antidupe 0.0.2

Image deduplicator using CNN, Cosine Similarity, Image Hashing, Structural Similarity Index Measurement, and Euclidean Distance

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Antidupe

Image deduplicator using CNN, Cosine Similarity, Image Hashing, Structural Similarity Index Measurement, and Euclidean Distance

Installation

You can install Antidupe using pip:

pip install antidupe

Usage

Basic Usage

from antidupe import Antidupe
from PIL import Image

# Initialize Antidupe
antidupe = Antidupe()

# Load images (as numpy arrays or PIL.Image objects)
image1 = Image.open("image1.jpg")
image2 = Image.open("image2.jpg")

# Check for duplicates
is_duplicate = antidupe.predict([image1, image2])

if is_duplicate:
    print("Duplicate images detected!")
else:
    print("Images are not duplicates.")

Customizing Thresholds

You can customize the similarity thresholds for each technique during runtime or initialization:

# Initialize Antidupe with custom thresholds
custom_thresholds = {
    'ih': 0.2,   # Image Hash
    'ssim': 0.2, # SSIM
    'cs': 0.2,   # Cosine Similarity
    'cnn': 0.2   # CNN
}
antidupe = Antidupe(limits=custom_thresholds)

# Check for duplicates
is_duplicate = antidupe.predict([image1, image2])

Debugging

You can enable debug mode to print debugging messages:

# Initialize Antidupe with debug mode enabled
antidupe = Antidupe(debug=True)

# Check for duplicates
is_duplicate = antidupe.predict([image1, image2])

Changing Limits During Runtime

You can change the similarity thresholds during runtime:

# Set new limits during runtime
new_thresholds = {
    'ih': 0.1,
    'ssim': 0.1,
    'cs': 0.1,
    'cnn': 0.1
}
antidupe.set_limits(limits=new_thresholds)

# Check for duplicates
is_duplicate = antidupe.predict([image1, image2])

Requirements

• Python 3.x
• NumPy
• Pillow
• imagehash
• torch
• efficientnet_pytorch
• torchvision

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License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.