Face-Mask-Detection-Jetson-Nano

A fast face mask recognition running at 44-5 FPS on a Jetson Nano.

This is a fast C++ implementation of two deep learning models found in the public domain.

The first is face detector of Linzaer running on a ncnn framework.
https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB.

The second is the Paddle Lite mask detection which classifies the found faces.
https://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite/demo/cxx/mask_detection.

The frame rate depends on the number of detected faces and can be calculated as follows:
FPS = 1.0/(0.022 + 0.008 x #Faces) when overclocked to 2014 MHz.

Paper: https://arxiv.org/abs/1905.00641.pdf
Size: 320x320

Special made for a Jetson Nano see Q-engineering deep learning examples

New version 2.0.

A new and superior version with only TensorFlow Lite for a Jetson Nano see GitHub

Dependencies.

April 4 2021: Adapted for ncnn version 20210322 or later

To run the application, you have to:

• The Paddle Lite framework installed. Install Paddle
  
• The Tencent ncnn framework installed. Install ncnn
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

  Running the app.

  To extract and run the network in Code::Blocks
  $ mkdir MyDir
  $ cd MyDir
  $ wget https://github.com/Qengineering/Face-Mask-Detection-Jetson-Nano/archive/refs/heads/main.zip
  $ unzip -j master.zip
  Remove master.zip and README.md as they are no longer needed.
  $ rm master.zip
  $ rm README.md
  
  Your MyDir folder must now look like this:
  Face_1.jpg
  Face_2.jpg
  Face_3.jpg
  Face_Mask_Video.mp4
  mask_detector_opt2.nb
  MaskUltra.cpb
  mask_ultra.cpp
  UltraFace.cpp
  UltraFace.hpp
  RFB-320.bin
  RFB-320.param
  slim_320.bin
  slim_320.param
  

  Notes.

  The directories in the Code::Blocks project file will probably need to be adapted to the naming convention you are using.
  The camera input used is a simple OpenCV webcam. The GStreamer is not used in this example for symplicity reasons.
  
  The RFB-320 model recognizes slightly more faces than slim_320 at the expense of a little bit of speed. It is up to you.
  Note that the compilation of the Paddle Lite framework in your application may take a while.
  
  See the Raspberry Pi video at https://youtu.be/LDPXgJv3wAk