Book Image

Practical Computer Vision

By : Abhinav Dadhich
Book Image

Practical Computer Vision

By: Abhinav Dadhich

Overview of this book

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects. With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.
Table of Contents (12 chapters)

Libraries and installation

Before we begin, it is required that we install each library. There are two major methods of installing a library:

  • We download the source code and build binaries by compiling the code
  • We can directly download binaries and put them in relevant directories

While downloading pre-built binaries is a faster method, however, due to the difference of platforms or non-availability of binaries may force to build a library from source. If readers are using different OS then the mentioned in the following sections, they might come across such a situation. Once installed a library, it can be used with programs or other libraries.

Since it is crucial to have libraries that are not affected by other installations, we will be using Python-based environments in most of the book. This helps in keeping track of libraries installed and also separates different environment...