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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-computer-vision-with-python-3-2-in-1/
课程评论：没有评论
课程名称：Python：计算机视觉与Python 3：2合1 课程概述： 本课程旨在通过学习强大的Python模块，使参与者快速原型设计和开发图像处理及计算机视觉的生产级代码。课程专注于帮助学员构建更智能、更快速、更复杂且更实用的计算机视觉应用程序。您将学习的具体应用包括光学字符识别、物体跟踪以及建立一个可通过互联网访问的计算机视觉即服务平台。此外，您将掌握先进的图像分类技术以及在视频中查找和识别人的技能。本学习路径包括两门完整的课程，分别是《Python 3.x用于计算机视觉》和《Python 3计算机视觉项目》。 在第一门课程中，您将了解图像处理的基础，学习在计算机视觉中常用的特征和过滤器，并实施特征检测算法（如LBP和ORB）。同时，您将理解卷积神经网络以识别图像中的模式。该课程使用了三个图像处理库：Pillow、Scikit-Image和OpenCV，以实现不同的计算机视觉算法。 第二门课程开始于在主要操作系统中设置Anaconda Python及其前沿的第三方计算机视觉库。您将学习先进的技术来对图像进行分类和在人群视频中查找和识别个体。接着，您将学习如何使用OpenCV和TensorFlow等强大的视觉和机器学习工具来增强Python的功能。最后，您将掌握面部特征检测并开发通用图像分类器。 课程结束时，您将能够构建能够在现实场景中有效运作的计算机视觉应用程序。 讲师简介： Saurabh Kapur 是德里印德拉普拉斯科技大学的计算机科学学生，专注于计算机视觉、数值分析和算法设计，对竞争编程充满热情，并积极参与物联网应用的开发。 Matthew Rever 是一家国家实验室的图像处理与计算机视觉工程师，拥有多年自动化复杂科学数据分析和控制先进仪器的经验，并致力于使最新的计算机视觉发展惠及各类开发者。
Python comes with many freely available powerful modules for handling images, mathematical computing, and data mining which makes it an ideal language for rapidly prototyping and developing production-grade codes for image processing and computer vision. If you wish to build computer vision applications that are smarter, faster, more complex, and more practical with Python 3, then you should surely go for this Learning Path. This comprehensive 2-in-1 course aims to equip you to build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that you will learn in this course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet. You will also learn state-of-the-art techniques to classify images, and to find and identify humans within videos. This learning path will give you a versatile range of computer vision techniques with Python 3, which you will put to work in building your own computer vision applications. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Python 3.x for Computer Vision, starts off with an introduction to image processing. You will then learn features and filters in computer vision. You will also implement feature detection algorithms such as LBP and ORB. Finally, you will understand convolutional neural networks to learn patterns in images. Throughout this course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms. The second course, Computer Vision Projects with Python 3, starts off by showing you how to set up Anaconda Python for the major OSes with cutting-edge third-party libraries for computer vision. You will then learn state-of-the-art techniques to classify images and find and identify humans within videos. Next, you will learn to augment Python with the powerful vision and machine learning tools such as OpenCV and TensorFlow. Finally, you will learn to detect facial features and develop a general image classifier.By the end of this Learning Path, you will be able to build computer vision applications that are capable of working in real-world scenarios effectively.About the Authors: Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi. His interests are in computer vision, numerical analysis, and algorithm design. He often spends time-solving competitive programming questions. Saurabh also enjoys working on IoT applications and tinkering with hardware. He likes to spend his free time playing or watching cricket.Matthew Rever is an image processing and computer vision engineer at a major national laboratory. He has years of experience automating the analysis of complex scientific data, as well as the control of sophisticated instruments. He has applied computer vision technology to save a great many hours of valuable human labor. He is also enthusiastic about making the latest developments in computer vision accessible to developers of all backgrounds.
