Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

clustimage is a python package for unsupervised clustering of images.

License

NotificationsYou must be signed in to change notification settings

erdogant/clustimage

Repository files navigation

PythonPypiDocsLOCDownloadsDownloadsLicenseForksIssuesProject StatusDOIMediumColabDonate

clustimage is a Python library to detect natural groups or clusters of images. Multiple steps are pipelined where images are processed, features extracted, and the clusters evaluated across the feature space.The optimal number of clusters is determined using methods such as *silhouette, dbindex, and derivatives* in combination with clustering methods, such as *agglomerative, kmeans, dbscan and hdbscan*.clustimage allows you to determine the most robust clustering by efficiently searching across the parameters and by evaluating the clusters.Besides clustering of images, the ``clustimage`` library can also find the most similar images for a new, unseen sample. ⭐️Star it if you like it⭐️


clustimage overcomes the following challenges:

* 1. Robustly groups similar images.* 2. Returns the unique images.* 3. Finds highly similar images for a given input image.* 4. Cluster on datetime or latlon coordinates when using photos.

clustimage is fun because:

* It does not require a learning process.* 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 can map photos on an interactive map with thumbnails and cluster labels so that you can easily structure your photos.* It provided many plots to improve the  understanding of the feature-space and sample-sample relationships* It is built on core statistics, such as PCA, HOG, EXIF data, and many more, and therefore it does not have a dependency block.* It works out of the box.

⭐️ Star this repo if you like it ⭐️

Blogs

  • Read theblog to get a structured overview how to cluster images.

On thedocumentation pages you can find detailed information about the working of theclustimage with many examples.

Installation

It is advisable to create a new environment (e.g. with Conda).
conda create -n env_clustimage python=3.8conda activate env_clustimage
Install bnlearn from PyPI
pip install clustimage# new installpip install -U clustimage# update to latest version
Directly install from GitHub source
pip install git+https://github.com/erdogant/clustimage
Import clustimage package
fromclustimageimportclustimage

Examples

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

Examples Mnist dataset:

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


Examples Flower dataset:





Support

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.

[8]ページ先頭

©2009-2025 Movatter.jp