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. Use the skimage.filters module's unsharp_mask() function with different values of the radius and amount parameters to sharpen an image.
  2. Use the PIL ImageFilter module's UnsharpMask() function with different values of the radius and percentparameters to sharpen an image.
  3. Sharpen a color (RGB) image using the sharpen kernel [[0,-1,0],[-1,5,-1],[0,-1,0]]. (Hint: use SciPy signal module's convolve2d() function for each of the color channels one by one.)
  4. With the SciPy ndimage module, sharpen a color image directly (without sharpening individual color channels one by one).
  5. Compute and display a Gaussian pyramid with the lena gray-scale input image using theskimage.transformmodule'spyramid_laplacian()function.
  1. Construct the Gaussian pyramid with the reduce() function of the transform module of scikit-image and Laplacian pyramid from the Gaussian pyramid and expand() function, with the algorithm discussed.
  2. Compute the Laplacian pyramid for an image and construct the original image from it.
  3. Show...