This is the code repository for Hands-On Image Processing with Python, published by Packt.

Expert techniques for advanced image analysis and effective interpretation of image data

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.

This book covers the following exciting features:

• Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python
• Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python
• Do morphological image processing and segment images with different algorithms
• Learn techniques to extract features from images and match images
• Write Python code to implement supervised / unsupervised machine learning algorithms for image processing
• Use deep learning models for image classification, segmentation, object detection, transfer learning and neural style transfer

If you feel this book is for you, get your copy today!

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

viewer = viewer.ImageViewer(im)
viewer.show()

Following is what you need for this book: This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.

With the following software and hardware list you can run all code files present in the book (Chapter 1-12).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Python Machine Learning - Second Edition [Packt] [Amazon]

• TensorFlow Machine Learning Cookbook - Second Edition [Packt] [Amazon]

Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses/specializations. He is a regular blogger on his blog sites (https://sandipanweb.wordpress.com/, https://sandipandey.wixsite.com/simplydatascience, https://www.datasciencecentral.com/profile/SandipanDey and https://sandipanumbc.tumblr.com/) and is a machine learning education enthusiast.

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