This chapter and subsequent chapters are focused on Python (a programming language) and OpenCV (a computer vision library) concepts in connection with computer vision, machine learning, and deep learning techniques (among others). Therefore, Python (https://www.python.org/) and OpenCV (https://opencv.org/) should be installed on your computer. Moreover, some Python packages related to scientific computing and data science should also be installed (for example, NumPy (http://www.numpy.org/) or Matplotlib (https://matplotlib.org/)).

Additionally, it is recommended that you install an integrated development environment (IDE) software package because it facilitates computer programmers with software development. In this sense, a Python-specific IDE is recommended. The de facto Python IDE is PyCharm, which can be downloaded from https://www.jetbrains.com/pycharm...

Python is an interpreted high-level and general-purpose programming language with a dynamic type system and automatic memory management. The official home of the Python programming language is https://www.python.org/. The popularity of Python has risen steadily over the past decade. This is because Python is a very important programming language in some of today's most exciting and challenging technologies. Artificial intelligence (AI), machine learning, neural networks, deep learning, Internet of Things (IoT), and robotics (among others) rely on Python.

Here are some advantages of Python:

• Python is considered a perfect language for scientific computing, mainly for four reasons:
  • It is very easy to understand.
  • It has support (via packages) for scientific computing.
  • It removes many of the complexities other programming languages have.
  • It has a simple...

OpenCV is a programming library with real-time computer vision capabilities and it is free for both academic and commercial use (BSD license). In this section, an introduction about the OpenCV library will be given, including its main modules and other useful information in connection with the library.

OpenCV (since version 2) is divided into several modules, where each module can be understood, in general, as being dedicated to one group of computer vision problems. This division can be seen in the next diagram, where the main modules are shown:

OpenCV modules are shortly described here:

• core: Core functionality. Core functionality is a module defining basic...

OpenCV, Python, and AI-related packages can be installed on most operating systems. We will see how to install these packages by means of different approaches.

Additionally, at the end of this chapter, an introduction to Jupyter Notebook is given due to the popularity of these documents, which can be run to perform data analysis.

In this section, you will see how to install Python, OpenCV, and any other package globally. Specific instructions are given for both Linux and Windows operating systems.

virtualenv (https://pypi.org/project/virtualenv/) is a very popular tool that creates isolated Python environments for Python libraries. virtualenv allows multiple Python projects that have different (and sometimes conflicting) requirements. In a technical way, virtualenv works by installing some files under a directory (for example, env/).

Additionally, virtualenv modifies the PATH environment variable to prefix it with a custom bin directory (for example, env/bin/). Additionally, an exact copy of the Python or Python3 binary is placed in this directory. Once this virtual environment is activated, you can install packages in the virtual environment using pip. virtualenv is also recommended by the PyPA (https://packaging.python.org/guides/tool-recommendations/). Therefore, we will see how to install OpenCV or any other...

In the next section, we are going to create virtual environments with PyCharm, which is a Python IDE. But before doing that, we are going to discuss IDEs. An IDE is a software application that facilitates computer programmers with software development. IDEs present a single program where all the development is done. In connection with Python IDEs, two approaches can be found:

• General editors and IDEs with Python support
• Python-specific editors and IDEs

In the first category (general IDEs), some examples should be highlighted:

• Eclipse + PyDev
• Visual Studio + Python Tools for Visual Studio
• Atom + Python extension

In the second category, here are some Python-specific IDEs:

• PyCharm: One of the best full-featured, dedicated IDEs for Python. PyCharm installs quickly and easily on Windows, macOS, and Linux platforms...

Conda (https://conda.io/docs/) is an open source package-management and environment-management system (provides virtual environment capabilities) that runs on many operating systems (for example, Windows, macOS, and Linux). Conda installs, runs, and updates packages and their dependencies. Conda can create, save, load, and switch between environments.

Anaconda is a downloadable, free, open source, high-performance Python and R distribution. Anaconda comes with conda, conda build, Python, and more than 100 open source scientific packages and their dependencies. Using the conda install command, you can easily install popular open source packages for data science from...

So far, we have seen how to install Python, OpenCV, and a few other packages (numpy and matplotlib) from scratch, or using Anaconda distribution, which includes many popular data-science packages. In this way, some knowledge about the main packages for scientific computing, data science, machine learning, and computer vision is a key point because they offer powerful computational tools. Throughout this book, many Python packages will be used. Not all of the cited packages in this section will, but a comprehensive list is provided for the sake of completeness in order to show the potential of Python in topics related to the content of this book:

• NumPy (http://www.numpy.org/) provides support for large, multi-dimensional arrays. NumPy is a key library in computer vision because...

The Jupyter Notebook an open source web application that allows you to edit and run documents via a web browser. These documents, which are called Notebook documents (or notebooks), contain code (more than 40 programming languages, including Python, are supported) and rich text elements (paragraphs, equations, figures). The Jupyter Notebook can be executed on a local computer or can be installed on a remote server. You can start with notebooks, trying them online or installing the Jupyter Notebook.

First, go to https://jupyter.org/try. You will see something like this:

To try Jupyter with Python online, click on the Python option, or paste this URL into your web browser: https...

The project structure is the way you organize all the files inside a folder in a way that the project best accomplishes the objectives. We are going to start with a .py script (sampleproject.py) that should be with other files in order to complete the information about this script – dependencies, license, how to install it, or how to test it. A common approach for structuring this basic project is as follows:

sampleproject/
│
├── .gitignore
├── sampleproject.py
├── LICENSE
├── README.rst
├── requirements.txt
├── setup.py
└── tests.py

sampleproject.py—if your project is only a single Python source file, then put it into the directory and name it something related to your project.

Based on the minimal project structure that was shown in the previous section, we are going to create our first Python and OpenCV project. This project has the following structure:

helloopencv/
│
├── images/
│
├── .gitignore
├── helloopencv.py
├── LICENSE
├── README.rst
├── requirements.txt
├── setup.py
└── helloopencvtests.py

README.rst (.rst extension) follows a basic structure, which was shown in the previous section. Python and ReStructuredText (RST) are deeply linked—RST is the format of docutils and sphinx (the de facto standard for documenting python code). RST is used both to document objects via docstrings, and to write additional documentation. If you go to...

In this first chapter, we covered the main steps to set up OpenCV and Python to build your computer vision projects. At the beginning of this chapter, we quickly looked at the main concepts in this book – Artificial Intelligence, machine learning, neural networks, and deep learning. Then we explored the OpenCV library, including the history of the library and its main modules. As OpenCV and other packages can be installed in many operating systems and in different ways, we covered the main approaches.

Specifically, we saw how to install Python, OpenCV, and other packages globally or in a virtual environment. In connection with the installation of the packages, we introduced Anaconda/Miniconda and Conda, because we can also create and manage virtual environments. Additionally, Anaconda/Miniconda comes with many open source scientific packages, including SciPy and...

1. What is a virtual environment?
2. What is the connection between pip, virtualenv, pipenv, Anaconda, and conda?
3. What is the Jupyter Notebook?
4. What are the main packages to work with computer vision in Python?
5. What does pip install -r requirements.txt do?
6. What is an IDE and why should you use one during the development of your projects?
7. Under what license is OpenCV published?

The following references will help you dive deeper into concepts presented in this chapter:

• Python Machine Learning:
  • Python Machine Learning, by Sebastian Raschka: https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning

• Python Deep Learning:
  • Python Deep Learning Projects, by Rahul Kumar, Matthew Lamons: https://www.packtpub.com/big-data-and-business-intelligence/python-deep-learning-projects