File size: 4,730 Bytes
5a5e5cf | 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 | #!/usr/bin/env python3
"""Create one event-centered visual audit sheet per scenario family."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import cv2
import numpy as np
PROJECT_ROOT = Path(__file__).resolve().parents[2]
DEFAULT_DATASET_ROOT = PROJECT_ROOT / "closed_dataset"
DEFAULT_RUN_ROOT = DEFAULT_DATASET_ROOT / "acceptance_final_v1_runs"
DEFAULT_OUTPUT = DEFAULT_DATASET_ROOT / "acceptance_final_v1/contact_sheets"
PHYSICAL_COMPLETION_EVENTS = {
"pedestrian_crossing_completed",
"cut_in_completed",
"obstacle_reveal_completed",
"hazard_actor_cleared_route",
"flow_actor_cleared",
}
def _json_lines(path: Path) -> list[dict]:
return [
json.loads(line)
for line in path.read_text(encoding="utf-8").splitlines()
if line.strip()
]
def _event_frame(run_dir: Path) -> int:
timeline = _json_lines(run_dir / "event_timeline.jsonl")
clock = _json_lines(run_dir / "logs/frame_clock.jsonl")
start = next(item for item in timeline if item.get("event") == "hazard_started")
completion = next(
item
for item in timeline
if item.get("event") in PHYSICAL_COMPLETION_EVENTS
)
target_game_time = (
float(start["game_time"]) + float(completion["game_time"])
) / 2.0
sim_offset = float(clock[0]["sim_time"])
target_sim_time = sim_offset + target_game_time
nearest = min(clock, key=lambda item: abs(float(item["sim_time"]) - target_sim_time))
return int(nearest["dataset_frame"])
def _read_video_frame(video: Path, frame_index: int) -> np.ndarray:
capture = cv2.VideoCapture(str(video))
if not capture.isOpened():
raise RuntimeError(f"cannot open {video}")
capture.set(cv2.CAP_PROP_POS_FRAMES, frame_index)
ok, frame = capture.read()
capture.release()
if not ok:
raise RuntimeError(f"cannot read frame {frame_index} from {video}")
return frame
def _tile(frame: np.ndarray, title: str, subtitle: str) -> np.ndarray:
width, height = 384, 216
frame = cv2.resize(frame, (width, height), interpolation=cv2.INTER_AREA)
canvas = np.zeros((height + 54, width, 3), dtype=np.uint8)
canvas[54:] = frame
cv2.putText(
canvas,
title,
(8, 21),
cv2.FONT_HERSHEY_SIMPLEX,
0.52,
(255, 255, 255),
1,
cv2.LINE_AA,
)
cv2.putText(
canvas,
subtitle,
(8, 44),
cv2.FONT_HERSHEY_SIMPLEX,
0.43,
(190, 220, 255),
1,
cv2.LINE_AA,
)
return canvas
def build(dataset_root: Path, run_root: Path, output: Path) -> None:
manifest = json.loads((dataset_root / "manifest.json").read_text(encoding="utf-8"))
grouped: dict[str, list[dict]] = {}
for route in manifest["routes"]:
grouped.setdefault(route["family"], []).append(route)
output.mkdir(parents=True, exist_ok=True)
for family, routes in sorted(grouped.items()):
tiles = []
for route in sorted(routes, key=lambda item: item["route_id"]):
route_id = route["route_id"]
matches = list(run_root.glob(f"*/{route_id}"))
if len(matches) != 1:
raise RuntimeError(f"expected one run directory for {route_id}")
run_dir = matches[0]
videos = list((run_dir / "videos").glob("*_pdm_lite_rgb.mp4"))
if len(videos) != 1:
raise RuntimeError(f"expected one RGB video for {route_id}")
frame_index = _event_frame(run_dir)
frame = _read_video_frame(videos[0], frame_index)
subtitle = (
f"{route['town']} | {route['weather_profile'].replace('_', ' ')}"
f" | event frame {frame_index}"
)
tiles.append(_tile(frame, route_id, subtitle))
rows = [
np.concatenate(tiles[index : index + 5], axis=1)
for index in range(0, len(tiles), 5)
]
sheet = np.concatenate(rows, axis=0)
path = output / f"{family}.jpg"
if not cv2.imwrite(str(path), sheet, [cv2.IMWRITE_JPEG_QUALITY, 92]):
raise RuntimeError(f"failed to write {path}")
print(path)
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--dataset-root", type=Path, default=DEFAULT_DATASET_ROOT)
parser.add_argument("--run-root", type=Path, default=DEFAULT_RUN_ROOT)
parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
args = parser.parse_args()
build(args.dataset_root.resolve(), args.run_root.resolve(), args.output.resolve())
return 0
if __name__ == "__main__":
raise SystemExit(main())
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