CARIA.2.1

Update for the final presentation
huge change with previous version

server-trainer/model_training/CV2_UsersIdentification-ModelTraining.py

@@ -0,0 +1,59 @@
+import cv2
+import os
+import numpy as np
+import pickle
+from pathlib import Path
+
+# Définir les chemins
+IMAGE_DIR = Path(r"W:/CARIA/images/avatars")
+LABELS_FILE = Path("server-ia/data/modeles/CV2/labels.pickle")
+TRAINER_FILE = Path("server-ia/data/modeles/CV2/trainner.yml")
+
+# Initialiser les variables
+current_id = 0
+label_ids = {}
+x_train = []
+y_labels = []
+
+def process_images(image_dir):
+    global current_id, label_ids, x_train, y_labels
+    print("Début du traitement des images...")
+    for root, _, files in os.walk(image_dir):
+        if files:
+            label = Path(root).name
+            for file in files:
+                if file.lower().endswith(".jpg"):
+                    path = Path(root) / file
+                    if label not in label_ids:
+                        label_ids[label] = current_id
+                        current_id += 1
+                    id_ = label_ids[label]
+                    image = cv2.imread(str(path), cv2.IMREAD_GRAYSCALE)
+                    if image is not None:
+                        x_train.append(image)
+                        y_labels.append(id_)
+    print(f"Traitement terminé. {len(x_train)} images chargées.")
+
+def save_data():
+    print("Sauvegarde des étiquettes...")
+    with open(LABELS_FILE, "wb") as f:
+        pickle.dump(label_ids, f)
+    print(f"Étiquettes sauvegardées dans {LABELS_FILE}.")
+
+def train_recognizer():
+    print("Entraînement du reconnaisseur...")
+    x_train_np = np.array(x_train)
+    y_labels_np = np.array(y_labels)
+    recognizer = cv2.face.LBPHFaceRecognizer_create()
+    recognizer.train(x_train_np, y_labels_np)
+    recognizer.save(str(TRAINER_FILE))
+    print(f"Modèle entraîné et sauvegardé dans {TRAINER_FILE}.")
+
+def main():
+    process_images(IMAGE_DIR)
+    save_data()
+    train_recognizer()
+    print("Traitement complet. Les modèles ont été sauvegardés.")
+
+if __name__ == "__main__":
+    main()