57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from main import analysis_mode
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if analysis_mode == "plan":
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dataset_path = "data/catalogueSymbol"
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allowed_classes = ['Figure1', 'Figure2', 'Figure3', 'Figure4', 'Figure5', 'Figure6']
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model_path = "models/bayesian_modelPLAN.pth"
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else:
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dataset_path = "data/catalogue"
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allowed_classes = ['2', 'd', 'I', 'n', 'o', 'u']
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model_path = "models/bayesian_modelPAGE.pth"
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from src.classifiers.bayesian import BayesianClassifier
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from collections import defaultdict
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import os
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import cv2
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import numpy as np
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# Initialisation
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bayesian_model = BayesianClassifier()
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print("Début de l'entraînement...")
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class_features = defaultdict(list)
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total_images = 0
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# Parcours des classes dans le dataset
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for class_name in os.listdir(dataset_path):
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if class_name not in allowed_classes:
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continue
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class_folder_path = os.path.join(dataset_path, class_name)
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if not os.path.isdir(class_folder_path):
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continue
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if class_name not in bayesian_model.classes:
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bayesian_model.classes.append(class_name)
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for image_name in os.listdir(class_folder_path):
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image_path = os.path.join(class_folder_path, image_name)
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image = cv2.imread(image_path)
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if image is not None:
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features = bayesian_model.extract_features(image)
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for feature in features:
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class_features[class_name].append(feature)
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total_images += 1
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# Calcul des statistiques pour chaque classe
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for class_name in bayesian_model.classes:
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if class_name in class_features:
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features = np.array(class_features[class_name])
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bayesian_model.feature_means[class_name] = np.mean(features, axis=0)
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bayesian_model.feature_variances[class_name] = np.var(features, axis=0) + 1e-6
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bayesian_model.class_priors[class_name] = len(features) / total_images
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print("Entraînement terminé.")
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bayesian_model.save_model(model_path)
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print(f"Modèle sauvegardé dans : {model_path}")
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