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>
309 lines
12 KiB
Python
309 lines
12 KiB
Python
"""暗区识别（dark_region）单元测试（照搬旧项目 test_ceus_dark_region.py）。
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旧标题：B7 暗区识别（dark_region）单元测试。
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合成"亮 ROI 内嵌暗斑"图像钉死六个 port 的真实行为，并核对评分公式的三个硬编码
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常量（64.0 / 4.0 / 2.0）与时间一致性 IoU。测试用真实 OpenCV 像素管线（非 mock），
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覆盖：otsu 检出暗斑、auto_ensemble 择优、min_area/min_area_ratio 过滤碎片、
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exclude_prompt_color 剔绿、enabled=False 返回空且保留帧对齐、逐帧轮廓重采样 64 点。
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"""
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import numpy as np
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from cadx.ceus import (
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CeusConfig,
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InteriorDarkRegionConfig,
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apply_dark_morphology,
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dark_candidate_mask,
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extract_dark_region_masks,
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extract_dark_region_polygons,
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score_dark_candidate,
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split_dark_components,
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)
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from conftest import load_ceus_section
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# 配置不设默认值：旧测试靠默认值构造的配置，改为在 config/ceus.yaml 的取值上 model_copy。
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_CONFIG = CeusConfig.model_validate(load_ceus_section())
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# 剔绿框色用的 HSV 阈值：旧代码没传时回退的写死阈值与 prompt_source 相同，这里显式传入。
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_PROMPT = _CONFIG.prompt_source
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def _dark_cfg(**overrides: object) -> InteriorDarkRegionConfig:
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"""ceus.yaml 的暗区段，按需改几项（代替旧的 `InteriorDarkRegionConfig(...)`）。"""
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return _CONFIG.interior_dark_region.model_copy(update=overrides)
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# ---------- 合成样本 ----------
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_H, _W = 120, 160
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# 整幅 ROI 多边形：占据画面中央的大矩形。
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_ROI_POLYGON = np.array(
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[[20.0, 20.0], [140.0, 20.0], [140.0, 100.0], [20.0, 100.0]], dtype=np.float32
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)
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# ROI 内的暗斑位置（一个明确的低灰度方块）。
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_DARK_BOX = (slice(45, 75), slice(60, 100)) # (y, x)，30×40=1200 px
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def _bright_roi_with_dark_box() -> np.ndarray:
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"""构造一帧 BGR：全黑画布上铺亮 ROI（灰度 200），其内嵌暗斑（灰度 40）。"""
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frame = np.zeros((_H, _W, 3), dtype=np.uint8)
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# 亮 ROI 区
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frame[20:100, 20:140] = 200
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# 暗斑
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frame[_DARK_BOX] = 40
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return frame
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def _roi_mask() -> np.ndarray:
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from cadx.ceus import polygon_to_mask
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return polygon_to_mask(_ROI_POLYGON, _H, _W)
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def _gray(frame: np.ndarray) -> np.ndarray:
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import cv2
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return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# ---------- dark_candidate_mask / split / morphology ----------
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def test_otsu_candidate_detects_dark_box() -> None:
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"""otsu 法应在 ROI 内检出暗斑（暗斑像素被标记，亮区不被标记）。"""
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cfg = _dark_cfg()
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frame = _bright_roi_with_dark_box()
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gray = _gray(frame)
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sample = _roi_mask()
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dark = dark_candidate_mask(gray, sample, "otsu", cfg)
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assert dark.dtype == bool
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# 暗斑中心应被标记
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assert dark[60, 80]
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# 亮区中心不应被标记
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assert not dark[30, 30]
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# 暗斑覆盖率应显著（形态学后仍是大块）
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assert dark.sum() > 500
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def test_split_components_filters_small_fragments() -> None:
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"""min_area / min_area_ratio 联合过滤：组件阈 = max(min_area, ROI面积×ratio)。"""
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sample = _roi_mask()
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roi_area = float(sample.sum())
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# 造一个 ~1200px 暗块 + 一个 ~16px 碎片
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dark = np.zeros((_H, _W), dtype=bool)
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dark[45:75, 60:100] = True # 1200 px
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dark[10:14, 10:14] = True # 16 px 碎片（且在 ROI 外，会被 roi_mask 裁掉）
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dark[50:54, 30:34] = True # 16 px 碎片（ROI 内）
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cfg = _dark_cfg(min_area=300, min_area_ratio=0.015)
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# 阈值 = max(300, roi_area*0.015)
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threshold = max(300.0, roi_area * 0.015)
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assert threshold >= 300.0
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components = split_dark_components(dark, sample, cfg)
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# 只剩大暗块
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assert len(components) == 1
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assert components[0].sum() >= 300
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def test_morphology_intersects_sample_mask() -> None:
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"""形态学结果须与 sample_mask 取交（不外溢 ROI）。"""
