File size: 5,604 Bytes
3688d57 | 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 | #!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "datasets>=4.0",
# "huggingface_hub>=1.27", # hf://buckets in HfFileSystem (1.6) + prefix-collision fix (1.27)
# "pillow",
# "numpy",
# "pycocotools>=2.0.11",
# ]
# ///
"""Render detection overlays from a dataset in this directory's schema -- and PROVE they rendered.
Draws boxes (and masks, when masks_rle is present) over the embedded images and writes PNGs.
Before reporting success it pixel-diffs every render against its source image: a page with
instances whose render is identical to the source means the overlay silently failed (alpha
bugs, empty mask lists, wrong-column reads -- all observed in real runs, twice shown to a
human as "done"). Any blank render exits nonzero and names the file.
uv run render-detections.py <ns>/<teacher-or-training-dataset> --limit 10 --out previews/
uv run render-detections.py "hf://buckets/<ns>/<bucket>/dataset/train.parquet" --out previews/
"""
import argparse
import io
import json
import sys
from pathlib import Path
import numpy as np
from datasets import Image as HFImage
from datasets import load_dataset
from PIL import Image, ImageDraw
COLORS = [
(255, 210, 0),
(80, 200, 120),
(90, 160, 255),
(230, 90, 80),
(200, 120, 220),
(255, 150, 50),
]
def main():
p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
p.add_argument(
"data", help="dataset repo id, or a parquet path/glob (hf:// or local)"
)
p.add_argument("--split", default="train")
p.add_argument("--limit", type=int, default=10)
p.add_argument("--out", default="previews")
p.add_argument("--bbox-format", default="yolo", choices=["yolo", "coco_xywh"])
p.add_argument("--no-masks", action="store_true")
p.add_argument(
"--min-pixels",
type=int,
default=1,
help="a page with instances whose render changed fewer pixels than this is BLANK "
"(default 1: any drawn pixel proves the overlay; a 50x50 box on a 3000px scan is real)",
)
args = p.parse_args()
if "://" in args.data or args.data.endswith(".parquet"):
ds = load_dataset("parquet", data_files=args.data, split="train")
else:
ds = load_dataset(args.data, split=args.split)
assert "image" in ds.column_names, (
"no image column in this dataset — nothing to render over"
)
ds = ds.select(range(min(args.limit, len(ds))))
out = Path(args.out)
out.mkdir(parents=True, exist_ok=True)
blank, rendered, skipped = [], 0, []
# undecoded bytes: a corrupt image or an error row is skipped, not a crash inside datasets
for row in ds.cast_column("image", HFImage(decode=False)).with_format(None):
raw = row["image"]
try:
src = (
Image.open(io.BytesIO(raw["bytes"])).convert("RGB")
if raw and raw.get("bytes")
else None
)
except Exception: # noqa: BLE001 -- any decode failure means "skip this row"
src = None
if src is None or row.get("error"):
skipped.append(row["image_id"])
continue
im = src.copy()
w, h = im.size
n = len(row["objects"]["bbox"])
if not args.no_masks and row.get("masks_rle"):
from pycocotools import mask as mask_utils
overlay = Image.new("RGBA", im.size, (0, 0, 0, 0))
for i, rle in enumerate(json.loads(row["masks_rle"])):
seg = mask_utils.decode({**rle, "counts": rle["counts"].encode()})
if seg.shape != (h, w):
seg = np.asarray(Image.fromarray(seg).resize((w, h), Image.NEAREST))
r, g, b = COLORS[i % len(COLORS)]
tint = np.zeros((h, w, 4), np.uint8)
tint[seg > 0] = (r, g, b, 110)
overlay = Image.alpha_composite(overlay, Image.fromarray(tint))
im = Image.alpha_composite(im.convert("RGBA"), overlay).convert("RGB")
draw = ImageDraw.Draw(im)
for i, bbox in enumerate(row["objects"]["bbox"]):
if args.bbox_format == "yolo":
cx, cy, bw, bh = bbox
box = [
(cx - bw / 2) * w,
(cy - bh / 2) * h,
(cx + bw / 2) * w,
(cy + bh / 2) * h,
]
else:
x, y, bw, bh = bbox
box = [x, y, x + bw, y + bh]
draw.rectangle(box, outline=COLORS[i % len(COLORS)], width=max(3, w // 400))
name = f"{row['image_id']}_{n}inst.png"
im.save(out / name)
# ---- the point of this script: prove the overlay exists ----
changed = int(np.any(np.asarray(src) != np.asarray(im), axis=-1).sum())
if n > 0 and changed < args.min_pixels:
blank.append(name)
elif n > 0:
rendered += 1
print(
f"{name}: {n} instances, {changed} pixels changed ({changed / (w * h):.2%})"
)
if blank:
sys.exit(
f"BLANK RENDERS ({len(blank)}): {blank} — overlays did not draw; do not show these to a human."
)
if skipped:
print(f"skipped {len(skipped)} undecodable/error rows, e.g. {skipped[:3]}")
if rendered == 0:
sys.exit(
"No page with instances was rendered — nothing verified; increase --limit."
)
print(f"OK: {rendered} non-empty renders verified against source pixels -> {out}/")
if __name__ == "__main__":
main()
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