"""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）。 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 [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 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 被当成重复帧。左半是不变的灰阶画面。 手算的预期： - 绿框在右半 → 造影画面是右半，分割那一半是左半 [0, 200)； - 绿框挪到左半局部坐标：x = 250 − 200 = 50 到 369 − 200 = 169； - 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， 超过 40 秒的视频末尾，未定位。 """ import json import math from pathlib import Path import cv2 import numpy as np import pytest from cadx.ceus import ( ANCHOR_SLOTS, CeusConfig, LognormalParameters, NoValidContourError, SegmentationInput, evaluate_lognormal, keyframe_images, pack_tic_curves, pack_trajectory, prepare_segmentation, quant_summary, quantify_case, render_case_overlay, render_tic_png, tic_plot_markers, unpack_tic_curves, unpack_trajectory, ) _WIDTH, _HEIGHT, _SPLIT = 400, 200, 200 _FPS, _FRAMES = 10.0, 400 _LESION = (250, 60, 369, 159) # 整帧坐标 x1, y1, x2, y2（含端点） _CYST = (290, 95, 320, 125) _BOLUS = LognormalParameters(area=1300.0, mu=2.4, sigma=0.45, arrival_offset_s=4.0, baseline=40.0) _INJECTION_OFFSET_S = 5.0 def _write_video(path: Path) -> None: rng = np.random.default_rng(3) times = np.arange(_FRAMES) / _FPS intensity = evaluate_lognormal(times, _BOLUS) writer = cv2.VideoWriter(str(path), cv2.VideoWriter.fourcc(*"mp4v"), _FPS, (_WIDTH, _HEIGHT)) x1, y1, x2, y2 = _LESION for index in range(_FRAMES): frame = np.zeros((_HEIGHT, _WIDTH, 3), dtype=np.uint8) frame[:, :_SPLIT] = 120 frame[:, _SPLIT + 1 :] = 30 lesion = np.clip( intensity[index] + rng.normal(0.0, 6.0, (y2 - y1 + 1, x2 - x1 + 1)), 0, 255 ) frame[y1 : y2 + 1, x1 : x2 + 1] = lesion.astype(np.uint8)[:, :, None] cx1, cy1, cx2, cy2 = _CYST frame[cy1 : cy2 + 1, cx1 : cx2 + 1] = 10 cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), thickness=3) writer.write(frame) writer.release() @pytest.fixture(scope="module") def config() -> CeusConfig: from conftest import load_ceus_section return CeusConfig.model_validate(load_ceus_section()) @pytest.fixture(scope="module") def prepared(tmp_path_factory: pytest.TempPathFactory, config: CeusConfig) -> SegmentationInput: path = tmp_path_factory.mktemp("ceus") / "synthetic.mp4" _write_video(path) return prepare_segmentation(path, config) def _lesion_masks(prepared: SegmentationInput) -> np.ndarray: """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）。""" view = prepared.contrast.target_view masks = np.zeros((prepared.frame_count, view.height, view.width), dtype=bool) x1, y1, x2, y2 = _LESION masks[:, y1 + 3 : y2 - 2, x1 - _SPLIT + 3 : x2 - _SPLIT - 2] = True return masks def test_prepare_segmentation_finds_contrast_half_and_maps_prompts( prepared: SegmentationInput, ) -> None: assert prepared.frame_count == _FRAMES assert prepared.frame_size == (_WIDTH, _HEIGHT) assert prepared.contrast.side == "right" target = prepared.contrast.target_view assert (target.x1, target.x2) == (0, prepared.split_x) assert abs(prepared.split_x - _SPLIT) <= 1 assert len(prepared.target_frames) == _FRAMES assert all(frame.shape == (target.height, target.width, 3) for frame in prepared.target_frames) assert sorted(prepared.prompt_masks) == [p.frame_index for p in prepared.contrast.prompt_frames] assert 0 in prepared.prompt_masks xs = np.where(prepared.prompt_masks[0])[1] # 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。 assert abs(int(xs.min()) - 50) <= 5 assert abs(int(xs.max()) - 169) <= 5 def test_quantify_case_end_to_end(prepared: SegmentationInput, config: CeusConfig) -> None: result = quantify_case(prepared, _lesion_masks(prepared), _INJECTION_OFFSET_S, config) assert result.trajectory.source_polygons.shape == (_FRAMES, 64, 2) assert result.lesion_fit_ready is True assert list(result.anchors) == list(ANCHOR_SLOTS) for slot in ("D1", "D2", "D3"): assert result.anchors[slot].status == "ok", result.anchors[slot].reason assert result.anchors["F1"].frame_index == 150 assert result.anchors["F1"].frame_time_video_s == pytest.approx(15.0, abs=0.06) assert result.anchors["F2"].status == "not_covered" assert result.anchors["F2"].reason is not None assert result.perfusion["roi_perf"] is not None peak_time = _BOLUS.arrival_offset_s + math.exp(_BOLUS.mu - _BOLUS.sigma**2) assert result.perfusion["roi_perf"].ttp_s == pytest.approx(peak_time, abs=0.5) # 画面上没有紫圈。 assert result.pan_detected_fraction == 0.0 assert result.tic.reference_valid is False # 暗区（囊）被找出来，去暗区曲线比病灶整体亮。 peak_row = result.tic.rows[round(peak_time * _FPS)] assert peak_row["dark_area_px"] > 0 assert peak_row["roi_perf_raw"] > peak_row["roi_all_raw"] restored = unpack_trajectory(pack_trajectory(result.trajectory)) assert restored.calibration["contrast_side"] == "right" curves = unpack_tic_curves(pack_tic_curves(result.curves)) assert curves.injection_offset_s == pytest.approx(_INJECTION_OFFSET_S) summary = quant_summary(result) json.dumps(summary, allow_nan=False) assert summary["anchor_frames"]["F1"]["frame_index"] == 150 assert summary["perfusion_metrics"]["roi_perf"] is not None markers = tic_plot_markers(result.anchors) assert [marker.label.split()[0] for marker in markers][:3] == ["D1", "D2", "D3"] assert render_tic_png(result.tic, markers=markers).startswith(b"\x89PNG") assert len(render_case_overlay(prepared, result)) > 0 images = keyframe_images(prepared, result.anchors) assert "F1" in images and "F2" not in images assert all(image.shape == (_HEIGHT, _WIDTH, 3) for image in images.values()) def test_empty_segmentation_fails_case_at_step_4( prepared: SegmentationInput, config: CeusConfig ) -> None: empty = np.zeros_like(_lesion_masks(prepared)) with pytest.raises(NoValidContourError) as raised: quantify_case(prepared, empty, _INJECTION_OFFSET_S, config) assert raised.value.step == 4 def test_mask_shape_mismatch_is_rejected(prepared: SegmentationInput, config: CeusConfig) -> None: masks = _lesion_masks(prepared)[:-1] with pytest.raises(ValueError, match="分割掩膜"): quantify_case(prepared, masks, _INJECTION_OFFSET_S, config)