Image Hashing and Sorting Script

This Python script processes a folder of .jpg images to identify duplicates and unique images based on perceptual hashing. It organizes the images into separate folders for unique images and duplicates.

Features

• Crops images to remove borders before processing.
• Computes perceptual hashes for images using the imagehash library.
• Detects duplicate images based on a configurable similarity threshold.
• Organizes images into unique and duplicates folders.
• Groups duplicates by their original image.

Requirements

• Python 3.6 or higher
• Required Python libraries:
  • Pillow
  • imagehash

Install the dependencies using pip:

pip install Pillow imagehash 

Usage
Run the script:

python duplicates_uniques.py

Enter the path to the folder containing .jpg images when prompted.

The script will process the images and create a processed_images folder in the current directory with the following structure:

processed_images/
├── unique/
└── duplicates/
    ├── <original_image_1>/
    └──├── <dulicate_original_image>/
       ├── <dulicate_original_image>/
    ├── <original_image_2>/
    └──├── <dulicate_original_image>/
       ├── <dulicate_original_image>/
       ├── ...

Configuration

Cropping: The script crops 1200 pixels from each side of the image. You can adjust this value in the crop_box function.
Hash Size: The hash size for perceptual hashing is set to 16 by default. You can modify it in the compute_hash function.
Threshold: The similarity threshold for detecting duplicates is set to 5 by default. You can change it in the compare_images function.

Example Output

Unique images are saved in the unique folder.
Duplicates are grouped by their original image in the duplicates folder.