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

The scikit-image filter.rank module


The scikit-image's filter.rank module provides functions to implement morphological filters; for example, the morphological median filter and morphological contrast enhancement filter. The following sections demonstrate a couple of these filters.

Morphological contrast enhancement

The morphological contrast enhancement filter operates on each pixel by considering only the pixels in a neighborhood defined by a structuring element. It replaces the central pixel either by the local minimum or the local maximum pixel in the neighborhood, depending on which one the original pixel is closest to. The following code block shows a comparison of the output obtained using the morphological contrast enhancement filter and the exposure module's adaptive histogram equalization, with both the filters being local:

from skimage.filters.rank import enhance_contrast

def plot_gray_image(ax, image, title):
    ax.imshow(image, vmin=0, vmax=255, cmap=pylab.cm.gray), ax.set_title...