed [#1434](https://github.com/roboflow/supervision/pull/1434): [`InferenceSlicer`](https://supervision.roboflow.com/0.23.0/detection/tools/inference_slicer/) now features an `overlap_ratio_wh` parameter, making it easier to compute slice sizes when handling overlapping slices.
|
||||
|
||||
- Fix [#1448](https://github.com/roboflow/supervision/pull/1448): Various annotator type issues have been resolved, supporting expanded error handling.
|
||||
|
||||
- Fix [#1348](https://github.com/roboflow/supervision/pull/1348): Introduced a new method for [seeking to a specific video frame](https://supervision.roboflow.com/latest/utils/video/#supervision.utils.video.get_video_frames_generator), addressing cases where traditional seek methods were failing. It can be enabled with `iterative_seek=True`.
|
||||
- Fix [#1348](https://github.com/roboflow/supervision/pull/1348): Introduced a new method for [seeking to a specific video frame](https://supervision.roboflow.com/0.23.0/utils/video/#supervision.utils.video.get_video_frames_generator), addressing cases where traditional seek methods were failing. It can be enabled with `iterative_seek=True`.
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
|
@ -133,7 +133,7 @@ for frame in sv.get_video_frames_generator(
|
|||
|
||||
### 0.22.0 <small>Jul 12, 2024</small>
|
||||
|
||||
- Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.ClassificationDataset) allowing to load the images into memory only when necessary (lazy loading).
|
||||
- Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.ClassificationDataset) allowing to load the images into memory only when necessary (lazy loading).
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
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@ -166,15 +166,15 @@ for path, image, annotation in ds_train:
|
|||
# loads image on demand
|
||||
```
|
||||
|
||||
- Added [#1296](https://github.com/roboflow/supervision/pull/1296): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_lmm) now supports parsing results from the [Florence 2](https://huggingface.co/microsoft/Florence-2-large) model, extending the capability to handle outputs from this Large Multimodal Model (LMM). This includes detailed object detection, OCR with region proposals, segmentation, and more. Find out more in our [Colab notebook](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb).
|
||||
- Added [#1296](https://github.com/roboflow/supervision/pull/1296): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_lmm) now supports parsing results from the [Florence 2](https://huggingface.co/microsoft/Florence-2-large) model, extending the capability to handle outputs from this Large Multimodal Model (LMM). This includes detailed object detection, OCR with region proposals, segmentation, and more. Find out more in our [Colab notebook](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb).
|
||||
|
||||
- Added [#1232](https://github.com/roboflow/supervision/pull/1232) to support keypoint detection with Mediapipe. Both [legacy](https://colab.research.google.com/github/googlesamples/mediapipe/blob/main/examples/pose_landmarker/python/%5BMediaPipe_Python_Tasks%5D_Pose_Landmarker.ipynb) and [modern](https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker/python) pipelines are supported. See [`sv.KeyPoints.from_mediapipe`](https://supervision.roboflow.com/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe) for more.
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||||
- Added [#1232](https://github.com/roboflow/supervision/pull/1232) to support keypoint detection with Mediapipe. Both [legacy](https://colab.research.google.com/github/googlesamples/mediapipe/blob/main/examples/pose_landmarker/python/%5BMediaPipe_Python_Tasks%5D_Pose_Landmarker.ipynb) and [modern](https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker/python) pipelines are supported. See [`sv.KeyPoints.from_mediapipe`](https://supervision.roboflow.com/0.22.0/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe) for more.
|
||||
|
||||
- Added [#1316](https://github.com/roboflow/supervision/pull/1316): [`sv.KeyPoints.from_mediapipe`](https://supervision.roboflow.com/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe) extended to support FaceMesh from Mediapipe. This enhancement allows for processing both face landmarks from `FaceLandmarker`, and legacy results from `FaceMesh`.
|
||||
- Added [#1316](https://github.com/roboflow/supervision/pull/1316): [`sv.KeyPoints.from_mediapipe`](https://supervision.roboflow.com/0.22.0/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe) extended to support FaceMesh from Mediapipe. This enhancement allows for processing both face landmarks from `FaceLandmarker`, and legacy results from `FaceMesh`.
|
||||
|
||||
- Added [#1310](https://github.com/roboflow/supervision/pull/1310): [`sv.KeyPoints.from_detectron2`](https://supervision.roboflow.com/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_detectron2) is a new `KeyPoints` method, adding support for extracting keypoints from the popular [Detectron 2](https://github.com/facebookresearch/detectron2) platform.
|
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- Added [#1310](https://github.com/roboflow/supervision/pull/1310): [`sv.KeyPoints.from_detectron2`](https://supervision.roboflow.com/0.22.0/keypoint/core/#supervision.keypoint.core.KeyPoints.from_detectron2) is a new `KeyPoints` method, adding support for extracting keypoints from the popular [Detectron 2](https://github.com/facebookresearch/detectron2) platform.
|
||||
|
||||
- Added [#1300](https://github.com/roboflow/supervision/pull/1300): [`sv.Detections.from_detectron2`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) now supports segmentation models detectron2. The resulting masks can be used with [`sv.MaskAnnotator`](https://supervision.roboflow.com/latest/annotators/#supervision.annotators.core.MaskAnnotator) for displaying annotations.
|
||||
- Added [#1300](https://github.com/roboflow/supervision/pull/1300): [`sv.Detections.from_detectron2`](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_detectron2) now supports segmentation models detectron2. The resulting masks can be used with [`sv.MaskAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.MaskAnnotator) for displaying annotations.
|
||||
|
||||
```python
|
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import supervision as sv
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|
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@ -196,7 +196,7 @@ mask_annotator = sv.MaskAnnotator()
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annotated_frame = mask_annotator.annotate(scene=image.copy(), detections=detections)
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```
|
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|
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- Added [#1277](https://github.com/roboflow/supervision/pull/1277): if you provide a font that supports symbols of a language, [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator.annotate) will draw them on your images.
|
||||
- Added [#1277](https://github.com/roboflow/supervision/pull/1277): if you provide a font that supports symbols of a language, [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator.annotate) will draw them on your images.
|
||||
- Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
|
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|
||||
```python
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@ -232,25 +232,25 @@ obb_annotator = OrientedBoxAnnotator()
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annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detections)
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```
|
||||
|
||||
- Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.TraceAnnotator.annotate).
|
||||
- Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.TraceAnnotator.annotate).
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
|
||||
`BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator).
|
||||
The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
|
||||
|
||||
- Added [#975](https://github.com/roboflow/supervision/pull/975) 📝 New Cookbooks: serialize detections into [json](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-json.ipynb) and [csv](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-csv.ipynb).
|
||||
|
||||
- Added [#1290](https://github.com/roboflow/supervision/pull/1290): Mostly an internal change, our file utility function now support both `str` and `pathlib` paths.
|
||||
|
||||
- Added [#1340](https://github.com/roboflow/supervision/pull/1340): Two new methods for converting between bounding box formats - [`xywh_to_xyxy`](https://supervision.roboflow.com/latest/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/latest/detection/utils/#supervision.detection.utils.xcycwh_to_xyxy)
|
||||
- Added [#1340](https://github.com/roboflow/supervision/pull/1340): Two new methods for converting between bounding box formats - [`xywh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xcycwh_to_xyxy)
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_inference) instead.
|
||||
`from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
|
|
@ -274,17 +274,17 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
|
|||
|
||||
!!! failure "Removed"
|
||||
|
||||
`ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/latest/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
|
||||
`ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/latest/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
|
||||
`FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
|
||||
|
||||
### 0.21.0 <small>Jun 5, 2024</small>
|
||||
|
||||
- Added [#500](https://github.com/roboflow/supervision/pull/500): [`sv.Detections.with_nmm`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.with_nmm) to perform non-maximum merging on the current set of object detections.
