https://github.com/coffeecms/coffee_py_face_data_management
Python & OpenCV Face ID Management System
https://github.com/coffeecms/coffee_py_face_data_management
Last synced: 3 months ago
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Python & OpenCV Face ID Management System
- Host: GitHub
- URL: https://github.com/coffeecms/coffee_py_face_data_management
- Owner: coffeecms
- Created: 2024-09-16T06:32:49.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-09-16T06:42:58.000Z (9 months ago)
- Last Synced: 2025-01-08T08:40:15.588Z (5 months ago)
- Language: Python
- Size: 8.79 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Face ID Management System - https://blog.lowlevelforest.com/
## Overview
This system manages Face IDs for workers, allowing you to load face data from images, search for the closest face data based on a base64 image, and handle concurrent searches. It supports both CPU and GPU processing modes for enhanced accuracy and speed.
## Features
- Load face ID data from images.
- Search for the closest face data based on a base64 image.
- Handle concurrent searches efficiently.
- Support both CPU and GPU processing modes.
## Requirements
- Python 3.8+
- OpenCV with CUDA (for GPU mode)
- NumPy
- scikit-learn
- facenet-pytorch
- faiss-cpu or faiss-gpu
- aiohttp (for asynchronous requests)
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/your-repo/face-id-management.git
cd face-id-management
```
2. **Install dependencies:**
Create a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
```
Install required Python libraries:
```bash
pip install opencv-python-headless numpy scikit-learn facenet-pytorch faiss-cpu aiohttp
```
For GPU support in Faiss, install `faiss-gpu` instead:
```bash
pip install faiss-gpu
```
To use GPU processing mode in OpenCV, ensure OpenCV is built with CUDA support.
## Configuration
- **Processing Mode:**
Set `PROCESSING_MODE` to `'cpu'` or `'gpu'` in the script to choose the processing mode.
- **Server Endpoint:**
Update the `url` variable in the `main_search` function to your server endpoint for handling search requests.
## Usage
1. **Loading Face Data from Images:**
Place images in a directory and call the `load_face_id_from_images` function:
```python
directory = 'path/to/your/image/folder'
data = load_face_data()
duplicates = load_face_id_from_images(directory, data, PROCESSING_MODE)
```
This function will process all images in the specified directory, add face data to `data.json`, and check for duplicate `user_id`s.
2. **Searching for Face Data:**
Convert base64 image strings to vectors and find the closest face data:
```python
base64_images = [
'base64_image_string_1',
'base64_image_string_2',
# Add more base64 image strings
]
results = loop.run_until_complete(main_search(base64_images, PROCESSING_MODE))
```
This will perform concurrent searches for all provided base64 images.
3. **Example Base64 Image Conversion:**
To convert an image to a base64 string:
```python
import base64
with open('path/to/image.jpg', 'rb') as image_file:
base64_string = base64.b64encode(image_file.read()).decode('utf-8')
```
## Running the System
To run the system and test the functionalities:
```bash
python your_script.py
```
Ensure you replace `your_script.py` with the actual script name.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.