OpenCV: Computer Vision Projects with Python

Book Description

Get savvy with OpenCV and actualize cool computer vision applications

About This Book

• Use OpenCV’s Python bindings to capture video, manipulate images, and track objects
• Learn about the different functions of OpenCV and their actual implementations.
• Develop a series of intermediate to advanced projects using OpenCV and Python

Who This Book Is For

This learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV’s application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV.

What You Will Learn

• Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect – all on Windows, Mac or Ubuntu
• Apply “curves” and other color transformations to simulate the look of old photos, movies, or video games
• Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image
• Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
• Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
• Detect and recognize street signs using a cascade classifier and support vector machines (SVMs)
• Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs
• Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features

In Detail

OpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3’s Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, we’ll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

• OpenCV Computer Vision with Python by Joseph Howse
• OpenCV with Python By Example by Prateek Joshi
• OpenCV with Python Blueprints by Michael Beyeler

Style and approach

This course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3’s Python API, and develop superb computer vision applications. Through this comprehensive course, you’ll learn to create computer vision applications from scratch to finish and more!.

Table of Contents

Module 1
Chapter 1. Setting Up Opencv
Chapter 2. Handling Files, Cameras, And Guis
Chapter 3. Filtering Images
Chapter 4. Tracking Faces With Haar Cascades
Chapter 5. Detecting Foreground/Background Regions And Depth

Module 2
Chapter 1. Detecting Edges And Applying Image Filters
Chapter 2. Cartoonizing An Image
Chapter 3. Detecting And Tracking Different Body Parts
Chapter 4. Extracting Features From An Image
Chapter 5. Creating A Panoramic Image
Chapter 6. Seam Carving
Chapter 7. Detecting Shapes And Segmenting An Image
Chapter 8. Object Tracking
Chapter 9. Object Recognition
Chapter 10. Stereo Vision And 3D Reconstruction
Chapter 11. Augmented Reality

Module 3
Chapter 1. Fun With Filters
Chapter 2. Hand Gesture Recognition Using A Kinect Depth Sensor
Chapter 3. Finding Objects Via Feature Matching And Perspective Transforms
Chapter 4. 3D Scene Reconstruction Using Structure From Motion
Chapter 5. Tracking Visually Salient Objects
Chapter 6. Learning To Recognize Traffic Signs
Chapter 7. Learning To Recognize Emotions On Faces

中文：

书名：OpenCV: Computer Vision Projects with Python

Get savvy with OpenCV and actualize cool computer vision applications

About This Book

• 使用OpenCV的Python绑定来捕获视频、处理图像和跟踪对象
• 了解OpenCV的不同功能及其实际实现。
• 使用OpenCV和Python开发一系列中高级项目

这本书是为谁写的

这条学习之路是为那些对Python有一定实用知识并想要试用OpenCV的人准备的。此学习途径将带您从使用OpenCV的计算机视觉应用程序的初学者成长为专家。OpenCV的应用程序非常庞大，而这条学习路径是让您彻底熟悉OpenCV的最好资源。

你将学到什么

• 在Windows、Mac或Ubuntu上安装OpenCV和相关软件，如Python、NumPy、SciPy、OpenNI和SensorKinect
• 应用曲线和其他颜色变换来模拟旧照片、电影或视频游戏的外观
• 对图像应用几何变换、执行图像过滤并将图像转换为类似卡通的图像
• 实时识别手势，并根据Microsoft Kinect传感器的输出执行手形分析
• 利用2D摄像机运动和常用的摄像机重新投影技术重建3D真实场景
• Detect and recognize street signs using a cascade classifier and support vector machines (SVMs)
• 基于卷积神经网络和支持向量机的人脸表情识别
• 增强您的OpenCV2技能并学习如何使用新的OpenCV3功能

In Detail

OpenCV是一种最先进的计算机视觉库，允许进行各种图像和视频处理操作。OpenCV for Python使我们能够实时运行计算机视觉算法。这条学习路径建议教授以下主题。首先，我们将学习如何开始使用OpenCV和OpenCV3的Python API，并开发一个跟踪身体部位的计算机视觉应用程序。然后，我们将构建令人惊叹的中级计算机视觉应用程序，例如使对象从图像中消失，识别不同的形状，从图像重建3D地图，以及构建增强现实应用程序。最后，我们将转向更高级的项目，例如手势识别，跟踪视觉上突出的对象，以及分别使用支持向量机和多层感知器识别人脸上的交通标志和情绪。

此学习路径将Packt必须提供的一些最好的功能组合在一个完整的、经过精心策划的程序包中。它包括来自以下Packt产品的内容：

• 用Python实现OpenCV计算机视觉，作者：Joseph Howse
• OpenCV with Python By Example by Prateek Joshi
• 由Michael Beyeler提供的OpenCV和Python蓝图

风格和方法

本课程旨在创建一条流畅的学习途径，教您如何入门，学习如何开始使用OpenCV和OpenCV 3&8217；的Python API，并开发出色的计算机视觉应用程序。通过这门综合性课程，您将学习从头到尾创建计算机视觉应用程序，甚至更多！

Table of Contents

Module 1
第1章.设置OpenCV
第2章.处理文件、相机和图形用户界面
Chapter 3. Filtering Images
第4章：使用Haar Cascade跟踪人脸
第5章.检测前景/背景区域和深度

Module 2
第1章.边缘检测和图像滤镜的应用
第2章：将图像动画化
第三章.检测和跟踪身体的不同部位
第4章.从图像中提取特征
Chapter 5. Creating A Panoramic Image
第六章.缝线雕刻
第7章.形状检测和图像分割
第8章.目标跟踪
第9章.物体识别
第十章立体视觉和三维重建
第11章.增强现实

Module 3
第1章：滤镜的乐趣
第二章：使用Kinect深度传感器进行手势识别
第3章：通过特征匹配和透视变换查找对象
第四章：基于运动结构的三维场景重建
第5章：跟踪视觉上突出的对象
第六章：学习识别交通标志
第七章：学会识别面部表情