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