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cfg = _dark_cfg(morphology_kernel=5)
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sample = _roi_mask()
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dark = np.zeros((_H, _W), dtype=bool)
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dark[45:75, 60:100] = True
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dark[0:5, 0:5] = True # ROI 外的块
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out = apply_dark_morphology(dark, sample, cfg)
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assert not out[2, 2] # ROI 外被裁掉
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assert (out & ~sample).sum() == 0
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# ---------- score_dark_candidate（三硬编码常量） ----------
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def test_score_contrast_constant_64() -> None:
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"""明暗对比项 = max(0, (median_bright - median_dark) / 64.0)。
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用无碎片惩罚 + 无时间项 + None preferred_range 的配置隔离对比项贡献。
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"""
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cfg = _dark_cfg(fragment_penalty_weight=0.0, temporal_consistency_weight=0.0)
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frame = _bright_roi_with_dark_box()
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gray = _gray(frame)
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sample = _roi_mask()
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component = np.zeros((_H, _W), dtype=bool)
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component[_DARK_BOX] = True
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score = score_dark_candidate(gray, sample, [component], None, cfg)
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union = component & sample
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median_bright = float(np.median(gray[(~union) & sample]))
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median_dark = float(np.median(gray[union & sample]))
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contrast = max(0.0, (median_bright - median_dark) / 64.0)
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area_ratio = float(union.sum()) / max(1.0, float(sample.sum()))
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area_score = min(area_ratio, 1.0 - area_ratio) * 2.0
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expected = contrast + area_score
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assert score == expected
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def test_score_empty_components_returns_sentinel() -> None:
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"""空组件列表返回极小值哨兵（择优时永远不会被选）。"""
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cfg = _dark_cfg()
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gray = _gray(_bright_roi_with_dark_box())
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sample = _roi_mask()
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assert score_dark_candidate(gray, sample, [], None, cfg) == -1e9
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def test_score_fragment_penalty_and_temporal_iou() -> None:
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"""碎片惩罚 = w×max(0, 块数−1)；时间一致性 = temporal_weight × IoU(union, prev)。"""
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cfg = _dark_cfg(fragment_penalty_weight=0.08, temporal_consistency_weight=0.35)
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frame = _bright_roi_with_dark_box()
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gray = _gray(frame)
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sample = _roi_mask()
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comp_a = np.zeros((_H, _W), dtype=bool)
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comp_a[45:75, 60:80] = True
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comp_b = np.zeros((_H, _W), dtype=bool)
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comp_b[45:75, 80:100] = True
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components = [comp_a, comp_b]
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union = (comp_a | comp_b) & sample
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prev = comp_a.copy() # 上一帧暗部 = comp_a
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score = score_dark_candidate(gray, sample, components, prev, cfg)
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from cadx.ceus import mask_iou
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median_bright = float(np.median(gray[(~union) & sample]))
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median_dark = float(np.median(gray[union & sample]))
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contrast = max(0.0, (median_bright - median_dark) / 64.0)
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area_ratio = float(union.sum()) / max(1.0, float(sample.sum()))
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area_score = min(area_ratio, 1.0 - area_ratio) * 2.0
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fragment_penalty = 0.08 * 1 # 2 块 → max(0, 2-1)=1
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temporal = mask_iou(union, prev) * 0.35
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expected = contrast + area_score + temporal - fragment_penalty
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assert abs(score - expected) < 1e-9
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def test_score_preferred_range_slope_4() -> None:
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"""preferred_area_ratio_range 命中时 area_score=1，否则 max(0, 1 - dist×4.0)。"""
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# 这个键旧 schema 里没有（旧测试用 object.__setattr__ 注入），现在是配置字段。
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cfg = _dark_cfg(
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fragment_penalty_weight=0.0,
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temporal_consistency_weight=0.0,
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preferred_area_ratio_range=[0.001, 0.005],
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)
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frame = _bright_roi_with_dark_box()
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gray = _gray(frame)
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sample = _roi_mask()
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component = np.zeros((_H, _W), dtype=bool)
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component[_DARK_BOX] = True # 1200/roi_area
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score = score_dark_candidate(gray, sample, [component], None, cfg)
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union = component & sample
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area_ratio = float(union.sum()) / max(1.0, float(sample.sum()))
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median_bright = float(np.median(gray[(~union) & sample]))
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median_dark = float(np.median(gray[union & sample]))
