litecv 0.1.0

A lightweight, fast, and easy-to-use Python computer vision library

LiteCV - Lightweight Computer Vision Library

A lightweight, fast, and easy-to-use Python computer vision library

Features • Installation • Quick Start • Documentation • Examples • Contributing • License

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About

LiteCV is a lightweight Python computer vision library designed for simplicity and performance. Inspired by OpenCV, it provides essential image processing operations, real-time camera capture, and a collection of filters optimized for both desktop and mobile devices. Built on top of industry-standard libraries like Pillow, pygame, and numpy, LiteCV offers a clean, intuitive API for computer vision tasks.

Why LiteCV?

• Lightweight: Minimal dependencies, fast imports
• Easy to Learn: Simple, intuitive API inspired by OpenCV
• Fast: Optimized for real-time processing
• Mobile-Friendly: Designed with mobile devices in mind
• Well-Documented: Comprehensive examples and API docs
• Active Development: Regularly maintained and updated

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Features

Core Image Operations

• ✅ Image I/O: Load, save, and convert images
• ✅ Resizing: Resize by pixels or percentage with quality options
• ✅ Color Conversion: RGB, Grayscale, and color space transformations
• ✅ Enhancements: Brightness, contrast, saturation, sharpness adjustments

Filters & Effects

• 🎨 Basic Filters: Grayscale, Blur, Edges, Sepia
• 🎨 Advanced Filters: Cartoon, Sketch, Thermal, Night Vision, Infrared
• 🎨 Motion Detection: Real-time motion tracking
• 🎨 Customizable: Easy to extend with custom filters

Drawing & Graphics

• 🖼️ Shapes: Draw circles, rectangles, and lines
• 🖼️ Text: Render text with custom fonts and sizes
• 🖼️ Composition: Concatenate and blend images

Camera & Real-Time Processing

• 📹 Live Camera Feed: Real-time camera capture with pygame
• 📹 Filter Application: Apply filters to live video streams
• 📹 Interactive UI: Built-in GUI for camera control
• 📹 Frame Control: Get individual frames or stream frames

Advanced Features

• 🔍 Object Detection: Basic object detection framework
• 🎬 Video Processing: Video frame extraction and processing
• 📊 NumPy Integration: Direct array access for advanced operations
• ⚡ Caching: Smart caching for performance optimization

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Installation

From Local Repository (Development)

cd litecv
pip install -e .

From PyPI (When Published)

pip install litecv

System Requirements

• Python: 3.10 or higher
• Operating System: Windows, macOS, Linux
• Camera (optional): For real-time camera features

Dependencies

• pygame>=2.6.1 - Camera capture and real-time display
• Pillow>=10.0.0 - Image processing
• numpy>=1.26.0 - Array operations

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Quick Start

1. Create and Save an Image

from litecv import new_image

# Create a 400x300 pixel image
img = new_image(400, 300, color='lightblue')

# Add text
img.draw_text('Hello LiteCV!', (50, 50), color='darkblue', size=24)

# Add shapes
img.draw_circle((200, 150), 50, color='red', fill='yellow')
img.draw_rectangle((100, 100), (300, 200), color='green', width=2)

# Save the image
img.save('my_image.jpg')

2. Apply Filters

from litecv import open_image, FilterType, StreamingFilter

# Open an existing image
img = open_image('my_image.jpg')

# Apply a filter
filter = StreamingFilter(FilterType.GRAYSCALE)
result = filter.apply(img.copy())

# Save the result
result.save('my_image_grayscale.jpg')

3. Real-Time Camera

from litecv import RealTimeCameraApp

# Create and start the camera app
app = RealTimeCameraApp(resolution=(800, 600), camera_resolution=(640, 480))
app.start()

# Use keyboard controls:
# 1-9: Switch filters
# 0: Original (no filter)
# ESC: Exit

4. Image Manipulation

from litecv import open_image, concatenate, blend_images

# Open images
img1 = open_image('image1.jpg')
img2 = open_image('image2.jpg')

# Resize
img1.resize(320, 240)

# Adjust properties
img1.brightness(1.2)  # 20% brighter
img1.contrast(0.9)    # 10% lower contrast
img1.saturation(1.5)  # 50% more colorful

# Concatenate images
combined = concatenate([img1, img2], direction='horizontal')
combined.save('combined.jpg')

# Blend images
blended = blend_images(img1, img2, alpha=0.5)
blended.save('blended.jpg')

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Available Filters

LiteCV includes 10+ built-in filters accessible via FilterType enum:

