roboflow/supervision

## 👋 你好 **我们为您编写可复用的计算机视觉工具。** 无论您是需要从硬盘加载数据集，在图像或视频上绘制检测结果，还是计算区域内的检测数量。您都可以信赖我们！🤝 ## 💻 安装 在 [**Python>=3.9**](https://www.python.org/) 环境中使用 Pip 安装 supervision 包。 ``` pip install supervision ``` 在我们的 [指南](https://roboflow.github.io/supervision/) 中阅读更多关于 conda、mamba 以及从源码安装的内容。 ## 🔥 快速入门 ### 模型 Supervision 旨在与模型无关。只需接入任何分类、检测或分割模型即可。为了您的方便，我们为 Ultralytics、Transformers、MMDetection 或 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（如 `rfdetr`）已经直接返回 `sv.Detections`。 使用 `pip install pillow rfdetr` 安装此示例的可选依赖项。 ``` import supervision as sv from PIL import Image from rfdetr import RFDETRSmall image = Image.open(...) 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)。 import supervision as sv from PIL import Image from inference import get_model image = Image.open(...) 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 提供了各种高度可定制的 [标注器](https://supervision.roboflow.com/latest/detection/annotators/)，让您能够针对具体用例组合出完美的可视化效果。 ``` import cv2 import supervision as sv image = cv2.imread(...) 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 提供了一组 [工具](https://supervision.roboflow.com/latest/datasets/core/)，允许您以支持的格式之一加载、拆分、合并和保存数据集。 ``` 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] # 按需加载图像 for path, image, annotation in ds: # loads image on demand pass ```

👉 更多数据集工具

- load 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 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 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 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 sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ).as_pascal_voc( images_directory_path=..., annotations_directory_path=..., )

## 🎬 教程 想要学习如何使用 Supervision？探索我们的 [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 [Cookbooks](https://supervision.roboflow.com/develop/cookbooks/)！

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## 💜 使用 supervision 构建 您用 supervision 构建了什么很酷的东西吗？[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 如何帮助您更快、更可靠地构建计算机视觉应用程序。

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