|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/computer-vision-2022-masterclass-with-opencv4-and-python/
课程评论：没有评论
课程名称：掌握深度学习、OpenCV4 和 Python 的计算机视觉 课程概述： 本课程是进入计算机视觉领域的终极指南。我们将从基础知识开始，包括图像形成和特性，进行基本的图像处理（读取/写入图像和视频 + 图像操作），使计算机视觉应用通过滑块和鼠标事件变得互动，掌握计算机视觉技术（分割、滤波和特征），最后深入学习高级计算机视觉主题，例如目标检测、跟踪和识别。课程最后，我们将开发一个完整的端到端视觉授权系统（安全访问）。 课程结构包括以下主要部分： 1. 计算机视觉基础 2. 图像处理基础（编码） 3. 计算机视觉基础101（理论 + 编码） 4. 高级 [检测]（理论 + 编码） 5. 高级 [跟踪]（理论 + 编码） 6. 项目：PeopleTrackr（人群监控系统） 7. 高级 [识别]（理论 + 编码） 8. 项目：EasyAttend（现场考勤系统） 9. 项目：Secure Access（端到端项目开发与部署） 10. 再见 在从基础到高级的每个主题中，将伴随有编码课程与理论。每个主题都有编程任务，以测试您的知识。我们将利用Python面向对象编程实践，以实现更好的开发效果。 学习成果： - 学会读取/写入图像和视频 + 图像处理 - 使用滑块和鼠标事件创建互动的计算机视觉应用 - 理解和应用计算机视觉技术，如变换、滤波、分割和特征提取 - 理解、训练和部署高级主题，如目标检测、跟踪和识别 - 通过完成每个主题的任务测试自己的知识 项目： - 人群监控系统 PeopleTrackr - 课堂和办公室的现场考勤系统 EasyAttend - 端到端视觉授权系统 Secure Access 算法： - 面部识别算法，如 LBP 和 Dlib 实现 - LBP（快速-不够精确） - Dlib 实现（慢-精确） - 单对象跟踪器 CSRT、KCF - 多对象跟踪器 DeepSort（慢-精确） - 目标检测方法 Haar Cascades（快速-不够精确）和 YoloV3（慢-精确） 计算机视觉技术： - Sift、Orb 特征匹配 - Canny 边缘检测 - 二元、Otsu 和自适应阈值 - Kmeans 分割 - 凸包近似 先修要求： - 软件要求：OpenCV4 - 技能要求：基本 Python 编程 - 动机：积极的学习态度 所有代码参考均可在本课程的 GitHub 仓库中获取。如果您有任何疑问，可以随时与我们联系并查看所有免费的预览内容。
This course is your ultimate guide for entering into the realm of Computer Vision. We will start from the very basics i.e Image Formation and Characteristics, Perform basic image processing (Read/Write Image & Video + Image Manipulation), make CV applications interactive using Trackbars and Mouse events, build your skillset with Computer Vision techniques (Segmentation, Filtering & Features) before finally Mastering Advanced Computer Vision Topics i.e Object Detection, Tracking, and recognition. Right at the end, we will develop a complete end-to-end Visual Authorization System (Secure Access). The course is structured with below main headings.Computer Vision FundamentalsImage Processing Basics (Coding)CV-101 (Theory + Coding)Advanced [Detecion] (Theory + Coding)Advanced [Tracking] (Theory + Coding)Project: PeopleTrackr ( Crowd Monitoring System )Advanced [Recognition] (Theory + Coding)Project: EasyAttend ( Live Attendance System )Project: Secure Access (End-to-end project development & deployment)GoodbyeFrom Basics to Advanced, each topic will accompany a coding session along with theory. Programming assignments are also available for testing your knowledge. Python Object Oriented programming practices will be utilized for better development.Learning Outcomes - Computer VisionRead/Write Image & Video + Image ManipulationInteractive CV applications with Trackbars & MouseEventsLearn CV Techniques i.e (Transformation, Filtering, Segmentation, and Features)Understand, train, and deploy advanced topics i.e (Object Detection, Tracking, and Recognition) Test your knowledge by completing assignments with each topic. [Project-1] PeopleTrackr: Crowd Monitoring System [Project-2] EasyAttend: Live attendance System for Classrooms and offices.[Final-Project] Secure Access: End-to-end Visual Authorization System for your Computer.- AlgorithmsFacial recognition algorithms like LBP and Dlib-ImplementationLBP (Fast-Less accurate)Dlib-Implementation (Slow-Accurate)Single Object TrackersCSRT, KCFMultiple Object TrackersDeepSort (Slow-Accurate)Object DetectionHaar Cascades (Fast-Less accurate)YoloV3 (Slow-Accurate)Computer Vision TechniquesSift Orb Feature MatchingCanny Edge detectionBinary, Otsu, and Adaptive ThresholdingKmeans SegmentationConvex hull ApproximationPre-Course RequirmentsSoftware BasedOpenCV4PythonSkill BasedBasic Python ProgrammingMotivated mind:)All the codes for reference are available on the GitHub repository of this course.Get a good idea by going through all of our free previews available and feel free to contact us in case of any confusion :)
