This is the code repository for Practical Computer Vision, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
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.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
import numpy as np
import matplotlib.pyplot as plt
import cv2The list of software needed for this book is as follows: Anaconda distribution v5.0.1 OpenCV v3.3.0 TensorFlow v1.4.0 Keras v2.1.2
To run all of the code effectively, Ubuntu 16.04 is preferable, with Nvidia GPU and at least 4 GB of RAM. The code will also run without GPU support.
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