clorps 0.1.0

CLORPS: A module for CLIP, LPIPS, and ORB based image similarity.

CLORPS

CLORPS is a Python package for calculating image similarity based on three complementary techniques: CLIP embeddings, LPIPS (Learned Perceptual Image Patch Similarity), and ORB (Oriented FAST and Rotated BRIEF). It provides a combined similarity score for one image compared to another image or a set of target images. Ideal for image retrieval, similarity-based ranking, and image comparison tasks.

Features

• CLIP Embeddings: Uses OpenAI’s CLIP model for embedding-based similarity.
• LPIPS Similarity: Calculates perceptual similarity using LPIPS.
• ORB Keypoint Matching: Traditional ORB-based similarity for structural comparison.
• Combined Score: Normalizes and combines the three scores for a final similarity metric.

Installation

pip install clorps

Usage

from clorps import CLORPS

# Initialize CLORPS instance
clorps_instance = CLORPS()

# Paths to input and target images
input_image_path = "/path/to/input/image.jpg"
target_image_paths = ["/path/to/target/image1.jpg", "/path/to/target/image2.jpg"]

# Calculate combined similarity scores
combined_scores = clorps_instance.calculate_combined_similarity(input_image_path, target_image_paths)
print("Combined similarity scores:", combined_scores)

Input: Path to the input image and either a single target image path or a list of target image paths. Output: A list of similarity scores, one for each target image.

Requirements:

• Python 3.6+
• torch
• open_clip_torch
• lpips
• numpy
• scikit-learn
• Pillow
• opencv-python

LICENSE This project is licensed under the MIT License.