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import tensorflow as tf
import pickle
import numpy as np
import pandas as pd
def load_model_and_processor():
"""Load the trained model and processor."""
# Load the complete model
model = tf.keras.models.load_model('model.h5')
# Load the processor
with open('processor.pkl', 'rb') as f:
processor = pickle.load(f)
return model, processor
def predict_sign(model, processor, landmark_data):
"""
Predict sign from landmark data.
Args:
model: Loaded Keras model
processor: SignLanguageProcessor instance
landmark_data: DataFrame with columns ['frame', 'row_id', 'x', 'y', 'z']
Returns:
predicted_class: Predicted sign class
confidence: Prediction confidence
"""
# Process the landmark data
X, _ = processor.process_dataset(landmark_data)
if len(X) == 0:
return None, 0.0
# Make prediction
predictions = model.predict(X)
predicted_class = np.argmax(predictions, axis=1)[0]
confidence = np.max(predictions, axis=1)[0]
# Convert back to sign name if mapping exists
if hasattr(processor, 'index_to_sign'):
sign_name = processor.index_to_sign[predicted_class]
return sign_name, confidence
return predicted_class, confidence
# Example usage
if __name__ == "__main__":
# Load model and processor
model, processor = load_model_and_processor()
# Example landmark data (replace with your actual data)
# landmark_data = pd.read_csv('your_landmark_data.csv')
# Make prediction
# predicted_sign, confidence = predict_sign(model, processor, landmark_data)
# print(f"Predicted sign: {predicted_sign}, Confidence: {confidence:.3f}")
print("Model and processor loaded successfully!")
print(f"Model input shape: {model.input_shape}")
print(f"Model output shape: {model.output_shape}")
print(f"Number of classes: {processor.sign_count}") |