iomgaa
1de88a5d0a
- 内存：prepare_segmentation 改为流式解码，只留分屏和配准的抽样帧，绿框逐帧在两半上找； 分割那一半由 SegmentationInput.target_frames() 到用时再解码，segment_video 边读边写 jpg； MedSAM2 init_state 的读帧换成直接在存放设备上建张量（不改 MedSAM2 源码，结果逐位相同）； 传播结果写进预分配数组。帧数最多的一段视频常驻内存峰值 11.2 GiB → 2.3 GiB，分辨率最大的 一段原代码被 OOM 杀掉、现为 4.2 GiB；两段的掩码和全部产物与改前逐字节相同，33 段视频的 第 1–2 步结果与改前一致，29 例配准与 review.csv 一致。 - 快照：ceus_quant 开跑时写 snapshot/（git、ceus.yaml、review.csv、uv.lock、segment.json）， 配置和核对表从快照副本读；收尾由新函数 runs.finish_snapshot 只新建 finished.json。 build 与 ceus_align_review 同样改用 finish_snapshot 记结束时间。 - build：summary.json 加 video_sha256；装 CEUS 结果时核对快照里的 delivery_dir、逐例核对 视频 SHA-256 和推注时间，不同就整次停下；新字段做类型检查，不进 tic.json。 来源：主会话任务（问题 1–3 及补充的 delivery_dir 比对）。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
182 lines
7.9 KiB
Python
182 lines
7.9 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。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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- 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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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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NoValidContourError,
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SegmentationInput,
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VideoFile,
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crop_frame,
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keyframe_images,
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locate_contrast_side,
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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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register_halves,
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render_case_overlay,
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render_tic_png,
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split_views,
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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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from fake_ceus_video import BOLUS, FPS, FRAMES, HEIGHT, LESION, SPLIT, WIDTH, write_video
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_INJECTION_OFFSET_S = 5.0
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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, green_box=True)
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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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# 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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target_frames = list(prepared.target_frames())
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assert len(target_frames) == FRAMES
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assert all(frame.shape == (target.height, target.width, 3) for frame in 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_streaming_prepare_matches_whole_video_in_memory(
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prepared: SegmentationInput, config: CeusConfig
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) -> None:
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"""逐帧解码的第 1–2 步与「整段帧读进内存再交给各步」逐像素相同（改成流式之前就是后一种）。"""
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frames = [frame for _, frame in VideoFile(prepared.video_path).iter_frames()]
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split_x, left, right = split_views(frames, config.views)
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registration = register_halves(
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frames, left, right, config.registration, config.prompt_source, config.pan_source
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)
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contrast = locate_contrast_side(frames, left, right, config.prompt_source, config.keyframes)
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assert prepared.split_x == split_x
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assert prepared.registration == registration
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assert prepared.contrast.side == contrast.side
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assert (prepared.contrast.contrast_view, prepared.contrast.target_view) == (
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contrast.contrast_view,
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contrast.target_view,
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)
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assert [p.frame_index for p in prepared.contrast.prompt_frames] == [
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p.frame_index for p in contrast.prompt_frames
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]
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for ours, whole in zip(prepared.contrast.prompt_frames, contrast.prompt_frames, strict=True):
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assert np.array_equal(ours.mask, whole.mask)
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target_frames = list(prepared.target_frames())
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assert len(target_frames) == len(frames)
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for ours, frame in zip(target_frames, frames, strict=True):
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assert np.array_equal(ours, crop_frame(frame, contrast.target_view))
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assert np.array_equal(
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prepared.first_contrast_frame, crop_frame(frames[0], contrast.contrast_view)
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)
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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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