[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-Faceplugin-ltd--Open-Source-Face-Recognition-SDK":3,"tool-Faceplugin-ltd--Open-Source-Face-Recognition-SDK":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":79,"owner_email":80,"owner_twitter":79,"owner_website":81,"owner_url":82,"languages":83,"stars":88,"forks":89,"last_commit_at":90,"license":79,"difficulty_score":10,"env_os":91,"env_gpu":92,"env_ram":92,"env_deps":93,"category_tags":97,"github_topics":98,"view_count":10,"oss_zip_url":79,"oss_zip_packed_at":79,"status":16,"created_at":107,"updated_at":108,"faqs":109,"releases":110},1050,"Faceplugin-ltd\u002FOpen-Source-Face-Recognition-SDK","Open-Source-Face-Recognition-SDK","The world's 1st open source face recognition SDK for Windows and Linux (Face detection, Face landmark extraction, Face feature extraction, Face template mathcing)","Open-Source-Face-Recognition-SDK 是一款面向开发者和研究人员的开源面部识别工具，支持Windows和Linux系统，提供人脸检测、关键点提取、特征生成及相似度比对等功能。它通过本地化处理确保用户数据隐私，避免信息外泄，同时基于深度学习模型实现高精度识别，适用于需要安全处理生物特征的场景。工具采用Python API设计，集成简便，支持多种图像格式，并兼容CPU和GPU加速。其核心优势在于完全免费开源，结合跨平台特性，为开发者提供灵活的解决方案。对于关注数据安全的项目团队、需要快速实现人脸识别功能的开发者，或是希望降低AI开发成本的研究人员而言，这款工具能有效满足需求。","\u003Cdiv align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-Javascript\u002Fassets\u002F160750757\u002F657130a9-50f2-486d-b6d5-b78bcec5e6e2.png\" alt=\"Face Recognition SDK Logo\" width=\"200\"\u002F>\n  \n  # Open Source Face Recognition SDK\n  \n  **The world's first completely free and open-source face recognition SDK for Windows and Linux**\n  \n  [![Platform](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fplatform-Windows%20%7C%20Linux-blue.svg)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FOpen-Source-Face-Recognition-SDK)\n  [![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.9+-blue.svg)](https:\u002F\u002Fwww.python.org\u002F)\n  [![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-Open%20Source-green.svg)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FOpen-Source-Face-Recognition-SDK)\n  [![Privacy](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fprivacy-On--Premise%20Only-brightgreen.svg)](https:\u002F\u002Ffaceplugin.com\u002F)\n\u003C\u002Fdiv>\n\n---\n\n## 🚀 Overview\n\nThe **Open Source Face Recognition SDK** by [Faceplugin](https:\u002F\u002Ffaceplugin.com\u002F) is a powerful, privacy-focused solution for integrating face recognition capabilities into your applications. Built with deep learning models, this SDK provides high-accuracy face detection and recognition while ensuring complete data privacy through on-premise processing.\n\n### ✨ Key Features\n\n- 🔒 **100% On-Premise**: All processing happens locally - no data leaves your device\n- 🎯 **High Accuracy**: Powered by state-of-the-art deep learning models\n- ⚡ **Real-Time Processing**: Fast face detection and recognition capabilities\n- 🔧 **Easy Integration**: Simple Python APIs for seamless development\n- 🌐 **Cross-Platform**: Compatible with Windows and Linux systems\n- 📱 **GPU Optional**: Works efficiently on CPU-only systems\n- 🆓 **Completely Free**: Open source with no licensing fees\n\n### 🎯 Current Capabilities\n\n- Face detection and bounding box extraction\n- Facial landmark detection\n- Feature embedding generation\n- Face similarity comparison\n- Support for multiple image formats (JPG, PNG, etc.)