47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
import os
|
|
import shutil
|
|
import random
|
|
|
|
SOURCE_DIR = "../dataset"
|
|
TARGET_DIR = "dataset_model2"
|
|
|
|
TRAIN_RATIO = 0.8
|
|
VAL_RATIO = 0.10
|
|
TEST_RATIO = 0.10
|
|
|
|
random.seed(42)
|
|
|
|
VALID_CLASSES = ["hawar_daun", "karat_daun", "sehat"]
|
|
|
|
for class_name in VALID_CLASSES:
|
|
class_path = os.path.join(SOURCE_DIR, class_name)
|
|
|
|
images = [
|
|
img for img in os.listdir(class_path)
|
|
if img.lower().endswith((".jpg", ".png", ".jpeg"))
|
|
]
|
|
|
|
random.shuffle(images)
|
|
|
|
total = len(images)
|
|
train_end = int(total * TRAIN_RATIO)
|
|
val_end = train_end + int(total * VAL_RATIO)
|
|
|
|
splits = {
|
|
"train": images[:train_end],
|
|
"val": images[train_end:val_end],
|
|
"test": images[val_end:]
|
|
}
|
|
|
|
for split_name, split_images in splits.items():
|
|
target_dir = os.path.join(TARGET_DIR, split_name, class_name)
|
|
os.makedirs(target_dir, exist_ok=True)
|
|
|
|
for img in split_images:
|
|
src = os.path.join(class_path, img)
|
|
dst = os.path.join(target_dir, img)
|
|
shutil.copy2(src, dst)
|
|
|
|
print(f"✅ {class_name} diproses")
|
|
|
|
print("🎉 Model 2 dataset siap!") |