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import numpy as np
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import joblib
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import json
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import argparse
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import tensorflow as tf
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from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
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from ml_gestures_dynamic.sequence_utils import load_sequences_from_csv
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def evaluate(data_path, model_path, max_len=30, test_size=0.2):
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# Загружаем данные
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X_train, X_test, y_train, y_test, le = load_sequences_from_csv(data_path, max_len, test_size)
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# Загружаем модель
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model = tf.keras.models.load_model(model_path)
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# Загружаем классы (или используем le из данных)
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try:
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classes_path = model_path.replace('.h5', '_classes.pkl')
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with open(classes_path, 'rb') as f:
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saved_classes = joblib.load(f)
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# Проверяем соответствие классов
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if list(saved_classes) != list(le.classes_):
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print("Warning: classes in model and data differ. Using data classes.")
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classes = le.classes_
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else:
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classes = saved_classes
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except:
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classes = le.classes_
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# Предсказание
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y_pred = np.argmax(model.predict(X_test), axis=1)
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acc = accuracy_score(y_test, y_pred)
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report = classification_report(y_test, y_pred, target_names=classes, output_dict=True)
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cm = confusion_matrix(y_test, y_pred).tolist()
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print(f"Test accuracy: {acc:.4f}")
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print("\nClassification Report:")
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for cls in classes:
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print(f"{cls}: precision={report[cls]['precision']:.3f}, recall={report[cls]['recall']:.3f}, f1={report[cls]['f1-score']:.3f}")
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print("\nConfusion Matrix:")
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for row in cm:
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print(row)
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# Сохраняем отчёт
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report_path = model_path.replace('.h5', '_evaluation_report.json')
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with open(report_path, 'w') as f:
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json.dump({
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'accuracy': acc,
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'classification_report': report,
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'confusion_matrix': cm
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}, f, indent=2)
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print(f"\nEvaluation report saved to {report_path}")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Оценка LSTM для динамических жестов')
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parser.add_argument('--data', required=True, help='Путь к CSV-файлу или папке с CSV-файлами')
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parser.add_argument('--model', required=True, help='Путь к обученной модели (.h5)')
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parser.add_argument('--max_len', type=int, default=30, help='Длина последовательности (должна совпадать с обучением)')
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parser.add_argument('--test_size', type=float, default=0.2, help='Доля тестовой выборки')
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args = parser.parse_args()
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evaluate(args.data, args.model, args.max_len, args.test_size)
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