Live Emoji Prediction Project - Part 2

Overview

• This part of the project focuses on finalizing the emoji prediction system.
• Previously covered in Part 1: Data collection, recommended to watch for better understanding.

Learning Objectives

• Finalize project by loading collected data, training the model, and running inference.
• Create and implement two Python scripts:
  • data_training.py for training the model
  • inference.py for making predictions.

Data Collection

• Run data_collection.py multiple times to gather emoji data:
  • Collected data files:
    • hello.npy
    • good_luck.npy
    • nope.npy
• Ensure all data files end with .npy to filter correctly.

Data Training Script (data_training.py)

1. Imports Needed:

   • os for file handling.
   • numpy as np for array manipulation.
   • cv2 if using OpenCV.

2. Load Data Files:

   • Use os.listdir() to retrieve all files in the directory.
   • Filter files to only include those ending with .npy.

3. Initialize Data Arrays:

   • Create x for features and y for labels initially set to empty.
   • Concatenate loaded data from files into x and create corresponding y labels.
   • Ensure y is set as an array of integers corresponding to the labels.

4. Handle Label Encoding:

   • Create a dictionary for emoji labels to unique integers.
   • Convert the y data into categorical format using to_categorical from Keras utilities.

5. Model Creation:

   • Use Keras to define the model architecture with input and dense layers.
   • Compile model with rmsprop optimizer and categorical_crossentropy loss function.

6. Training the Model:

   • Fit the model on x and y data for a defined number of epochs.
   • It's suggested to shuffle data to mix the training samples effectively.
   • Save the trained model and labels into files for later use.

Inference Script (inference.py)

1. Setup:

   • Import necessary libraries: cv2, numpy, mediapipe, and Keras model loading utilities.
   • Load the trained model and labels saved from the training phase.

2. Capture Video:

   • Initialize video capture from the webcam.
   • Process frames to detect hands using mediapipe hands landmark solutions.

3. Prediction Logic:

   • Convert captured landmarks into the right shape for model input.
   • Use the model to predict the emoji based on the captured hand gestures.
   • Output the predictions dynamically on the capture window.

Improvements and Considerations

• Ensure error handling for file operations and model predictions.
• Consider adding more emojis and ensuring model retraining as new data is collected.

Conclusion

• Successfully implemented a project to predict emojis based on hand gestures.
• Future work could involve refining model performance and expanding the emoji set.

Call to Action

• Viewers encouraged to engage with questions or problems in the comments.
• Reminder to like the video and subscribe for upcoming content.

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Emoji: 🚀