Filter	Description	Use Case
GRAYSCALE	Convert to grayscale	B&W photography, preprocessing
EDGES	Edge detection	Object boundary detection
BLUR	Gaussian blur	Smoothing, privacy masking
SEPIA	Vintage sepia tone	Retro effects
CARTOON	Cartoon effect	Artistic rendering
SKETCH	Pencil sketch	Art simulation
THERMAL	Thermal imaging	Thermal effect
NIGHT_VISION	Night vision effect	Low-light simulation
INFRARED	Infrared imaging	IR effect
MOTION_DETECT	Motion detection	Movement tracking

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Documentation

API Reference

See docs/api.md for complete API documentation including:

• AdvancedLiteImage class methods
• StreamingFilter usage
• CameraFeed real-time control
• Utility functions

Usage Guide

See docs/usage.md for:

• Detailed usage examples
• Best practices
• Performance optimization tips
• Common workflows

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Examples

The examples/ folder contains production-ready demo scripts:

Basic Operations

• basic_image.py - Create and draw on images
• utilities.py - Image concatenation and blending

Filters

• filters_demo.py - Apply all available filters

Real-Time

• camera_app.py - Interactive camera application

Advanced

• object_detection.py - Object detection demo
• video_demo.py - Video frame processing

Branding

• logo_demo.py - Access logo assets programmatically

Running Examples

# Run any example with:
python examples/<filename>.py

# For instance:
python examples/basic_image.py
python examples/filters_demo.py
python examples/camera_app.py

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Project Structure

litecv/
├── litecv/
│   ├── __init__.py          # Package entry point
│   └── _litecv.py           # Core implementation
├── examples/
│   ├── basic_image.py       # Basic operations
│   ├── filters_demo.py      # Filter demonstrations
│   ├── camera_app.py        # Real-time camera
│   ├── object_detection.py  # Detection example
│   ├── utilities.py         # Utility operations
│   ├── video_demo.py        # Video processing
│   ├── logo_demo.py         # Logo utilities
│   └── README.md            # Examples documentation
├── docs/
│   ├── api.md               # Complete API reference
│   └── usage.md             # Usage guide
├── logo/
│   ├── logo.png             # Main logo
│   ├── litecv_logo.svg      # Vector logo
│   └── generate_logo.py     # Logo generation script
├── tests/
│   ├── test_import.py       # Import tests
│   └── test_examples.py     # Example validation
├── README.md                # This file
├── LICENSE                  # MIT License
├── setup.cfg                # Package configuration
└── pyproject.toml           # Build configuration

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Performance Tips

For Real-Time Applications

# Use optimize_speed=True for mobile/real-time
img.resize(320, 240, optimize_speed=True)
img.blur(radius=2, optimize_speed=True)

# Cache numpy arrays to avoid repeated conversions
arr = img.to_numpy()  # Cached internally

Memory Optimization

# Work with copies to preserve originals
filtered = img.copy()
filtered.blur(5)

# Load images in batches
images = [open_image(f) for f in file_list[:10]]

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Troubleshooting

Camera Not Detected

# Check available cameras
import pygame.camera
pygame.camera.init()
cameras = pygame.camera.list_cameras()
print("Available cameras:", cameras)

Import Errors

Ensure all dependencies are installed:

pip install pygame pillow numpy

Image Codec Issues

For specific formats, install additional codecs:

# For WebP support
pip install Pillow-webp

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Related Projects

LowMind

LowMind is a companion project providing AI and machine learning utilities. Install it with:

pip install lowmind

Visit the LowMind documentation for more information.

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Contributing

We welcome contributions! To contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

git clone https://github.com/yourusername/litecv.git
cd litecv
pip install -e .
python -m pytest tests/

Code Style

• Follow PEP 8
• Use type hints where possible
• Write docstrings for all public methods
• Add tests for new features

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Roadmap

• GPU acceleration with CUDA
• More object detection models
• Video file I/O
• Face detection and recognition
• Machine learning integration
• Mobile app support
• Web interface
• Performance benchmarks

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License

LiteCV is licensed under the MIT License - see LICENSE file for details.

MIT License

Copyright (c) 2026 LiteCV Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...

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Changelog

Version 0.1.0 (2026-05-03)

• Initial release
• Core image operations
• 10+ built-in filters
• Real-time camera support
• Object detection framework
• Comprehensive documentation
• 8 example scripts

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Support

• 📖 Documentation
• 💬 Issues
• 📧 Contact: [e-mail]

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Acknowledgments

• Built with Pillow
• Camera support via Pygame
• Array operations with NumPy
• Inspired by OpenCV

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Made with ❤️ by the LiteCV team