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## ð hello
**We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! ð¤
## ð» install
Pip install the supervision package in a
[**3.11>=Python>=3.8**](https://www.python.org/) environment.
```bash
pip install supervision[desktop]
```
Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).
## ð¥ quickstart
### [detections processing](https://roboflow.github.io/supervision/detection/core/)
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
>>> model = YOLO('yolov8s.pt')
>>> result = model(IMAGE)[0]
>>> detections = sv.Detections.from_ultralytics(result)
>>> len(detections)
5
```
<details close>
<summary>ð more detections utils</summary>
- Easily switch inference pipeline between supported object detection/instance segmentation models
```python
>>> import supervision as sv
>>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
>>> sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
>>> mask_generator = SamAutomaticMaskGenerator(sam)
>>> sam_result = mask_generator.generate(IMAGE)
>>> detections = sv.Detections.from_sam(sam_result=sam_result)
```
- [Advanced filtering](https://roboflow.github.io/supervision/quickstart/detections/)
```python
>>> detections = detections[detections.class_id == 0]
>>> detections = detections[detections.confidence > 0.5]
>>> detections = detections[detections.area > 1000]
```
- Image annotation
```python
>>> import supervision as sv
>>> box_annotator = sv.BoxAnnotator()
>>> annotated_frame = box_annotator.annotate(
... scene=IMAGE,
... detections=detections
... )
```
</details>
### [datasets processing](https://roboflow.github.io/supervision/dataset/core/)
```python
>>> import supervision as sv
>>> dataset = sv.DetectionDataset.from_yolo(
... images_directory_path='...',
... annotations_directory_path='...',
... data_yaml_path='...'
... )
>>> dataset.classes
['dog', 'person']
>>> len(dataset)
1000
```
<details close>
<summary>ð more dataset utils</summary>
- Load object detection/instance segmentation datasets in one of the supported formats
```python
>>> dataset = sv.DetectionDataset.from_yolo(
... images_directory_path='...',
... annotations_directory_path='...',
... data_yaml_path='...'
... )
>>> dataset = sv.DetectionDataset.from_pascal_voc(
... images_directory_path='...',
... annotations_directory_path='...'
... )
>>> dataset = sv.DetectionDataset.from_coco(
... images_directory_path='...',
... annotations_path='...'
... )
```
- Loop over dataset entries
```python
>>> for name, image, labels in dataset:
... print(labels.xyxy)
array([[404. , 719. , 538. , 884.5 ],
[155. , 497. , 404. , 833.5 ],
[ 20.154999, 347.825 , 416.125 , 915.895 ]], dtype=float32)
```
- Split dataset for training, testing, and validation
```python
>>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
>>> test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
>>> len(train_dataset), len(test_dataset), len(valid_dataset)
(700, 150, 150)
```
- Merge multiple datasets
```python
>>> ds_1 = sv.DetectionDataset(...)
>>> len(ds_1)
100
>>> ds_1.classes
['dog', 'person']
>>> ds_2 = sv.DetectionDataset(...)
>>> len(ds_2)
200
>>> ds_2.classes
['cat']
>>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
>>> len(ds_merged)
300
>>> ds_merged.classes
['cat', 'dog', 'person']
```
- Save object detection/instance segmentation datasets in one of the supported formats
```python
>>> dataset.as_yolo(
... images_directory_path='...',
... annotations_directory_path='...',
... data_yaml_path='...'
... )
>>> dataset.as_pascal_voc(
... images_directory_path='...',
... annotations_directory_path='...'
... )
>>> dataset.as_coco(
... images_directory_path='...',
... annotations_path='...'
... )
```
- Convert labels between supported formats
```python
>>> sv.DetectionDataset.from_yolo(
... images_directory_path='...',
... annotations_directory_path='...',
... data_yaml_path='...'
... ).as_pascal_voc(
... images_directory_path='...',
... annotations_directory_path='...'
... )
```
- Load classification datasets in one of the supported formats
```python
>>> cs = sv.ClassificationDataset.from_folder_structure(
... root_directory_path='...'
... )
```
- Save classification datasets in one of the supported formats
```python
>>> cs.as_folder_structure(
... root_directory_path='...'
... )
```
</details>
### [model evaluation](https://roboflow.github.io/supervision/metrics/detection/)
```python
>>> import supervision as sv
>>> dataset = sv.DetectionDataset.from_yolo(...)
>>> def callback(image: np.ndarray) -> sv.Detections:
... ...
>>> confusion_matrix = sv.ConfusionMatrix.benchmark(
... dataset = dataset,
... callback = callback
... )
>>> confusion_matrix.matrix
array([
[0., 0., 0., 0.],
[0., 1., 0., 1.],
[0., 1., 1., 0.],
[1., 1., 0., 0.]
])
```
<details close>
<summary>ð more metrics</summary>
- Mean average precision (mAP) for object detection tasks.
```python
>>> import supervision as sv
>>> dataset = sv.DetectionDataset.from_yolo(...)
>>> def callback(image: np.ndarray) -> sv.Detections:
... ...
>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
... dataset = dataset,
... callback = callback
... )
>>> mean_average_precision.map50_95
0.433
```
</details>
## ð¬ tutorials
<p align="left">
<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><img src="https://github.com/roboflow/supervision/assets/26109316/54afdf1c-218c-4451-8f12-627fb85f1682" alt="Traffic Analysis with Y
可重复使用的计算机视觉工具
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可重复使用的计算机视觉工具 （123个子文件） 
CITATION.cff 684B CNAME 25B extra.css 92B .gitattributes 26B .gitignore 2KB .gitignore 6B
404.html 206B demo.ipynb 3.51MB poetry.lock 245KB changelog.md 22KB README.md 13KB filter_detections.md 8KB CONTRIBUTING.md 6KB annotators.md 6KB CODE_OF_CONDUCT.md 5KB index.md 2KB README.md 2KB README.md 1KB README.md 1KB LICENSE.md 1KB utils.md 491B detection.md 356B datasets.md 344B video.md 303B utils.md 294B polygon_zone.md 163B notebook.md 125B image.md 99B file.md 84B inference_slicer.md 84B core.md 71B trackers.md 65B core.md 56B core.md 51B process_video.md 30B evaluate_model.md 30B detect_and_annotate.md 30B track_objects.md 30B core.py 32KB detection.py 31KB core.py 27KB core.py 25KB core.py 16KB test_utils.py 16KB utils.py 14KB test_detection.py 13KB test_coco.py 10KB test_core.py 9KB pascal_voc.py 9KB test_yolo.py 9KB yolo.py 9KB script.py 8KB video.py 8KB test_core.py 8KB line_counter.py 8KB coco.py 7KB kalman_filter.py 7KB inference_slicer.py 7KB test_utils.py 7KB polygon_zone.py 5KB utils.py 5KB annotate.py 5KB test_pascal_voc.py 5KB utils.py 4KB file.py 4KB color.py 4KB utils.py 4KB test_utils.py 3KB core.py 3KB image.py 3KB notebook.py 3KB script.py 2KB matching.py 2KB __init__.py 2KB test_core.py 2KB test_dataclasses.py 2KB core.py 2KB basetrack.py 1KB test_color.py 1KB utils.py 1KB utils.py 793B internal.py 421B base.py 249B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B共 123 条
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