feat(ceus): 两半坐标换算改为每段视频配准，加目视核对脚本
- 第 1 步分屏后在两半扇形区内用边缘图归一化互相关估 (dx, dy)，再用逐帧多块模板匹配的中位数独立核对； 两者各分量之差超过门槛（15 像素）这一例第 1 步失败。删掉「两个视图左上角相减」的实现与测试。 - 门槛依据：34 例中走完第 1 步的 29 例，差值中位 2、95 分位 6.2、最大 13；参数与门槛在同一批视频上调出。 - scripts/ceus_align_review.py：只做第 1 步，出扇形区棋盘图与边缘叠加图，混入错开 15–30 像素的 阴性对照、随机编号；计分时任一对照未被判「没对齐」即作废。 - ceus_quant 读 review.csv：不通过的病例第 1 步失败；表里没有这段视频或平移对不上即停。 - 配准与紫圈颜色参数进分割缓存键。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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@@ -3,7 +3,8 @@
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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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被当成重复帧。两半各有一块位置相同、带纹理、随时间轻微起伏的扇形区（`fake_split_scene`，
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配准要靠它），左半是灰阶画面，右半（造影）调暗。
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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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@@ -14,19 +15,15 @@
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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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@@ -39,36 +36,11 @@ from cadx.ceus import (
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unpack_tic_curves,
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unpack_trajectory,
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)
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from fake_ceus_video import BOLUS, FPS, FRAMES, HEIGHT, LESION, SPLIT, WIDTH, write_video
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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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@@ -79,7 +51,7 @@ def config() -> CeusConfig:
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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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write_video(path, green_box=True)
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return prepare_segmentation(path, config)
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@@ -87,21 +59,25 @@ 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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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.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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# 两半扇形对齐：两种配准都估出 (0, 0)；造影在右半，平移取反后仍是 (0, 0)。
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assert prepared.registration.correlation == (0, 0)
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assert prepared.registration.disagreement_px <= 1
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assert prepared.offset == (0, 0)
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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 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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@@ -114,7 +90,7 @@ def test_prepare_segmentation_finds_contrast_half_and_maps_prompts(
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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.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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@@ -124,13 +100,13 @@ def test_quantify_case_end_to_end(prepared: SegmentationInput, config: CeusConfi
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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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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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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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@@ -150,7 +126,7 @@ def test_quantify_case_end_to_end(prepared: SegmentationInput, config: CeusConfi
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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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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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