https://github.com/felipeversiane/face-cluster
application that receives a dataset of faces and creates a cluster of images that have similarity.
https://github.com/felipeversiane/face-cluster
docker face-clustering face-recognition hierarchical-clustering python
Last synced: 2 months ago
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application that receives a dataset of faces and creates a cluster of images that have similarity.
- Host: GitHub
- URL: https://github.com/felipeversiane/face-cluster
- Owner: felipeversiane
- Created: 2024-12-04T19:43:36.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-06T18:34:47.000Z (6 months ago)
- Last Synced: 2025-02-01T14:21:17.286Z (4 months ago)
- Topics: docker, face-clustering, face-recognition, hierarchical-clustering, python
- Language: Python
- Homepage:
- Size: 555 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Face Cluster
This project uses [Docker](https://www.docker.com) to manage dependencies and containerize the environment, enabling easy deployment and execution of facial recognition and clustering tasks.
## Prerequisites:
Ensure you have the following installed:
- Docker installed on your system.
- Docker Compose for managing multi-container applications.
## Libs:
The system leverages Python libraries such as:
- OpenCV: Utilized for image processing and face detection.
- Scikit-learn: Used for clustering the facial encodings using algorithms like DBSCAN.
- Imutils: A utility library to assist with common image-processing tasks.
- Numpy: Essential for numerical operations, especially when handling large sets of encodings.
- Face Recognition: A deep learning-based library for detecting and encoding faces.
## Installation:
Follow these steps to get the project up and running:
### 1. Clone repository
Use this to copy the source code in your machine:
```bash
git clone https://github.com/felipeversiane/face-cluster.git
```
### 2. Build and running:
Use this to build, download the dependences and running app :
```bash
docker-compose up --build
```
With this command, the code will be executed, first running the `encode_faces.py` file and then the `cluster_faces.py` file. This will create two folders called results and encodings, in which you will have the result of the executed code.
## Directory structure:
- `/src` : Contains the Python source code for face encoding `encode_faces.py` and clustering `cluster_faces.py`.
- `/database` : Folder for storing the input images used for generating face encodings and clusters.
- `encodings/` : Directory where the serialized facial encodings will be saved after processing.
- `results/` : The output folder where clustered images and results will be saved.
## Results:
The cluster results will be stored in the results folder, where it will be possible to see each label/cluster created with their respective images
Example:
- label1: image0.jpg, image1.jpg, image14.jpg ....
- label2: ....
## Environment Variables:
- ENCODINGS_PATH: Path to the serialized encodings file.
- FACE_DATA_PATH: Path to store face data during processing.
- CLUSTERING_RESULT_PATH: Path where clustering results will be saved.
## Usage in Docker:
- `encode-faces` processes images and generates face encodings.
- `cluster-faces` uses DBSCAN to group faces into clusters, storing them in the designated result path.
## Notes:
- The project is designed to be extensible, allowing additional services like a web interface for image upload or real-time face recognition.
- The current focus is on the core functionality of encoding and clustering faces.