46dea3304f
This commit integrates the backend API for fetching and updating report data. It also includes a normalization function to handle data consistency between the API and local storage. Co-authored-by: anthonymuncher <anthonymuncher@gmail.com>
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
import os
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import shutil
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import random
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from pathlib import Path
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# ---------- CONFIG ----------
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SRC_DIR = Path("dataset_downloads") # where new images are
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DST_DIR = Path("dataset") # your main dataset folder
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TRAIN_SPLIT = 0.8 # 80% train, 20% val
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# Classes to process
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NEW_CLASSES = ["broken streetlight", "drainage"]
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for cls in NEW_CLASSES:
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src_class_dir = SRC_DIR / cls
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if not src_class_dir.exists():
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print(f"⚠️ Source folder not found: {src_class_dir}")
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continue
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# Prepare destination folders
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train_dest = DST_DIR / "train" / cls
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val_dest = DST_DIR / "val" / cls
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train_dest.mkdir(parents=True, exist_ok=True)
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val_dest.mkdir(parents=True, exist_ok=True)
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# List all images
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images = list(src_class_dir.glob("*.*")) # jpg, png, jpeg
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random.shuffle(images)
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# Split
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split_idx = int(len(images) * TRAIN_SPLIT)
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train_imgs = images[:split_idx]
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val_imgs = images[split_idx:]
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# Move images
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for img in train_imgs:
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shutil.move(str(img), train_dest / img.name)
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for img in val_imgs:
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shutil.move(str(img), val_dest / img.name)
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print(f"✅ Class '{cls}' added: {len(train_imgs)} train, {len(val_imgs)} val")
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print("All new classes are organized and ready for training!")
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