Basic knowledge of Python and image processing is required to understand and run the code, along with access to a few online image datasets and the book's GitHub link.

Python 3.5+ (Python 3.7.4 was used to test the code) is needed with Anaconda preferably installed for the Windows users, along with Jupyter (to view/run notebooks).

All the code was tested on Windows 10 (Pro) with 32 GB RAM and an Intel i7-series processor. However, the code should require little/no change to be run on Linux.

You will need to install all the required Python packages using pip3.

Access to a GPU is recommended to run the recipes involving training with deep learning (that is, training that involves libraries such as TensorFlow, Keras, and PyTorch) much faster. The code that is best run with a GPU was tested on an Ubuntu 16.04 machine with an Nvidia Tesla K80 GPU (with CUDA 10.1).

A basic math background is also needed to understand the concepts in the book.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

To access the notebooks and images, clone the repository from this URL: https://github.com/PacktPublishing/Python-Image-Processing-Cookbook.

Install Python 3.7 and the necessary libraries as and when required. Install Anaconda/Jupyter and open the notebooks for each chapter. Run the code for each recipe. Follow the instructions for each recipe for any additional steps (for instance, you may need to download a pre-trained model or an image dataset).

Some additional exercises are provided for most of the recipes in a There's more... section to test your understanding. Perform them independently and have fun!