feat(build): 任务书 02 第四阶段代码——build 装入 CEUS 结果

- build.yaml 加必填字段 ceus_run（CEUS 运行目录或 null）。 - 不为 null 时按视频号找结果；在 failed.csv 里或缺结果目录的整例跳过，记 C15；关键帧图沿用 C14 检查。 - 资料库超声一路加 keyframes/<槽位>.png（去附加数据重编码）和 tic.json（白名单逐字段构造：四条曲线的拟合成败与 R²、灌注指标、紫圈曲线是否可用、7 个槽位的定位与视频时间或原因）。 - manifest 的 CEUS 来源写运行号；泄露扫描已覆盖 tic.json，补命中测试。 - 真实构建等配准改完、全量 CEUS 运行之后再做。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-16 05:55:57 -04:00
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+"""测试用假 CEUS 运行目录：布局照 `scripts/ceus_quant.py` 写出的（failed.csv、snapshot/、
+`<视频号>/summary.json`、`<视频号>/keyframes/<槽位>.png`），数值全部编造，不调显卡。
+
+summary.json 故意带上 tic.json 不该有的字段（视频号、分割缓存键、帧号、拟合参数、失败原因代码、
+推注时间、紫圈检出率），用来测 build 只按白名单挑字段。关键帧 PNG 带文本块和 EXIF。
+"""
+
+import csv
+import json
+from pathlib import Path
+from typing import Any
+
+from PIL import Image, PngImagePlugin
+
+SLOTS = ("B0", "D1", "D2", "D3", "D4", "F1", "F2")
+CURVES = ("roi_all", "roi_perf", "roi_pan", "background_circle")
+UNLOCATED_REASON = "拟合曲线上没有 D4 时刻（编造的原因）"
+
+
+def _perfusion(scale: float) -> dict[str, Any]:
+    names = ("PE_au", "WiAUC_au_s", "RT_s", "mTTl_s", "TTP_s", "WiR_au_per_s", "WiPI_au")
+    more = ("WoAUC_au_s", "WiWoAUC_au_s", "FT_s", "WoR_au_per_s")
+    return {
+        "signal_domain": "db_display",
+        "time_origin": "video_start",
+        "area_curve": "fitted_minus_baseline",
+        "values": {name: scale * (i + 1) for i, name in enumerate((*names, *more))},
+        "supporting_times_s": {"TI": 3.0, "mTT": 9.5, "TO": None},
+    }
+
+
+def _fit(success: bool, r2: float | None) -> dict[str, Any]:
+    return {
+        "success": success,
+        "failure_reason": None if success else "credible_peak_not_found",
+        "curve_model": "lognormal",
+        "baseline_source": "observed",
+        "valid_samples": 120,
+        "duplicate_samples": 3,
+        "parameters": {
+            "area": 1.0,
+            "mu": 2.0,
+            "sigma": 0.5,
+            "arrival_offset_s": 4.0,
+            "baseline": 9,
+        },
+        "quality": {"r2": r2, "nrmse": 0.1, "residual_noise_ratio": 1.2, "multi_peak_flag": False},
+        "model_arrival_time_video_s": 4.0,
+        "model_landmark_times_video_s": {"D2": 6.0, "D3": 9.0, "D4": 15.0},
+        "model_landmark_intensities": {"D2": 1.0, "D3": 2.0, "D4": 1.5},
+    }
+
+
+def summary(video_id: str, located: tuple[str, ...]) -> dict[str, Any]:
+    """一例的 summary.json：`located` 里的槽位定位到（视频时间=槽位序号×2.5 秒），余下未定位。"""
+    anchors = {
+        slot: {
+            "frame_index": index * 25 if slot in located else None,
+            "frame_time_video_s": index * 2.5 if slot in located else None,
+            "time_since_injection_s": index * 2.5 + 5.0 if slot in located else None,
+            "status": "ok" if slot in located else "anchor_undefined",
+            "reason": None if slot in located else UNLOCATED_REASON,
+            "anchor_time_video_s": index * 2.5,
+            "time_error_s": 0.0 if slot in located else None,
+            "source_curve": "roi_perf",
+        }
+        for index, slot in enumerate(SLOTS)
+    }
+    return {
+        "video_id": video_id,
+        "segment_cache_key": "f" * 64,
+        "injection_offset_s": 5.0,
+        "reference_valid": False,
+        "pan_detected_fraction": 0.4,
+        "early_truncation_flag": False,
+        "lesion_fit_ready": True,
+        "fits": {
+            "roi_all": _fit(True, 0.93),
+            "roi_perf": _fit(True, 0.91),
+            "roi_pan": _fit(False, None),
+            "background_circle": _fit(False, 0.2),
+        },
+        "perfusion_metrics": {"roi_all": _perfusion(1.0), "roi_perf": None},
+        "anchor_frames": anchors,
+        "available_dynamic_slots": [s for s in ("B0", "D1", "D2", "D3", "D4") if s in located],
+    }
+
+
+def keyframe_png(path: Path, shade: int) -> None:
+    """一张 8 位 RGB 关键帧，带文本块和 EXIF（写进资料库前要去掉）。"""
+    pixels = bytes(v for i in range(12) for v in (shade, i * 20, 200 - i * 10))
+    image = Image.frombytes("RGB", (4, 3), pixels)
+    info = PngImagePlugin.PngInfo()
+    info.add_text("Source", "U999_roi2.mp4")
+    exif = Image.Exif()
+    exif[0x010E] = "U999"  # ImageDescription
+    image.save(path, format="PNG", pnginfo=info, exif=exif.tobytes())
+
+
+def write_run(
+    run_dir: Path,
+    results: dict[str, tuple[str, ...]],
+    failed: list[tuple[str, int, str]],
+) -> Path:
+    """写一个已完成的运行目录：`results` 是视频号 → 定位到的槽位，`failed` 是 failed.csv 的行。"""
+    (run_dir / "snapshot").mkdir(parents=True)
+    (run_dir / "snapshot" / "snapshot.json").write_text("{}", encoding="utf-8")
+    with (run_dir / "failed.csv").open("w", encoding="utf-8", newline="") as handle:
+        writer = csv.writer(handle)
+        writer.writerow(("video_id", "step", "reason"))
+        writer.writerows(failed)
+    for video_id, located in results.items():
+        (run_dir / video_id / "keyframes").mkdir(parents=True)
+        content = json.dumps(summary(video_id, located), ensure_ascii=False, indent=2)
+        (run_dir / video_id / "summary.json").write_text(content, encoding="utf-8")
+        for shade, slot in enumerate(located):
+            keyframe_png(run_dir / video_id / "keyframes" / f"{slot}.png", shade * 30)
+    return run_dir