iomgaa
150eb5f6ae
- 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>
169 lines
7.0 KiB
Python
169 lines
7.0 KiB
Python
"""ceus 对外两段接口的端到端测试：合成一段分屏造影视频，第二段喂合成掩膜（不调分割模型）。
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视频 400×200、10 fps、40 秒（400 帧），分屏线在 x=200（这一列全黑）。造影画面在右半：病灶是整帧
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[250, 369] × [60, 159] 的方块，医生的绿框是它的空心描边；病灶内部亮度按 Lognormal 团注曲线从
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基线 40 升到约 170 再回落，内部另有一块一直发黑的囊（暗区）；每帧加一点随机噪声，免得相邻帧
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被当成重复帧。左半是不变的灰阶画面。
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手算的预期：
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- 绿框在右半 → 造影画面是右半，分割那一半是左半 [0, 200)；
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- 绿框挪到左半局部坐标：x = 250 − 200 = 50 到 369 − 200 = 169；
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- 推注时间 X = 5 秒 → F1 在视频第 20 − 5 = 15 秒（第 150 帧），F2 在第 60 − 5 = 55 秒，
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超过 40 秒的视频末尾，未定位。
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"""
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import json
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import math
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from pathlib import Path
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import cv2
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import numpy as np
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import pytest
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from cadx.ceus import (
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ANCHOR_SLOTS,
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CeusConfig,
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LognormalParameters,
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NoValidContourError,
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SegmentationInput,
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evaluate_lognormal,
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keyframe_images,
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pack_tic_curves,
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pack_trajectory,
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prepare_segmentation,
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quant_summary,
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quantify_case,
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render_case_overlay,
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render_tic_png,
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tic_plot_markers,
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unpack_tic_curves,
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unpack_trajectory,
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)
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_WIDTH, _HEIGHT, _SPLIT = 400, 200, 200
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_FPS, _FRAMES = 10.0, 400
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_LESION = (250, 60, 369, 159) # 整帧坐标 x1, y1, x2, y2（含端点）
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_CYST = (290, 95, 320, 125)
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_BOLUS = LognormalParameters(area=1300.0, mu=2.4, sigma=0.45, arrival_offset_s=4.0, baseline=40.0)
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_INJECTION_OFFSET_S = 5.0
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def _write_video(path: Path) -> None:
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rng = np.random.default_rng(3)
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times = np.arange(_FRAMES) / _FPS
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intensity = evaluate_lognormal(times, _BOLUS)
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writer = cv2.VideoWriter(str(path), cv2.VideoWriter.fourcc(*"mp4v"), _FPS, (_WIDTH, _HEIGHT))
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x1, y1, x2, y2 = _LESION
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for index in range(_FRAMES):
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frame = np.zeros((_HEIGHT, _WIDTH, 3), dtype=np.uint8)
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frame[:, :_SPLIT] = 120
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frame[:, _SPLIT + 1 :] = 30
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lesion = np.clip(
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intensity[index] + rng.normal(0.0, 6.0, (y2 - y1 + 1, x2 - x1 + 1)), 0, 255
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)
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frame[y1 : y2 + 1, x1 : x2 + 1] = lesion.astype(np.uint8)[:, :, None]
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cx1, cy1, cx2, cy2 = _CYST
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frame[cy1 : cy2 + 1, cx1 : cx2 + 1] = 10
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), thickness=3)
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writer.write(frame)
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writer.release()
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@pytest.fixture(scope="module")
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def config() -> CeusConfig:
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from conftest import load_ceus_section
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return CeusConfig.model_validate(load_ceus_section())
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@pytest.fixture(scope="module")
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def prepared(tmp_path_factory: pytest.TempPathFactory, config: CeusConfig) -> SegmentationInput:
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path = tmp_path_factory.mktemp("ceus") / "synthetic.mp4"
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_write_video(path)
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return prepare_segmentation(path, config)
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def _lesion_masks(prepared: SegmentationInput) -> np.ndarray:
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"""合成「分割结果」：每帧都是病灶方块挪到左半局部坐标后的位置（略往里收 3 像素）。"""
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view = prepared.contrast.target_view
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masks = np.zeros((prepared.frame_count, view.height, view.width), dtype=bool)
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x1, y1, x2, y2 = _LESION
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masks[:, y1 + 3 : y2 - 2, x1 - _SPLIT + 3 : x2 - _SPLIT - 2] = True
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return masks