\n\n---\n\n## 🛠️ Installation\n\n### Prerequisites\n\n- **Python 3.9 or higher**\n- **Anaconda** (recommended for dependency management)\n- **Windows or Linux** operating system\n\n### Setup Instructions\n\n1. **Install Anaconda** (if not already installed)\n   ```bash\n   # Download from: https:\u002F\u002Fwww.anaconda.com\u002Fproducts\u002Fdistribution\n   ```\n\n2. **Create and activate conda environment**\n   ```bash\n   conda create -n facesdk python=3.9\n   conda activate facesdk\n   ```\n\n3. **Install dependencies**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **Test the installation**\n   ```bash\n   python run.py\n   ```\n\n---\n\n## 📖 Quick Start\n\n### Basic Usage\n\n```python\nfrom face_recognition_sdk import FaceRecognition\n\n# Initialize the SDK\nface_sdk = FaceRecognition()\n\n# Process an image\nimage_path = \"path\u002Fto\u002Fyour\u002Fimage.jpg\"\nface_info = face_sdk.GetImageInfo(image_path, faceMaxCount=10)\n\n# Compare two faces\nsimilarity = face_sdk.get_similarity(feature1, feature2)\n```\n\n### Example: Face Comparison\n\n```python\n# Compare two images\nimage1 = \"test\u002F1.jpg\"\nimage2 = \"test\u002F2.png\"\n\n# Get face information from both images\nfaces1 = face_sdk.GetImageInfo(image1, faceMaxCount=1)\nfaces2 = face_sdk.GetImageInfo(image2, faceMaxCount=1)\n\nif faces1 and faces2:\n    # Compare the first face from each image\n    similarity = face_sdk.get_similarity(faces1[0]['embedding'], faces2[0]['embedding'])\n    print(f\"Similarity: {similarity}%\")\n    \n    # Check if it's the same person (threshold = 75)\n    is_same_person = similarity >= 75\n    print(f\"Same person: {is_same_person}\")\n```\n\n---\n\n## 🔧 API Reference\n\n### Core Functions\n\n#### `GetImageInfo(image_path, faceMaxCount)`\nExtracts face information from an image.\n\n**Parameters:**\n- `image_path` (str): Path to the input image\n- `faceMaxCount` (int): Maximum number of faces to detect\n\n**Returns:**\n- List of dictionaries containing:\n  - `bbox`: Face bounding box coordinates\n  - `landmarks`: Facial landmark points\n  - `embedding`: Feature embedding vector\n\n#### `get_similarity(feature1, feature2)`\nCompares two face feature embeddings.\n\n**Parameters:**\n- `feature1` (array): First face embedding\n- `feature2` (array): Second face embedding\n\n**Returns:**\n- Similarity score (0-100), where higher values indicate greater similarity\n\n### Configuration\n\n- **Default Threshold**: 75 (for determining if two faces belong to the same person)\n- **Supported Formats**: JPG, PNG, BMP, TIFF\n- **Face Detection**: Automatic detection of multiple faces per image\n\n---\n\n## 🎯 Use Cases\n\nThis SDK is ideal for various applications:\n\n### 🔐 Security & Authentication\n- **Access Control Systems**: Secure entry points with face recognition\n- **User Authentication**: Biometric login for applications\n- **Surveillance**: Real-time monitoring and alerting\n\n### 👥 Business Applications\n- **Time & Attendance**: Automated employee check-in\u002Fcheck-out\n- **Customer Analytics**: Retail customer tracking and analytics\n- **Smart Offices**: Automated visitor management\n\n### 📱 Mobile & IoT\n- **Smart Devices**: Integration with IoT devices\n- **Mobile Apps**: Face recognition in mobile applications\n- **Augmented Reality**: AR applications with facial recognition\n\n---\n\n## 🏢 Enterprise Solutions\n\nFor higher accuracy requirements and enterprise features, contact us for our commercial SDK offerings:\n\n- **Enhanced Accuracy Models**: Superior recognition performance\n- **Liveness Detection**: Anti-spoofing capabilities\n- **Multi-Platform Support**: Android, iOS, Web, and more\n- **Technical Support**: Professional assistance and documentation\n\n**Contact us at:** [info@faceplugin.com](mailto:info@faceplugin.com)\n\n---\n\n## 📚 Related Products\n\nExplore our complete suite of face recognition and biometric solutions:\n\n### Mobile SDKs\n- [Android (Java\u002FKotlin)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-Android)\n- [iOS (Objective-C\u002FSwift)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-iOS)\n- [React Native](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-React-Native)\n- [Flutter](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-Flutter)\n\n### Web & Desktop\n- [JavaScript](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-LivenessDetection-Javascript)\n- [React](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-LivenessDetection-React)\n- [Vue.js](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-LivenessDetection-Vue)\n- [.NET MAUI](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-.Net)\n- [.NET WPF](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-WPF-.Net)\n\n### Specialized Solutions\n- [Liveness Detection](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFace-Liveness-Detection-SDK)\n- [Palm Recognition](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FPalm-Recognition)\n- [ID Card Recognition](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FID-Card-Recognition)\n- [Document Liveness Detection](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FID-Document-Liveness-Detection)\n\n---\n\n## 🤝 Support & Contact\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"mailto:info@faceplugin.com\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEmail-info@faceplugin.com-blue.svg?logo=gmail\" alt=\"Email\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Ft.me\u002Ffaceplugin\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTelegram-@faceplugin-blue.svg?logo=telegram\" alt=\"Telegram\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwa.me\u002F+19382025720\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWhatsApp-faceplugin-green.svg?logo=whatsapp\" alt=\"WhatsApp\"\u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n### 📞 Get in Touch\n- **Email**: [info@faceplugin.com](mailto:info@faceplugin.com)\n- **Telegram**: [@faceplugin](https:\u002F\u002Ft.me\u002Ffaceplugin)\n- **WhatsApp**: [+1 (938) 202-5720](https:\u002F\u002Fwa.me\u002F+19382025720)\n- **Website**: [faceplugin.com](https:\u002F\u002Ffaceplugin.com\u002F)\n\n---\n\n\u003Cdiv align=\"center\">\n  \u003Csub>Made with ❤️ by \u003Ca href=\"https:\u002F\u002Ffaceplugin.com\">Faceplugin\u003C\u002Fa>\u003C\u002Fsub>\n\u003C\u002Fdiv>\n","\u003Cdiv align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-Javascript\u002Fassets\u002F160750757\u002F657130a9-50f2-486d-b6d5-b78bcec5e6e2.png\" alt=\"人脸识别SDK Logo\" width=\"200\"\u002F>\n  \n  # 开源人脸识别SDK\n  \n  **全球首个完全免费且开源的人脸识别SDK，支持Windows和Linux系统**\n  \n  [![平台](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fplatform-Windows%20%7C%20Linux-blue.svg)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FOpen-Source-Face-Recognition-SDK)\n  [![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.9+-blue.svg)](https:\u002F\u002Fwww.python.org\u002F)\n  [![许可证](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-开源-green.svg)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FOpen-Source-Face-Recognition-SDK)\n  [![隐私](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F隐私-仅限本地处理-brightgreen.svg)](https:\u002F\u002Ffaceplugin.com\u002F)\n\u003C\u002Fdiv>\n\n---\n\n## 🚀 概述\n\n由[Faceplugin](https:\u002F\u002Ffaceplugin.com\u002F)开发的**开源人脸识别SDK**，是一款专注于隐私保护的解决方案，可将人脸识别功能集成到您的应用程序中。基于深度学习模型构建，该SDK在保证数据隐私（通过本地处理）的同时，提供高精度的人脸检测与识别能力。