|
||||
- Added [#500](https://github.com/roboflow/supervision/pull/500): [`sv.Detections.with_nmm`](https://supervision.roboflow.com/0.21.0/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/latest/detection/core/#supervision.detection.core.Detections.from_lmm) allowing to parse Large Multimodal Model (LMM) text result into [`sv.Detections`](https://supervision.roboflow.com/latest/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.
|
||||
- Added [#1221](https://github.com/roboflow/supervision/pull/1221): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/0.21.0/detection/core/#supervision.detection.core.Detections.from_lmm) allowing to parse Large Multimodal Model (LMM) text result into [`sv.Detections`](https://supervision.roboflow.com/0.21.0/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
|
||||
|
|
@ -303,7 +303,7 @@ detections.class_id
|
|||
# array([0])
|
||||
```
|
||||
|
||||
- Added [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.VertexLabelAnnotator`](https://supervision.roboflow.com/latest/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator.annotate) allowing to annotate every vertex of a keypoint skeleton with custom text and color.
|
||||
- Added [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.VertexLabelAnnotator`](https://supervision.roboflow.com/0.21.0/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
|
||||
|
|
@ -321,15 +321,15 @@ annotated_frame = edge_annotator.annotate(
|
|||
)
|
||||
```
|
||||
|
||||
- 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 [#1147](https://github.com/roboflow/supervision/pull/1147): [`sv.KeyPoints.from_inference`](https://supervision.roboflow.com/0.21.0/keypoint/core/#supervision.keypoint.core.KeyPoints.from_inference) allowing to create [`sv.KeyPoints`](https://supervision.roboflow.com/0.21.0/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 [#1138](https://github.com/roboflow/supervision/pull/1138): [`sv.KeyPoints.from_yolo_nas`](https://supervision.roboflow.com/0.21.0/keypoint/core/#supervision.keypoint.core.KeyPoints.from_yolo_nas) allowing to create [`sv.KeyPoints`](https://supervision.roboflow.com/0.21.0/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.
|
||||
- Added [#1163](https://github.com/roboflow/supervision/pull/1163): [`sv.mask_to_rle`](https://supervision.roboflow.com/0.21.0/datasets/utils/#supervision.dataset.utils.rle_to_mask) and [`sv.rle_to_mask`](https://supervision.roboflow.com/0.21.0/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 [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.InferenceSlicer`](https://supervision.roboflow.com/0.21.0/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.
|
||||
- Changed [#1178](https://github.com/roboflow/supervision/pull/1178): [`sv.InferenceSlicer`](https://supervision.roboflow.com/0.21.0/detection/tools/inference_slicer/) adding instance segmentation model support.
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -356,9 +356,9 @@ 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 [#1228](https://github.com/roboflow/supervision/pull/1228): [`sv.LineZone`](https://supervision.roboflow.com/0.21.0/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.
|
||||
- Changed [#1163](https://github.com/roboflow/supervision/pull/1163): [`sv.DetectionDataset.from_coco`](https://supervision.roboflow.com/0.21.0/datasets/core/#supervision.dataset.core.DetectionDataset.from_coco) and [`sv.DetectionDataset.as_coco`](https://supervision.roboflow.com/0.21.0/datasets/core/#supervision.dataset.core.DetectionDataset.as_coco) adding support for run-length encoding (RLE) mask format.
|
||||
|
||||
### 0.20.0 <small>April 24, 2024</small>
|
||||
|
||||
|
|
@ -485,11 +485,11 @@ annotated_frame = crop_annotator.annotate(
|
|||
)
|
||||
```
|
||||
|
||||
- Changed [#827](https://github.com/roboflow/supervision/pull/827): [`sv.ByteTrack.reset`](/0.19.0/tracking/#supervision.tracking.ByteTrack.reset) allowing users to clear trackers state, enabling the processing of multiple video files in sequence.
|
||||
- Changed [#827](https://github.com/roboflow/supervision/pull/827): [`sv.ByteTrack.reset`](/0.19.0/trackers/#supervision.tracker.ByteTrack.reset) allowing users to clear trackers state, enabling the processing of multiple video files in sequence.
|
||||
|
||||
- Changed [#802](https://github.com/roboflow/supervision/pull/802): [`sv.LineZoneAnnotator`](/0.19.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) allowing to hide in/out count using `display_in_count` and `display_out_count` properties.
|
||||
|
||||
- Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/tracking/#supervision.tracking.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
|
||||
- Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/trackers/#supervision.tracker.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
|
|
@ -916,7 +916,7 @@ array([
|
|||
|
||||
- Added [#101](https://github.com/roboflow/supervision/pull/101): ability to extract masks from YOLOv8 result using [`sv.Detections.from_yolov8`](/0.8.0/detection/core/#supervision.detection.core.Detections.from_yolov8). Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.
|
||||
|
||||
- Added [#122](https://github.com/roboflow/supervision/pull/122): ability to crop image using [`sv.crop`](/latest/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.
|
||||
- Added [#122](https://github.com/roboflow/supervision/pull/122): ability to crop image using [`sv.crop`](/0.9.0/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.
|
||||
|
||||
- Added [#120](https://github.com/roboflow/supervision/pull/120): ability to conveniently save multiple images into directory using [`sv.ImageSink`](/0.9.0/utils/image/#imagesink). Here is an example showing how to save every tenth video frame as a separate image.
|
||||
|
||||
|
|
@ -928,11 +928,11 @@ array([
|
|||
... sink.save_image(image=image)
|
||||
```
|
||||
|
||||
- Fixed [#106](https://github.com/roboflow/supervision/issues/106): inconvenient handling of [`sv.PolygonZone`](/0.8.0/supervision/detection/tools/polygon_zone/#polygonzone) coordinates. Now `sv.PolygonZone` accepts coordinates in the form of `[[x1, y1], [x2, y2], ...]` that can be both integers and floats.
|
||||
- Fixed [#106](https://github.com/roboflow/supervision/issues/106): inconvenient handling of [`sv.PolygonZone`](/0.8.0/detection/tools/polygon_zone/#polygonzone) coordinates. Now `sv.PolygonZone` accepts coordinates in the form of `[[x1, y1], [x2, y2], ...]` that can be both integers and floats.
|
||||
|
||||
### 0.8.0 <small>May 17, 2023</small>
|
||||
|
||||
- Added [#100](https://github.com/roboflow/supervision/pull/100): support for dataset inheritance. The current `Dataset` got renamed to `DetectionDataset`. Now [`DetectionDataset`](/0.8.0/supervision/dataset/core/#detectiondataset) inherits from `BaseDataset`. This change was made to enforce the future consistency of APIs of different types of computer vision datasets.
|
||||
- Added [#100](https://github.com/roboflow/supervision/pull/100): support for dataset inheritance. The current `Dataset` got renamed to `DetectionDataset`. Now [`DetectionDataset`](/0.8.0/dataset/core/#detectiondataset) inherits from `BaseDataset`. This change was made to enforce the future consistency of APIs of different types of computer vision datasets.
|
||||
- Added [#100](https://github.com/roboflow/supervision/pull/100): ability to save datasets in YOLO format using [`DetectionDataset.as_yolo`](/0.8.0/dataset/core/#supervision.dataset.core.DetectionDataset.as_yolo).
