Pure Tensorflow 2+ DBSCAN algorithm
Initially this DBSCAN implementation was developed for Face Recognition problem.
Therefore, this module assumes that a feature_matrix containing information about image embeddings will be fed into the algorithm or adjacency_matrix, which describes a cosine distance between embeddings.
Anyway, I hope this implementation will be useful and helpful a bit.
List of input types:
adjacency_matrix- matrix NxN, where each cell is a distance (for instance, cosine similarity) between frames (where N is the number of frames)feature_matrix- matrix NxM, where each frame is an embedding with the length M (and N is a number of frames)
import tensorflow as tf
from dbscan import DBSCAN
dbscan = DBSCAN(eps=0.4, min_samples=1)
adjacency_matrix = tf.random.uniform((16, 16), dtype=tf.float32)
labels = dbscan(adjacency_matrix)