File size: 7,798 Bytes
974f31e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | #!/usr/bin/env python3
"""Create and optionally publish a resized HF dataset for the HyperView Space."""
from __future__ import annotations
import argparse
import json
import os
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
import pandas as pd
from PIL import Image
from datasets import Dataset, Image as HFImage
PROJECT_ROOT = Path(__file__).resolve().parents[2]
DEFAULT_DATASET_ROOT = PROJECT_ROOT / "kaggle_jaguar_dataset_v2"
DEFAULT_CORESET_CSV = PROJECT_ROOT / "data/validation_coreset.csv"
DEFAULT_OUTPUT_DIR = PROJECT_ROOT / "HyperViewDemoHuggingFaceSpace/dataset_build"
DEFAULT_REPO_ID = os.environ.get("HF_DATASET_REPO", "hyper3labs/jaguar-hyperview-demo")
def utc_now() -> str:
return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Build resized train+validation demo dataset with split tags for HyperView."
)
parser.add_argument("--dataset_root", type=Path, default=DEFAULT_DATASET_ROOT)
parser.add_argument("--coreset_csv", type=Path, default=DEFAULT_CORESET_CSV)
parser.add_argument("--output_dir", type=Path, default=DEFAULT_OUTPUT_DIR)
parser.add_argument("--repo_id", type=str, default=DEFAULT_REPO_ID)
parser.add_argument("--config_name", type=str, default="default")
parser.add_argument("--image_size", type=int, default=384)
parser.add_argument("--jpeg_quality", type=int, default=90)
parser.add_argument(
"--image_variant",
type=str,
default="foreground_only",
choices=["foreground_only", "full_rgb"],
)
parser.add_argument("--max_samples", type=int, default=None)
parser.add_argument("--private", action="store_true")
parser.add_argument("--hf_token_env", type=str, default="HF_TOKEN")
parser.add_argument("--no_push", action="store_true")
return parser.parse_args()
def load_rows(dataset_root: Path, coreset_csv: Path, max_samples: int | None) -> pd.DataFrame:
train_csv = dataset_root / "train.csv"
if not train_csv.exists():
raise FileNotFoundError(f"Missing train.csv at {train_csv}")
train_df = pd.read_csv(train_csv)
coreset_df = pd.read_csv(coreset_csv)
coreset_filenames = set(coreset_df["filename"].astype(str).tolist())
train_df = train_df.copy()
train_df["filename"] = train_df["filename"].astype(str)
train_df["label"] = train_df["ground_truth"].astype(str)
train_df["split_tag"] = np.where(train_df["filename"].isin(coreset_filenames), "validation", "train")
train_df["sample_id"] = train_df["filename"]
if max_samples is not None:
train_df = train_df.iloc[: int(max_samples)].copy()
return train_df[["filename", "label", "split_tag", "sample_id"]]
def load_variant_image(image_path: Path, image_variant: str) -> Image.Image:
if image_variant == "foreground_only":
rgba = Image.open(image_path).convert("RGBA")
rgba_np = np.array(rgba, dtype=np.uint8)
rgb = rgba_np[:, :, :3]
alpha = rgba_np[:, :, 3]
mask = (alpha > 0).astype(np.uint8)
cutout_rgb = (rgb * mask[:, :, np.newaxis]).astype(np.uint8)
return Image.fromarray(cutout_rgb, mode="RGB")
return Image.open(image_path).convert("RGB")
def build_resized_images(
rows_df: pd.DataFrame,
dataset_root: Path,
output_images_dir: Path,
image_size: int,
jpeg_quality: int,
image_variant: str,
) -> pd.DataFrame:
source_images_dir = dataset_root / "train"
if not source_images_dir.exists():
raise FileNotFoundError(f"Missing image directory: {source_images_dir}")
output_images_dir.mkdir(parents=True, exist_ok=True)
records: list[dict[str, str]] = []
for _, row in rows_df.iterrows():
filename = str(row["filename"])
src = source_images_dir / filename
if not src.exists():
raise FileNotFoundError(f"Missing source image: {src}")
image = load_variant_image(src, image_variant=image_variant)
image = image.resize((int(image_size), int(image_size)), Image.Resampling.BICUBIC)
dst_name = f"{Path(filename).stem}.jpg"
dst = output_images_dir / dst_name
image.save(dst, format="JPEG", quality=int(jpeg_quality), optimize=True)
records.append(
{
"image": str(dst),
"label": str(row["label"]),
"filename": filename,
"split_tag": str(row["split_tag"]),
"sample_id": str(row["sample_id"]),
}
)
return pd.DataFrame(records)
def build_hf_dataset(records_df: pd.DataFrame) -> Dataset:
payload = {
"image": records_df["image"].tolist(),
"label": records_df["label"].tolist(),
"filename": records_df["filename"].tolist(),
"split_tag": records_df["split_tag"].tolist(),
"sample_id": records_df["sample_id"].tolist(),
}
dataset = Dataset.from_dict(payload)
dataset = dataset.cast_column("image", HFImage())
return dataset
def maybe_push_to_hub(
dataset: Dataset,
repo_id: str,
config_name: str,
private: bool,
hf_token_env: str,
no_push: bool,
) -> str:
if no_push:
return "skipped (--no_push)"
token = os.environ.get(hf_token_env)
if not token:
raise RuntimeError(
f"Missing Hugging Face token in environment variable {hf_token_env}."
)
dataset.push_to_hub(
repo_id=repo_id,
config_name=config_name,
token=token,
private=bool(private),
)
return f"pushed:{repo_id}:{config_name}"
def main() -> int:
args = parse_args()
output_dir = args.output_dir.resolve()
images_out = output_dir / "images"
dataset_out = output_dir / "hf_dataset"
output_dir.mkdir(parents=True, exist_ok=True)
rows_df = load_rows(
dataset_root=args.dataset_root.resolve(),
coreset_csv=args.coreset_csv.resolve(),
max_samples=args.max_samples,
)
if rows_df.empty:
raise RuntimeError("No dataset rows found for publish pipeline.")
records_df = build_resized_images(
rows_df=rows_df,
dataset_root=args.dataset_root.resolve(),
output_images_dir=images_out,
image_size=int(args.image_size),
jpeg_quality=int(args.jpeg_quality),
image_variant=args.image_variant,
)
dataset = build_hf_dataset(records_df)
dataset.save_to_disk(str(dataset_out))
publish_status = maybe_push_to_hub(
dataset=dataset,
repo_id=args.repo_id,
config_name=args.config_name,
private=args.private,
hf_token_env=args.hf_token_env,
no_push=args.no_push,
)
metadata = {
"generated_at_utc": utc_now(),
"dataset_root": str(args.dataset_root.resolve()),
"coreset_csv": str(args.coreset_csv.resolve()),
"output_dir": str(output_dir),
"repo_id": args.repo_id,
"config_name": args.config_name,
"image_size": int(args.image_size),
"jpeg_quality": int(args.jpeg_quality),
"image_variant": args.image_variant,
"num_rows": int(len(records_df)),
"split_counts": records_df["split_tag"].value_counts().to_dict(),
"push_status": publish_status,
}
metadata_path = output_dir / "publish_metadata.json"
metadata_path.write_text(json.dumps(metadata, indent=2), encoding="utf-8")
print("=== HyperView demo dataset pipeline complete ===")
print(f"Rows: {len(records_df)}")
print(f"HF dataset saved to: {dataset_out}")
print(f"Push status: {publish_status}")
print(f"Metadata: {metadata_path}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
|