OpenCV轻松入门：面向Python

OpenCV轻松入门_python-opencvpdf_opencv

OpenCV轻松入门，面向Python

OpenCV 3.x with Python By Example, 2nd Edition-Packt Publishing(2018).pdf

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementations. Who this book is for This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV and Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors and matrices. What this book covers Chapter 1, Applying Geometric Transformations to Images, explains how to apply geometric transformations to images. In this chapter, we will discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. The chapter will begin with the procedure of installing OpenCV-Python on multiple platforms, such as Mac OS X, Linux, and Windows. You will also learn how to manipulate an image in various ways, such as resizing and changing color spaces. Chapter 2, Detecting Edges and Applying Image Filters, shows how to use fundamental image- processing operators and how we can use them to build bigger projects. We will discuss why we need edge detection and how it can be used in various different ways in computer vision applications. We will discuss image filtering and how we can use it to apply various visual effects to photos. Chapter 3, Cartoonizing an Image, shows how to cartoonize a given image using image filters and other transformations. We will see how to use the webcam to capture a live video stream. We will discuss how to build a real-time application, where we extract information from each frame in the stream and display the result. Preface Chapter 4, Detecting and Tracking Different Body Parts, shows how to detect and track faces in a live video stream. We will discuss the face detection pipeline and see how we can use it to detect and track different parts of the face, such as eyes, ears, mouth, and nose. Chapter 5, Extracting Features from an Image, is about detecting the salient points (called keypoints) in an image. We will discuss why these salient points are important and how we can use them to understand the image's content. We will talk about the different techniques that can be used to detect salient points and extract features from an image. Chapter 6, Seam Carving, shows how to do content-aware image resizing. We will discuss how to detect interesting parts of an image and see how we can resize a given image without deteriorating those interesting parts. Chapter 8, Detecting Shapes and Segmenting an Image, shows how to perform image segmentation. We will discuss how to partition a given image into its constituent parts in the best possible way. You will also learn how to separate the foreground from the background in an image. Chapter 8, Object Tracking, shows you how to track different objects in a live video stream. At the end of this chapter, you will be able to track any object in a live video stream that is captured through the webcam. Chapter 9, Object Recognition, shows how to build an object recognition system. We will discuss how to use this knowledge to build a visual search engine. Chapter 10, Augmented Reality, shows how to build an augmented reality application. By the end of this chapter, you will be able to build a fun augmented reality project using the webcam. Chapter 11, Machine Learning by Artificial Neural Network, shows how to build advanced image classifiers and object recognition using the latest OpenCV implementations. By the end of this chapter, you will be able to understand how neural networks work and how to apply them to machine learning to build advance images tools.

OpenCV with Python By Example 独家书签修正版 无水印pdf 0分

原pdf书签不正常（非发行版pdf），2016.02.16本人对书签进行了修正。 Paperback: 296 pages Publisher: Packt Publishing - ebooks Account (September 2015) Language: English ISBN-10: 1785283936 ISBN-13: 978-1785283932 Build real-world computer vision applications and develop cool demos using OpenCV for Python About This Book Learn how to apply complex visual effects to images using geometric transformations and image filters Extract features from an image and use them to develop advanced applications Build algorithms to help you understand the image content and perform visual searches Who This Book Is For This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on. What You Will Learn Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image Detect and track various body parts such as the face, nose, eyes, ears, and mouth Stitch multiple images of a scene together to create a panoramic image Make an object disappear from an image Identify different shapes, segment an image, and track an object in a live video Recognize an object in an image and build a visual search engine Reconstruct a 3D map from images Build an augmented reality application

OpenCV 3.x with Python By Example(2nd) epub

OpenCV 3.x with Python By Example(2nd) 英文epub 第2版 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Python语言实现+OpenCV3计算机视觉第二版withPython2016.06

