TensorFlow_Lite_Face_Mask_RPi_64-bits

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TensorFlow_Lite_Face_Mask_RPi_64-bits

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Face mask detection on a bare Raspberry Pi 4 wit 32 or 64-bit OS

TensorFlow_Lite_Face_Mask_RPi_64-bits 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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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)
# TensorFlow_Lite_Face_Mask_RPi_64-bits
![output image](https://qengineering.eu/images/Mask_2_RPi.jpg) 
## TensorFlow Lite Face Mask detection running on a bare Raspberry Pi 4.
[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) 
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 detection ___2.0___. 
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. 
### Update 8-29-2022. 
- Added GPIO outputs.

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## Benchmark.
| Model | RPi 4 64os 1900 MHz | RPi 4 64os 1500 MHz |
|  :------------: |  :------------: | :-------------: |
| ssd_mobilenet_v2.tflite | 8.1 FPS | 7.5 FPS |
| ssd_mobilenet_v2_fpnlite.tflite | 6.3 FPS | 5.8 FPS |

Special made for a Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)

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## Dependencies.
To run the application, you have to:
- TensorFlow Lite framework installed. [Install TensorFlow Lite](https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-64-os.html) 
- OpenCV 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```)

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## Running the app.
To extract and run the network in Code::Blocks 
$ mkdir *MyDir* 
$ cd *MyDir* 
$ wget https://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 
Your *MyDir* 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.

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## WebCam.
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`

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

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[![paypal](https://qengineering.eu/images/TipJarSmall4.png)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)

![output image](https://qengineering.eu/images/Mask_5_RPi.jpg) 
![output image](https://qengineering.eu/images/Mask_1_RPi.jpg) 
![output image](https://qengineering.eu/images/Mask_49_RPi.jpg)

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