base code test for training a model with basil and tomatoes images to include in step 3 of previous code to improve answer

test2_with_training/scripts/prepare_data.py

@@ -0,0 +1,50 @@
+import os
+import shutil
+from sklearn.model_selection import train_test_split
+
+# 1) Definisci il nome delle classi e la corrispondente cartella in data/all_plants
+#    ASSICURATI che questi nomi corrispondano ESATTAMENTE alle directory in data/all_plants/
+classes = {
+    "basil":  "basil",   # es: data/all_plants/basil/
+    "tomato": "tomato"   # es: data/all_plants/tomato/
+}
+
+# 2) Percorsi principali
+src_root = "data/all_plants"
+dst_root = "data/basil_tomato"
+
+# 3) Per ciascuna classe, prendi tutte le immagini e crea split train/val
+for label, folder in classes.items():
+    src_folder = os.path.join(src_root, folder)
+    if not os.path.isdir(src_folder):
+        raise FileNotFoundError(f"Cartella non trovata: {src_folder}")
+    
+    # Elenca file (filtra solo immagini .jpg/.png se vuoi)
+    all_images = [f for f in os.listdir(src_folder)
+                  if os.path.isfile(os.path.join(src_folder, f))
+                     and f.lower().endswith((".jpg", ".jpeg", ".png"))]
+    
+    # 80% train, 20% validation
+    train_imgs, val_imgs = train_test_split(
+        all_images, test_size=0.2, random_state=42, shuffle=True
+    )
+    
+    # Copia train
+    dst_train = os.path.join(dst_root, "train", label)
+    os.makedirs(dst_train, exist_ok=True)
+    for img in train_imgs:
+        shutil.copy(
+            os.path.join(src_folder, img),
+            os.path.join(dst_train, img)
+        )
+    
+    # Copia validation
+    dst_val = os.path.join(dst_root, "val", label)
+    os.makedirs(dst_val, exist_ok=True)
+    for img in val_imgs:
+        shutil.copy(
+            os.path.join(src_folder, img),
+            os.path.join(dst_val, img)
+        )
+
+    print(f"[{label}] Copiate {len(train_imgs)} immagini in train/ e {len(val_imgs)} in val/")