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contrast = max(0.0, (median_bright - median_dark) / 64.0)
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low, high = 0.001, 0.005
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if low <= area_ratio <= high:
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area_score = 1.0
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else:
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nearest = low if area_ratio < low else high
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area_score = max(0.0, 1.0 - abs(area_ratio - nearest) * 4.0)
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assert abs(score - (contrast + area_score)) < 1e-9
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# ---------- extract_dark_region_masks ----------
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def test_auto_ensemble_selects_dark_box() -> None:
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"""auto_ensemble 跑 candidate_methods 择优，应选出覆盖暗斑的暗块。"""
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cfg = _dark_cfg(threshold_method="auto_ensemble")
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frame = _bright_roi_with_dark_box()
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masks, union = extract_dark_region_masks(frame, _ROI_POLYGON, cfg, None, _PROMPT)
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assert masks, "auto_ensemble 应检出暗块"
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assert union is not None
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# 暗斑中心被选中暗块覆盖
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assert union[60, 80]
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# 不外溢 ROI
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assert (union & ~_roi_mask()).sum() == 0
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def test_exclude_prompt_color_removes_green() -> None:
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"""exclude_prompt_color=True 时 ROI 内绿像素从分析中剔除（不计入暗 / 亮统计）。"""
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frame = _bright_roi_with_dark_box()
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# 在 ROI 内画一块纯绿（BGR），它在 HSV 绿区间内，灰度也偏低
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frame[80:95, 30:60] = (0, 255, 0)
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cfg_on = _dark_cfg(exclude_prompt_color=True)
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_masks_on, union_on = extract_dark_region_masks(frame, _ROI_POLYGON, cfg_on, None, _PROMPT)
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# 绿块不应进入选中暗块
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assert union_on is not None
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assert not union_on[87, 45], "绿像素应被剔除，不计入暗块"
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def test_disabled_returns_empty_polys_but_keeps_alignment() -> None:
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"""enabled=False：每帧返回空多边形列表，但帧数与输入对齐（§5 不变量）。"""
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cfg = _dark_cfg(enabled=False)
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frames = [_bright_roi_with_dark_box() for _ in range(3)]
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polys = [_ROI_POLYGON for _ in range(3)]
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result = extract_dark_region_polygons(frames, polys, cfg, _PROMPT)
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assert len(result) == 3
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assert all(frame_polys == [] for frame_polys in result)
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def test_polygons_resampled_to_64_points_and_aligned() -> None:
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"""逐帧暗块轮廓重采样到 contour_points=64 点；帧对齐保留（N 帧进 N 帧出）。"""
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cfg = _dark_cfg(contour_points=64)
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frames = [_bright_roi_with_dark_box() for _ in range(4)]
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polys = [_ROI_POLYGON for _ in range(4)]
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result = extract_dark_region_polygons(frames, polys, cfg, _PROMPT)
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assert len(result) == 4
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# 每帧至少一个暗块多边形（暗斑稳定）
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for frame_polys in result:
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assert frame_polys, "每帧应检出暗斑多边形"
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for poly in frame_polys:
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assert poly.shape == (64, 2)
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assert poly.dtype == np.float32
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def _bright_roi_with_donut_dark() -> np.ndarray:
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"""构造一帧 BGR：亮 ROI（200）内嵌暗斑（40），暗斑中心再挖一个亮洞（200）→ 甜甜圈暗区。"""
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frame = np.zeros((_H, _W, 3), dtype=np.uint8)
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frame[20:100, 20:140] = 200 # 亮 ROI
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frame[45:75, 60:100] = 40 # 暗斑（1200 px）
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frame[55:65, 72:88] = 200 # 中心亮洞（10×16=160 px）→ 暗区成环
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return frame
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def test_preserve_holes_keeps_hole_contour() -> None:
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"""preserve_holes=True：甜甜圈暗区的多边形列表须含外轮廓 + 洞轮廓（≥2 个）。
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回归 Codex 发现的 Critical——洞由 split_dark_components 挖回 mask，
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_significant_contours 须沿 hierarchy 追加子轮廓（参考 P:685-692），否则洞被丢弃。
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"""
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cfg = _dark_cfg(preserve_holes=True, contour_points=64)
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frames = [_bright_roi_with_donut_dark()]
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polys = [_ROI_POLYGON]
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result = extract_dark_region_polygons(frames, polys, cfg, _PROMPT)
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assert len(result) == 1
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frame_polys = result[0]
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# 外环 + 中心洞 → 至少两个多边形
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assert len(frame_polys) >= 2, f"应含外轮廓 + 洞，实得 {len(frame_polys)} 个"
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# 每个多边形均重采样到 64 点
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assert all(p.shape == (64, 2) for p in frame_polys)
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# 存在一个明显小于外环的多边形（洞）——按面积区分
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from cadx.ceus import polygon_area
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areas = sorted(polygon_area(p) for p in frame_polys)
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assert areas[0] < areas[-1], "洞多边形面积应显著小于外轮廓"
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