\n\n### ✨ 核心特性\n\n- 🔒 **100% 本地处理**：所有处理均在本地完成 - 数据不会离开您的设备\n- 🎯 **高精度**：基于最先进的深度学习模型\n- ⚡ **实时处理**：快速的人脸检测与识别能力\n- 🔧 **易集成**：通过简单的Python API实现无缝开发\n- 🌐 **跨平台**：兼容Windows和Linux系统\n- 📱 **可选GPU**：在仅CPU运行的系统上也能高效工作\n- 🆓 **完全免费**：开源无许可费用\n\n### 🎯 当前功能\n\n- 人脸检测与边界框提取\n- 面部关键点检测\n- 特征嵌入生成\n- 人脸相似度比较\n- 支持多种图像格式（JPG、PNG等）\n\n---\n\n## 🛠️ 安装\n\n### 先决条件\n\n- **Python 3.9 或更高版本**\n- **Anaconda**（推荐用于依赖管理）\n- **Windows 或 Linux** 操作系统\n\n### 安装步骤\n\n1. **安装Anaconda**（如未安装）\n   ```bash\n   # 下载地址：https:\u002F\u002Fwww.anaconda.com\u002Fproducts\u002Fdistribution\n   ```\n\n2. **创建并激活conda环境**\n   ```bash\n   conda create -n facesdk python=3.9\n   conda activate facesdk\n   ```\n\n3. **安装依赖**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **测试安装**\n   ```bash\n   python run.py\n   ```\n\n---\n\n## 📖 快速入门\n\n### 基本用法\n\n```python\nfrom face_recognition_sdk import FaceRecognition\n\n# 初始化SDK\nface_sdk = FaceRecognition()\n\n# 处理图像\nimage_path = \"path\u002Fto\u002Fyour\u002Fimage.jpg\"\nface_info = face_sdk.GetImageInfo(image_path, faceMaxCount=10)\n\n# 比较两个面孔\nsimilarity = face_sdk.get_similarity(feature1, feature2)\n```\n\n### 示例：人脸比较\n\n```python\n# 比较两张图片\nimage1 = \"test\u002F1.jpg\"\nimage2 = \"test\u002F2.png\"\n\n# 获取两张图片的人脸信息\nfaces1 = face_sdk.GetImageInfo(image1, faceMaxCount=1)\nfaces2 = face_sdk.GetImageInfo(image2, faceMaxCount=1)\n\nif faces1 and faces2:\n    # 比较每张图片中的第一个面孔\n    similarity = face_sdk.get_similarity(faces1[0]['embedding'], faces2[0]['embedding'])\n    print(f\"相似度: {similarity}%\")\n    \n    # 判断是否为同一个人（阈值 = 75）\n    is_same_person = similarity >= 75\n    print(f\"同一个人: {is_same_person}\")\n```\n\n---\n\n## 🔧 API 参考\n\n### 核心函数\n\n#### `GetImageInfo(image_path, faceMaxCount)`\n从图像中提取人脸信息。\n\n**参数：**\n- `image_path` (str)：输入图像路径\n- `faceMaxCount` (int)：检测的最大人脸数\n\n**返回：**\n- 包含以下字段的字典列表：\n  - `bbox`：人脸边界框坐标\n  - `landmarks`：面部关键点坐标\n  - `embedding`：特征嵌入向量\n\n#### `get_similarity(feature1, feature2)`\n比较两个面孔的特征嵌入。\n\n**参数：**\n- `feature1` (数组)：第一个面孔的嵌入\n- `feature2` (数组)：第二个面孔的嵌入\n\n**返回：**\n- 相似度分数（0-100），数值越高表示越相似\n\n### 配置\n\n- **默认阈值**：75（用于判断两个面孔是否属于同一个人）\n- **支持格式**：JPG、PNG、BMP、TIFF\n- **人脸检测**：自动检测每张图像中的多个人脸\n\n---\n\n## 🎯 应用场景\n\n该SDK适用于多种应用：\n\n### 🔐 安全与认证\n- **门禁控制系统**：通过人脸识别实现安全入口\n- **用户认证**：应用程序的生物识别登录\n- **监控**：实时监控与警报\n\n### 👥 商业应用\n- **考勤系统**：自动化员工打卡\n- **客户分析**：零售客户追踪与分析\n- **智能办公室**：自动化访客管理\n\n### 📱 移动与物联网\n- **智能设备**：与物联网设备集成\n- **移动应用**：移动应用中的人脸识别\n- **增强现实**：AR应用中的面部识别\n\n---\n\n## 🏢 企业解决方案\n\n对于更高精度需求和企业功能，请联系我们获取商业SDK：\n\n- **增强精度模型**：更优越的识别性能\n- **活体检测**：防伪能力\n- **多平台支持**：Android、iOS、Web等\n- **技术支持**：专业协助与文档\n\n**联系方式**：[info@faceplugin.com](mailto:info@faceplugin.com)\n\n---\n\n## 📚 相关产品\n\n探索我们完整的人脸识别和生物识别解决方案：\n\n### 移动SDKs\n- [Android (Java\u002FKotlin)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-Android)\n- [iOS (Objective-C\u002FSwift)](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-iOS)\n- [React Native](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-React-Native)\n- [Flutter](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-Flutter)\n\n### Web & 台式机\n- [JavaScript](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-LivenessDetection-Javascript)\n- [React](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-LivenessDetection-React)\n- [Vue.js](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-LivenessDetection-Vue)\n- [.NET MAUI](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-.Net)\n- [.NET WPF](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFaceRecognition-WPF-.Net)\n\n### 专用解决方案\n- [活体检测](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FFace-Liveness-Detection-SDK)\n- [掌纹识别](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FPalm-Recognition)\n- [身份证识别](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FID-Card-Recognition)\n- [文档活体检测](https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd\u002FID-Document-Liveness-Detection)\n\n## 🤝 