clustimage 1.2.0

Python package clustimage is for unsupervised clustering of images.

clustimage

• clustimage is a python package for unsupervised clustering of images.

Installation

• Install clustimage from PyPI (recommended). clustimage is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
• A new environment can be created as following:

conda create -n env_clustimage python=3.8
conda activate env_clustimage

• Install from pypi

pip install -U clustimage

Import the clustimage package

from clustimage import Clustimage

Simple example using data-array as an input.

# Load library
import matplotlib.pyplot as plt
from clustimage import Clustimage
# init
cl = Clustimage()
# Load example digit data
X = cl.import_example(data='digits')

print(X)
# Each row is an image that can be plotted after reshaping:
plt.imshow(X[0,:].reshape(8,8), cmap='binary')
# array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],
#        [ 0.,  0.,  0., ..., 10.,  0.,  0.],
#        [ 0.,  0.,  0., ..., 16.,  9.,  0.],
#        ...,
#        [ 0.,  0.,  0., ...,  9.,  0.,  0.],
#        [ 0.,  0.,  0., ...,  4.,  0.,  0.],
#        [ 0.,  0.,  6., ...,  6.,  0.,  0.]])
# 
# Preprocessing and feature extraction
results = cl.fit_transform(X)

# Scatter
cl.scatter()
# Plot dendrogram
cl.dendrogram()
# Plot the clustered images
cl.plot(cmap='binary')

images with flowers to cluster.

# Load library
from clustimage import Clustimage
# init
cl = Clustimage(method='pca', embedding='tsne')
# load example with flowers
path_to_imgs = cl.import_example(data='flowers')
# Preprocessing and feature extraction
results = cl.fit_transform(path_to_imgs, min_clust=10)
# Scatter
cl.scatter(dotsize=50)
# Plot dendrogram
cl.dendrogram()
# Plot clustered images
cl.plot(ncols=5)

# Predict
results_find = cl.find(path_to_imgs[0:5], k=None, alpha=0.05)
cl.plot_find()
cl.scatter()

Make prediction for unseen input image.

# Predict
results_find = cl.find(path_to_imgs[0:5], alpha=0.05)
cl.plot_find()
cl.scatter()

images with faces to cluster.

from clustimage import Clustimage
# Init
cl = Clustimage(method='pca', grayscale=True, params_pca={'n_components':14})
# Load example with faces
pathnames = cl.import_example(data='faces')
# Detect faces
face_results = cl.detect_faces(pathnames)
# Cluster
results = cl.fit_transform(face_results['facepath'])

# Plot faces
cl.plot_faces()
# Dendrogram depicts the clustering of the faces
cl.dendrogram()

# Make various other plots
cl.scatter()
# Make plot
cl.plot(ncols=2, show_hog=True)

References

• https://github.com/erdogant/clustimage

Citation

Please cite in your publications if this is useful for your research (see citation).

Maintainers

• Erdogan Taskesen, github: erdogant

Contribute

• All kinds of contributions are welcome!
• If you wish to buy me a Coffee for this work, it is very appreciated :)

Licence

See LICENSE for details.