https://github.com/ruhyadi/vision-fr
Face recognition with ONNX Runtime, YOLOX, InsightFace, PostgreSQL, pgvector, and FastAPI.
https://github.com/ruhyadi/vision-fr
cpu face-recognition fastapi insightface onnx onnxruntime pgvector postgresql yolox
Last synced: 2 months ago
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Face recognition with ONNX Runtime, YOLOX, InsightFace, PostgreSQL, pgvector, and FastAPI.
- Host: GitHub
- URL: https://github.com/ruhyadi/vision-fr
- Owner: ruhyadi
- Created: 2024-07-05T12:33:23.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-07-07T18:04:21.000Z (10 months ago)
- Last Synced: 2025-01-02T02:24:02.887Z (4 months ago)
- Topics: cpu, face-recognition, fastapi, insightface, onnx, onnxruntime, pgvector, postgresql, yolox
- Language: Python
- Homepage:
- Size: 39.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Face Recognition
## Introduction
Face Recognition is a computer vision task of identifying and verifying a person from a digital image or video frame. Face recognition systems can be used for various applications such as security, surveillance, access control, and more.
This repository contains an **End-to-End Face Recognition API** implemented using [ONNX Runtime](https://onnxruntime.ai/), [PostgreSQL](https://www.postgresql.org/) ([pgvector](https://github.com/pgvector/pgvector)), and [FastAPI](https://fastapi.tiangolo.com/). We also combining [YOLOx](https://github.com/Megvii-BaseDetection/YOLOX) for face detection and [InsigtFace](https://github.com/deepinsight/insightface) for face recognition in ONNX format. With pgvector (a vector similarity search extension for PostgreSQL), we can store and search for face embeddings efficiently. Lastly, we use FastAPI to expose the face recognition API.
## Docker Image
We provide a production-ready Docker image for convenience. You can pull the image from [Docker Hub](https://hub.docker.com/repository/docker/ruhyadi/vision-fr/general) and run it directly. The details are as follows:
| Version | Docker Image | Description |
| ------- | -------------------------- | --------------------------------------------------------------------------------------- |
| v1.0.0 | `ruhyadi/vision-fr:v1.0.0` | Contains YOLOx_s model for face detection and MobileFaceNet model for face recognition. |
## Supported Models
The following models are supported in this repository:
### Face Detection
| Model Name | Description | Input Shape | Output Shape | Link |
| ------------ | -------------------------------------------------------------------------------------------- | ----------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------- |
| YOLOx s face | YOLOx small model for face detection. Trained on WIDER FACE dataset. **ONNX NMS supported**. | (-1, 3, 640, 640) | (`num_dets`, `boxes`, `classes`, `scores`) | [yoloxs_face.onnx](https://github.com/ruhyadi/vision-fr/releases/download/v1.0.0/yoloxs_face.onnx) |
### Face Recognition
| Model Name | Description | Input Shape | Output Shape | Link |
| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- | ------------ | ---------------------------------------------------------------------------------------------- |
| MobileFaceNet | MobileFaceNet model for face recognition. Trained on WebFace600K. Official model from [InsightFace](https://github.com/deepinsight/insightface/tree/master/model_zoo). | (-1, 3, 112, 112) | (-1, 512) | [w600k_mbf.onnx](https://github.com/ruhyadi/vision-fr/releases/download/v1.0.0/w600k_mbf.onnx) |
## Getting Started
### Prerequisites
We recommend using Docker either for development or production. You can install Docker by following the instructions [here](https://docs.docker.com/get-docker/).
Next, you can clone this repository and navigate to the project directory:
```bash
git clone https://github.com/ruhyadi/vision-fr
cd vision-fr
```
### Production
To run the Face Recognition API in production, you can use the provided Docker image. You can pull the image from Docker Hub and run it directly. The details are as follows:
```bash
docker pull ruhyadi/vision-fr:latest
```
We provide a `docker-compose.onnx.prod.yaml` file to run the API with PostgreSQL. You can run the following command to start the services:
```bash
docker compose -f docker-compose.onnx.prod.yaml up -d
```
The swagger documentation will be available at `http://localhost:7030`.
### Development
We encourage you to use [devcontainer](https://code.visualstudio.com/docs/devcontainers/containers) for development. Please make sure you have installed [Visual Studio Code](https://code.visualstudio.com/) and [Remote - Containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) extension.
We provide a `devcontainer/onnx/devcontainer.json` file to build the ONNX development container.
Before you start devcontainer, you need to start the PostgreSQL service. You can run the following command to start the PostgreSQL service:
```bash
docker compose -f docker-compose.devel.db.yaml up -d
```
Next, you can press `Ctrl+Shift+P` to open the command palette. Then, type and select `Remote-Containers: Reopen in Container`. The development container will be built and you can start developing.
## API Endpoints
The details of endpoints can be found in the Swagger documentation. Here the brief description of the endpoints:
| Method | Endpoint | Description |
| ------ | ------------------------------- | ------------------------------------------- |
| GET | `/api/v1/engine/face` | Get list of all faces data in the database. |
| POST | `/api/v1/engine/face/register` | Register a new face to the database. |
| POST | `/api/v1/engine/face/recognize` | Compare a face with the database. |
| DELETE | `/api/v1/engine/face/{id}` | Delete a face from the database. |
## Acknowledgements
- [ONNX Runtime](https://onnxruntime.ai/): ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
- [PostgreSQL](https://www.postgresql.org/): PostgreSQL is a powerful, open-source object-relational database system.
- [pgvector](https://github.com/pgvector/pgvector): pgvector is a vector similarity search extension for PostgreSQL.
- [FastAPI](https://fastapi.tiangolo.com/): FastAPI is a modern, fast (high-performance), web framework for building APIs with Python.
- [YOLOx](https://github.com/Megvii-BaseDetection/YOLOX): YOLOX is a high-performance anchor-free YOLO with ONNX NMS supported.
- [InsigtFace](https://github.com/deepinsight/insightface): InsightFace is an open-source 2D and 3D face analysis toolkit.