Packt.OpenCV.3.x.with.Python.By.Example.2nd.Edition.2018下载

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Packt.OpenCV.3.x.with.Python.By.Example.2nd.Edition.2018.Code
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Packt.OpenCV.3.x.with.Python.By.Example.2nd.Edition.2018.Code

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.

转自：https://china-testing.github.io/opencv_books.html @Machine Learning for OpenCV - 2017.pdf 机器学习不再仅仅是一个流行语，它就在我们身边：从保护您的电子邮件到自动标记图片中的朋友，到预测您喜欢的电影。计算机视觉是当今最令人兴奋的机器学习应用领域之一，深度学习驱动创新系统，如自动驾驶汽车和谷歌的De...

[it-ebooks]电子书列表 [2014]: Learning Objective-C by Developing iPhone Games || Leverage Xcode and Objective-C to develop iPhone games http://it-ebooks.info/book/3544/Learning Web App Devel...

在本章中，我们探讨了在生产系统中部署训练好的 PyTorch 深度学习模型的世界。在下一章中，我们将探讨与在 PyTorch 中使用模型相关的另一个实用方面，这在训练和验证深度学习模型时能极大地节省时间和资源。首先，我们将定义一个符合 Optuna 要求的模型对象。所谓 Optuna 兼容，是指在模型定义代码中添加 Optuna 提供的 API，以便对模型超参数进行参数化。首先，我们导入必要的库，如下所示：optuna库将在整个练习中管理超参数搜索。接下来，我们定义模型架构。

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