Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Image Processing with Python

You're reading from   Hands-On Image Processing with Python Expert techniques for advanced image analysis and effective interpretation of image data

Arrow left icon
Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Length 492 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
1. Getting Started with Image Processing 2. Sampling, Fourier Transform, and Convolution FREE CHAPTER 3. Convolution and Frequency Domain Filtering 4. Image Enhancement 5. Image Enhancement Using Derivatives 6. Morphological Image Processing 7. Extracting Image Features and Descriptors 8. Image Segmentation 9. Classical Machine Learning Methods in Image Processing 10. Deep Learning in Image Processing - Image Classification 11. Deep Learning in Image Processing - Object Detection, and more 12. Additional Problems in Image Processing 1. Other Books You May Enjoy Index

Questions


  1. Use the scikit-image library's functions to read a collection of images and display them as a montage.
  2. Use the scipy ndimage and misc modules' functions to zoom, crop, resize, and apply Affine transformation to an image.
  3. Create a Python remake of the Gotham Instagram filter (https://github.com/lukexyz/CV-Instagram-Filters) (hint: manipulate an image with the PIL split(), merge(), and numpy interp() functions to create a channel interpolation (https://www.youtube.com/watch?v=otLGDpBglEA&feature=player_embedded)).
  4. Use scikit-image's warp() function to implement the swirl transform. Note that the swirl transform can also be expressed with the following equations:
  1. Implement the wave transform (hint: use scikit-image's warp()) given by the following:
  1. Use PIL to load an RGB .png file with a palette and convert into a grayscale image. This problem is taken from this post: https://stackoverflow.com/questions/51676447/python-use-pil-to-load-png-file-gives-strange-results/51678271#51678271. Convert the following RGB image (from the VOC2012 dataset) into a grayscale image by indexing the palette:
  1. Make a 3D plot for each of the color channels of the parrot image used in this chapter (hint: use the mpl_toolkits.mplot3d module's plot_surface() function and NumPy's meshgrid() function).
  1. Use scikit-image's transform module's ProjectiveTransform to estimate the homography matrix from a source to a destination image and use the inverse() function to embed the Lena image (or yours) in the blank canvas as shown in the following:

Input Image

Output Image

First try to solve the problems on your own. For your reference, the solutions can be found here: https://sandipanweb.wordpress.com/2018/07/30/some-image-processing-problems/.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime