{"id":13445196,"url":"https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits","last_synced_at":"2025-03-20T20:32:01.569Z","repository":{"id":112946102,"uuid":"274450628","full_name":"Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits","owner":"Qengineering","description":"Face mask detection on Raspberry Pi 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image](https://qengineering.eu/images/SDcard16GB_tiny.jpg) Find this example on our [SD-image](https://github.com/Qengineering/RPi-image)\n# Face Mask Detection on Raspberry Pi 64 bits\n![output image]( https://qengineering.eu/images/FamilyOut.jpg )\n\n## A fast face mask recognition running at 24-5 FPS on bare a Raspberry Pi 4.\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\u003cbr/\u003e\nThis is a fast C++ implementation of two deep learning models found in the public domain. \u003cbr/\u003e\u003cbr/\u003e\nThe first is face detector of Linzaer running on a ncnn framework.\u003cbr/\u003e \nhttps://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB. \u003cbr/\u003e\u003cbr/\u003e\nThe second is the Paddle Lite mask detection which classifies the found faces.\u003cbr/\u003e \nhttps://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite/demo/cxx/mask_detection. \u003cbr/\u003e\u003cbr/\u003e\nThe frame rate depends on the number of detected faces and can be calculated as follows: \u003cbr/\u003e\nFPS = 1.0/(0.04 + 0.01 x #Faces) when overclocked to 1950 MHz. \u003cbr/\u003e\u003cbr/\u003e\nPaper: https://arxiv.org/abs/1905.00641.pdf \u003cbr/\u003e\nSize: 320x320 \u003cbr/\u003e\u003cbr/\u003e\nSpecial made for a bare Raspberry Pi see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html) \u003cbr/\u003e\n### New version 2.0.\nA 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) \u003cbr/\u003e\n## Dependencies.\nTo run the application, you have to:\n- 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) \u003cbr/\u003e\n- The Paddle Lite framework installed. [Install Paddle](https://qengineering.eu/install-paddle-on-raspberry-pi-4.html) \u003cbr/\u003e\n- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) \u003cbr/\u003e\n- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html) \u003cbr/\u003e\n- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)\n## Running the app.\nTo extract and run the network in Code::Blocks \u003cbr/\u003e\n$ mkdir *MyDir* \u003cbr/\u003e\n$ cd *MyDir* \u003cbr/\u003e\n$ wget https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits/archive/refs/heads/master.zip \u003cbr/\u003e\n$ unzip -j master.zip \u003cbr/\u003e\nRemove master.zip and README.md as they are no longer needed. \u003cbr/\u003e \n$ rm master.zip \u003cbr/\u003e\n$ rm README.md \u003cbr/\u003e \u003cbr/\u003e\nYour *MyDir* folder must now look like this: \u003cbr/\u003e \nFace_1.jpg \u003cbr/\u003e\nFace_2.jpg \u003cbr/\u003e\nFace_3.jpg \u003cbr/\u003e\nFace_Mask_Video.mp4 \u003cbr/\u003e\nMaskUltra.cpb \u003cbr/\u003e\nmask_ultra.cpp \u003cbr/\u003e\nUltraFace.cpp \u003cbr/\u003e\nUltraFace.hpp \u003cbr/\u003e\nRFB-320.bin \u003cbr/\u003e\nRFB-320.param \u003cbr/\u003e\nslim_320.bin \u003cbr/\u003e\nslim_320.param \u003cbr/\u003e\n \u003cbr/\u003e\nThe RFB-320 model recognizes slightly more faces than slim_320 at the expense of a little bit of speed. It is up to you.\u003cbr/\u003e\nNote that the compilation of the Paddle Lite framework in your application can take minutes (\u003e 3 min). \u003cbr/\u003e \u003cbr/\u003e\nSee the video at https://youtu.be/LDPXgJv3wAk\n\n","funding_links":[],"categories":["Application projects"],"sub_categories":["Detection"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FQengineering%2FFace-Mask-Detection-Raspberry-Pi-64-bits","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FQengineering%2FFace-Mask-Detection-Raspberry-Pi-64-bits","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FQengineering%2FFace-Mask-Detection-Raspberry-Pi-64-bits/lists"}