支持与联系\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"mailto:info@faceplugin.com\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEmail-info@faceplugin.com-blue.svg?logo=gmail\" alt=\"Email\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Ft.me\u002Ffaceplugin\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTelegram-@faceplugin-blue.svg?logo=telegram\" alt=\"Telegram\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwa.me\u002F+19382025720\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWhatsApp-faceplugin-green.svg?logo=whatsapp\" alt=\"WhatsApp\"\u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n### 📞 联系我们\n- **邮件**: [info@faceplugin.com](mailto:info@faceplugin.com)\n- **Telegram**: [@faceplugin](https:\u002F\u002Ft.me\u002Ffaceplugin)\n- **WhatsApp**: [+1 (938) 202-5720](https:\u002F\u002Fwa.me\u002F+19382025720)\n- **网站**: [faceplugin.com](https:\u002F\u002Ffaceplugin.com\u002F)\n\n---\n\n\u003Cdiv align=\"center\">\n  \u003Csub>由 \u003Ca href=\"https:\u002F\u002Ffaceplugin.com\">Faceplugin\u003C\u002Fa> 用心打造\u003C\u002Fsub>\n\u003C\u002Fdiv>","# Open-Source-Face-Recognition-SDK 快速上手指南\n\n## 环境准备\n- **系统要求**：Windows 或 Linux\n- **前置依赖**：\n  - Python 3.9 或更高版本\n  - Anaconda（推荐用于依赖管理）\n  - 国内镜像源建议：`pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple`\n\n## 安装步骤\n1. **安装 Anaconda**  \n   ```bash\n   # 下载地址：https:\u002F\u002Fwww.anaconda.com\u002Fproducts\u002Fdistribution\n   ```\n\n2. **创建并激活环境**  \n   ```bash\n   conda create -n facesdk python=3.9\n   conda activate facesdk\n   ```\n\n3. **安装依赖**  \n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **测试安装**  \n   ```bash\n   python run.py\n   ```\n\n## 基本使用\n```python\nfrom face_recognition_sdk import FaceRecognition\n\n# 初始化 SDK\nface_sdk = FaceRecognition()\n\n# 处理图片\nimage_path = \"path\u002Fto\u002Fyour\u002Fimage.jpg\"\nface_info = face_sdk.GetImageInfo(image_path, faceMaxCount=10)\n\n# 比较人脸\nsimilarity = face_sdk.get_similarity(feature1, feature2)\n```\n\n### 示例：人脸对比\n```python\nimage1 = \"test\u002F1.jpg\"\nimage2 = \"test\u002F2.png\"\n\nfaces1 = face_sdk.GetImageInfo(image1, faceMaxCount=1)\nfaces2 = face_sdk.GetImageInfo(image2, faceMaxCount=1)\n\nif faces1 and faces2:\n    similarity = face_sdk.get_similarity(faces1[0]['embedding'], faces2[0]['embedding'])\n    print(f\"Similarity: {similarity}%\")\n    is_same_person = similarity >= 75\n    print(f\"Same person: {is_same_person}\")\n```","某小型企业需要实现员工考勤系统，但受限于预算和数据安全要求，无法采用商业人脸识别方案。  \n\n### 没有 Open-Source-Face-Recognition-SDK 时  \n- 需要购买商业SDK，年费高达2万元，且需支付额外的云服务费用  \n- 人脸数据需上传至云端处理，存在隐私泄露风险  \n- 开发团队需自行集成深度学习模型，开发周期长达2个月  \n- 识别准确率不足90%，导致考勤记录频繁出错  \n- 系统仅支持Windows平台，无法适配新采购的Linux服务器  \n- CPU处理速度慢，单次识别耗时超过3秒  \n- 需要额外购买GPU卡才能满足实时处理需求  \n\n### 使用 Open-Source-Face-Recognition-SDK 后  \n- 免费获取完整功能，无需支付任何许可费用  \n- 本地部署实现数据闭环，完全避免隐私泄露风险  \n- 提供简单Python API，2天内完成系统集成  \n- 识别准确率提升至98.7%，误判率降低90%  \n- 支持Windows\u002FLinux双平台，无缝适配新服务器  \n- CPU即可实现20帧\u002F秒的实时处理速度  \n- 无需额外硬件投入，降低30%的运维成本  \n\n核心价值：开源SDK以零成本解决了企业级人脸识别系统的隐私安全、开发效率和跨平台兼容性难题。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FFaceplugin-ltd_Open-Source-Face-Recognition-SDK_0bc501a4.png","Faceplugin-ltd","FacePlugIn Ltd","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FFaceplugin-ltd_e85405d5.png","",null,"info@faceplugin.com","https:\u002F\u002Ffaceplugin.com\u002F","https:\u002F\u002Fgithub.com\u002FFaceplugin-ltd",[84],{"name":85,"color":86,"percentage":87},"Python","#3572A5",100,842,108,"2026-04-04T15:53:47","Windows, Linux","未说明",{"notes":94,"python":95,"dependencies":96},"建议使用 conda 管理环境，首次运行需下载约 5GB 模型文件","3.9+",[],[14,13],[99,100,101,102,103,104,105,106],"deep-learning","face-detection","face-landmark-detection","face-recognition","identity-verification","machine-learning","open-source","python","2026-03-27T02:49:30.150509","2026-04-06T06:45:53.665893",[],[]]