%20%2018-02-2021%2018_33_18%20(2).png){kind=logo}
## :innocent: Motivation
In the present scenario due to Covid-19, there is no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. Also, the absence of large datasets of __‘with_mask’__ images has made this task more cumbersome and challenging.
## The sharing of the PPT and Project Report costs ₹1000 ($14)
If interested :email: chandrikadeb7@gmail.com
## :hourglass: Project Demo
:movie_camera: [YouTube Demo Link](https://youtu.be/wYwW7gAYyxw)
:computer: [Dev Link](https://dev.to/chandrikadeb7/face-mask-detection-my-major-project-3fj3)
[](https://face-mask--detection-app.herokuapp.com/)
requirements.txt [See here](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/requirements.txt)
## 🚀 Installation
1. Clone the repo
```
$ git clone https://github.com/chandrikadeb7/Face-Mask-Detection.git
```
2. Change your directory to the cloned repo
```
$ cd Face-Mask-Detection
```
3. Create a Python virtual environment named 'test' and activate it
```
$ virtualenv test
```
```
$ source test/bin/activate
```
4. Now, run the following command in your Terminal/Command Prompt to install the libraries required
```
$ pip3 install -r requirements.txt
```
## :bulb: Working
1. Open terminal. Go into the cloned project directory and type the following command:
```
$ python3 train_mask_detector.py --dataset dataset
```
2. To detect face masks in an image type the following command:
```
$ python3 detect_mask_image.py --image images/pic1.jpeg
```
3. To detect face masks in real-time video streams type the following command:
```
$ python3 detect_mask_video.py
```
## :key: Results
#### Our model gave 98% accuracy for Face Mask Detection after training via tensorflow-gpu==2.5.0
####
#### We got the following accuracy/loss training curve plot
## Streamlit app
Face Mask Detector webapp using Tensorflow & Streamlit
command
```
$ streamlit run app.py
```
## Images
Upload Images
Results
## :clap: And it's done! Feel free to mail me for any doubts/query :email: chandrikadeb7@gmail.com ## :handshake: Contribution Feel free to **file a new issue** with a respective title and description on the the [Face-Mask-Detection](https://github.com/chandrikadeb7/Face-Mask-Detection/issues) repository. If you already found a solution to your problem, **I would love to review your pull request**! ## :trophy: Awards Awarded Runners Up position in [Amdocs Innovation India ICE Project Fair]( https://www.amdocs.com/)  ## :raising_hand: Cited by: 1. https://osf.io/preprints/3gph4/ 2. https://link.springer.com/chapter/10.1007/978-981-33-4673-4_49 3. https://ieeexplore.ieee.org/abstract/document/9312083/ 4. https://link.springer.com/chapter/10.1007/978-981-33-4673-4_48 5. https://www.researchgate.net/profile/Akhyar_Ahmed/publication/344173985_Face_Mask_Detector/links/5f58c00ea6fdcc9879d8e6f7/Face-Mask-Detector.pdf ## 👏 Appreciation Selected in [Devscript Winter Of Code](https://devscript.tech/woc/)  Selected in [Script Winter Of Code](https://swoc.tech/project.html)  Seleted in [Student Code-in](https://scodein.tech/)  ## :heart: Owner Made with :heart: by [Chandrika Deb](https://github.com/chandrikadeb7) ## :+1: Credits * [https://www.pyimagesearch.com/](https://www.pyimagesearch.com/) * [https://www.tensorflow.org/tutorials/images/transfer_learning](https://www.tensorflow.org/tutorials/images/transfer_learning) ## :handshake: Our Contributors [CONTRIBUTORS.md](/CONTRIBUTORS.md) ## :eyes: Code of Conduct You can find our Code of Conduct [here](/CODE_OF_CONDUCT.md). ## :raising_hand: Citation You are allowed to cite any part of the code or our dataset. You can use it in your Research Work or Project. Remember to provide credit to the Maintainer Chandrika Deb by mentioning a link to this repository and her GitHub Profile. Follow this format: - Author's name - Chandrika Deb - Date of publication or update in parentheses. - Title or description of document. - URL. --- ## Internet of Things Device Setup ### Expected Hardware * [Raspberry Pi 4 4GB with a case](https://www.canakit.com/raspberry-pi-4-4gb.html) * [5MP OV5647 PiCamera from Arducam](https://www.arducam.com/docs/cameras-for-raspberry-pi/native-raspberry-pi-cameras/5mp-ov5647-cameras/) ### Getting Started * Setup the Raspberry Pi case and Operating System by following the Getting Started section on page 3 at `documentation/CanaKit-Raspberry-Pi-Quick-Start-Guide-4.0.pdf` or https://www.canakit.com/Media/CanaKit-Raspberry-Pi-Quick-Start-Guide-4.0.pdf * With NOOBS, use the recommended operating system * Setup the PiCamera * Assemble the PiCamera case from Arducam using `documentation/Arducam-Case-Setup.pdf` or https://www.arducam.com/docs/cameras-for-raspberry-pi/native-raspberry-pi-cameras/5mp-ov5647-cameras/ * [Attach your PiCamera module to the Raspberry Pi and enable the camera](https://projects.raspberrypi.org/en/projects/getting-started-with-picamera/2) ### Raspberry Pi App Installation & Execution > Run these commands after cloning the project | Commands | Time to completion | |------------------------------------------------------------------------------------------------------------------------------|--------------------| | sudo apt install -y libatlas-base-dev liblapacke-dev gfortran | 1min | | sudo apt install -y libhdf5-dev libhdf5-103 | 1min | | pip3 install -r requirements.txt | 1-3 mins | | wget "https://raw.githubusercontent.com/PINTO0309/Tensorflow-bin/master/tensorflow-2.4.0-cp37-none-linux_armv7l_download.sh" | less than 10 secs | | ./tensorflow-2.4.0-cp37-none-linux_armv7l_download.sh | less than 10 secs | | pip3 install tensorflow-2.4.0-cp37-none-linux_armv7l.whl | 1-3 mins | --- ## :eyes: License MIT © [Chandrika Deb](https://github.com/chandrikadeb7/Face-Mask-Detection/blob/master/LICENSE)