https://github.com/asutoshpati/face-clustering-using-dbscan
Demonstrates face clustering using DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm.
https://github.com/asutoshpati/face-clustering-using-dbscan
dbscan dbscan-algorithm face-clustering face-dataset face-encodings face-recognition opencv python
Last synced: 29 days ago
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Demonstrates face clustering using DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm.
- Host: GitHub
- URL: https://github.com/asutoshpati/face-clustering-using-dbscan
- Owner: AsutoshPati
- License: mit
- Created: 2021-06-14T14:58:21.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-04-16T09:49:57.000Z (about 2 years ago)
- Last Synced: 2025-04-07T00:51:16.555Z (about 2 months ago)
- Topics: dbscan, dbscan-algorithm, face-clustering, face-dataset, face-encodings, face-recognition, opencv, python
- Language: Python
- Homepage:
- Size: 99.6 KB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Face Clustering Algorithm
---
This code shows face clustering using [DBSCAN](
https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (Density Based Spatial Clustering of
Applications with Noise) algorithm. Using this codes you can create face database of fine images (by removing blurred
images) and then you can easily apply face clustering on the newly created database.
After clustering unique faces you can also find best pictures from each cluster.
If you find this code helpful please don't forget to give star and follow.
---
## Requirements
> pip install dlib@https://pypi.python.org/packages/da/06/bd3e241c4eb0a662914b3b4875fc52dd176a9db0d4a2c915ac2ad8800e9e/dlib-19.7.0-cp36-cp36m-win_amd64.whl#md5=b7330a5b2d46420343fbed5df69e6a3f
> pip install face-recognition==1.3.0
> pip install opencv-python==4.5.2.52
> pip install scikit-learn==0.24.2
## How to use
* Just put all the cropped face (as this code doesn't have crop face code) images into "face_images" directory.
(you can add the crop face code in "create_face_database" function at the marked place in "get_encodings.py")
* Then run the "example.py" file to stat the face database making procedure. If you want to use it in your code; then
you need to put "get_encodings.py" in your project directory and have to use the function "create_face_database".
```python
import get_encodings as enc
root_path = 'face_images' # path to your
dir_name, data_len = enc.create_face_database(root_path)
```
* Once face database is created it will start doing face cluster immediately after completing the result directory where
clustered faces are stored will be displayed. If you want to add "face_cluster.py" to your project directory and have
to use "do_cluster" function.
```python
import face_cluster as cluster
dir_name = '' # path to face database directory
unq_faces, res_dir = cluster.do_cluster(dir_name)
```
* You can find best picture from each class using "get_best_pics" function once clustering is completed, and you have
number of clusters and path to result directory.
```python
import face_cluster as cluster
unq_faces = 0 # num of clusters after clustering
res_dir = '' # path to stored face cluster result
unq_fine_faces = cluster.get_best_pics(unq_faces, res_dir)
```