可重复使用的计算机视觉工具

<div align="center"> <p> <a align="center" href="" target="https://supervision.roboflow.com"> <img width="100%" src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529" > </a> </p> <br> [notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro) <br> [](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) [](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) [](https://huggingface.co/spaces/Roboflow/Annotators) [](https://discord.gg/GbfgXGJ8Bk) [](https://squidfunk.github.io/mkdocs-material/) </div> ## 馃憢 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! 馃 [](https://github.com/orgs/roboflow/projects/10) ## 馃捇 install Pip install the supervision package in a [**Python>=3.8**](https://www.python.org/) environment. ```bash pip install supervision ``` Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/). ## 馃敟 quickstart ### models Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created [connectors](https://supervision.roboflow.com/latest/detection/core/#detections) for the most popular libraries like Ultralytics, Transformers, or MMDetection. ```python import cv2 import supervision as sv from ultralytics import YOLO image = cv2.imread(...) model = YOLO('yolov8s.pt') result = model(image)[0] detections = sv.Detections.from_ultralytics(result) len(detections) # 5 ``` <details> <summary>馃憠 more model connectors</summary> - inference Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key). ```python import cv2 import supervision as sv from inference.models.utils import get_roboflow_model image = cv2.imread(...) model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>) result = model.infer(image)[0] detections = sv.Detections.from_inference(result) len(detections) #聽5 ``` </details> ### annotators Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/latest/annotators/), allowing you to compose the perfect visualization for your use case. ```python import cv2 import supervision as sv image = cv2.imread(...) detections = sv.Detections(...) bounding_box_annotator = sv.BoundingBoxAnnotator() annotated_frame = bounding_box_annotator.annotate( scene=image.copy(), detections=detections ) ``` https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce ### datasets Supervision provides a set of [utils](https://supervision.roboflow.com/latest/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats. ```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) #聽1000 ``` <details close> <summary>馃憠 more dataset utils</summary> - load ```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=... ) ``` - split ```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) #聽(700, 150, 150) ``` - merge ```python ds_1 = sv.DetectionDataset(...) len(ds_1) #聽100 ds_1.classes #聽['dog', 'person'] ds_2 = sv.DetectionDataset(...) len(ds_2) # 200 ds_2.classes #聽['cat'] ds_merged = sv.DetectionDataset.merge([ds_1, ds_2]) len(ds_merged) #聽300 ds_merged.classes #聽['cat', 'dog', 'person'] ``` - save ```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 ```python sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=... ).as_pascal_voc( images_directory_path=..., annotations_directory_path=... ) ``` </details> ## 馃幀 tutorials <p align="left"> <a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/61a444c8-b135-48ce-b979-2a5ab47c5a91" alt="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source" width="300px" align="left" /></a> <a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><strong>Speed Estimation & Vehicle Tracking | Computer Vision | Open Source</strong></a> <div><strong>Created: 11 Jan 2024</strong> | <strong>Updated: 11 Jan 2024</strong></div> <br/> Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.</p> <br/> <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 YOLOv8 and ByteTrack - Vehicle Detection and Tracking" width="300px" align="left" /></a> <a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><strong>Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking</strong></a> <div><strong>Created: 6 Sep 2023</strong> | <strong>Updated: 6 Sep 2023</strong></div> <br/> In this video, we explore real-time traffic analysis using YOLOv8 and ByteTrack to detect and track vehicles on aerial images. Harnessing the power of Python and Supervision, we delve deep into assigning cars to specific entry zones and understanding their direction of movement. By visualizing their paths, we gain insights into traffic flow across bustling roundabouts... </p> ## 馃挏