|
||||
|
||||
```python
|
||||
|
|
|
|||
|
|
@ -25,12 +25,12 @@ These features are phased out due to better alternatives or potential issues in
|
|||
|
||||
### 0.22.0
|
||||
|
||||
- [`Detections.from_froboflow`](detection/core.md/#supervision.detection.core.Detections.from_roboflow) is removed as of `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
|
||||
- [`Detections.from_roboflow`](detection/core.md/#supervision.detection.core.Detections.from_roboflow) is removed as of `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
|
||||
- The method `Color.white()` was removed as of `supervision-0.22.0`. Use the constant `Color.WHITE` instead.
|
||||
- The method `Color.black()` was removed as of `supervision-0.22.0`. Use the constant `Color.BLACK` instead.
|
||||
- The method `Color.red()` was removed as of `supervision-0.22.0`. Use the constant `Color.RED` instead.
|
||||
- The method `Color.green()` was removed as of `supervision-0.22.0`. Use the constant `Color.GREEN` instead.
|
||||
- The method `Color.blue()` was removed as of `supervision-0.22.0`. Use the constant `Color.BLUE` instead.
|
||||
- The method [`ColorPalette.default()`](draw/color.md/#supervision.draw.color.ColorPalette.default) was removed as of `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](draw/color.md/#supervision.draw.color.ColorPalette.DEFAULT) instead.
|
||||
- The method `ColorPalette.default()` was removed as of `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](draw/color/#supervision.draw.color.ColorPalette.DEFAULT) instead.
|
||||
- `BoxAnnotator` was removed as of `supervision-0.22.0`, however `BoundingBoxAnnotator` was immediately renamed to `BoxAnnotator`. Use [`BoxAnnotator`](detection/annotators.md/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](detection/annotators.md/#supervision.annotators.core.LabelAnnotator) instead of the old `BoxAnnotator`.
|
||||
- The method [`FPSMonitor.__call__`](utils/video.md/#supervision.utils.video.FPSMonitor.__call__) was removed as of `supervision-0.22.0`. Use the attribute [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ comments: true
|
|||
|
||||
Supervision provides a seamless process for annotating predictions generated by various
|
||||
object detection and segmentation models. This guide shows how to perform inference
|
||||
with the [Inference](https://github.com/roboflow/inference),
|
||||
with the [Inference](https://github.com/roboflow/inference),
|
||||
[Ultralytics](https://github.com/ultralytics/ultralytics) or
|
||||
[Transformers](https://github.com/huggingface/transformers) packages. Following this,
|
||||
you'll learn how to import these predictions into Supervision and use them to annotate
|
||||
|
|
@ -69,7 +69,7 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
|
||||
=== "Inference"
|
||||
|
||||
We can do so using the [`sv.Detections.from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
|
||||
We can do so using the [`sv.Detections.from_inference`](/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```{ .py hl_lines="2 8" }
|
||||
import cv2
|
||||
|
|
@ -84,7 +84,7 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
|
||||
=== "Ultralytics"
|
||||
|
||||
We can do so using the [`sv.Detections.from_ultralytics`](detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
|
||||
We can do so using the [`sv.Detections.from_ultralytics`](/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```{ .py hl_lines="2 8" }
|
||||
import cv2
|
||||
|
|
@ -99,7 +99,7 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
|
||||
=== "Transformers"
|
||||
|
||||
We can do so using the [`sv.Detections.from_transformers`](detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
|
||||
We can do so using the [`sv.Detections.from_transformers`](/latest/detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```{ .py hl_lines="2 19-21" }
|
||||
import torch
|
||||
|
|
@ -135,7 +135,7 @@ You can load predictions from other computer vision frameworks and libraries usi
|
|||
|
||||
## Annotate Image with Detections
|
||||
|
||||
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator) classes.
|
||||
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
|
|
@ -217,7 +217,7 @@ Finally, we can annotate the image with the predictions. Since we are working wi
|
|||
|
||||
## Display Custom Labels
|
||||
|
||||
By default, [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator)
|
||||
By default, [`sv.LabelAnnotator`](/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator)
|
||||
will label each detection with its `class_name` (if possible) or `class_id`. You can
|
||||
override this behavior by passing a list of custom `labels` to the `annotate` method.
|
||||
|
||||
|
|
@ -320,9 +320,9 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
|
|||
## Annotate Image with Segmentations
|
||||
|
||||
If you are running the segmentation model
|
||||
[`sv.MaskAnnotator`](/latest/annotators/#supervision.annotators.core.MaskAnnotator)
|
||||
[`sv.MaskAnnotator`](/latest/detection/annotators/#supervision.annotators.core.MaskAnnotator)
|
||||
is a drop-in replacement for
|
||||
[`sv.BoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoxAnnotator)
|
||||
[`sv.BoxAnnotator`](/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator)
|
||||
that will allow you to draw masks instead of boxes.
|
||||
|
||||
=== "Inference"
|
||||
|
|
|
|||
|
|
@ -148,7 +148,7 @@ enabling the continuous following of the object's motion path across different f
|
|||
|
||||
Annotating the video with tracking IDs helps in distinguishing and following each object
|
||||
distinctly. With the
|
||||
[`sv.LabelAnnotator`](/latest/annotators.md/#supervision.annotators.core.LabelAnnotator)
|
||||
[`sv.LabelAnnotator`](/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator)
|
||||
in Supervision, we can overlay the tracker IDs and class labels on the detected objects,
|
||||
offering a clear visual representation of each object's class and unique identifier.
|
||||
|
||||
|
|
@ -230,7 +230,7 @@ offering a clear visual representation of each object's class and unique identif
|
|||
|
||||
Adding traces to the video involves overlaying the historical paths of the detected
|
||||
objects. This feature, powered by the
|
||||
[`sv.TraceAnnotator`](/latest/annotators/#supervision.annotators.core.TraceAnnotator),
|
||||
[`sv.TraceAnnotator`](/latest/detection/annotators/#supervision.annotators.core.TraceAnnotator),
|
||||
allows for visualizing the trajectories of objects, helping in understanding the
|
||||
movement patterns and interactions between objects in the video.
|
||||
|
||||
|
|
|
|||
|
|
@ -35,20 +35,17 @@ You can install `supervision` in a
|
|||
|
||||
!!! example "pip install (recommended)"
|
||||
|
||||
=== "headless"
|
||||
The headless installation of `supervision` is designed for environments where graphical user interfaces (GUI) are not needed, making it more lightweight and suitable for server-side applications.
|
||||
=== "pip"
|
||||
|
||||
[](https://badge.fury.io/py/supervision)
|
||||
[](https://pypistats.org/packages/supervision)
|
||||
[](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
|
||||
[](https://badge.fury.io/py/supervision)
|
||||
|
||||
```bash
|
||||
pip install supervision
|
||||
```
|
||||
|
||||
=== "desktop"
|
||||
If you require the full version of `supervision` with GUI support you can install the desktop version. This version includes the GUI components of OpenCV, allowing you to display images and videos on the screen.
|
||||
|
||||
```bash
|
||||
pip install "supervision[desktop]"
|
||||
```
|
||||
|
||||
!!! example "conda/mamba install"
|
||||
|
||||
=== "conda"
|
||||
|
|
@ -81,11 +78,8 @@ You can install `supervision` in a
|
|||
source venv/bin/activate
|
||||
pip install --upgrade pip
|
||||
|
||||
# headless install
|
||||
# installation
|
||||
pip install -e "."
