asutoshpati / face-clustering-using-dbscan

Demonstrates face clustering using DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm.

Face Clustering Algorithm

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This code shows face clustering using DBSCAN (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.

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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".

  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.

  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.

  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)