clustimage
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 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:
Click on the underneath scatterplot to zoom-in and see ALL the images in the scatterplot
Example: Plot the explained variance
Example: Plot the unique images
Example: Plot the dendrogram
Examples Flower dataset:
Example: cluster the flower dataset
Example: Make scatterplot with clusterlabels
Example: Plot the unique images per cluster
Example: Plot the images in a particular cluster
Example: Make prediction for unseen input image
Example: Clustering of faces on images
Example: Break up the steps
Example: Extract images belonging to clusters
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.
Release files for clustimage 1.7.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| clustimage-1.7.1.tar.gz | 59.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| clustimage-1.7.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 117.7 kB
Release files / clustimage-1.7.1.tar.gz
| Download URL | clustimage-1.7.1.tar.gz |
|---|---|
| Size | 59.4 kB |
| Tags | Source |
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Release files / clustimage-1.7.1-py3-none-any.whl
| Download URL | clustimage-1.7.1-py3-none-any.whl |
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| Size | 58.2 kB |
| Tags | Python 3 |
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