|
||||
|
||||
# desktop install
|
||||
pip install -e ".[desktop]"
|
||||
```
|
||||
|
||||
=== "poetry"
|
||||
|
|
@ -99,18 +93,15 @@ You can install `supervision` in a
|
|||
poetry env use python3.10
|
||||
poetry shell
|
||||
|
||||
# headless install
|
||||
# installation
|
||||
poetry install
|
||||
|
||||
# desktop install
|
||||
poetry install --extras "desktop"
|
||||
```
|
||||
|
||||
## 🚀 Quickstart
|
||||
|
||||
<div class="grid cards" markdown>
|
||||
|
||||
- __Detect and Annotate__
|
||||
- **Detect and Annotate**
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -118,7 +109,7 @@ You can install `supervision` in a
|
|||
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/detect_and_annotate.md)
|
||||
|
||||
- __Track Objects__
|
||||
- **Track Objects**
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -126,7 +117,7 @@ You can install `supervision` in a
|
|||
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/track_objects.md)
|
||||
|
||||
- __Detect Small Objects__
|
||||
- **Detect Small Objects**
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -134,25 +125,26 @@ You can install `supervision` in a
|
|||
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/detect_small_objects.md)
|
||||
|
||||
- > __Count Objects Crossing Line__
|
||||
- **Count Objects Crossing Line**
|
||||
|
||||
---
|
||||
|
||||
Explore methods to accurately count and analyze objects crossing a predefined line
|
||||
|
||||
- > __Filter Objects in Zone__
|
||||
[:octicons-arrow-right-24: Notebook](https://supervision.roboflow.com/latest/notebooks/count-objects-crossing-the-line/)
|
||||
|
||||
- **Filter Objects in Zone**
|
||||
|
||||
---
|
||||
|
||||
Master the techniques to selectively filter and focus on objects within a specific zone
|
||||
|
||||
- **Cheatsheet**
|
||||
- **Cheatsheet**
|
||||
|
||||
***
|
||||
---
|
||||
|
||||
Access a quick reference guide to the most common `supervision` functions
|
||||
|
||||
[:octicons-arrow-right-24: Cheatsheet](https://roboflow.github.io/cheatsheet-supervision/)
|
||||
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -287,10 +287,10 @@
|
|||
],
|
||||
"source": [
|
||||
"# Create a bounding box annotator object.\n",
|
||||
"bounding_box = sv.BoundingBoxAnnotator()\n",
|
||||
"box_annotator = sv.BoxAnnotator()\n",
|
||||
"\n",
|
||||
"# Annotate our frame with detections.\n",
|
||||
"annotated_frame = bounding_box.annotate(scene=frame.copy(), detections=detections)\n",
|
||||
"annotated_frame = box_annotator.annotate(scene=frame.copy(), detections=detections)\n",
|
||||
"\n",
|
||||
"# Display the frame.\n",
|
||||
"sv.plot_image(annotated_frame)"
|
||||
|
|
@ -302,7 +302,7 @@
|
|||
"id": "o8SsyCid6YV3"
|
||||
},
|
||||
"source": [
|
||||
"Notice that we create a `box_annoator` variable by initalizing a [BoundingBoxAnnotator](https://supervision.roboflow.com/latest/annotators/#boundingboxannotator). We can change the color and thickness, but for simplicity we keep the defaults. There are a ton of easy to use [annotators](https://supervision.roboflow.com/latest/annotators/) available in the Supervision package other than a bounding box that are fun to play with."
|
||||
"Notice that we create a `box_annotator` variable by initalizing a [BoxAnnotator](https://supervision.roboflow.com/latest/detection/annotators/#boxannotator). We can change the color and thickness, but for simplicity we keep the defaults. There are a ton of easy to use [annotators](https://supervision.roboflow.com/latest/detection/annotators/) available in the Supervision package other than a bounding box that are fun to play with."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -342,7 +342,7 @@
|
|||
" detections = sv.Detections.from_inference(result)\n",
|
||||
"\n",
|
||||
" # Apply bounding box to detections on a copy of the frame.\n",
|
||||
" annotated_frame = bounding_box.annotate(\n",
|
||||
" annotated_frame = box_annotator.annotate(\n",
|
||||
" scene=frame.copy(),\n",
|
||||
" detections=detections\n",
|
||||
" )\n",
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -50,6 +50,10 @@
|
|||
<p class="card repo-card" data-name="Serialise Detections to a JSON File"
|
||||
data-labels="DETECTIONS,JSON SINK,INFERENCE" data-version="v0.21.0" data-author="onuralpszr"></p>
|
||||
</a>
|
||||
<a href="/develop/notebooks/small-object-detection-with-sahi">
|
||||
<p class="card repo-card" data-name="Small Object Detection with SAHI"
|
||||
data-labels="DETECTIONS,SAHI,SMALL,OBJECT,INFERENCE" data-version="v0.23.0" data-author="ediardo"></p>
|
||||
</a>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import argparse
|
||||
import os
|
||||
from typing import Dict, Iterable, List, Set
|
||||
from typing import Dict, Iterable, List, Optional, Set
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -77,7 +77,7 @@ class VideoProcessor:
|
|||
roboflow_api_key: str,
|
||||
model_id: str,
|
||||
source_video_path: str,
|
||||
target_video_path: str = None,
|
||||
target_video_path: Optional[str] = None,
|
||||
confidence_threshold: float = 0.3,
|
||||
iou_threshold: float = 0.7,
|
||||
) -> None:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import argparse
|
||||
from typing import Dict, Iterable, List, Set
|
||||
from typing import Dict, Iterable, List, Optional, Set
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -74,7 +74,7 @@ class VideoProcessor:
|
|||
self,
|
||||
source_weights_path: str,
|
||||
source_video_path: str,
|
||||
target_video_path: str = None,
|
||||
target_video_path: Optional[str] = None,
|
||||
confidence_threshold: float = 0.3,
|
||||
iou_threshold: float = 0.7,
|
||||
) -> None:
|
||||
|
|
|
|||
|
|
@ -259,13 +259,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
|
|||
|
||||
[[package]]
|
||||
name = "build"
|
||||
version = "1.2.1"
|
||||
version = "1.2.2"
|
||||
description = "A simple, correct Python build frontend"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "build-1.2.1-py3-none-any.whl", hash = "sha256:75e10f767a433d9a86e50d83f418e83efc18ede923ee5ff7df93b6cb0306c5d4"},
|
||||
{file = "build-1.2.1.tar.gz", hash = "sha256:526263f4870c26f26c433545579475377b2b7588b6f1eac76a001e873ae3e19d"},
|
||||
{file = "build-1.2.2-py3-none-any.whl", hash = "sha256:277ccc71619d98afdd841a0e96ac9fe1593b823af481d3b0cea748e8894e0613"},
|
||||
{file = "build-1.2.2.tar.gz", hash = "sha256:119b2fb462adef986483438377a13b2f42064a2a3a4161f24a0cca698a07ac8c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2179,13 +2179,13 @@ requests = "*"
|
|||
|
||||
[[package]]
|
||||
name = "mkdocs-git-revision-date-localized-plugin"
|
||||
version = "1.2.7"
|
||||
version = "1.2.9"
|
||||
description = "Mkdocs plugin that enables displaying the localized date of the last git modification of a markdown file."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocs_git_revision_date_localized_plugin-1.2.7-py3-none-any.whl", hash = "sha256:d2b30ccb74ec8e118298758d75ae4b4f02c620daf776a6c92fcbb58f2b78f19f"},
