Hand-Gesture-Recognition-Rock-Paper-Scissor

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Hand-Gesture-Recognition-Rock-Paper-Scissor

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Hand Gesture Recognition and Modification was based on transfer learning Inception v3 model using Keras with Tensorflow backend trained on 4 classes - rock, paper, scissors, and nothing hand signs. The final trained model resulted in an accuracy of 97.05%. The model was deployed using Streamlit on Heroku Paas.

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// repository documentation

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<p align="center">
<a href="https://dscommunity.in">
	<img src="https://github.com/Data-Science-Community-SRM/template/blob/master/Header.png?raw=true" width=80%/>
</a>
	<h2 align="center"> RPSNet: Hand Gesture Recognition and Modification </h2>
	<h4 align="center"> Image classification of Rock🌍,Paper📜 and Scissors✂ hand symbols <h4>
</p>
	
## Preview

- Output:
<p align="center">
  <img src="Graphs_Results/screen_recording.gif" alt="screen recording of output" width="576" height="324">
</p>

- Browse images from your system and try out the image recognition model!

## Functionalities
- [ ]  This model ([Model_4_classes.h5](https://drive.google.com/file/d/1ztXjjs2kKJJZ0CbO8JRXEerAMMwKAOtF/view?usp=sharing)) has been trained to detect 4 classes of objects: Paper 📜, Rock 🌍 , Scissors ✂ and Nothing(Case of No hand sign) using transfer learning on the InceptionV3 model till layer ‘mixed7’, followed by a Dense layer with 256 nodes (RelU), and a softmax layer with 4 output nodes using Keras with Tensorflow backend. 
- [ ]  It was trained using the RMSprop optimizer with a batch size of 32 for 100 epochs. Input size of the images were (150, 150, 3). The images were rescaled and augmented before training. ([TrainInception_4classes.ipynb](https://github.com/Data-Science-Community-SRM/hand-gesture-recognition-and-modification/blob/master/Inception-Model/TrainInception_4classes.ipynb))
- [ ]  The [h5 weights file](https://drive.google.com/file/d/1ztXjjs2kKJJZ0CbO8JRXEerAMMwKAOtF/view?usp=sharing) of the Inception v3 model has been integrated as a [Streamlit app](https://github.com/Data-Science-Community-SRM/hand-gesture-recognition-and-modification/blob/master/Streamlit%20App/app.py).
The Streamlit app has been deployed on Heroku PaaS
- [ ]  The final trained model resulted in an accuracy of 97.05% on the test set with 237 images.
- [ ]  The model can be visualized using the file Visualize_4_classes.py and Uses OpenCV library and the webcam to do the same. Each frame is flipped, resized to 150x150 and then normalized before feeding into the network to make a prediction.

Plot of training and validation accuracy versus the number of epochs:

Plot of training and validation loss versus the number of epochs:

<br>

## Instructions to run

* Pre-requisites:
	- The software requirements are listed in the [requirements.txt](https://github.com/Data-Science-Community-SRM/hand-gesture-recognition-and-modification/blob/master/requirements.txt) file.

* Installation
```bash
< mkdir -p ~/.streamlit/

echo "\
[server]\n\
headless = true\n\
port = $PORT\n\
enableCORS = false\n\
\n\
" > ~/.streamlit/config.toml >
```

* Execution:

- https://rps-streamlit-app.herokuapp.com/
    - The program can be quit by hitting ‘Q’.

```bash
< web: sh setup.sh && streamlit run app.py >
```

## Contribute 👨‍👨‍👧‍👦

Thanks for taking the time to contribute!

The following is a set of guidelines for contributing to Hand Gesture Recognition and Modification. Please check out the [Contribute.md](https://github.com/Data-Science-Community-SRM/hand-gesture-recognition-and-modification/blob/master/Contribute.md) . These are just guidelines, not rules, so use your best judgement and feel free to propose changes to this document in a pull request.
If you have any questions, open an issue.

## License

This project is licensed under the MIT License - see the [License.md](https://github.com/Data-Science-Community-SRM/hand-gesture-recognition-and-modification/blob/master/License.md) file for details

[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://badges.mit-license.org)

<p align="center">
	Made with :heart: by <a href="https://dscommunity.in">DS Community SRM</a>
</p>

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