59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
import os
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import shutil
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import random
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SOURCE_DIR = "../dataset"
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TARGET_DIR = "dataset_model1"
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TRAIN_RATIO = 0.8
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VAL_RATIO = 0.10
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TEST_RATIO = 0.10
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random.seed(42)
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JAGUNG_CLASSES = ["hawar_daun", "karat_daun", "sehat"]
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for split in ["train", "val", "test"]:
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os.makedirs(os.path.join(TARGET_DIR, split, "jagung"), exist_ok=True)
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os.makedirs(os.path.join(TARGET_DIR, split, "non_jagung"), exist_ok=True)
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for class_name in os.listdir(SOURCE_DIR):
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class_path = os.path.join(SOURCE_DIR, class_name)
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if not os.path.isdir(class_path):
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continue
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images = [
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img for img in os.listdir(class_path)
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if img.lower().endswith((".jpg", ".png", ".jpeg"))
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]
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random.shuffle(images)
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total = len(images)
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train_end = int(total * TRAIN_RATIO)
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val_end = train_end + int(total * VAL_RATIO)
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splits = {
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"train": images[:train_end],
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"val": images[train_end:val_end],
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"test": images[val_end:]
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}
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# 🔥 Tentukan label
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if class_name in JAGUNG_CLASSES:
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label = "jagung"
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else:
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label = "non_jagung"
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for split_name, split_images in splits.items():
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target_dir = os.path.join(TARGET_DIR, split_name, label)
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for img in split_images:
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src = os.path.join(class_path, img)
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dst = os.path.join(target_dir, f"{class_name}_{img}")
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shutil.copy2(src, dst)
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print(f"✅ {class_name} → {label}")
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print("🎉 Model 1 dataset siap!") |