|
||||
{file = "mkdocs_git_revision_date_localized_plugin-1.2.7.tar.gz", hash = "sha256:2f83b52b4dad642751a79465f80394672cbad022129286f40d36b03aebee490f"},
|
||||
{file = "mkdocs_git_revision_date_localized_plugin-1.2.9-py3-none-any.whl", hash = "sha256:dea5c8067c23df30275702a1708885500fadf0abfb595b60e698bffc79c7a423"},
|
||||
{file = "mkdocs_git_revision_date_localized_plugin-1.2.9.tar.gz", hash = "sha256:df9a50873fba3a42ce9123885f8c53d589e90ef6c2443fe3280ef1e8d33c8f65"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2194,6 +2194,11 @@ GitPython = "*"
|
|||
mkdocs = ">=1.0"
|
||||
pytz = "*"
|
||||
|
||||
[package.extras]
|
||||
all = ["GitPython", "babel (>=2.7.0)", "click", "codecov", "mkdocs (>=1.0)", "mkdocs-gen-files", "mkdocs-git-authors-plugin", "mkdocs-material", "mkdocs-static-i18n", "pytest", "pytest-cov", "pytz"]
|
||||
base = ["GitPython", "babel (>=2.7.0)", "mkdocs (>=1.0)", "pytz"]
|
||||
dev = ["click", "codecov", "mkdocs-gen-files", "mkdocs-git-authors-plugin", "mkdocs-material", "mkdocs-static-i18n", "pytest", "pytest-cov"]
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-jupyter"
|
||||
version = "0.24.8"
|
||||
|
|
@ -2215,13 +2220,13 @@ pygments = ">2.12.0"
|
|||
|
||||
[[package]]
|
||||
name = "mkdocs-material"
|
||||
version = "9.5.34"
|
||||
version = "9.5.35"
|
||||
description = "Documentation that simply works"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocs_material-9.5.34-py3-none-any.whl", hash = "sha256:54caa8be708de2b75167fd4d3b9f3d949579294f49cb242515d4653dbee9227e"},
|
||||
{file = "mkdocs_material-9.5.34.tar.gz", hash = "sha256:1e60ddf716cfb5679dfd65900b8a25d277064ed82d9a53cd5190e3f894df7840"},
|
||||
{file = "mkdocs_material-9.5.35-py3-none-any.whl", hash = "sha256:44e069d87732d29f4a2533ae0748fa0e67e270043270c71f04d0fba11a357b24"},
|
||||
{file = "mkdocs_material-9.5.35.tar.gz", hash = "sha256:0d233d7db067ac896bf22ee7950eebf2b1eaf26c155bb27382bf4174021cc117"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2257,13 +2262,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "mkdocstrings"
|
||||
version = "0.26.0"
|
||||
version = "0.26.1"
|
||||
description = "Automatic documentation from sources, for MkDocs."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocstrings-0.26.0-py3-none-any.whl", hash = "sha256:1aa227fe94f88e80737d37514523aacd473fc4b50a7f6852ce41447ab23f2654"},
|
||||
{file = "mkdocstrings-0.26.0.tar.gz", hash = "sha256:ff9d0de28c8fa877ed9b29a42fe407cfe6736d70a1c48177aa84fcc3dc8518cd"},
|
||||
{file = "mkdocstrings-0.26.1-py3-none-any.whl", hash = "sha256:29738bfb72b4608e8e55cc50fb8a54f325dc7ebd2014e4e3881a49892d5983cf"},
|
||||
{file = "mkdocstrings-0.26.1.tar.gz", hash = "sha256:bb8b8854d6713d5348ad05b069a09f3b79edbc6a0f33a34c6821141adb03fe33"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2622,7 +2627,7 @@ files = [
|
|||
name = "opencv-python"
|
||||
version = "4.10.0.84"
|
||||
description = "Wrapper package for OpenCV python bindings."
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "opencv-python-4.10.0.84.tar.gz", hash = "sha256:72d234e4582e9658ffea8e9cae5b63d488ad06994ef12d81dc303b17472f3526"},
|
||||
|
|
@ -2645,33 +2650,6 @@ numpy = [
|
|||
{version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "opencv-python-headless"
|
||||
version = "4.10.0.84"
|
||||
description = "Wrapper package for OpenCV python bindings."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "opencv-python-headless-4.10.0.84.tar.gz", hash = "sha256:f2017c6101d7c2ef8d7bc3b414c37ff7f54d64413a1847d89970b6b7069b4e1a"},
|
||||
{file = "opencv_python_headless-4.10.0.84-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:a4f4bcb07d8f8a7704d9c8564c224c8b064c63f430e95b61ac0bffaa374d330e"},
|
||||
{file = "opencv_python_headless-4.10.0.84-cp37-abi3-macosx_12_0_x86_64.whl", hash = "sha256:5ae454ebac0eb0a0b932e3406370aaf4212e6a3fdb5038cc86c7aea15a6851da"},
|
||||
{file = "opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:46071015ff9ab40fccd8a163da0ee14ce9846349f06c6c8c0f2870856ffa45db"},
|
||||
{file = "opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:377d08a7e48a1405b5e84afcbe4798464ce7ee17081c1c23619c8b398ff18295"},
|
||||
{file = "opencv_python_headless-4.10.0.84-cp37-abi3-win32.whl", hash = "sha256:9092404b65458ed87ce932f613ffbb1106ed2c843577501e5768912360fc50ec"},
|
||||
{file = "opencv_python_headless-4.10.0.84-cp37-abi3-win_amd64.whl", hash = "sha256:afcf28bd1209dd58810d33defb622b325d3cbe49dcd7a43a902982c33e5fad05"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
|
||||
{version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
|
||||
{version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
|
||||
{version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
|
||||
{version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "overrides"
|
||||
version = "7.7.0"
|
||||
|
|
@ -3189,13 +3167,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "pytest"
|
||||
version = "8.3.2"
|
||||
version = "8.3.3"
|
||||
description = "pytest: simple powerful testing with Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pytest-8.3.2-py3-none-any.whl", hash = "sha256:4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5"},
|
||||
{file = "pytest-8.3.2.tar.gz", hash = "sha256:c132345d12ce551242c87269de812483f5bcc87cdbb4722e48487ba194f9fdce"},
|
||||
{file = "pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2"},
|
||||
{file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3820,29 +3798,29 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.6.3"
|
||||
version = "0.6.5"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "ruff-0.6.3-py3-none-linux_armv6l.whl", hash = "sha256:97f58fda4e309382ad30ede7f30e2791d70dd29ea17f41970119f55bdb7a45c3"},
|
||||
{file = "ruff-0.6.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:3b061e49b5cf3a297b4d1c27ac5587954ccb4ff601160d3d6b2f70b1622194dc"},
|
||||
{file = "ruff-0.6.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:34e2824a13bb8c668c71c1760a6ac7d795ccbd8d38ff4a0d8471fdb15de910b1"},
|
||||
{file = "ruff-0.6.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bddfbb8d63c460f4b4128b6a506e7052bad4d6f3ff607ebbb41b0aa19c2770d1"},
|
||||
{file = "ruff-0.6.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ced3eeb44df75353e08ab3b6a9e113b5f3f996bea48d4f7c027bc528ba87b672"},
|
||||
{file = "ruff-0.6.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:47021dff5445d549be954eb275156dfd7c37222acc1e8014311badcb9b4ec8c1"},
|
||||
{file = "ruff-0.6.3-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:7d7bd20dc07cebd68cc8bc7b3f5ada6d637f42d947c85264f94b0d1cd9d87384"},
|
||||
{file = "ruff-0.6.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:500f166d03fc6d0e61c8e40a3ff853fa8a43d938f5d14c183c612df1b0d6c58a"},
