Hands-On-Image-Processing-with-Python-Second-Edition

Hands-On Image Processing with Python, Second Edition, Published by Packt

Book Chapters

1. Getting Started with Digital Image Processing
2. Image Manipulation
3. More Image Manipulation
4. Sampling & Fourier Transform
5. Convolution & Spatial / Frequency Domain Filtering
6. Frequency Domain Filtering
7. Image Enhancement
8. More Image Enhancement using Derivatives
9. Image Restoration
10. Morphological Image Processing
11. Image Regsitration - Extracting Features and Descriptors
12. Image Segmentation
13. Classical Machine Learning Methods
14. Deep Learning Methods - Image Classification
15. Deep Learning Methods - Object Detection, and more
16. Generative AI - GAN, Diffusion, and more
17. Additional Problems in Image Processing & Computer Vision

Setting up different Image Processing Libraries in Python

The next few paragraphs describe how to install different image processing libraries and setup the environment for writing codes to process images using the classical image processing techniques in python. In the last few chapters of the book, we shall need to use a different setup when we shall use deep-learning-based methods.

1 Installing pip

We are going to use the pip (or pip3) tool to install the libraries, so we need to install pip first, in case it's not installed. As mentioned here, pip is already installed if we use python 3 >=3.4 downloaded from python.org or if we are working in a Virtual Environment created by virtualenv or pyvenv. Just we need to make sure to upgrade pip. How to install pip for different OS / platforms can be found here.

2 Installing Some Image Processing Libraries in Python

In python, there are many libraries which we can use for image processing. Some of the following libraries we are going to use are as below:

• numpy
• scipy
• scikit-image
• PIL (pillow)
• wand
• opencv-python
• scikit-learn
• mahotas
• matplotlib
• seaborn

The library matplotlib and seaborn will primarily be used for display purpose, where as numpy will be used for storing an image. The scikit-learn library will be used for building machine-learning models for image processing, scipy will be used mainly for image enhancements. The scikit-image, mahotas and opencv will be used for different image processing algorithms.

The following code block shows how the libraries that we are going to use can be downloaded and installed with pip from a python prompt (interactive mode) or in jupyter notebook:

!pip install numpy
!pip install scipy
!pip install scikit-image
!pip install scikit-learn
!pip install matplotlib
!pip install pillow
!pip install mahotas
!pip install opencv-python
!pip install jupyter
!pip install -U wand

There may be some additional installation instructions, depending on the OS platform one is going to use. We suggest the reader to go through the documentation sites for each of the libraries to get a detailed platform-specific installation instructions for each library. For example, for the scikit-image library, detailed installation instructions for different OS platforms can be found here. Also, one should be familiar to websites such as stackoverflow to resolve platform-dependent installation issues for different libraries.

Finally, we can verify whether a library is properly installed or not by importing it from the python prompt. If the library is imported successfully (no error message is thrown), then we don't have any installation issue. We can print the version of the library installed by printing it to the console.

The following code block shows the same for the python library scikit-image.

import skimage
print(skimage.__version__)

0.17.2

As can be seen from above, an example output of the above code block 0.17.2.

The following code-block shows the version of PIL and numpy libraries, respectively, as a tuple:

import PIL
import numpy
PIL.__version__, numpy.__version__

('8.3.2', '1.21.6')

Let's ensure that we have the latest versions for all the libraries.

3 Installing the Anaconda Distribution

We also recommend to download and install the latest version of the anaconda distribution from here, this will eliminate the need of explicit installation of many python packages.

More about installing anaconda for different OS can be found ''.

4 Installing Jupyter Notebook

We are going to use jupyter notebooks to write our python code. So we need to install the jupyter package first from a python prompt with>>> pip install jupyter and then launch the jupyter notebook app in the browser using>>> jupyter notebook where we can create new python notebooks and choose a kernel. If we use anaconda, we do not need to install jupyter explicitly, the latest anaconda distribution comes with jupyter.

More about running jupyter notebooks can be found here.

We can even install a python package from inside a notebook cell, e.g., we can install scipy with the command !pip install scipy

For more information on installing Jupyter please refer to: http://jupyter.readthedocs.io/en/latest/install.html

Code Reproducibility

To ensure reproduciblity, a requirements.txt file has been added corresponding to each chapter, to the book’s GitHub repository.

To install all required packages with compatible versions, run:

pip install -r requirements.txt

This ensures all dependencies match the versions used in the book's code examples.