feat(ceus,segment): 任务书 02 第二阶段——分割和后半段算法

- ceus 迁入 polygons、dark_region、reference_region、tic_fitting、tic、anchor_frames、 perfusion_metrics、trajectory、tic_curves、tic_plot、overlay，旧测试照搬（改 import 与配置读取）。 - 对外两段接口：prepare_segmentation（第 1–2 步）与 quantify_case（第 4–9 步）。 - 第 3 条：删掉 D1 取医生标注时刻的模式，D1 只从拟合曲线算。 - 第 4 条：F1/F2 取视频第 20−X、60−X 秒（X 为视频第 0 秒时已推注的秒数），落在视频外记未定位。 - 绿框融合权重、紫圈颜色阈值照搬；关键帧容差窗口的出处写进 ceus.yaml 注释。 - segment：同一进程调用 MedSAM2，设置写进 ceus.yaml 的 segment 段，全部必填；gpu 测试真跑通过。 - 依赖加 scipy、matplotlib、medsam2（可编辑安装，不编译 CUDA 扩展）；uv 只解析 linux x86_64。 - 导入契约：只有 segment 引用 torch、sam2；segment 不引用其他 cadx 模块。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-16 05:07:02 -04:00
parent 9a916c65a5
commit 150eb5f6ae
35 changed files with 10655 additions and 505 deletions
@@ -0,0 +1,313 @@
+"""TIC 折线图单元测试：缺测不许画成零（照搬旧项目 test_ceus_tic_plot.py）。"""
+
+from typing import Any
+
+import cv2
+import numpy as np
+import pytest
+from matplotlib.axes import Axes
+
+from cadx.ceus import (
+    CURVE_NAMES,
+    BaselineResult,
+    LognormalFitResult,
+    TicPlotMarker,
+    TicResult,
+    render_tic_png,
+)
+
+_ROI_ALL_BGR = (184, 99, 37)
+_ROI_PERF_BGR = (0, 103, 212)
+_ROI_PERF_RAW_BGR = (194, 219, 245)
+_ROI_PAN_BGR = (237, 58, 124)
+_ROI_PAN_RAW_BGR = (251, 208, 224)
+_MANUAL_AT_BGR = (30, 30, 220)
+
+_FRAME_COUNT = 21
+# 去暗区曲线中间这一段没采到（NaN），落在时间轴 35%~65% 之间。
+_GAP = range(7, 14)
+
+
+def _invalid_baselines() -> dict[str, BaselineResult]:
+    """四条曲线都给一个无效基线——这张图只读 ``rows``，基线怎么样它不关心。"""
+    return {
+        name: BaselineResult(mean=None, sd=None, frame_start=-1, frame_end=-1, valid=False)
+        for name in CURVE_NAMES
+    }
+
+
+def _result(
+    *,
+    perf_all_nan: bool,
+    perf_gap: bool,
+    pan_available: bool = False,
+    alternate_background: bool = False,
+    perf_fit_success: bool | None = None,
+    pan_fit_success: bool | None = None,
+    pan_fitted_all_nan: bool = False,
+    perf_processed_value: float | None = None,
+    perf_singleton: bool = False,
+) -> TicResult:
+    """造一份 ``roi_perf`` 可缺失或断开的 ``TicResult``。"""
+    rows: list[dict[str, Any]] = []
+    for index in range(_FRAME_COUNT):
+        perf = float("nan") if perf_all_nan else 90.0 + 10.0 * (index % 2)
+        if perf_singleton:
+            perf = 95.0 if index == _FRAME_COUNT // 2 else float("nan")
+        if perf_gap and index in _GAP:
+            perf = float("nan")
+        values = {
+            "roi_all": 10.0 * index,
+            "roi_perf": perf,
+            "roi_pan": 180.0 + index if pan_available else float("nan"),
+            "background_circle": (150.0 - index if alternate_background else 40.0 + index),
+        }
+        row: dict[str, Any] = {"frame_index": index, "time_sec": float(index)}
+        for name, value in values.items():
+            row[f"{name}_raw"] = value
+            row[f"{name}_processed"] = (
+                perf_processed_value
+                if name == "roi_perf" and perf_processed_value is not None
+                else value
+            )
+        rows.append(row)
+
+    fits: dict[str, LognormalFitResult] = {}
+    for name in CURVE_NAMES:
+        values = np.asarray([row[f"{name}_processed"] for row in rows], dtype=float)
+        success = bool(np.isfinite(values).any())
+        if name == "roi_perf" and perf_fit_success is not None:
+            success = perf_fit_success
+        if name == "roi_pan" and pan_fit_success is not None:
+            success = pan_fit_success
+        fitted = values.copy()
+        if name == "roi_pan" and pan_fitted_all_nan:
+            fitted[:] = np.nan
+        fits[name] = LognormalFitResult(
+            success=success,
+            failure_reason="" if success else "no_data",
+            algorithm_version="lognormal-v2",
