File size: 8,856 Bytes
f894a3c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | # /// script
# requires-python = ">=3.11"
# dependencies = ["datasets>=4.0.0", "huggingface-hub", "pillow", "matplotlib"]
# ///
"""
Local visual inspection for the output of qwen3vl-detect.py.
Given an output dataset (produced by qwen3vl-detect.py) and optionally its
source dataset (with ground-truth bboxes), renders side-by-side PNGs of
predicted detections vs ground truth, one per row.
Output: a /tmp/<slug>-viz/ directory of PNGs and a per-row text summary
(detection counts, label histograms, bbox value ranges).
Usage:
uv run inspect-detections.py OUTPUT_DATASET [--split SPLIT] [--source SOURCE_DATASET]
If --source is provided, the script will also load the source dataset, pull
ground-truth `objects` (bbox + category), auto-discover class names from the
ClassLabel feature, and overlay GT boxes on a second panel for comparison.
"""
import argparse
import json
import os
from collections import Counter
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datasets import load_dataset
def render_box(ax, x1, y1, x2, y2, color, lw=1.2, label=None, fontsize=7):
rect = mpatches.Rectangle(
(x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor=color, linewidth=lw
)
ax.add_patch(rect)
if label:
ax.text(
x1,
y1,
label,
fontsize=fontsize,
color="white",
bbox=dict(
boxstyle="round,pad=0.15",
facecolor=color,
alpha=0.75,
edgecolor="none",
),
va="bottom",
ha="left",
)
def discover_class_names(ds) -> list[str]:
"""Pull class names from an `objects.category` or `objects.category_id`
ClassLabel feature on a HF dataset. Returns [] if not found."""
feats = ds.features
if "objects" not in feats:
return []
obj_feat = feats["objects"]
# objects may be List[Dict[...]] or Sequence(Dict[...]) — both expose `feature`
inner = getattr(obj_feat, "feature", None) or obj_feat
if not hasattr(inner, "keys"):
return []
for key in ("category", "category_id"):
if key in inner:
cat_feat = inner[key]
# Sequence(ClassLabel) or ClassLabel
cl = getattr(cat_feat, "feature", None) or cat_feat
names = getattr(cl, "names", None)
if names:
return list(names)
return []
def main() -> None:
parser = argparse.ArgumentParser(
description="Render Qwen3-VL detection output side-by-side with ground truth."
)
parser.add_argument("output_dataset", help="HF dataset ID with detections column")
parser.add_argument(
"--split", default="train", help="Split of output_dataset (default: train)"
)
parser.add_argument(
"--source",
default=None,
help="Optional source dataset with ground-truth `objects` for GT overlay panel.",
)
parser.add_argument(
"--source-split", default=None, help="Split of source dataset (default: same as --split)"
)
parser.add_argument(
"--max-rows", type=int, default=None, help="Render only the first N rows"
)
parser.add_argument(
"--out-dir",
default=None,
help="Output directory (default: /tmp/<output-slug>-viz/)",
)
args = parser.parse_args()
slug = args.output_dataset.split("/")[-1]
out_dir = args.out_dir or f"/tmp/{slug}-viz"
os.makedirs(out_dir, exist_ok=True)
print(f"Loading {args.output_dataset} split={args.split}…")
ds = load_dataset(args.output_dataset, split=args.split)
if args.max_rows:
ds = ds.select(range(min(args.max_rows, len(ds))))
print(f"rows={len(ds)} cols={ds.column_names}")