基于Python编程语言的opencv，介绍图像变换方法, Python代码实现DEMO。

OpenCV编程入门电子书及配套源码

毛星云的OpenCV编程入门电子书及配套源码，是Opencv入门的经典书籍

OpenCV官方教程中文版（For Python）.pdf

OpenCV官方教程中文版,介绍的很详细，建议初学者可以好好看下。

OpenCV 3计算机视觉 Python语言实现(第二版) PDF+代码

OpenCV 3计算机视觉 Python语言实现(第二版) PDF+代码

OpenCV官方教程中文版(For Python)pdf

OpenCV官方教程中文版（For Python） 段力辉 译 Python-OpenCV

opencv-python(python3.6 64位)

本资源为windows系统下python3.6编译好的opencv，可以直接pip install安装，有需要的可以下载一下。

Python+OpenCv项目代码

Python和OpenCv的项目实践代码，供小伙伴们下载参考。

opencv_python

这个opencv包对应Python3.6版本，也是opencv_python包的最新版本。 opencv广泛用于计算机视觉开发，人脸检测和车牌号识别都是靠opencv算法的巨大支撑。

OpenCV Python中文教程

python上面的opencv程序开发方便快捷，而且借助python的向量库和数学库性能也不差，很适合做验证和测试。这里提供一个比较入门的中文教程。

python-opencv教程中文版

翻译OpenCV 的官方文档，内容全面，对各种的算 法的描述简单易懂，而且不拘泥于长篇大论的数学推导，非常适合想使用 OpenCV 解决实际问题的人，对他们来说具体的数学原理并不重要，重要 是能解决实际问题。

opencv-python 3.4.3.18

opencv-python 3.4.3.18

Opencv-python中文教程

大部分opencv教程都以c++为实现，这本是简单的python实现

openCV python

python OpenCV包，python 3.6不支持，python2.7貌似可用

OpenCV_with_Python_By_Example

OpenCV_with_Python_By_Example

OpenCV with Python By Example.pdf

OpenCV with Python By Example.pdf 带书签无水印。这本书用很多例子教你学opencv，很棒

OpenCV with Python By Example

OpenCV with Python By Example - Prateek Joshi,学习opencv和python的

源码OpenCV 3.x with Python By Example 2nd

Code for OpenCV 3.x with Python By Example - Second Edition: Make the most of OpenCV and Python to build applications for object recognition and augmented reality

Python OpenCV实践+案例教程+代码实现

Python3+OpenCV3+CaseStudy+code，示例入门，上手简单

Learning OpenCV 3 Computer Vision with Python(PACKT,2ed,2015)

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV’s API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application. What You Will Learn Install and familiarize yourself with OpenCV 3’s Python API Grasp the basics of image processing and video analysis Identify and recognize objects in images and videos Detect and recognize faces using OpenCV Train and use your own object classifiers Learn about machine learning concepts in a computer vision context Work with artificial neural networks using OpenCV Develop your own computer vision real-life application

OPENCV電子書-詳細說明理論與實際的應用

OPENCV電子書，此電子檔是一本原文書的電子檔案，詳細說明理論與實際的應用

基于Python+pytorch的图像处理+附完整代码图像处理，能够轻松实现图像的读取、显示、裁剪等还有机器学习等操作

Python+PyTorch：图像处理界的“瑞士军刀” 在图像处理这个充满魔法的世界里，Python和PyTorch这对黄金搭档，就像一位技艺高超的魔法师和一把无所不能的“瑞士军刀”，总能轻松解决各种看似棘手的难题。它们以高效、灵活和强大的特性，引领着图像处理技术的发展潮流，让无数开发者为之倾倒。Python，这位优雅的魔法师，以其简洁易懂的语法和丰富的库资源，赢得了广大开发者喜爱。无论是数据处理、机器学习还是深度学习，Python都能轻松应对，展现出其无与伦比的魅力。在图像处理领域，Python更是如鱼得水，通过OpenCV、PIL等库，能够轻松实现图像的读取、显示、裁剪、缩放、滤波等操作，让图像在指尖起舞。而PyTorch，这把图像处理界的“瑞士军刀”，则以其灵活性和易用性，成为深度学习领域的翘楚。它拥有强大的自动求导功能，能够轻松构建和训练复杂的神经网络模型。在图像处理中，PyTorch能够助力开发者构建出各种高效的图像识别、分割、生成等模型，让图像焕发出新的生机。想象一下，当你掌握了Python和PyTorch这对黄金搭档，就如同掌握了一把魔法杖和一把瑞士军刀。必然大可作为

