This is a step-by-step tutorial to set up a Python environment for the Computer Vision course. This setup will involve installing Python, essential libraries like OpenCV, NumPy, and Matplotlib, and using a virtual environment to keep your project dependencies organized.

Step 1: Install Python

1. Download Python:

   • Go to the official Python website: https://www.python.org/downloads/
   • Download the latest stable version of Python (ensure it's Python 3.x).

2. Install Python:

   • During installation, check the option “Add Python to PATH” so you can use Python from the command line.
   • Follow the installation wizard.

3. Verify the Installation:

   • Open a terminal (Command Prompt on Windows, Terminal on macOS/Linux).
   • Type the following to verify that Python was installed correctly:
     python --version

Step 2: Install pip (Python's package installer)

pip typically comes with Python, but if it's not installed, follow these steps:

• Check if pip is installed:
  pip --version

• If it's not installed, you can install it by following the official instructions.

Step 3: Set Up a Virtual Environment

A virtual environment helps to isolate your Python projects and avoid conflicts between different dependencies.

1. Install virtualenv (if not already installed):
   pip install virtualenv

2. Create a new virtual environment:

   • Navigate to your project folder in the terminal:
     cd path/to/your/project-folder
   • Create a virtual environment called cv_env:
     virtualenv cv_env

3. Activate the virtual environment:

• Windows:
  cv_env\Scripts\activate

• macOS/Linux:
  source cv_env/bin/activate

  Once activated, your terminal should show the virtual environment name in parentheses (e.g., (cv_env)).

Step 4: Install Required Libraries for Computer Vision

You need to install several essential libraries to work on computer vision tasks:

1. Install OpenCV: OpenCV is a library for real-time computer vision tasks.
   pip install opencv-python

2. Install NumPy: NumPy is used for handling arrays, which are crucial for image manipulation in computer vision.
   pip install numpy

3. Install Matplotlib: Matplotlib is a library for plotting and visualizing images.
   pip install matplotlib

4. Install Scikit-Image: Scikit-Image provides algorithms for image processing tasks.
   pip install scikit-image

5. Install Jupyter Notebook (Optional but Recommended): Jupyter Notebook allows you to write and execute Python code in a web-based environment, which is great for experimenting with code.
   pip install notebook

Step 5: Verify the Installation

Create a Python file (e.g., test_installation.py) or open a Jupyter Notebook and write the following code to check if everything is installed correctly:

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load an image using OpenCV
img = cv2.imread('sample_image.jpg')

# Check if the image was loaded correctly
if img is not None:
    print("OpenCV is working correctly!")
else:
    print("Error loading the image!")

# Display the image using Matplotlib
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title('Test Image')
plt.show()

Run this code (python test_installation.py) in your virtual environment to verify that everything is working. You should see the message “OpenCV is working correctly!” and the image should display in a plot.

Step 6: Deactivate the Virtual Environment

When you're done working in your virtual environment, you can deactivate it by running the following command:

deactivate

Step 7: Managing Dependencies (Optional)

To keep track of all the libraries you’ve installed, you can generate a requirements.txt file:

pip freeze > requirements.txt

This file can later be used to install the same dependencies in another environment by running:

pip install -r requirements.txt

Step 8: Working with Jupyter Notebooks (Optional)

1. Launch Jupyter Notebook: In your terminal, inside the virtual environment, type:
   jupyter notebook

2. Create a New Notebook: In the web interface, click on New and select Python 3 to create a new notebook.

3. Start Coding: Write and execute your Python code in the browser. For example, start by importing libraries:

   import cv2
   import numpy as np
   import matplotlib.pyplot as plt
   

Step 9: Additional Libraries for Deep Learning

Since the course covers deep learning topics, you will also need libraries like TensorFlow or PyTorch. We will be using PyTorch:

Install PyTorch: Follow instructions from PyTorch's official website to install the appropriate version for your system.

Summary

You have now set up a Python environment for your computer vision course. This environment includes essential libraries like OpenCV, NumPy, and Matplotlib. You’ve also created a virtual environment to keep your project dependencies clean and installed Jupyter Notebook for interactive coding. Finally, you have installed PyTorch for deep learning.