+            curve_model="lognormal",
+            smoothing_method="",
+            baseline_source="observed",
+            candidate=values.copy(),
+            fitted=fitted,
+            processed=values.copy(),
+            valid_sample_mask=np.isfinite(values),
+            duplicate_sample_mask=np.zeros(_FRAME_COUNT, dtype=bool),
+            parameters=None,
+            r2=float("nan"),
+            nrmse=float("nan"),
+            residual_noise_ratio=float("nan"),
+            fit_start_s=0.0,
+            fit_end_s=float(_FRAME_COUNT - 1),
+            multi_peak_flag=False,
+            d1_time_s=None,
+            d2_time_s=None,
+            d3_time_s=None,
+            d4_time_s=None,
+        )
+    return TicResult(
+        rows=rows,
+        parameters=None,
+        baselines=_invalid_baselines(),
+        fits=fits,
+        early_truncation_flag=False,
+        reference_valid=pan_available,
+        pan_detected_fraction=1.0 if pan_available else 0.0,
+    )
+
+
+def _decode(png: bytes) -> np.ndarray:
+    canvas = cv2.imdecode(np.frombuffer(png, dtype=np.uint8), cv2.IMREAD_COLOR)
+    assert canvas is not None, "渲染出来的字节解不回一张图"
+    return canvas
+
+
+def _render(*, perf_all_nan: bool, perf_gap: bool) -> np.ndarray:
+    return _decode(
+        render_tic_png(_result(perf_all_nan=perf_all_nan, perf_gap=perf_gap), markers=())
+    )
+
+
+def _color_pixels(canvas: np.ndarray, color: tuple[int, int, int]) -> np.ndarray:
+    difference = np.abs(canvas.astype(np.int16) - np.asarray(color, dtype=np.int16))
+    return np.all(difference <= 8, axis=2)
+
+
+def test_all_nan_curve_is_not_drawn_at_zero() -> None:
+    """``roi_perf`` 整条 NaN 时不画；同一条有数据时画得出来。
+
+    第二句是要害：只断言「没画」的话，一个「什么都不画」的实现照样能过。
+    """
+    absent = _color_pixels(_render(perf_all_nan=True, perf_gap=False), _ROI_PERF_BGR)
+    present = _color_pixels(_render(perf_all_nan=False, perf_gap=False), _ROI_PERF_BGR)
+
+    assert int(absent.sum()) == 0, "整条 NaN 的曲线被画出来了——多半是被当成了 0"
+    assert int(present.sum()) > 0, "同一条曲线有数据时反而没画出来"
+
+
+def test_nan_gap_breaks_the_polyline_instead_of_dipping_to_zero() -> None:
+    canvas = _render(perf_all_nan=False, perf_gap=True)
+    perf_pixels = _color_pixels(canvas, _ROI_PERF_BGR)
+    height, width = canvas.shape[:2]
+    inside_gap = perf_pixels[
+        int(0.08 * height) : int(0.62 * height),
+        int(0.45 * width) : int(0.55 * width),
+    ]
+
+    assert int(inside_gap.sum()) == 0, "缺口那一段仍有去暗区曲线像素——折线没有断开"
+    assert int(perf_pixels.sum()) > 0, "整条曲线都没画出来——断开断过头了"
+
+
+def test_curves_with_data_are_still_drawn() -> None:
+    """有数据的曲线照画不误（防止「遇 NaN 就整条放弃」）。"""
+    canvas = _render(perf_all_nan=True, perf_gap=True)
+    assert int(_color_pixels(canvas, _ROI_ALL_BGR).sum()) > 0
+
+
+def test_isolated_raw_sample_is_visible() -> None:
+    result = _result(
+        perf_all_nan=True,
+        perf_gap=False,
+        perf_fit_success=False,
+        perf_singleton=True,
+    )
+    canvas = _decode(render_tic_png(result, markers=()))
+    height, width = canvas.shape[:2]
+    plot_center = _color_pixels(canvas, _ROI_PERF_RAW_BGR)[
+        int(0.2 * height) : int(0.65 * height),
+        int(0.45 * width) : int(0.55 * width),
+    ]
+
+    assert int(plot_center.sum()) > 0
+
+
+def test_rejected_fit_keeps_raw_but_hides_processed_curve() -> None:
+    result = _result(
+        perf_all_nan=False,
+        perf_gap=False,
+        perf_fit_success=False,
+        perf_processed_value=170.0,
+    )
+    canvas = _decode(render_tic_png(result, markers=()))
+
+    assert int(_color_pixels(canvas, _ROI_PERF_RAW_BGR).sum()) > 0
+    assert int(_color_pixels(canvas, _ROI_PERF_BGR).sum()) == 0
+
+
+def test_pan_reference_raw_and_fit_are_drawn_when_available() -> None:
