imgemb 0.2.1

A lightweight Python library for generating image embeddings with semantic search

Imgemb

pypi.org

A lightweight Python library for generating and comparing image embeddings using various methods. This library provides tools for image similarity search, clustering, and comparison.

Features

• Multiple embedding methods:
  • Average Color: Simple RGB color averaging
  • Grid-based: Divides image into grid cells and computes color features
  • Edge-based: Uses Sobel edge detection and histogram features
  • CLIP-based: Semantic embeddings for natural language search
• Command-line interface (CLI) for easy usage
• Normalization options for embeddings
• Tools for finding similar images in a directory
• Support for batch processing
• Semantic Search:
  • Natural language queries for image search
  • Zero-shot image classification
  • Cross-modal understanding between text and images
  • GPU acceleration support

Installation

From PyPI (Recommended)

pip install imgemb

From Source

git clone https://github.com/aryanraj2713/image_embeddings.git
cd image_embeddings
pip install -e ".[dev]"  # Install with development dependencies

Quick Start

Using as a Python Library

from imgemb import ImageEmbedder

# Initialize embedder
embedder = ImageEmbedder(
    method='grid',           # 'average_color', 'grid', or 'edge'
    grid_size=(4, 4),       # For grid method
    normalize=True          # Whether to normalize embeddings
)

# Generate embedding for a single image
embedding = embedder.embed_image('path/to/image.jpg')

# Compare two images
similarity = embedder.compare_images('image1.jpg', 'image2.jpg')

# Find similar images in a directory
similar_images = embedder.find_similar_images(
    'query.jpg',
    'path/to/image/directory',
    top_k=5
)

Semantic Search

from imgemb import SemanticSearcher

# Initialize searcher
searcher = SemanticSearcher()

# Index a directory of images
searcher.index_directory("path/to/images")

# Search using natural language
results = searcher.search("a photo of a dog playing in the park", top_k=5)

# Print results
for path, score in results:
    print(f"{path}: {score:.3f}")

Using the Command Line Interface

1. Compare two images:

imgemb compare image1.jpg image2.jpg --method grid --grid-size 4 4

2. Generate embeddings for images:

imgemb generate path/to/images/ --output embeddings.json --method edge

3. Find similar images:

imgemb find-similar query.jpg image/directory/ -k 5 --method grid

Embedding Methods

Average Color

Computes the mean RGB values of the entire image. Simple but effective for basic color-based similarity.

embedder = ImageEmbedder(method='average_color')

Grid-based

Divides the image into a grid and computes mean RGB values for each cell. Better for capturing spatial color distribution.

embedder = ImageEmbedder(method='grid', grid_size=(4, 4))

Edge-based

Uses Sobel edge detection and histogram features. Good for capturing structural similarities.

embedder = ImageEmbedder(method='edge')

Development

Setup Development Environment

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

Running Tests

pytest tests/ -v

Contributing

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

License

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

Requirements

• Python ≥ 3.8
• OpenCV (opencv-python)
• NumPy
• Matplotlib
• scikit-learn

Citation

If you use this library in your research, please cite:

@software{image_embeddings,
  title = {Image Embeddings: A Lightweight Library for Image Similarity},
  author = {Raj, Aryan},
  year = {2024},
  url = {https://github.com/aryanraj2713/image_embeddings}
}