> [!NOTE] > 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 > [English](./README.en.md) · [原始项目](https://github.com/roboflow/supervision) · [上游 README](https://github.com/roboflow/supervision/blob/HEAD/README.md) > 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。

[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)
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📑 目录

- [👋 你好](#-hello) - [💻 安装](#-install) - [🔥 快速入门](#-quickstart) - [模型](#models) - [标注器](#annotators) - [数据集](#datasets) - [🎬 教程](#-tutorials) - [💜 使用 Supervision 构建](#-built-with-supervision) - [📚 文档](#-documentation) - [🏆 贡献](#-contribution)

## 👋 你好 **我们是你在计算机视觉（computer vision）领域的必备工具包。** 从数据加载到实时区域计数，我们提供各类基础模块，让你可以专注于围绕模型构建应用。🤝 ## 💻 安装在 [**Python>=3.10**](https://www.python.org/) 环境中，使用 pip 安装 supervision 包。 ```bash pip install supervision ``` 如需了解 conda、mamba 及从源码安装的更多内容，请参阅我们的[指南](https://roboflow.github.io/supervision/). ## 🔥 快速入门 ### 模型 Supervision 在设计上是模型无关（model agnostic）的。你可以直接接入任意分类、检测或分割模型。为方便使用，我们为 Ultralytics、Transformers、MMDetection、Inference 等最流行的库创建了[连接器](https://supervision.roboflow.com/latest/detection/core/#detections)。其他集成方式（如 `rfdetr`）已能直接返回 `sv.Detections`。使用 `pip install pillow rfdetr` 安装本示例所需的可选依赖。 ```python import supervision as sv from PIL import Image from rfdetr import RFDETRSmall image = Image.open("path/to/image.jpg") model = RFDETRSmall() detections = model.predict(image, threshold=0.5) len(detections) # 5 ```

👉 更多模型连接器

- inference 使用 [Inference](https://github.com/roboflow/inference) 运行需要 [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key). ```python import supervision as sv from PIL import Image from inference import get_model image = Image.open("path/to/image.jpg") model = get_model(model_id="rfdetr-small", api_key="ROBOFLOW_API_KEY") result = model.infer(image)[0] detections = sv.Detections.from_inference(result) len(detections) # 5 ```

### 标注器 Supervision 提供大量高度可自定义的[标注器](https://supervision.roboflow.com/latest/detection/annotators/),，可让你为具体场景组合出完美的可视化效果。 ```python import cv2 import supervision as sv image = cv2.imread("path/to/image.jpg") # Assuming detections are obtained from a model detections = sv.Detections(...) box_annotator = sv.BoxAnnotator() annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections) ``` https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce ### 数据集 Supervision 提供一组[工具函数](https://supervision.roboflow.com/latest/datasets/core/)，支持以支持的格式之一加载、拆分、合并和保存数据集。 ```python import supervision as sv from roboflow import Roboflow project = Roboflow().workspace("WORKSPACE_ID").project("PROJECT_ID") dataset = project.version("PROJECT_VERSION").download("coco") ds = sv.DetectionDataset.from_coco( images_directory_path=f"{dataset.location}/train", annotations_path=f"{dataset.location}/train/_annotations.coco.json", ) path, image, annotation = ds[0] # loads image on demand for path, image, annotation in ds: # loads image on demand pass ```

👉 更多数据集工具

- 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) # (700, 150, 150) ``` - merge ```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 ```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=..., ) ```

## 🎬 教程想了解如何使用 Supervision？可浏览我们的[操作指南](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/),、[端到端示例](./examples)、[速查表](https://roboflow.github.io/cheatsheet-supervision/), 和 [cookbook](https://supervision.roboflow.com/develop/cookbooks/)!

Dwell Time Analysis with Computer Vision | Real-Time Stream Processing

创建时间：2024 年 4 月 5 日

了解如何使用计算机视觉分析等待时间并优化流程。本教程涵盖目标检测、跟踪，以及计算在指定区域内的停留时间。可将这些技术用于提升零售、交通管理等场景下的客户体验。

速度估算与车辆跟踪 | 计算机视觉 | 开源

创建日期：2024 年 1 月 11 日

学习如何使用 YOLO、ByteTrack 和 Roboflow Inference 跟踪并估算车辆速度。本综合教程涵盖目标检测（object detection）、多目标跟踪（multi-object tracking）、检测结果过滤、透视变换（perspective transformation）、速度估算、可视化改进等内容。

## 💜 使用 Supervision 构建你是否使用 supervision 构建了很酷的项目？[告诉我们！](https://github.com/roboflow/supervision/discussions/categories/built-with-supervision) https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4 https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900 https://github.com/roboflow/supervision/assets/26109316/3ac6982f-4943-4108-9b7f-51787ef1a69f ## 📚 文档访问我们的[文档](https://roboflow.github.io/supervision) 页面，了解 supervision 如何帮助你更快、更可靠地构建计算机视觉应用。 ## 🏆 贡献我们欢迎你的反馈！请查阅我们的[贡献指南](.github/CONTRIBUTING.md)开始参与。感谢 🙏 所有贡献者！