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

Edges-based/region-based segmentation


This example, taken from the examples in the scikit-image documentation, demonstrates how to segment objects from a background by first using edge-based and then using region-based segmentation algorithms. The coins image from skimage.data is used as the input image, which shows several coins outlined against a darker background. The next code block displays the grayscale image and its intensity histogram:

coins = data.coins()
hist = np.histogram(coins, bins=np.arange(0, 256), normed=True)
fig, axes = pylab.subplots(1, 2, figsize=(20, 10))
axes[0].imshow(coins, cmap=pylab.cm.gray, interpolation='nearest')
axes[0].axis('off'), axes[1].plot(hist[1][:-1], hist[0], lw=2)
axes[1].set_title('histogram of gray values')
pylab.show()

Edge-based segmentation

In this example, we will try to delineate the contours of the coins usingedge-based segmentation. To do this, the first step is to get the edges of features using the Canny edge detector, demonstrated by the...