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https://gitlvb.teallvbs.xyz/IQ.Lvbs/IQ.Pilot.git
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IQ.Lvbs CI [bot]
244 lines
7.3 KiB
Python
244 lines
7.3 KiB
Python
#!/usr/bin/env python3
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import argparse
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import csv
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import hashlib
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import json
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import math
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import shutil
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import subprocess
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import tempfile
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from pathlib import Path
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try:
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from PIL import Image, ImageChops, ImageStat
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except ModuleNotFoundError:
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Image = None
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ImageChops = None
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ImageStat = None
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DEFAULT_PLANNER_ROI = (0.22, 0.54, 0.78, 0.97)
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def run(cmd: list[str]) -> str:
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result = subprocess.run(cmd, check=True, capture_output=True, text=True)
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return result.stdout
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def ffprobe_video(path: Path) -> dict:
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payload = run([
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"ffprobe",
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"-v", "error",
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"-print_format", "json",
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"-show_streams",
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"-show_format",
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str(path),
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])
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return json.loads(payload)
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def parse_fraction(value: str) -> float:
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if "/" in value:
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num, den = value.split("/", 1)
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return float(num) / float(den)
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return float(value)
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def extract_frames(video_path: Path, output_dir: Path) -> list[Path]:
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output_dir.mkdir(parents=True, exist_ok=True)
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run([
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"ffmpeg",
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"-loglevel", "error",
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"-i", str(video_path),
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"-vsync", "0",
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str(output_dir / "frame_%06d.png"),
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"-y",
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])
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return sorted(output_dir.glob("frame_*.png"))
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def file_hash(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as f:
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for chunk in iter(lambda: f.read(1024 * 1024), b""):
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digest.update(chunk)
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return digest.hexdigest()
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def crop_box(width: int, height: int, roi: tuple[float, float, float, float]) -> tuple[int, int, int, int]:
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left = int(width * roi[0])
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top = int(height * roi[1])
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right = int(width * roi[2])
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bottom = int(height * roi[3])
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return left, top, right, bottom
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def rms_diff(prev_img, cur_img) -> float:
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diff = ImageChops.difference(prev_img, cur_img)
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return float(ImageStat.Stat(diff).rms[0])
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def summarize(values: list[float]) -> dict[str, float]:
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if not values:
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return {"count": 0, "avg": 0.0, "max": 0.0, "min": 0.0}
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return {
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"count": len(values),
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"avg": sum(values) / len(values),
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"max": max(values),
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"min": min(values),
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}
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def write_contact_sheet(flagged: list[Path], output_path: Path, *, columns: int = 3) -> None:
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if Image is None or not flagged:
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return
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images = []
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for path in flagged:
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with Image.open(path) as img:
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images.append(img.convert("RGB").copy())
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thumb_w = min(img.width for img in images)
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thumb_h = min(img.height for img in images)
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rows = math.ceil(len(images) / columns)
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sheet = Image.new("RGB", (thumb_w * columns, thumb_h * rows), color=(0, 0, 0))
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for idx, img in enumerate(images):
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thumb = img.resize((thumb_w, thumb_h))
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x = (idx % columns) * thumb_w
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y = (idx // columns) * thumb_h
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sheet.paste(thumb, (x, y))
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sheet.save(output_path)
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def audit_video(video_path: Path, output_dir: Path, roi: tuple[float, float, float, float]) -> dict:
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output_dir.mkdir(parents=True, exist_ok=True)
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temp_root = Path(tempfile.mkdtemp(prefix="iqpilot_video_audit_"))
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try:
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frames = extract_frames(video_path, temp_root / "frames")
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probe = ffprobe_video(video_path)
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streams = probe.get("streams", [])
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video_stream = next((stream for stream in streams if stream.get("codec_type") == "video"), {})
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fps = parse_fraction(video_stream.get("avg_frame_rate", "0")) if video_stream.get("avg_frame_rate") else 0.0
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duration = float(probe.get("format", {}).get("duration", 0.0) or 0.0)
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records: list[dict] = []
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duplicate_runs: list[int] = []
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current_duplicate_run = 0
