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328 lines
12 KiB
Markdown
328 lines
12 KiB
Markdown
<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 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。
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> [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)
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> 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。
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<div align="center">
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<p>
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<a align="center" href="https://supervision.roboflow.com" target="_blank">
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<img
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width="100%"
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src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
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>
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</a>
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</p>
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<br>
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[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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<br>
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[](https://badge.fury.io/py/supervision) [](https://pypistats.org/packages/supervision) [](LICENSE.md) [](https://badge.fury.io/py/supervision) [](https://codecov.io/gh/roboflow/supervision)
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[](https://snyk.io/advisor/python/supervision) [](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) [](https://huggingface.co/spaces/Roboflow/Annotators) [](https://discord.gg/GbfgXGJ8Bk)
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<div align="center">
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<a href="https://trendshift.io/repositories/124" target="_blank"><img src="https://trendshift.io/api/badge/repositories/124" alt="roboflow%2Fsupervision | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
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</div>
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</div>
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<details>
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<summary><strong>📑 目录</strong></summary>
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- [👋 你好](#-hello)
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- [💻 安装](#-install)
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- [🔥 快速入门](#-quickstart)
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- [模型](#models)
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- [标注器](#annotators)
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- [数据集](#datasets)
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- [🎬 教程](#-tutorials)
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- [💜 使用 Supervision 构建](#-built-with-supervision)
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- [📚 文档](#-documentation)
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- [🏆 贡献](#-contribution)
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</details>
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## 👋 你好
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**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。🤝
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## 💻 安装
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在 [**Python>=3.10**](https://www.python.org/) 环境中，使用 pip 安装 supervision 包。
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```bash
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pip install supervision
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```
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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/).
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## 🔥 快速入门
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### 模型
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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`。
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使用 `pip install pillow rfdetr` 安装本示例所需的可选依赖。
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```python
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import supervision as sv
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from PIL import Image
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from rfdetr import RFDETRSmall
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image = Image.open("path/to/image.jpg")
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model = RFDETRSmall()
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detections = model.predict(image, threshold=0.5)
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len(detections)
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# 5
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```
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<details>
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<summary>👉 更多模型连接器</summary>
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- inference
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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).
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```python
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import supervision as sv
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from PIL import Image
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from inference import get_model
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image = Image.open("path/to/image.jpg")
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model = get_model(model_id="rfdetr-small", api_key="ROBOFLOW_API_KEY")
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result = model.infer(image)[0]
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detections = sv.Detections.from_inference(result)
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len(detections)
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# 5
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```
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</details>
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### 标注器
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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。
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```python
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import cv2
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import supervision as sv
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image = cv2.imread("path/to/image.jpg")
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# Assuming detections are obtained from a model
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detections = sv.Detections(...)
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box_annotator = sv.BoxAnnotator()
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annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)
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```
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https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
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### 数据集
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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。
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```python
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import supervision as sv
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from roboflow import Roboflow
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project = Roboflow().workspace("WORKSPACE_ID").project("PROJECT_ID")
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dataset = project.version("PROJECT_VERSION").download("coco")
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ds = sv.DetectionDataset.from_coco(
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images_directory_path=f"{dataset.location}/train",
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annotations_path=f"{dataset.location}/train/_annotations.coco.json",
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)
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path, image, annotation = ds[0]
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# loads image on demand
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for path, image, annotation in ds:
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# loads image on demand
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pass
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```
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<details>
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<summary>👉 更多数据集工具</summary>
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- load
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```python
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dataset = sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...,
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)
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dataset = sv.DetectionDataset.from_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...,
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)
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dataset = sv.DetectionDataset.from_coco(
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images_directory_path=...,
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annotations_path=...,
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)
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```
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- split
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```python
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train_dataset, test_dataset = dataset.split(split_ratio=0.7)
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test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
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len(train_dataset), len(test_dataset), len(valid_dataset)
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# (700, 150, 150)
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```
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- merge
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```python
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ds_1 = sv.DetectionDataset(...)
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len(ds_1)
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# 100
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ds_1.classes
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# ['dog', 'person']
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ds_2 = sv.DetectionDataset(...)
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len(ds_2)
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# 200
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ds_2.classes
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# ['cat']
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ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
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len(ds_merged)
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# 300
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ds_merged.classes
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# ['cat', 'dog', 'person']
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```
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- save
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```python
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dataset.as_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...,
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)
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dataset.as_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...,
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)
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dataset.as_coco(
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images_directory_path=...,
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annotations_path=...,
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)
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```
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- convert
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```python
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sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...,
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).as_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...,
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)
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```
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</details>
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## 🎬 教程
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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/)!
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<br/>
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<p align="left">
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<a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><img src="https://github.com/user-attachments/assets/014cffc7-72b3-4c0a-bb89-6de265b2c06b" alt="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing" width="300px" align="left" /></a>
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<a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><strong>Dwell Time Analysis with Computer Vision | Real-Time Stream Processing</strong></a>
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<div><strong>创建时间：2024 年 4 月 5 日</strong></div>
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<br/>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。</p>
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<br/>
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<p align="left">
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<a href="https://youtu.be/uWP6UjDeZvY" title="速度估算与车辆跟踪 | 计算机视觉 | 开源"><img src="https://github.com/user-attachments/assets/b16b8e21-dc6c-4a73-a678-2f7d5d374793" alt="速度估算与车辆跟踪 | 计算机视觉 | 开源" width="300px" align="left" /></a>
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<a href="https://youtu.be/uWP6UjDeZvY" title="速度估算与车辆跟踪 | 计算机视觉 | 开源"><strong>速度估算与车辆跟踪 | 计算机视觉 | 开源</strong></a>
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<div><strong>创建日期：2024 年 1 月 11 日</strong></div>
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<br/>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。</p>
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## 💜 使用 Supervision 构建
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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)
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https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4
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https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900
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https://github.com/roboflow/supervision/assets/26109316/3ac6982f-4943-4108-9b7f-51787ef1a69f
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## 📚 文档
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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。
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## 🏆 贡献
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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！
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<p align="center">
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<a href="https://github.com/roboflow/supervision/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=roboflow/supervision" />
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</a>
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</p>
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<br>
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<div align="center">
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<a href="https://youtube.com/roboflow">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/youtube.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634652"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/roboflow-app.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949746649"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://www.linkedin.com/company/roboflow-ai/">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/linkedin.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633691"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://docs.roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/knowledge.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634511"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://discuss.roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/forum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://blog.roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/blog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605"
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width="3%"
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/>
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</a>
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</div>
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