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Practical Computer Vision

Practical Computer Vision

By : Abhinav Dadhich
1.5 (2)
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Practical Computer Vision

Practical Computer Vision

1.5 (2)
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)
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Datasets and libraries

In this appendix, we won't use a specific dataset; rather, we will use example values to show the workings. The libraries used are NumPy and scipy. In Chapter 2, Libraries, Development Platform, and Datasets we saw the installation of the Anaconda tool, which has both NumPy and SciPy; therefore, there is no need of a new installation.

If Anaconda is not installed, then to install Numpy and SciPy, use the following command:

pip install numpy scipy

To plot a figure, we will use matplotlib. This also comes with Anaconda; however, if there is a need for installation, use the following command:

pip install matplotlib

To begin with the codes in the chapter, we will use this common import:

import numpy as np
import scipy
import matplotlib.pyplot as plt

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