The aim of clustimage is to detect natural groups or clusters of images. It works using a multi-step proces of carefully pre-processing the images, extracting the features, and evaluating the optimal number of clusters across the feature space. The optimal number of clusters can be determined using well known methods suchs as silhouette, dbindex, and derivatives in combination with clustering methods, such as agglomerative, kmeans, dbscan and hdbscan. With clustimage we aim to determine the most robust clustering by efficiently searching across the parameter and evaluation the clusters. Besides clustering of images, the clustimage model can also be used to find the most similar images for a new unseen sample.

A schematic overview is as following:

clustimage overcomes the following challenges:

* 1. Robustly groups similar images.
* 2. Returns the unique images.
* 3. Finds higly similar images for a given input image.

clustimage is fun because:

* It does not require a learning proces.
* It can group any set of images.
* It can return only the unique() images.
* it can find highly similar images given an input image.
* It provided many plots to improve understanding of the feature-space and sample-sample relationships
* It is build on core statistics, such as PCA, HOG and many more, and therefore it does not has a dependency block.
* It works out of the box.

⭐️ Star this repo if you like it ⭐️

• Read the blog to get a structured overview how to cluster images.

On the documentation pages you can find detailed information about the working of the clustimage with many examples.

conda create -n env_clustimage python=3.8
conda activate env_clustimage

pip install clustimage            # new install
pip install -U clustimage         # update to latest version

pip install git+https://github.com/erdogant/clustimage

from clustimage import clustimage

The results obtained from the clustimgage library is a dictionary containing the following keys:

* img       : image vector of the preprocessed images
* feat      : Features extracted for the images
* xycoord   : X and Y coordinates from the embedding
* pathnames : Absolute path location to the image file
* filenames : File names of the image file
* labels    : Cluster labels

In this example we will be using a flattened grayscale image array loaded from sklearn. The unique detected clusters are the following:

Click on the underneath scatterplot to zoom-in and see ALL the images in the scatterplot

This project needs some love! ❤️ You can help in various ways.

* Become a Sponsor!
* Star this repo at the github page.
* Other contributions can be in the form of feature requests, idea discussions, reporting bugs, opening pull requests.
* Read more why becoming an sponsor is important on the Sponsor Github Page.

Cheers Mate.