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https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits

Face mask detection on Raspberry Pi 4
https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits

aarch64 armv8 cpp deep-learning face-detection face-mask face-mask-detection face-recognition high-fps ncnn ncnn-framework paddle-lite paddlepaddle raspberry-pi-4 ssd-model ubuntu

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Face mask detection on Raspberry Pi 4

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![output image](https://qengineering.eu/images/SDcard16GB_tiny.jpg) Find this example on our [SD-image](https://github.com/Qengineering/RPi-image)

# Face Mask Detection on Raspberry Pi 64 bits

![output image]( https://qengineering.eu/images/FamilyOut.jpg )



## A fast face mask recognition running at 24-5 FPS on bare a Raspberry Pi 4.

[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)



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/(0.04 + 0.01 x #Faces) when overclocked to 1950 MHz. 



Paper: https://arxiv.org/abs/1905.00641.pdf 


Size: 320x320 



Special made for a bare Raspberry Pi see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html) 


### New version 2.0.

A new and superior version with only __TensorFlow Lite__ for a bare Raspberry Pi see [GitHub](https://github.com/Qengineering/TensorFlow_Lite_Face_Mask_RPi_64-bits) 


## Dependencies.

To run the application, you have to:

- A raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. [Install 64-bit OS](https://qengineering.eu/install-raspberry-64-os.html) 


- The Paddle Lite framework installed. [Install Paddle](https://qengineering.eu/install-paddle-on-raspberry-pi-4.html) 


- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) 


- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html) 


- 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-Raspberry-Pi-64-bits/archive/refs/heads/master.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 


MaskUltra.cpb 


mask_ultra.cpp 


UltraFace.cpp 


UltraFace.hpp 


RFB-320.bin 


RFB-320.param 


slim_320.bin 


slim_320.param 


 


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 can take minutes (> 3 min). 
 


See the video at https://youtu.be/LDPXgJv3wAk