Face Detection API

This project is a Flask-based web service for face detection using the YOLOv8 model.

Features

• Face detection from uploaded images
• Real-time face detection from camera feed
• Adjustable confidence threshold and input size via API
• Display detection results on images and video stream

Requirements

• Python 3.9
• Flask
• OpenCV
• ONNX Runtime
• Pillow

Installation

1. Clone repository:

   git clone https://github.com/PongpreechaSuea/FaceDetection.git
   cd FaceDetection

2. Create and activate virtual environment:

   python -m venv venv
   source venv/bin/activate  # For Windows use `venv\Scripts\activate`

3. Install required packages:

   pip install -r requirements.txt

Configuration

Customize the src/config.py file as needed:

MODEL = "./model/yolov8n-face.onnx"
IMAGE_PATH = "image_debug.jpg"
IOU = 0.58
REG_MAX = 16
STRIDES = (8, 16, 32)
CONF = 0.3
IMGSIZE = 480
FREE_SPACE = 20 
HOST = "0.0.0.0"
PORT = 3000

Usage

Starting the Server

Start the FastAPI server:

python app.py

# Upload UI: http://0.0.0.0:3000/upload
# Webcam UI: http://0.0.0.0:3000/webcam
# Swagger UI: http://0.0.0.0:3000/docs

The server will run on http://0.0.0.0:3000 by default.

API Endpoints

Upload and Detect Faces from Image

• Endpoint: /upload

• Method: POST

• Description: Upload an image and detect faces

• Request: multipart/form-data

  • file: Image for face detection

• Response: JSON with detection results and base64 encoded image

Adjust Settings

• Endpoint: /seting

• Method: POST

• Description: Adjust confidence threshold and input size

• Request: Query parameters

  • conf_threshold: New confidence threshold value
  • input_size: New input size

• Response: JSON with status of the operation

Stream Video from Camera

• Endpoint: /video_feed
• Method: GET
• Description: Stream video from camera with real-time face detection
• Response: multipart/x-mixed-replace stream

Test Web Pages

• /upload: Page for uploading images
• /webcam: Page for opening camera and real-time face detection

Docker Support

This project includes Docker support for easy deployment.

Building and Running with Docker

Build the Docker image:

docker build -t face-detection-app .

Run the container:

docker run -p 3000:3000 face-detection-app

Using Docker Compose

Alternatively, you can use Docker Compose:

docker-compose up --build

This will build the image and start the container, mapping port 3000 on your host to port 3000 in the container.

Example Output