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exact_duplicate_indices: list[int] = []
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low_motion_indices: list[int] = []
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suspicious_paths: list[Path] = []
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full_rms_values: list[float] = []
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planner_rms_values: list[float] = []
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prev_hash = None
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prev_img = None
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prev_planner = None
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for idx, frame_path in enumerate(frames):
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frame_hash = file_hash(frame_path)
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exact_duplicate = prev_hash == frame_hash
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full_rms = 0.0
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planner_rms = 0.0
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if Image is not None:
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with Image.open(frame_path) as img:
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current = img.convert("L")
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planner = current.crop(crop_box(current.width, current.height, roi))
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if prev_img is not None:
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full_rms = rms_diff(prev_img, current)
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planner_rms = rms_diff(prev_planner, planner)
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full_rms_values.append(full_rms)
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planner_rms_values.append(planner_rms)
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prev_img = current.copy()
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prev_planner = planner.copy()
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if exact_duplicate:
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current_duplicate_run += 1
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exact_duplicate_indices.append(idx)
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elif current_duplicate_run:
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duplicate_runs.append(current_duplicate_run)
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current_duplicate_run = 0
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if idx > 0 and (exact_duplicate or planner_rms < 1.2 or full_rms < 1.0):
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low_motion_indices.append(idx)
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suspicious_paths.append(frame_path)
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records.append({
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"frame": idx,
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"exact_duplicate": exact_duplicate,
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"full_rms": round(full_rms, 4),
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"planner_rms": round(planner_rms, 4),
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})
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prev_hash = frame_hash
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if current_duplicate_run:
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duplicate_runs.append(current_duplicate_run)
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csv_path = output_dir / f"{video_path.stem}_frame_metrics.csv"
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with csv_path.open("w", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=["frame", "exact_duplicate", "full_rms", "planner_rms"])
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writer.writeheader()
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writer.writerows(records)
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flagged_dir = output_dir / f"{video_path.stem}_flagged_frames"
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flagged_dir.mkdir(parents=True, exist_ok=True)
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for path in suspicious_paths[:24]:
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shutil.copy2(path, flagged_dir / path.name)
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write_contact_sheet(sorted(flagged_dir.glob("*.png"))[:12], output_dir / f"{video_path.stem}_contact_sheet.png")
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summary = {
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"video": str(video_path),
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"frame_count": len(frames),
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"fps": fps,
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"duration_s": duration,
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"exact_duplicate_frames": len(exact_duplicate_indices),
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"max_duplicate_run": max(duplicate_runs) if duplicate_runs else 0,
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"duplicate_runs": duplicate_runs,
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"low_motion_frames": len(low_motion_indices),
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"full_rms": summarize(full_rms_values),
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"planner_rms": summarize(planner_rms_values),
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"planner_roi": {
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"left": roi[0],
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"top": roi[1],
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"right": roi[2],
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"bottom": roi[3],
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},
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"artifacts": {
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"metrics_csv": str(csv_path),
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"flagged_frames_dir": str(flagged_dir),
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"contact_sheet": str(output_dir / f"{video_path.stem}_contact_sheet.png"),
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},
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}
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summary_path = output_dir / f"{video_path.stem}_summary.json"
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summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n")
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return summary
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finally:
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shutil.rmtree(temp_root, ignore_errors=True)
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def main() -> None:
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parser = argparse.ArgumentParser(description="Audit an MP4 for duplicate/low-motion frame runs.")
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parser.add_argument("video", type=Path, help="Input MP4")
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parser.add_argument("--output-dir", type=Path, default=Path("video_audit"), help="Artifact output directory")
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parser.add_argument(
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"--planner-roi",
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default="0.22,0.54,0.78,0.97",
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help="ROI fractions left,top,right,bottom for planner-focused RMS stats",
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)
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args = parser.parse_args()
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roi = tuple(float(part.strip()) for part in args.planner_roi.split(","))
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if len(roi) != 4:
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raise ValueError("planner-roi must have four comma-separated floats")
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summary = audit_video(args.video, args.output_dir, roi)
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print(json.dumps(summary, indent=2, sort_keys=True))
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if __name__ == "__main__":
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main()
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