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clustimage
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:
<p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/schematic_overview.png" width="1000" /> </p>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.
* 4. Cluster on datetime or latlon coordinates when using photos.
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 can map photos on an interactive map with thumbnails and clusterlabels so that you easily structure your photos.
* 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, EXIF data 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 ⭐️
Blogs
- Read the blog to get a structured overview how to cluster images.
Documentation pages
On the documentation pages you can find detailed information about the working of the clustimage with many examples.
Installation
It is advisable to create a new environment (e.g. with Conda).
conda create -n env_clustimage python=3.8
conda activate env_clustimage
Install bnlearn from PyPI
pip install clustimage # new install
pip install -U clustimage # update to latest version
Directly install from github source
pip install git+https://github.com/erdogant/clustimage
Import clustimage package
from clustimage import clustimage
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:
Example: Clustering mnist dataset
In this example we will be using a flattened grayscale image array loaded from sklearn. The unique detected clusters are the following:
<p align="left"> <a href="https://erdogant.github.io/clustimage/pages/html/Examples.html#scatter-plot"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_fig2_tsne.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_fig21_tsne.png" width="400" /> </a> </p>Click on the underneath scatterplot to zoom-in and see ALL the images in the scatterplot
<p align="left"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/scatter_mnist_all.png" width="400" /> </p>Example: Plot the explained variance
<p align="left"> <a href="https://erdogant.github.io/clustimage/pages/html/Examples.html#cluster-evaluation"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_explained_var.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_clusters.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_fig1.png" width="600" /> </a> </p>Example: Plot the unique images
<p align="left"> <a href="https://erdogant.github.io/clustimage/pages/html/Examples.html#detect-unique-images"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_unique.png" width="300" /> </a> </p>Example: Plot the dendrogram
<p align="left"> <a href="https://erdogant.github.io/clustimage/pages/html/Examples.html#dendrogram"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_dendrogram.png" width="400" /> </a> </p> <hr>Examples Flower dataset:
Example: cluster the flower dataset
<p align="left"> <a href="https://erdogant.github.io/clustimage/pages/html/Examples.html#id5"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_sil_vs_nrclusters.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_silhouette.png" width="400" /> </a> </p>Example: Make scatterplot with clusterlabels
<p align="left"> <a href="https://erdogant.github.io/clustimage/pages/html/Examples.html#id7"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_scatter.png" width="300" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_scatter_imgs_mean.png" width="300" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_scatter_imgs.png" width="300" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_predict_scatter_all.png" width="300" /> </a> </p>Example: Plot the unique images per cluster
<p align="left"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_unique.png" width="400" /> </p> <p align="left"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_unique_mean.png" width="400" /> </p>Example: Plot the images in a particular cluster
<p align="left"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_cluster3.png" width="400" /> </p>Example: Make prediction for unseen input image
<p align="left"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_predict_1.png" width="600" /> </p> <p align="left"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_predict_2.png" width="600" /> </p> <p align="left"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_predict_scatter.png" width="600" /> </p> <hr>Example: Clustering of faces on images
<p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_sil_vs_nrclusters.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_set_max_clust.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_unique.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_scatter_no_img.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_scatter.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_cluster0.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_cluster3.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces1.png" width="400" /> </p> <hr>Example: Break up the steps
<hr>Example: Extract images belonging to clusters
<hr>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.