coo-img-sorter

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Image sorting tool-box. You need to have Python 3.8+ installed.

This project utilizes mathematical knowledge such as Euclidean Distance、Mean Squared Error to implement the (core) function of an image sorter.

Installation

pip install coo-img-sorter

Usage

...

TODO

• Add more image sorting algorithms:

  • Peak Signal-to-Noise Ratio (PSNR)

    Evaluate image similarity by calculating the signal-to-noise ratio between the original image and the compressed or processed image.

  • Cosine Similarity

    Treat images as pixel value vectors and calculate the cosine similarity between them. A cosine similarity value closer to 1 indicates that the image is more similar.

  • Perceptual Hashing

    Measure image similarity by comparing the Hamming distance between hash codes

  • Histogram Comparison

    Evaluate image similarity by calculating the color histogram of the image and comparing the differences between the histograms.

  • Local Feature Matching

    Evaluate image similarity by detecting key points in the image and comparing the similarity between feature descriptors.

  • Mean Squared Error (MSE)

    Calculate the average difference in pixel values between two images. A smaller MSE value indicates a more similar image.

  • Structural Similarity Index (SSIM)

    It considers differences in brightness, contrast, and structure, and provides a comprehensive similarity score. SSIM values closer to 1 indicate a more similar image.

  • RGB

    Calculate the similarity value of RGB between two sets of images using Euclidean distance.

• Classification speed

  • Asynchronous + multithreading