| """Build Dataset Viewer parquet shards: 1024px JPEG previews + per-image counts.""" |
| import io |
| from pathlib import Path |
|
|
| import pandas as pd |
| from datasets import Dataset, Features, Image as HFImage, Value |
| from PIL import Image, ImageFile |
| from tqdm import tqdm |
|
|
| |
| |
| ImageFile.LOAD_TRUNCATED_IMAGES = True |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| DATA = ROOT / "data" |
| OUT = ROOT / "parquet" |
| SHARDS = {"train": 4, "val": 1, "test": 1} |
| FEATURES = Features( |
| {"image": HFImage(), "berry_count": Value("int32"), "cluster_count": Value("int32")} |
| ) |
|
|
|
|
| def preview_bytes(path): |
| img = Image.open(path).convert("RGB") |
| img.thumbnail((1024, 1024)) |
| buf = io.BytesIO() |
| img.save(buf, format="JPEG", quality=85) |
| return buf.getvalue() |
|
|
|
|
| def main(): |
| OUT.mkdir(exist_ok=True) |
| meta = pd.read_csv(DATA / "metadata.csv") |
| for split, n_shards in SHARDS.items(): |
| rows = meta[meta.split == split].reset_index(drop=True) |
|
|
| def gen(rows=rows): |
| for r in tqdm(rows.itertuples(), total=len(rows), desc=split): |
| yield { |
| "image": {"bytes": preview_bytes(DATA / "imgs" / r.file_name), |
| "path": r.file_name}, |
| "berry_count": int(r.berry_count), |
| "cluster_count": int(r.cluster_count), |
| } |
|
|
| ds = Dataset.from_generator(gen, features=FEATURES) |
| for i in range(n_shards): |
| shard_path = OUT / f"{split}-{i:05d}-of-{n_shards:05d}.parquet" |
| ds.shard(num_shards=n_shards, index=i).to_parquet(shard_path) |
| print("wrote", shard_path, f"{shard_path.stat().st_size/1e6:.0f} MB") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
