Metadata-Version: 2.1
Name: clustimage
Version: 1.1.0
Summary: Python package clustimage is for unsupervised clustering of images.
Home-page: https://github.com/erdogant/clustimage
Author: Erdogan Taskesen
Author-email: erdogant@gmail.com
License: UNKNOWN
Download-URL: https://github.com/erdogant/clustimage/archive/1.1.0.tar.gz
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pca
Requires-Dist: distfit
Requires-Dist: clusteval
Requires-Dist: colourmap
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: tqdm
Requires-Dist: sklearn
Requires-Dist: matplotlib
Requires-Dist: scikit-image
Requires-Dist: scipy
Requires-Dist: opencv-python

# clustimage

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* 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:

```bash
conda create -n env_clustimage python=3.8
conda activate env_clustimage
```

* Install from pypi
```bash
pip install -U clustimage
```  

#### Import the clustimage package
```python
from clustimage import Clustimage
```

#### Simple example using data-array as an input.
```python
# 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')

```
<p align="center">
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_fig1.png" width="600" />
  <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_pca.png" width="400" />
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_fig2_tsne.png" width="600" />
  <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_dendrogram.png" width="400" />
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_cluster1.png" width="400" />
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_cluster4.png" width="400" />
</p>


#### images with flowers to cluster.
```python
# 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(dot_size=50)
# Plot dendrogram
cl.dendrogram()
# Plot clustered images
cl.plot(ncols=5)

# Predict
results_predict = cl.predict(path_to_imgs[0:5], k=None, alpha=0.05)
cl.plot_predict()
cl.scatter()

```
<p align="center">
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_plot1.png" width="400" />
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flowers_plot2.png" width="400" />
</p>


#### Make prediction for unseen input image.
```python
# Predict
results_predict = cl.predict(path_to_imgs[0:5], alpha=0.05)
cl.plot_predict()
cl.scatter()
```
<p align="center">
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/scatter_predict.png" width="400" />
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/flower_predict_example.png" width="400" />
</p>


#### images with faces to cluster.

```python
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)
```

<p align="center">
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces1.png" width="400" />
  <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/faces_dendrogram.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" />
</p>

#### 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](https://github.com/erdogant)

### Contribute
* All kinds of contributions are welcome!
* If you wish to buy me a <a href="https://www.buymeacoffee.com/erdogant">Coffee</a> for this work, it is very appreciated :)

### Licence
See [LICENSE](LICENSE) for details.


