Setting Up Your Development Environment

To start experimenting with computer vision, you'll need a place to write and run code. This involves setting up a development environment on your computer. Think of this as preparing your digital workbench with the necessary tools. For this course, and indeed for much of the computer vision field, we will use the Python programming language along with a few specialized libraries.

Python is a popular choice due to its readability, extensive collection of libraries, and strong community support. Libraries are pre-written collections of code that provide functionalities we can readily use, saving us from writing everything from scratch.

Why Use a Dedicated Environment?

Before installing anything, it's highly recommended to create an isolated environment for your project. Why? Different projects might require different versions of the same library. Installing everything globally (directly into your main Python installation) can lead to conflicts down the line. A virtual environment acts like a self-contained workspace for each project, keeping its dependencies separate.

Essential Tools

We'll need the following components:

1. Python: The core programming language. We'll assume you have Python 3 installed. If not, you can download it from the official Python website (python.org). During installation on Windows, make sure to check the box that says "Add Python to PATH".
2. pip: Python's package installer. It usually comes bundled with Python 3. We'll use pip to install the required libraries.
3. Virtual Environment Tool: Python includes venv for creating virtual environments.
4. OpenCV: The primary library for computer vision tasks. It provides a vast range of functions for image and video analysis.
5. NumPy: A fundamental library for numerical operations in Python. Images are essentially numerical data (arrays of pixel values), and NumPy provides efficient ways to manipulate these arrays. OpenCV relies heavily on NumPy.
6. Matplotlib: A library for creating plots and visualizations. We'll use it occasionally to display images or graphs like histograms.

Setting Up Your Workspace Step-by-Step

Let's walk through the setup process using the command line or terminal.

1. Open Your Terminal or Command Prompt:

   • On Windows, search for cmd or PowerShell.
   • On macOS, open the Terminal application (found in Applications > Utilities).
   • On Linux, open your preferred terminal emulator (e.g., gnome-terminal, konsole).

2. Create a Project Directory: It's good practice to have a dedicated folder for your course work.

   # Create a directory (e.g., 'cv_intro') and navigate into it
   mkdir cv_intro
   cd cv_intro
   

3. Create a Virtual Environment: We'll use Python's built-in venv module. We'll name our environment env (a common convention).

   # On macOS/Linux
   python3 -m venv env
   
   # On Windows
   python -m venv env
   

   This command creates a new directory named env containing a copy of the Python interpreter and a place to install libraries.

4. Activate the Virtual Environment: You need to activate the environment each time you work on the project in a new terminal session.

   # On macOS/Linux (bash/zsh)
   source env/bin/activate
   
   # On Windows (Command Prompt)
   .\env\Scripts\activate.bat
   
   # On Windows (PowerShell)
   .\env\Scripts\Activate.ps1
   

   Once activated, your terminal prompt will usually change to show the environment name (e.g., (env) Your-Computer:cv_intro user$). This indicates that python and pip commands will now use the versions within this isolated environment.

5. Install Libraries: Now, use pip to install OpenCV, NumPy, and Matplotlib. We'll install the main OpenCV module (opencv-python) which includes NumPy as a dependency, and also Matplotlib.

   pip install opencv-python matplotlib
   

   pip will download and install the specified packages and their dependencies into your active virtual environment.

6. Verify the Installation: A quick way to check if everything is installed correctly is to start a Python interpreter and try importing the libraries.

   • Type python (or python3 on some systems) in your activated terminal and press Enter. You should see the Python prompt (>>>).
   • Try importing the libraries:

     >>> import cv2
     >>> import numpy
     >>> import matplotlib
     >>> print(cv2.__version__)
     # Expected output: A version number, e.g., 4.9.0 (or similar)
     >>> exit()
     

   If you don't get any ModuleNotFoundError messages and the cv2.__version__ command prints a version number, your environment is ready! Type exit() to leave the Python interpreter.

Note on OpenCV Packages: You might see different OpenCV packages available via pip (e.g., opencv-contrib-python). For this introductory course, opencv-python contains the core functionalities we need. The contrib package includes additional, often experimental, modules.

Alternative: Using Anaconda

Many in data science and machine learning prefer using Anaconda, which is a distribution of Python and R that includes its own package manager (conda) and environment management system. If you already use Anaconda, you can create an environment and install packages using:

# Create a new environment named 'cv_intro_env' with python
conda create --name cv_intro_env python=3.9 # Or your preferred Python 3 version

# Activate the environment
conda activate cv_intro_env

# Install libraries (often uses different channel configurations)
conda install -c conda-forge opencv numpy matplotlib

While Anaconda is powerful, for simplicity and standard Python practice, we'll primarily refer to the pip and venv method throughout this course. Choose the method you are most comfortable with.

You now have a working Python environment equipped with the essential tools for computer vision. In the next section, we'll use this setup to load and display our first digital image. Remember to activate your virtual environment (source env/bin/activate or .\env\Scripts\activate) whenever you start a new terminal session to work on the course exercises.