Book Image

Hands-On Image Processing with Python

By : Sandipan Dey
Book Image

Hands-On Image Processing with Python

By: Sandipan Dey

Overview of this book

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Introducing YOLO v2 


YOLO, is a very popular and fully conventional algorithm that is used for detecting images. It gives a very high accuracy rate compared to other algorithms, and also runs in real time. As the name suggests, this algorithm looks only once at an image. This means that this algorithm requires only one forward propagation pass to make accurate predictions. 

In this section, we will detect objects in images with a fully convolutional network (FCN) deep learning model. Given an image with some objects (for example, animals, cars, and so on), the goal is to detect objects in those images using a pre-trained YOLO model, with bounding boxes.

Many of the ideas are from the two original YOLO papers, available at https://arxiv.org/abs/1506.02640 and https://arxiv.org/abs/1612.08242. But before diving into the YOLO model, let's first understand some prerequisite fundamental concepts.

Classifying and localizing images and detecting objects

Let's first understand the concepts regarding...