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34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
import numpy as np
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import tensorflow as tf
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import joblib
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from collections import deque
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from .feature_extractor import extract_sequence
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class DynamicGesturePredictor:
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def __init__(self, model_path, classes_path, window_size=30, threshold=0.7):
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self.model = tf.keras.models.load_model(model_path)
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with open(classes_path, 'rb') as f:
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self.classes = joblib.load(f)
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self.window_size = window_size
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self.buffer = deque(maxlen=window_size)
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self.threshold = threshold
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def add_frame(self, landmarks):
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if landmarks is None or np.isnan(landmarks).any():
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landmarks = np.zeros(99)
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self.buffer.append(landmarks)
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def predict(self):
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if len(self.buffer) < self.window_size:
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return None
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seq = extract_sequence(list(self.buffer))
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seq = np.expand_dims(seq, axis=0) # (1, window, 99)
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probs = self.model.predict(seq, verbose=0)[0]
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idx = np.argmax(probs)
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if probs[idx] > self.threshold and self.classes[idx] != 'none':
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return self.classes[idx]
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return None
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def reset(self):
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self.buffer.clear()
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