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def test_prepare_segmentation_finds_contrast_half_and_maps_prompts(
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prepared: SegmentationInput,
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) -> None:
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assert prepared.frame_count == _FRAMES
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assert prepared.frame_size == (_WIDTH, _HEIGHT)
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assert prepared.contrast.side == "right"
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target = prepared.contrast.target_view
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assert (target.x1, target.x2) == (0, prepared.split_x)
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assert abs(prepared.split_x - _SPLIT) <= 1
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assert len(prepared.target_frames) == _FRAMES
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assert all(frame.shape == (target.height, target.width, 3) for frame in prepared.target_frames)
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assert sorted(prepared.prompt_masks) == [p.frame_index for p in prepared.contrast.prompt_frames]
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assert 0 in prepared.prompt_masks
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xs = np.where(prepared.prompt_masks[0])[1]
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# 描边 3 像素宽、找绿框时再膨胀 2 像素、mp4 压缩会糊开边缘，容差放到 5 像素。
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assert abs(int(xs.min()) - 50) <= 5
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assert abs(int(xs.max()) - 169) <= 5
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def test_quantify_case_end_to_end(prepared: SegmentationInput, config: CeusConfig) -> None:
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result = quantify_case(prepared, _lesion_masks(prepared), _INJECTION_OFFSET_S, config)
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assert result.trajectory.source_polygons.shape == (_FRAMES, 64, 2)
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assert result.lesion_fit_ready is True
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assert list(result.anchors) == list(ANCHOR_SLOTS)
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for slot in ("D1", "D2", "D3"):
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assert result.anchors[slot].status == "ok", result.anchors[slot].reason
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assert result.anchors["F1"].frame_index == 150
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assert result.anchors["F1"].frame_time_video_s == pytest.approx(15.0, abs=0.06)
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assert result.anchors["F2"].status == "not_covered"
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assert result.anchors["F2"].reason is not None
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assert result.perfusion["roi_perf"] is not None
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peak_time = _BOLUS.arrival_offset_s + math.exp(_BOLUS.mu - _BOLUS.sigma**2)
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assert result.perfusion["roi_perf"].ttp_s == pytest.approx(peak_time, abs=0.5)
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# 画面上没有紫圈。
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assert result.pan_detected_fraction == 0.0
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assert result.tic.reference_valid is False
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# 暗区（囊）被找出来，去暗区曲线比病灶整体亮。
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peak_row = result.tic.rows[round(peak_time * _FPS)]
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assert peak_row["dark_area_px"] > 0
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assert peak_row["roi_perf_raw"] > peak_row["roi_all_raw"]
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restored = unpack_trajectory(pack_trajectory(result.trajectory))
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assert restored.calibration["contrast_side"] == "right"
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curves = unpack_tic_curves(pack_tic_curves(result.curves))
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assert curves.injection_offset_s == pytest.approx(_INJECTION_OFFSET_S)
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summary = quant_summary(result)
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json.dumps(summary, allow_nan=False)
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assert summary["anchor_frames"]["F1"]["frame_index"] == 150
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assert summary["perfusion_metrics"]["roi_perf"] is not None
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markers = tic_plot_markers(result.anchors)
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assert [marker.label.split()[0] for marker in markers][:3] == ["D1", "D2", "D3"]
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assert render_tic_png(result.tic, markers=markers).startswith(b"\x89PNG")
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assert len(render_case_overlay(prepared, result)) > 0
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images = keyframe_images(prepared, result.anchors)
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assert "F1" in images and "F2" not in images
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assert all(image.shape == (_HEIGHT, _WIDTH, 3) for image in images.values())
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def test_empty_segmentation_fails_case_at_step_4(
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prepared: SegmentationInput, config: CeusConfig
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) -> None:
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empty = np.zeros_like(_lesion_masks(prepared))
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with pytest.raises(NoValidContourError) as raised:
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quantify_case(prepared, empty, _INJECTION_OFFSET_S, config)
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assert raised.value.step == 4
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def test_mask_shape_mismatch_is_rejected(prepared: SegmentationInput, config: CeusConfig) -> None:
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masks = _lesion_masks(prepared)[:-1]
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with pytest.raises(ValueError, match="分割掩膜"):
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quantify_case(prepared, masks, _INJECTION_OFFSET_S, config)
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