python大作业 含爬虫、数据可视化、地图、报告、及源码（2016-2021全国各地区粮食产量）.rar

（含源码及报告）本程序分析了自2016年到2021年（外加）每年我国原油加工的产量，并且分析了2020年全国各地区原油加工量等，含饼状图，柱状图，折线图，数据在地图上显示。运行本程序需要requests、bs4、csv、pandas、matplotlib、pyecharts库的支持，如果缺少某库请自行安装后再运行。文件含6个excel表，若干个csv文件以及一个名字为render的html文件（需要用浏览器打开），直观的数据处理部分是图片以及html文件，可在地图中显示，数据处理的是excel文件。不懂可以扫文件中二维码在QQ里面问。

《点燃我温暖你》中李峋的同款爱心代码

python做的《点燃我温暖你》中李峋的同款爱心代码，最还原的

Python金融量化的高级库：TA-Lib-0.4.24（包含python3.7、3.8、3.9、3.10的32位和64位版本）

TA-Lib（Technical Analysis Library, 即技术分析库）是Python金融量化的高级库，涵盖了150多种股票、期货交易软件中常用的技术分析指标，如MACD、RSI、KDJ、动量指标、布林带等。但很多人安装指标计算ta-lib库就总报错，就可以在这里找到包下载后安装。 文件举例：TA_Lib‑0.4.24‑cp37‑cp37m‑win_amd64.whl 命名解释：包名-版本号-cp37代表适用于python3.7版本-win代表windows平台-amd64表示64位版本（与python版本要一致） 假定文件下载到d盘根目录，使用如下命令进行安装： pip install d:\TA_Lib‑0.4.24‑cp37‑cp37m‑win_amd64.whl 原文链接：https://blog.csdn.net/popboy29/article/details/126140862 建议使用360压缩进行解压。

第十五届蓝桥杯大赛软件赛省赛-PythonB组题目

您正在寻找的是第十五届蓝桥杯大赛软件赛省赛Python B组的题目全集。蓝桥杯大赛作为国内知名的计算机程序设计竞赛，一直以来都以其高水平的题目和严格的评选标准而备受瞩目。本次大赛的Python B组题目更是涵盖了众多编程领域的知识点，无论是算法设计、数据结构还是编程技巧，都考验了参赛者的深厚实力。 这份题目全集以PDF格式呈现，清晰易读，方便您随时查阅和学习。每一道题目都经过精心设计和筛选，旨在考察参赛者的编程思维、问题解决能力以及创新能力。无论您是正在准备参赛的选手，还是对编程感兴趣的爱好者，这份题目集都将为您提供一个极好的学习和挑战的平台。 通过这份题目集，您可以深入了解蓝桥杯大赛的出题风格和难度，熟悉各种编程问题的解题思路和方法，从而提升自己的编程能力和竞技水平。此外，这些题目也是极好的练习材料，可以帮助您巩固和拓展编程知识，提高解决实际问题的能力。 适用人群： 蓝桥杯大赛参赛选手 计算机专业学生 编程爱好者 对算法和数据结构有兴趣的学习者 资源特点： 高质量的题目设计，涵盖广泛的知识点 清晰易读的PDF格式，方便查阅和学习 提供解题思路和方法，有助于提升编程能力

大麦网抢票脚本【Python脚本】

Python脚本，使用Selenium 模拟浏览器操作。 在使用 Chrome 浏览器，用户可以使用鼠标滑动、按键点击以及键盘输入，作为信号输入设备向浏览器传达指令，浏览器收到指令后执行渲染。 这里提到的 Selenium WebDriver 是对浏览器提供的原生 API 进行封装，使用这套 API 可以操控浏览器的开启、关闭，打开网页，操作界面元素，控制 Cookie。简单说就是，可以通过写代码的方式来自动实现用户鼠标和键盘信号的输入。 由此实现模拟人为操作进行登录、验证、刷新网页以及点击购票等操作。