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Setting Up a Computer Vision Environment

Before writing Computer Vision code you need a working Python environment with the right libraries installed. This topic walks you through setup from scratch.

10 min•By Priygop Team•Updated 2026

What You Need

To start coding Computer Vision in Python you need:

1Python 3.8 or newer
2OpenCV (cv2): the main image processing library
3NumPy: for numerical operations on image arrays
4Matplotlib (optional): for displaying images in Jupyter notebooks

You will also want a code editor. VS Code is recommended because it has good Python support, a built-in terminal, and free extensions for Jupyter notebooks.

Diagram

Loading diagram…

Machine Learning follows a structured pipeline from data to deployment

Installation Commands

# Open your terminal and run these commands:

# Install OpenCV (full version with extra modules)
pip install opencv-python

# Or install the headless version (for servers without display)
# pip install opencv-python-headless

# Install NumPy (usually installed with OpenCV, but to be sure)
pip install numpy

# Install Matplotlib (for Jupyter notebook display)
pip install matplotlib

# Verify installations
python -c "import cv2; print('OpenCV version:', cv2.__version__)"
python -c "import numpy as np; print('NumPy version:', np.__version__)"

# Expected output:
# OpenCV version: 4.x.x
# NumPy version: 1.x.x

Verifying the Setup

# verification.py — run this to confirm everything works
import cv2
import numpy as np

print("✓ OpenCV installed:", cv2.__version__)
print("✓ NumPy installed:", np.__version__)

# Create a simple test image in memory
test_image = np.zeros((100, 100, 3), dtype=np.uint8)
test_image[25:75, 25:75] = [0, 255, 0]  # Green square

print("✓ Can create images:", test_image.shape)
print("✓ Setup complete — you are ready for Computer Vision!")

Topics in This Module

What You Need

To start coding Computer Vision in Python you need:

1Python 3.8 or newer
2OpenCV (cv2): the main image processing library
3NumPy: for numerical operations on image arrays
4Matplotlib (optional): for displaying images in Jupyter notebooks

You will also want a code editor. VS Code is recommended because it has good Python support, a built-in terminal, and free extensions for Jupyter notebooks.

Diagram

Loading diagram…

Machine Learning follows a structured pipeline from data to deployment

Installation Commands

# Open your terminal and run these commands:

# Install OpenCV (full version with extra modules)
pip install opencv-python

# Or install the headless version (for servers without display)
# pip install opencv-python-headless

# Install NumPy (usually installed with OpenCV, but to be sure)
pip install numpy

# Install Matplotlib (for Jupyter notebook display)
pip install matplotlib

# Verify installations
python -c "import cv2; print('OpenCV version:', cv2.__version__)"
python -c "import numpy as np; print('NumPy version:', np.__version__)"

# Expected output:
# OpenCV version: 4.x.x
# NumPy version: 1.x.x

Verifying the Setup

# verification.py — run this to confirm everything works
import cv2
import numpy as np

print("✓ OpenCV installed:", cv2.__version__)
print("✓ NumPy installed:", np.__version__)

# Create a simple test image in memory
test_image = np.zeros((100, 100, 3), dtype=np.uint8)
test_image[25:75, 25:75] = [0, 255, 0]  # Green square

print("✓ Can create images:", test_image.shape)
print("✓ Setup complete — you are ready for Computer Vision!")

Topics in This Module