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A fast C++ implementation of TensorFlow Lite Face Mask detector on a bare Raspberry Pi 4 with a 32 or 64-bit operating system. Ubuntu 18.04, or Ubuntu20.04 are also possible.
Once overclocked to 1900 MHz, your app runs at 8.3 FPS without any hardware accelerator.

You could call this Face Mask detection2.0.
The network used is a re-trained MobileNet V2 SSD. It has three classes: no maks, a mask, and wearing a mask incorrectly. Although the latter category is not very convincing, given the small size of training samples.
It's not the usual cascade of the two deep learning models, one face recognition and a second one that detects the masks.
This one model now recognizes not only the white masks, but also the black, colored and fancy masks.
Although it can detect more faces/masks in the same scene, the best result is still one face in front of the camera.

• Added GPIO outputs.

Special made for a Raspberry Pi 4 seeQ-engineering deep learning examples

To run the application, you have to:

• TensorFlow Lite framework installed.Install TensorFlow Lite
  
• OpenCV installed.Install OpenCV 4.5
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

To extract and run the network in Code::Blocks
$ mkdirMyDir
$ cdMyDir
$ wgethttps://github.com/Qengineering/TensorFlow_Lite_Face_Mask_RPi_64-bits/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md

YourMyDir folder must now look like this:
Face_Mask_Video.mp4
ssd_mobilenet_v2_fpnlite.tflite - more accurate
ssd_mobilenet_v2.tflite - somewhat faster
TestTensorFlow_Lite_Mask.cpb
FaceMask.cpp
Kapje_x.jpg - examples

Run TestTensorFlow_Lite.cpb with Code::Blocks.

If you want to use a camera please alter line 130 in main.cpp to
cv::VideoCapture cap(0); //WebCam
If you want to run a movie please alter line 130 in main.cpp to
cv::VideoCapture cap("Face_Mask_Video.mp4"); //Movie

https://www.kaggle.com/andrewmvd/face-mask-detection
https://github.com/tanhouren/Face_mask_detector