|
||||
{file = "ruff-0.6.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:42844ff678f9b976366b262fa2d1d1a3fe76f6e145bd92c84e27d172e3c34500"},
|
||||
{file = "ruff-0.6.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:70452a10eb2d66549de8e75f89ae82462159855e983ddff91bc0bce6511d0470"},
|
||||
{file = "ruff-0.6.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:65a533235ed55f767d1fc62193a21cbf9e3329cf26d427b800fdeacfb77d296f"},
|
||||
{file = "ruff-0.6.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:d2e2c23cef30dc3cbe9cc5d04f2899e7f5e478c40d2e0a633513ad081f7361b5"},
|
||||
{file = "ruff-0.6.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:d8a136aa7d228975a6aee3dd8bea9b28e2b43e9444aa678fb62aeb1956ff2351"},
|
||||
{file = "ruff-0.6.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:f92fe93bc72e262b7b3f2bba9879897e2d58a989b4714ba6a5a7273e842ad2f8"},
|
||||
{file = "ruff-0.6.3-py3-none-win32.whl", hash = "sha256:7a62d3b5b0d7f9143d94893f8ba43aa5a5c51a0ffc4a401aa97a81ed76930521"},
|
||||
{file = "ruff-0.6.3-py3-none-win_amd64.whl", hash = "sha256:746af39356fee2b89aada06c7376e1aa274a23493d7016059c3a72e3b296befb"},
|
||||
{file = "ruff-0.6.3-py3-none-win_arm64.whl", hash = "sha256:14a9528a8b70ccc7a847637c29e56fd1f9183a9db743bbc5b8e0c4ad60592a82"},
|
||||
{file = "ruff-0.6.3.tar.gz", hash = "sha256:183b99e9edd1ef63be34a3b51fee0a9f4ab95add123dbf89a71f7b1f0c991983"},
|
||||
{file = "ruff-0.6.5-py3-none-linux_armv6l.whl", hash = "sha256:7e4e308f16e07c95fc7753fc1aaac690a323b2bb9f4ec5e844a97bb7fbebd748"},
|
||||
{file = "ruff-0.6.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:932cd69eefe4daf8c7d92bd6689f7e8182571cb934ea720af218929da7bd7d69"},
|
||||
{file = "ruff-0.6.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:3a8d42d11fff8d3143ff4da41742a98f8f233bf8890e9fe23077826818f8d680"},
|
||||
{file = "ruff-0.6.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a50af6e828ee692fb10ff2dfe53f05caecf077f4210fae9677e06a808275754f"},
|
||||
{file = "ruff-0.6.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:794ada3400a0d0b89e3015f1a7e01f4c97320ac665b7bc3ade24b50b54cb2972"},
|
||||
{file = "ruff-0.6.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:381413ec47f71ce1d1c614f7779d88886f406f1fd53d289c77e4e533dc6ea200"},
|
||||
{file = "ruff-0.6.5-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:52e75a82bbc9b42e63c08d22ad0ac525117e72aee9729a069d7c4f235fc4d276"},
|
||||
{file = "ruff-0.6.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:09c72a833fd3551135ceddcba5ebdb68ff89225d30758027280968c9acdc7810"},
|
||||
{file = "ruff-0.6.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:800c50371bdcb99b3c1551d5691e14d16d6f07063a518770254227f7f6e8c178"},
|
||||
{file = "ruff-0.6.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e25ddd9cd63ba1f3bd51c1f09903904a6adf8429df34f17d728a8fa11174253"},
|
||||
{file = "ruff-0.6.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:7291e64d7129f24d1b0c947ec3ec4c0076e958d1475c61202497c6aced35dd19"},
|
||||
{file = "ruff-0.6.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:9ad7dfbd138d09d9a7e6931e6a7e797651ce29becd688be8a0d4d5f8177b4b0c"},
|
||||
{file = "ruff-0.6.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:005256d977021790cc52aa23d78f06bb5090dc0bfbd42de46d49c201533982ae"},
|
||||
{file = "ruff-0.6.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:482c1e6bfeb615eafc5899127b805d28e387bd87db38b2c0c41d271f5e58d8cc"},
|
||||
{file = "ruff-0.6.5-py3-none-win32.whl", hash = "sha256:cf4d3fa53644137f6a4a27a2b397381d16454a1566ae5335855c187fbf67e4f5"},
|
||||
{file = "ruff-0.6.5-py3-none-win_amd64.whl", hash = "sha256:3e42a57b58e3612051a636bc1ac4e6b838679530235520e8f095f7c44f706ff9"},
|
||||
{file = "ruff-0.6.5-py3-none-win_arm64.whl", hash = "sha256:51935067740773afdf97493ba9b8231279e9beef0f2a8079188c4776c25688e0"},
|
||||
{file = "ruff-0.6.5.tar.gz", hash = "sha256:4d32d87fab433c0cf285c3683dd4dae63be05fd7a1d65b3f5bf7cdd05a6b96fb"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -4107,17 +4085,17 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "tox"
|
||||
version = "4.18.0"
|
||||
version = "4.19.0"
|
||||
description = "tox is a generic virtualenv management and test command line tool"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "tox-4.18.0-py3-none-any.whl", hash = "sha256:0a457400cf70615dc0627eb70d293e80cd95d8ce174bb40ac011011f0c03a249"},
|
||||
{file = "tox-4.18.0.tar.gz", hash = "sha256:5dfa1cab9f146becd6e351333a82f9e0ade374451630ba65ee54584624c27b58"},
|
||||
{file = "tox-4.19.0-py3-none-any.whl", hash = "sha256:6e20a520db7710f6980b8ec96bde189d6b8cf41b327ec703b03e1a2a447b1aaf"},
|
||||
{file = "tox-4.19.0.tar.gz", hash = "sha256:66177d887f9d7ef8eaa9b58b187f7b865fa4c58650086c01336e82c9831e1867"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cachetools = ">=5.4"
|
||||
cachetools = ">=5.5"
|
||||
chardet = ">=5.2"
|
||||
colorama = ">=0.4.6"
|
||||
filelock = ">=3.15.4"
|
||||
|
|
@ -4129,8 +4107,8 @@ tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
|
|||
virtualenv = ">=20.26.3"
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2024.7.18)", "sphinx (>=7.4.7)", "sphinx-argparse-cli (>=1.16)", "sphinx-autodoc-typehints (>=2.2.3)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.11)"]
|
||||
testing = ["build[virtualenv] (>=1.2.1)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1)", "diff-cover (>=9.1.1)", "distlib (>=0.3.8)", "flaky (>=3.8.1)", "hatch-vcs (>=0.4)", "hatchling (>=1.25)", "psutil (>=6)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-xdist (>=3.6.1)", "re-assert (>=1.1)", "setuptools (>=70.3)", "time-machine (>=2.14.2)", "wheel (>=0.43)"]
|
||||
docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-argparse-cli (>=1.17)", "sphinx-autodoc-typehints (>=2.4)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=24.8)"]
|
||||
testing = ["build[virtualenv] (>=1.2.2)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1)", "diff-cover (>=9.1.1)", "distlib (>=0.3.8)", "flaky (>=3.8.1)", "hatch-vcs (>=0.4)", "hatchling (>=1.25)", "psutil (>=6)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-xdist (>=3.6.1)", "re-assert (>=1.1)", "setuptools (>=74.1.2)", "time-machine (>=2.15)", "wheel (>=0.44)"]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
|
|
@ -4227,24 +4205,24 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "types-pyyaml"
|
||||
version = "6.0.12.20240808"
|
||||
version = "6.0.12.20240917"
|
||||
description = "Typing stubs for PyYAML"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "types-PyYAML-6.0.12.20240808.tar.gz", hash = "sha256:b8f76ddbd7f65440a8bda5526a9607e4c7a322dc2f8e1a8c405644f9a6f4b9af"},
|
||||
{file = "types_PyYAML-6.0.12.20240808-py3-none-any.whl", hash = "sha256:deda34c5c655265fc517b546c902aa6eed2ef8d3e921e4765fe606fe2afe8d35"},
|
||||
