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f711f7f | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
from PIL import Image, ImageDraw
import psycopg
from psycopg import sql
def normalize_pg_url(url: str) -> str:
if url.startswith("postgresql+psycopg://"):
return "postgresql://" + url.split("postgresql+psycopg://", 1)[1]
return url
def save_grid(label: str, image_paths: list[Path], out_path: Path) -> None:
if not image_paths:
return
thumbs: list[Image.Image] = []
for path in image_paths:
thumbs.append(Image.open(path).convert("RGB").resize((128, 128), Image.Resampling.BILINEAR))
cols = 4
rows = (len(thumbs) + cols - 1) // cols
canvas = Image.new("RGB", (cols * 128, rows * 128 + 30), (255, 255, 255))
draw = ImageDraw.Draw(canvas)
draw.text((8, 6), f"{label} pgvector top-{len(image_paths)}", fill=(0, 0, 0))
for idx, thumb in enumerate(thumbs):
r = idx // cols
c = idx % cols
canvas.paste(thumb, (c * 128, r * 128 + 30))
out_path.parent.mkdir(parents=True, exist_ok=True)
canvas.save(out_path)
def parse_labels(raw: str) -> list[str]:
return [item.strip() for item in raw.split(",") if item.strip()]
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--db-url", required=True, help="PostgreSQL URL")
parser.add_argument("--data-root", default="data/quickdraw/images")
parser.add_argument("--embedding-table", default="quickdraw_embeddings")
parser.add_argument("--prototype-table", default="quickdraw_prototypes")
parser.add_argument("--embedding-source", default="quickdraw_train_v1")
parser.add_argument("--prototype-source", default="quickdraw_avg_v1")
parser.add_argument("--labels", required=True, help="Comma-separated labels")
parser.add_argument("--top-k", type=int, default=12)
parser.add_argument("--out-dir", default="data/quickdraw/pgvector_inspect")
args = parser.parse_args()
labels = parse_labels(args.labels)
if not labels:
raise RuntimeError("labels must not be empty")
data_root = Path(args.data_root)
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
db_url = normalize_pg_url(args.db_url)
summary: dict[str, dict[str, object]] = {}
query = sql.SQL(
"""
SELECT e.image_path, 1 - (e.embedding <=> p.embedding) AS similarity
FROM {embedding_table} e
JOIN {prototype_table} p
ON p.label = %s
AND p.source = %s
WHERE e.label = %s
AND e.source = %s
ORDER BY e.embedding <=> p.embedding
LIMIT %s
"""
).format(
embedding_table=sql.Identifier(args.embedding_table),
prototype_table=sql.Identifier(args.prototype_table),
)
with psycopg.connect(db_url) as conn:
with conn.cursor() as cur:
for label in labels:
cur.execute(
query,
(
label,
args.prototype_source,
label,
args.embedding_source,
args.top_k,
),
)
rows = cur.fetchall()
if not rows:
print(f"[warn] {label}: no rows found")
summary[label] = {"top_k": 0, "items": [], "output": None}
continue
items: list[dict[str, object]] = []
found_paths: list[Path] = []
missing_count = 0
for image_path, similarity in rows:
rel = Path(str(image_path))
abs_path = data_root / rel
exists = abs_path.exists()
if exists:
found_paths.append(abs_path)
else:
missing_count += 1
items.append(
{
"image_path": str(rel).replace("\\", "/"),
"absolute_path": str(abs_path),
"similarity": float(similarity),
"exists": exists,
}
)
out_png = out_dir / f"{label}_top{len(found_paths)}.png"
if found_paths:
save_grid(label, found_paths, out_png)
print(f"[done] {label}: wrote {out_png} (missing={missing_count})")
else:
print(f"[warn] {label}: all top-k files missing under {data_root}")
summary[label] = {
"top_k_requested": args.top_k,
"top_k_found_files": len(found_paths),
"missing_files": missing_count,
"output": str(out_png) if found_paths else None,
"items": items,
}
summary_path = out_dir / "summary.json"
summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
print(f"[done] summary: {summary_path}")
if __name__ == "__main__":
main()
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