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

Questions


  1. For classification of the mnist dataset using an FC layer with Keras, write a Python code fragment to visualize the output layer (what the neural network sees).
  2. For classification of the mnist dataset using the neural network with FC layers only and with the CNN with Keras, we have directly used the test dataset for evaluating the model while training it. Set aside a few thousand images from training images and create a validation dataset and train the model on the remaining images. Use the validation dataset to evaluate the model while training. At the end of training, use the model learned to predict the labels of the test dataset and evaluate the accuracy of the model. Does it increase?
  1. Use VGG-16/19, Resnet-50, and Inception V3 models (from Keras) to train (from scratch) on the mnist training images. What is the maximum accuracy you get on the test images?