+    result = _result(
+        perf_all_nan=False,
+        perf_gap=False,
+        pan_available=True,
+    )
+    canvas = _decode(render_tic_png(result, markers=()))
+
+    assert int(_color_pixels(canvas, _ROI_PAN_RAW_BGR).sum()) > 0
+    assert int(_color_pixels(canvas, _ROI_PAN_BGR).sum()) > 0
+
+
+def test_missing_pan_reference_is_not_drawn_at_zero() -> None:
+    canvas = _decode(render_tic_png(_result(perf_all_nan=False, perf_gap=False), markers=()))
+
+    assert int(_color_pixels(canvas, _ROI_PAN_RAW_BGR).sum()) == 0
+    assert int(_color_pixels(canvas, _ROI_PAN_BGR).sum()) == 0
+
+
+def test_rejected_pan_fit_draws_diagnostic_fitted_curve() -> None:
+    result = _result(
+        perf_all_nan=False,
+        perf_gap=False,
+        pan_available=True,
+        pan_fit_success=False,
+    )
+    canvas = _decode(render_tic_png(result, markers=()))
+
+    assert int(_color_pixels(canvas, _ROI_PAN_RAW_BGR).sum()) > 0
+    assert int(_color_pixels(canvas, _ROI_PAN_BGR).sum()) > 0
+
+
+def test_rejected_pan_fit_is_dashed_and_labeled(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    plotted_styles: list[dict[str, Any]] = []
+    original_plot = Axes.plot  # pyright: ignore[reportUnknownVariableType, reportUnknownMemberType]
+
+    def capture_plot(self: Axes, *args: Any, **kwargs: Any) -> Any:
+        plotted_styles.append(kwargs)
+        return original_plot(self, *args, **kwargs)
+
+    monkeypatch.setattr(Axes, "plot", capture_plot)
+    render_tic_png(
+        _result(
+            perf_all_nan=False,
+            perf_gap=False,
+            pan_available=True,
+            pan_fit_success=False,
+        ),
+        markers=(),
+    )
+
+    reference_fit = next(
+        style
+        for style in plotted_styles
+        if style.get("label") == "roi_pan reference Lognormal fit [QC rejected]"
+    )
+    assert reference_fit["linestyle"] == "--"
+
+
+def test_rejected_pan_fit_without_finite_model_does_not_fabricate_line() -> None:
+    result = _result(
+        perf_all_nan=False,
+        perf_gap=False,
+        pan_available=True,
+        pan_fit_success=False,
+        pan_fitted_all_nan=True,
+    )
+    canvas = _decode(render_tic_png(result, markers=()))
+
+    assert int(_color_pixels(canvas, _ROI_PAN_RAW_BGR).sum()) > 0
+    assert int(_color_pixels(canvas, _ROI_PAN_BGR).sum()) == 0
+
+
+def test_background_does_not_change_tic_plot() -> None:
+    regular = _result(perf_all_nan=False, perf_gap=False)
+    changed = _result(
+        perf_all_nan=False,
+        perf_gap=False,
+        alternate_background=True,
+    )
+    regular_canvas = _decode(render_tic_png(regular, markers=()))
+    changed_canvas = _decode(render_tic_png(changed, markers=()))
+
+    np.testing.assert_array_equal(regular_canvas, changed_canvas)
+
+
+def test_plot_uses_review_ready_canvas_size() -> None:
+    canvas = _render(perf_all_nan=False, perf_gap=False)
+
+    assert canvas.shape == (800, 1600, 3)
+
+
+def test_review_marker_is_drawn_without_changing_curve_data() -> None:
+    result = _result(perf_all_nan=False, perf_gap=False)
+    png = render_tic_png(
+        result,
+        markers=(TicPlotMarker("manual AT", 10.0, _MANUAL_AT_BGR),),
+    )
+    canvas = _decode(png)
+
+    assert int(_color_pixels(canvas, _MANUAL_AT_BGR).sum()) > 0
+    assert int(_color_pixels(canvas, _ROI_PERF_BGR).sum()) > 0
+
+
+def test_marker_outside_observed_time_is_not_attached_to_boundary() -> None:
+    result = _result(perf_all_nan=False, perf_gap=False)
+    png = render_tic_png(
+        result,
+        markers=(TicPlotMarker("manual AT", 100.0, _MANUAL_AT_BGR),),
+    )
+    canvas = _decode(png)
+
+    assert int(_color_pixels(canvas, _MANUAL_AT_BGR).sum()) == 0