https://github.com/rohankalbag/stone-paper-scissor

Hand gesture recognition from electromyographic data obtained from hand muscles used to win the nostalgic school game stone-paper-scissor
https://github.com/rohankalbag/stone-paper-scissor

classification deep-learning tensorflow-keras

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Hand gesture recognition from electromyographic data obtained from hand muscles used to win the nostalgic school game stone-paper-scissor

• Host: GitHub
• URL: https://github.com/rohankalbag/stone-paper-scissor
• Owner: rohankalbag
• Created: 2022-06-27T11:45:15.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-07-20T13:19:02.000Z (over 2 years ago)
• Last Synced: 2023-03-06T03:30:39.028Z (over 1 year ago)
• Topics: classification, deep-learning, tensorflow-keras
• Language: Jupyter Notebook
• Homepage:
• Size: 204 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Stone-Paper-Scissor

## Self Project

Making use of electromyographic data obtained from human hands connected to an electromyography (EMG) sensor to train a neural network powered by Tensorflow Keras, to predict whether the inputted hand gesture is Stone/Paper/Scissor and then predict the opposing move to defeat the user

## Instructions

- Open the `.ipynb` file in Google Colab

- Create a [Kaggle](https://www.kaggle.com/) account and obtain a `kaggle.json` to use its API 

- Train the model by running all the blocks in the `.ipynb` file

- Use an instance of `stone_paper_scissor` class to play the game

## Using the `stone_paper_scissor` class

```python

class stone_paper_scissor:  

  def winningmove(self, inp):

    if(inp==0):

      return "I play paper"

    elif(inp==1):

      return "I play scissor"

    else:

      return "I play stone"

  """

  move = 0; if user inputted stone

  move = 1; if user inputted paper

  move = 2; if user inputted scissor

  """

  def play_stone(self):

    move = 0

    sample = test_stone.sample()

    # obtain an exemplar EMG data sample for the user specified move

    pred = np.argmax(model.predict(sample), axis=1)

    print(self.winningmove(pred))

    if(move==pred):

      print("I won yay!")

    else:

      print("Nice, you won!")

  def play_paper(self):

    move = 1

    sample = test_paper.sample()

    # obtain an exemplar EMG data sample for the user specified move

    pred = np.argmax(model.predict(sample), axis=1)

    print(self.winningmove(pred))

    if(move==pred):

      print("I won yay!")

    else:

      print("Nice, you won!")

  def play_scissor(self):

    move = 2

    sample = test_scissor.sample()

    # obtain an exemplar EMG data sample for the user specified move

    pred = np.argmax(model.predict(sample), axis=1)

    print(self.winningmove(pred))

    if(move==pred):

      print("I won yay!")

    else:

      print("Nice, you won!")

```

- Create an instance the in following way 

> `instance = stone_paper_scissor()`

- Make use of the `play_stone()`, `play_scissor()`, `play_paper()` methods to make a move

> `instance.play_stone()` to play the stone move