Face Detection with InsightFace

This project provides a tool for detecting faces in images using the InsightFace library with the RetinaFace model. The tool processes images from a specified input folder, detects faces, and saves the results to a specified output folder with bounding boxes drawn around detected faces.

Features

Face detection using InsightFace's RetinaFace model. Batch processing of images in a specified folder. Automatic creation of output folders. Support for common image formats: PNG, JPG, JPEG, BMP, and TIFF.

Prerequisites

1. Python 3.6 or higher

2. OpenCV

3. InsightFace

Installation

Clone the repository:

git clone https://github.com/MahnazRazavi/FaceDetection.git
cd face-detection

Install required packages:

pip install opencv-python insightface

Usage

1. Prepare the input folder: Place the images you want to process in a folder. For example, create a folder named input and add your images to this folder.

2. Run the script: Execute the script with the input and output folder paths.

python face_detection.py

The script will read images from the input folder, detect faces, and save the results to the output folder.

Example

1. Place your images in the input folder.
2. Run the script:

python face_detection.py

3. The processed images with detected faces will be saved in the output folder.

Code Overview

FaceDetection Class:

init: Initializes the face detection model.

detect_faces: Detects faces in a given image and saves the result with bounding boxes drawn around detected faces.

process_images: Processes all images in the specified input folder.