/yoloface

Face detection using YOLOv3

Primary LanguagePythonMIT LicenseMIT

YOLOFace

Deep learning based Face detection using the YOLOv3 algorithm

Getting started

The YOLOv3 (You Only Look Once) is a state-of-the-art, real-time object detection algorithm. The published model recognizes 80 different objects in images and videos. For more details, you can refer to this paper.

YOLOv3's architecture

Imgur

Credit: Ayoosh Kathuria

OpenCV Deep Neural Networks (dnn module)

OpenCV dnn module supports running inference on pre-trained deep learning models from popular frameworks such as TensorFlow, Torch, Darknet and Caffe.

Prerequisites

  • tensorflow
  • opencv-python
  • opencv-contrib-python
  • numpy

Install the required packages by running the following command:

$ pip install -r requirements.txt

Note: This repositoty works on Python 3.x. Using Python virtual environment is highly recommended.

Usage

  • Clone this repository
$ git clone https://github.com/sthanhng/yoloface

  • For face detection, you should download the pre-trained YOLOv3 weights file which trained on the WIDER FACE: A Face Detection Benchmark dataset from this link and place it in the model-weights/ directory.

  • Run the following command:

image input

$ python yoloface.py --images samples/outside_000001.jpg --output-dir outputs/

video input

$ python yoloface.py --video samples/subway.mp4 --output-dir outputs/

webcam

$ python yoloface.py --src 1 --output-dir outputs/

Sample outputs

Imgur

Imgur

License

This project is licensed under the MIT License - see the LICENSE.md file for more details.

References