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

Some popular deep CNNs


In this section, let's discuss popular deep CNNs (for example, VGG-18/19, ResNet, and InceptionNet) used for image classification. The following screenshot shows single-crop accuracies (top-1 accuracy: how many times the correct label has the highest probability predicted by the CNN) of the most relevant entries submitted to the ImageNet challenge, from AlexNet (Krizhevsky et al., 2012), on the far left, to the best performing, Inception-v4 (Szegedy et al., 2016):

Also, we shall train a VGG-16 CNN with Keras to classify the cat images against the dog images.

VGG-16/19

The following screenshot shows the architecture of a popular CNN called VGG-16/19. The remarkable thing about the VGG-16 net is that, instead of having so many hyper-parameters, it lets you use a much simpler network where you focus on just having convolutional layers that are just 3 x 3 filters with a stride of 1 and that always use the same padding and make all the max pooling layers 2 x 2 with a stride...