# qwen3vl-detect preserves the source `objects` column on each output row,
# so GT is co-located with detections (no shuffled-index join needed).
# The source dataset (if provided) is used only to recover ClassLabel names
# since push_to_hub strips the ClassLabel typing on round-trip.
gt_names: list[str] = []
if args.source:
gt_split = args.source_split or args.split
print(f"Loading source {args.source} split={gt_split} for class names…")
gt_src = load_dataset(args.source, split=gt_split)
gt_names = discover_class_names(gt_src)
print(f"GT classes ({len(gt_names)}): {gt_names}")
elif "objects" in ds.column_names:
gt_names = discover_class_names(ds)
if gt_names:
print(f"GT classes from output features: {gt_names}")
for i in range(len(ds)):
row = ds[i]
img = row["image"]
W, H = img.size
info = json.loads(row["inference_info"])
dets = row["detections"]
# GT extraction from the output row itself (qwen3vl-detect preserves
# all input columns including `objects`). Handles both list-of-dicts
# and dict-of-lists shapes.
gt_cats, gt_bbox = [], []
if "objects" in row and row["objects"] is not None:
objs = row["objects"]
if isinstance(objs, dict):
gt_cats = objs.get("category", objs.get("category_id", []))
gt_bbox = objs.get("bbox", [])
else: # list of dicts
for o in objs:
if "category" in o:
gt_cats.append(o["category"])
elif "category_id" in o:
gt_cats.append(o["category_id"])
gt_bbox.append(o["bbox"])
bbox_vals = [v for d in dets for v in d["bbox"]]
bbox_max = max(bbox_vals) if bbox_vals else 0
bbox_min = min(bbox_vals) if bbox_vals else 0
qwen_hist = Counter(d["label"] for d in dets)
gt_hist = Counter(
gt_names[c] if 0 <= c < len(gt_names) else f"#{c}" for c in gt_cats
) if gt_names else Counter()
print(
f"\n=== row {i} image_id={row.get('image_id', '?')} orig={W}x{H} "
f"inference_image_size={info['image_size']} ==="
)
if gt_cats:
print(f" GT: {len(gt_cats)} objects {dict(gt_hist)}")
print(f" Qwen: {len(dets)} detections {dict(qwen_hist)}")
print(f" Qwen bbox range: [{bbox_min:.0f}, {bbox_max:.0f}] (image {W}x{H})")
# Render
n_panels = 2 if gt_cats else 1
fig, axes = plt.subplots(1, n_panels, figsize=(10 * n_panels, 12))
if n_panels == 1:
axes = [axes]
for ax in axes:
ax.imshow(img)
ax.set_xlim(0, W)
ax.set_ylim(H, 0)
ax.set_aspect("equal")
ax.axis("off")
# v1.1+ output stores bbox in pixel coords; v1 stored 0-1000 normalised.
needs_denorm = bbox_max <= 1001 and max(W, H) > 1001
sx = (W / 1000.0) if needs_denorm else 1.0
sy = (H / 1000.0) if needs_denorm else 1.0
title_extra = "(0-1000 → pixels)" if needs_denorm else "(pixel coords)"
axes[0].set_title(
f"Detections (n={len(dets)}) {title_extra} range [{bbox_min:.0f}, {bbox_max:.0f}]",
fontsize=11,
)
for d in dets:
b = d["bbox"]
if len(b) == 4:
render_box(axes[0], b[0] * sx, b[1] * sy, b[2] * sx, b[3] * sy, "#E03030", lw=1.0)
for d in dets[:10]:
b = d["bbox"]
if len(b) == 4:
render_box(
axes[0],
b[0] * sx,
b[1] * sy,
b[2] * sx,
b[3] * sy,
"#E03030",
lw=1.4,
label=d["label"][:18],
fontsize=8,
)
if gt_cats and len(axes) > 1:
palette = [
"#1f78b4", "#33a02c", "#ff7f00", "#6a3d9a", "#b15928",
"#e31a1c", "#fb9a99", "#a6cee3", "#b2df8a", "#fdbf6f",
"#cab2d6", "#ffff99",
]
color_by_name: dict[str, str] = {}
axes[1].set_title(f"Ground truth (n={len(gt_cats)})", fontsize=11)
for c, b in zip(gt_cats, gt_bbox):
if len(b) != 4:
continue
name = gt_names[c] if 0 <= c < len(gt_names) else f"#{c}"
color = color_by_name.setdefault(name, palette[len(color_by_name) % len(palette)])
x, y, w_, h_ = b # COCO xywh
render_box(axes[1], x, y, x + w_, y + h_, color, lw=2.0, label=name[:18], fontsize=8)
plt.tight_layout()
path = f"{out_dir}/row{i}_id{row.get('image_id', i)}.png"
plt.savefig(path, dpi=70, bbox_inches="tight")
plt.close()
print(f"\nDone. {len(ds)} rows. Open: {out_dir}")
if __name__ == "__main__":
main()
|