{file = "types-PyYAML-6.0.12.20240917.tar.gz", hash = "sha256:d1405a86f9576682234ef83bcb4e6fff7c9305c8b1fbad5e0bcd4f7dbdc9c587"},
|
||||
{file = "types_PyYAML-6.0.12.20240917-py3-none-any.whl", hash = "sha256:392b267f1c0fe6022952462bf5d6523f31e37f6cea49b14cee7ad634b6301570"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "types-requests"
|
||||
version = "2.32.0.20240712"
|
||||
version = "2.32.0.20240914"
|
||||
description = "Typing stubs for requests"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "types-requests-2.32.0.20240712.tar.gz", hash = "sha256:90c079ff05e549f6bf50e02e910210b98b8ff1ebdd18e19c873cd237737c1358"},
|
||||
{file = "types_requests-2.32.0.20240712-py3-none-any.whl", hash = "sha256:f754283e152c752e46e70942fa2a146b5bc70393522257bb85bd1ef7e019dcc3"},
|
||||
{file = "types-requests-2.32.0.20240914.tar.gz", hash = "sha256:2850e178db3919d9bf809e434eef65ba49d0e7e33ac92d588f4a5e295fffd405"},
|
||||
{file = "types_requests-2.32.0.20240914-py3-none-any.whl", hash = "sha256:59c2f673eb55f32a99b2894faf6020e1a9f4a402ad0f192bfee0b64469054310"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4501,10 +4479,9 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
|
|||
|
||||
[extras]
|
||||
assets = ["requests", "tqdm"]
|
||||
desktop = ["opencv-python"]
|
||||
metrics = ["pandas", "pandas-stubs"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.8"
|
||||
content-hash = "ab1a7151a2f59ad9a820550fca7361d9817ff8260e8b6d527b198b09d98612e4"
|
||||
content-hash = "86cf58c784053b04397cd55589f9704969531654dcb0c407435a81ea8305dd52"
|
||||
|
|
|
|||
|
|
@ -52,16 +52,14 @@ matplotlib = ">=3.6.0"
|
|||
pyyaml = ">=5.3"
|
||||
defusedxml = "^0.7.1"
|
||||
pillow = ">=9.4"
|
||||
opencv-python = { version = ">=4.5.5.64", optional = true }
|
||||
opencv-python-headless = ">=4.5.5.64"
|
||||
requests = { version = ">=2.26.0,<=2.32.3", optional = true }
|
||||
tqdm = { version = ">=4.62.3,<=4.66.5", optional = true }
|
||||
# pandas: picked lowest major version that supports Python 3.8
|
||||
pandas = { version = ">=2.0.0", optional = true }
|
||||
pandas-stubs = { version = ">=2.0.0.230412", optional = true }
|
||||
opencv-python = ">=4.5.5.64"
|
||||
|
||||
[tool.poetry.extras]
|
||||
desktop = ["opencv-python"]
|
||||
assets = ["requests", "tqdm"]
|
||||
metrics = ["pandas", "pandas-stubs"]
|
||||
|
||||
|
|
@ -111,24 +109,6 @@ tests = ["B201", "B301", "B318", "B314", "B303", "B413", "B412", "B410"]
|
|||
check = true
|
||||
imports = ["cv2", "supervision"]
|
||||
|
||||
[tool.black]
|
||||
target-version = ["py38"]
|
||||
line-length = 88
|
||||
include = '\.pyi?$'
|
||||
exclude = '''
|
||||
/(
|
||||
\.git
|
||||
| \.hg
|
||||
| \.mypy_cache
|
||||
| \.tox
|
||||
| \.venv
|
||||
| _build
|
||||
| buck-out
|
||||
| build
|
||||
| dist
|
||||
| docs
|
||||
)/
|
||||
'''
|
||||
|
||||
[tool.ruff]
|
||||
target-version = "py38"
|
||||
|
|
@ -166,7 +146,7 @@ indent-width = 4
|
|||
|
||||
[tool.ruff.lint]
|
||||
# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
|
||||
select = ["E", "F", "I", "A", "Q", "W"]
|
||||
select = ["E", "F", "I", "A", "Q", "W","RUF"]
|
||||
ignore = []
|
||||
# Allow autofix for all enabled rules (when `--fix`) is provided.
|
||||
fixable = [
|
||||
|
|
|
|||
|
|
@ -45,7 +45,7 @@ class Detections:
|
|||
The `sv.Detections` class in the Supervision library standardizes results from
|
||||
various object detection and segmentation models into a consistent format. This
|
||||
class simplifies data manipulation and filtering, providing a uniform API for
|
||||
integration with Supervision [trackers](/trackers/), [annotators](/detection/annotators/), and [tools](/detection/tools/line_zone/).
|
||||
integration with Supervision [trackers](/trackers/), [annotators](/latest/detection/annotators/), and [tools](/detection/tools/line_zone/).
|
||||
|
||||
=== "Inference"
|
||||
|
||||
|
|
@ -249,7 +249,7 @@ class Detections:
|
|||
results = model(image)[0]
|
||||
detections = sv.Detections.from_ultralytics(results)
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
if hasattr(ultralytics_results, "obb") and ultralytics_results.obb is not None:
|
||||
class_id = ultralytics_results.obb.cls.cpu().numpy().astype(int)
|
||||
|
|
@ -356,7 +356,7 @@ class Detections:
|
|||
result = model(img)
|
||||
detections = sv.Detections.from_tensorflow(result)
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
boxes = tensorflow_results["detection_boxes"][0].numpy()
|
||||
boxes[:, [0, 2]] *= resolution_wh[0]
|
||||
|
|
@ -431,7 +431,7 @@ class Detections:
|
|||
result = inference_detector(model, image)
|
||||
detections = sv.Detections.from_mmdetection(result)
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
return cls(
|
||||
xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
|
||||
|
|
@ -490,7 +490,7 @@ class Detections:
|
|||
id2label=model.config.id2label
|
||||
)
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
if (
|
||||
transformers_results.__class__.__name__ == "Tensor"
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ class LineZone:
|
|||
line_zone.in_count, line_zone.out_count
|
||||
# 7, 2
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -113,7 +113,7 @@ def from_florence_2(
|
|||
oriented bounding boxes.
|
||||
"""
|
||||
assert len(result) == 1, f"Expected result with a single element. Got: {result}"
|
||||
task = list(result.keys())[0]
|
||||
task = next(iter(result.keys()))
|
||||
if task not in SUPPORTED_TASKS_FLORENCE_2:
|
||||
raise ValueError(
|
||||
f"{task} not supported. Supported tasks are: {SUPPORTED_TASKS_FLORENCE_2}"
|
||||
|
|
|
|||
|
|
@ -183,7 +183,7 @@ def group_overlapping_boxes(
|
|||
ious = ious.flatten()
|
||||
|
||||
above_threshold = ious >= iou_threshold
|
||||
merge_group = [idx] + np.flip(order[above_threshold]).tolist()
|
||||
merge_group = [idx, *np.flip(order[above_threshold]).tolist()]
|
||||
merge_groups.append(merge_group)
|
||||
order = order[~above_threshold]
|
||||
return merge_groups
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@ class CSVSink:
|
|||
detections = sv.Detections.from_ultralytics(result)
|
||||
sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
def __init__(self, file_name: str = "output.csv") -> None:
|
||||
"""
|
||||
|
|
@ -104,7 +104,7 @@ class CSVSink:
|
|||
|
||||
@staticmethod
|
||||
def parse_detection_data(
|
||||
detections: Detections, custom_data: Dict[str, Any] = None
|
||||
detections: Detections, custom_data: Optional[Dict[str, Any]] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
parsed_rows = []
|
||||
for i in range(len(detections.xyxy)):
|
||||
|
|
@ -137,7 +137,7 @@ class CSVSink:
|
|||
return parsed_rows
|
||||
|
||||
def append(
|
||||
self, detections: Detections, custom_data: Dict[str, Any] = None
|
||||
self, detections: Detections, custom_data: Optional[Dict[str, Any]] = None
|
||||
) -> None:
|
||||
"""
|
||||
Append detection data to the CSV file.
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ class JSONSink:
|
|||
detections = sv.Detections.from_ultralytics(result)
|
||||
sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
def __init__(self, file_name: str = "output.json") -> None:
|
||||
"""
|
||||
|
|
@ -92,7 +92,7 @@ class JSONSink:
|
|||
|
||||
@staticmethod
|
||||
def parse_detection_data(
|
||||
detections: Detections, custom_data: Dict[str, Any] = None
|
||||
detections: Detections, custom_data: Optional[Dict[str, Any]] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
parsed_rows = []
|
||||
for i in range(len(detections.xyxy)):
|
||||
|
|
@ -126,7 +126,7 @@ class JSONSink:
|
|||
return parsed_rows
|
||||
|
||||
def append(
|
||||
self, detections: Detections, custom_data: Dict[str, Any] = None
|
||||
self, detections: Detections, custom_data: Optional[Dict[str, Any]] = None
|
||||
) -> None:
|
||||
"""
|
||||
Append detection data to the JSON file.
|
||||
|
|
|
|||
|
|
@ -53,7 +53,7 @@ class DetectionsSmoother:
|
|||
annotated_frame = box_annotator.annotate(frame.copy(), detections)
|
||||
sink.write_frame(annotated_frame)
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
|
||||
def __init__(self, length: int = 5) -> None:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -18,11 +18,11 @@ class KeyPoints:
|
|||
The `sv.KeyPoints` class in the Supervision library standardizes results from
|
||||
various keypoint detection and pose estimation models into a consistent format. This
|
||||
class simplifies data manipulation and filtering, providing a uniform API for
|
||||
integration with Supervision [keypoints annotators](/keypoint/annotators).
|
||||
integration with Supervision [keypoints annotators](/latest/keypoint/annotators).
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
Use [`sv.KeyPoints.from_ultralytics`](/keypoint/core/#supervision.keypoint.core.KeyPoints.from_ultralytics)
|
||||
Use [`sv.KeyPoints.from_ultralytics`](/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_ultralytics)
|
||||
method, which accepts [YOLOv8](https://github.com/ultralytics/ultralytics)
|
||||
pose result.
|
||||
|
||||
|
|
@ -40,7 +40,7 @@ class KeyPoints:
|
|||
|
||||
=== "Inference"
|
||||
|
||||
Use [`sv.KeyPoints.from_inference`](/keypoint/core/#supervision.keypoint.core.KeyPoints.from_inference)
|
||||
Use [`sv.KeyPoints.from_inference`](/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_inference)
|
||||
method, which accepts [Inference](https://inference.roboflow.com/) pose result.
|
||||
|
||||
```python
|
||||
|
|
@ -57,7 +57,7 @@ class KeyPoints:
|
|||
|
||||
=== "MediaPipe"
|
||||
|
||||
Use [`sv.KeyPoints.from_mediapipe`](/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe)
|
||||
Use [`sv.KeyPoints.from_mediapipe`](/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe)
|
||||
method, which accepts [MediaPipe](https://github.com/google-ai-edge/mediapipe)
|
||||
pose result.
|
||||
|
||||
|
|
@ -429,7 +429,7 @@ class KeyPoints:
|
|||
results = model.predict(image, conf=0.1)
|
||||
key_points = sv.KeyPoints.from_yolo_nas(results)
|
||||
```
|
||||
""" # noqa: E501 // docs
|
||||
"""
|
||||
if len(yolo_nas_results.prediction.poses) == 0:
|
||||
return cls.empty()
|
||||
|
||||
|
|
|
|||
|
|
@ -1,11 +1,11 @@
|
|||
from enum import Enum
|
||||
from typing import Dict, List, Tuple
|
||||
from typing import Dict, Tuple
|
||||
|
||||
Edges = List[Tuple[int, int]]
|
||||
Edges = Tuple[Tuple[int, int], ...]
|
||||
|
||||
|
||||
class Skeleton(Enum):
|
||||
COCO = [
|
||||
COCO: Edges = (
|
||||
(1, 2),
|
||||
(1, 3),
|
||||
(2, 3),
|
||||
|
|
@ -23,9 +23,9 @@ class Skeleton(Enum):
|
|||
(15, 13),
|
||||
(16, 14),
|
||||
(17, 15),
|
||||
]
|
||||
)
|
||||
|
||||
GHUM = [
|
||||
GHUM: Edges = (
|
||||
(1, 2),
|
||||
(1, 5),
|
||||
(2, 3),
|
||||
|
|
@ -61,9 +61,9 @@ class Skeleton(Enum):
|
|||
(29, 33),
|
||||
(30, 32),
|
||||
(31, 33),
|
||||
]
|
||||
)
|
||||
|
||||
FACEMESH_TESSELATION_NO_IRIS = [
|
||||
FACEMESH_TESSELATION_NO_IRIS: Edges = (
|
||||
(128, 35),
|
||||
(35, 140),
|
||||
(140, 128),
|
||||
|
|
@ -2620,9 +2620,9 @@ class Skeleton(Enum):
|
|||
(340, 449),
|
||||
(449, 256),
|
||||
(256, 340),
|
||||
]
|
||||
)
|
||||
|
||||
FACEMESH_TESSELATION = [
|
||||
FACEMESH_TESSELATION: Edges = (
|
||||
(474, 474),
|
||||
(475, 476),
|
||||
(476, 477),
|
||||
|
|
@ -2633,7 +2633,8 @@ class Skeleton(Enum):
|
|||
(471, 472),
|
||||
(472, 473),
|
||||
(473, 470),
|
||||
] + FACEMESH_TESSELATION_NO_IRIS
|
||||
*FACEMESH_TESSELATION_NO_IRIS,
|
||||
)
|
||||
|
||||
|
||||
SKELETONS_BY_EDGE_COUNT: Dict[int, Edges] = {}
|
||||
|
|
|
|||
|
|
@ -440,8 +440,8 @@ class ConfusionMatrix:
|
|||
class_names = classes if classes is not None else self.classes
|
||||
use_labels_for_ticks = class_names is not None and (0 < len(class_names) < 99)
|
||||
if use_labels_for_ticks:
|
||||
x_tick_labels = class_names + ["FN"]
|
||||
y_tick_labels = class_names + ["FP"]
|
||||
x_tick_labels = [*class_names, "FN"]
|
||||
y_tick_labels = [*class_names, "FP"]
|
||||
num_ticks = len(x_tick_labels)
|
||||
else:
|
||||
x_tick_labels = None
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
from contextlib import ExitStack as DoesNotRaise
|
||||
from typing import Dict, List, Tuple, Union
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
|
@ -21,7 +21,7 @@ def mock_coco_annotation(
|
|||
category_id: int = 0,
|
||||
bbox: Tuple[float, float, float, float] = (0.0, 0.0, 0.0, 0.0),
|
||||
area: float = 0.0,
|
||||
segmentation: Union[List[list], Dict] = None,
|
||||
segmentation: Optional[Union[List[list], Dict]] = None,
|
||||
iscrowd: bool = False,
|
||||
) -> dict:
|
||||
if not segmentation:
|
||||
|
|
|
|||
Loading…
Reference in New Issue