iomgaa
00ebbffb28
- 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>
509 lines
21 KiB
Python
509 lines
21 KiB
Python
"""polygons 的单元测试（照搬旧项目 test_ceus_polygons.py；坐标换算改为配准得到的平移 `offset`，
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去掉旧的有效区矩形和映射锚点参数。旧用例里两个视图左上角相同，平移取 (0, 0)，断言不变）。
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旧标题：B6 polygons.py 的单元测试：mask→平滑多边形 + 位置约束 + 绿框先验融合。
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参考 ``references/ROI/roi_tracking_pipeline.py`` P:649-965。覆盖核心真实场景
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（边缘场景靠实现内 assertion / 守卫，不在此堆假数据）：
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- ``masks_to_smoothed_polygons``：圆形 mask → (N, 64, 2) 平滑多边形；**空 mask 帧
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保留在序列**（§5 帧对齐不变量，N 帧进 N 帧出），且其多边形由时间插值得到（非空）。
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- ``apply_prompt_frame_position_constraints``（strength=1.0）：把每帧质心拉到 keyframe
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中心插值轨迹上（整体平移），**不改面积**；``enabled=False`` 原样返回。
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- ``fuse_prompt_prior_with_medsam``：``source != "prompt_prior"`` 门关 → 直接返回纯
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medsam；门开时 keyframe 帧用 ``keyframe_shape_strength`` 逐顶点混合，且面积被
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收紧到 ``[min, max] × prior_area``（findings §3.7）。
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"""
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from dataclasses import dataclass
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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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CeusConfig,
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ViewRect,
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apply_prompt_frame_position_constraints,
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fuse_prompt_prior_with_medsam,
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masks_to_smoothed_polygons,
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polygon_area,
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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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@dataclass
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class _RawShapeCfg:
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"""绕过 RoiShapeConfig 校验器的鸭子接口（直接喂超界 strength，验证函数内部 clip）。"""
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source: str = "prompt_prior"
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keyframe_shape_strength: float = 1.0
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propagation_shape_strength: float = 0.75
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min_area_ratio_to_prompt: float = 0.70
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max_area_ratio_to_prompt: float = 1.25
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@dataclass
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class _RawConstraintCfg:
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"""绕过 KeyframePositionConstraint 校验器（Literal mode + strength clip）的鸭子接口。"""
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enabled: bool = True
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mode: str = "center_interpolation"
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strength: float = 1.0
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def _disk_mask(height: int, width: int, center_xy: tuple[int, int], radius: int) -> np.ndarray:
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"""生成 ``(height, width)`` 的圆盘二值 mask（前景为 True），供轮廓提取测试用。"""
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canvas = np.zeros((height, width), dtype=np.uint8)
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cv2.circle(canvas, (int(center_xy[0]), int(center_xy[1])), int(radius), 255, -1)
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return canvas > 0
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def _circle_polygon(center_xy: tuple[float, float], radius: float, num_points: int) -> np.ndarray:
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"""生成绕指定中心的规则圆多边形 ``(num_points, 2)``（用作 medsam / prior 测试输入）。"""
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angles = np.linspace(0.0, 2.0 * np.pi, num_points, endpoint=False)
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poly = np.zeros((num_points, 2), dtype=np.float32)
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poly[:, 0] = center_xy[0] + radius * np.cos(angles)
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poly[:, 1] = center_xy[1] + radius * np.sin(angles)
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return poly
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def test_masks_to_smoothed_polygons_shape_and_frame_alignment() -> None:
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"""圆盘 mask → (N, 64, 2)；中间空 mask 帧保留在序列（§5 帧对齐不变量）。"""
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frame_count = 5
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contour_points = _CONFIG.smoothing.contour_points # 64
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masks_by_frame = {
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0: _disk_mask(120, 120, (60, 60), 20),
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1: _disk_mask(120, 120, (62, 60), 20),
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# frame 2 空（退化 / 漏检）——不得剔除，靠时间插值填补
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3: _disk_mask(120, 120, (64, 60), 20),
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4: _disk_mask(120, 120, (66, 60), 20),
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}
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polygons = masks_to_smoothed_polygons(masks_by_frame, frame_count, _CONFIG.smoothing)
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assert len(polygons) == frame_count, "N 帧进必须 N 帧出（§5 帧对齐，不剔除空帧）。"
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for poly in polygons:
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assert poly.shape == (contour_points, 2)
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assert poly.dtype == np.float32
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# 空帧（index 2）由相邻帧时间插值得到，应为有效非退化多边形
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assert polygon_area(polygons[2]) > 1.0, "空 mask 帧应被时间插值填补为有效多边形。"
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def test_masks_to_smoothed_polygons_all_empty_raises() -> None:
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"""全部帧无有效轮廓时无从插值，直接 RuntimeError（不静默兜底）。"""
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import pytest
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with pytest.raises(RuntimeError):
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masks_to_smoothed_polygons({}, 3, _CONFIG.smoothing)
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def test_position_constraint_pulls_center_to_keyframe_track_without_area_change() -> None:
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"""strength=1.0 把每帧质心拉到 keyframe 中心插值轨迹，且整体平移不改面积。"""
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target_eff = ViewRect(0, 0, 200, 200)
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offset = (0, 0)
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# 两个 keyframe：source mask 中心分别在 (40, 100) 与 (160, 100)
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keyframes = [
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{"frame_index": 0, "mask": _disk_mask(200, 200, (40, 100), 15)},
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{"frame_index": 4, "mask": _disk_mask(200, 200, (160, 100), 15)},
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]
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# 当前多边形都偏在 (100, 30)，期望被拉到插值中心轨迹（x 从 40 线性到 160）
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polygons = [_circle_polygon((100.0, 30.0), 12.0, 64) for _ in range(5)]
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areas_before = [polygon_area(poly) for poly in polygons]
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corrected = apply_prompt_frame_position_constraints(
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polygons,
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keyframes,
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offset,
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target_eff,
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_CONFIG.constraints.keyframe_position, # enabled、center_interpolation、strength=1.0
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)
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assert len(corrected) == len(polygons)
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# frame 0 质心 ≈ (40, 100)，frame 4 ≈ (160, 100)，frame 2 ≈ (100, 100)（线性插值）
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expected_x = [40.0, 70.0, 100.0, 130.0, 160.0]
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for frame_idx, poly in enumerate(corrected):
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center = poly.mean(axis=0)
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assert abs(float(center[0]) - expected_x[frame_idx]) < 3.0
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assert abs(float(center[1]) - 100.0) < 3.0
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# 整体平移：面积不变
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for before, poly in zip(areas_before, corrected, strict=True):
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assert abs(polygon_area(poly) - before) < 2.0
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def test_position_constraint_disabled_returns_input() -> None:
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"""enabled=False 时原样返回输入（门关）。"""
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target_eff = ViewRect(0, 0, 100, 100)
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offset = (0, 0)
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polygons = [_circle_polygon((50.0, 50.0), 10.0, 64)]
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keyframes = [{"frame_index": 0, "mask": _disk_mask(100, 100, (50, 50), 10)}]
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out = apply_prompt_frame_position_constraints(
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polygons,
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keyframes,
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offset,
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target_eff,
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_CONFIG.constraints.keyframe_position.model_copy(update={"enabled": False}),
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)
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assert out is polygons
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def test_fusion_gate_closed_returns_pure_medsam() -> None:
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"""roi_shape.source != 'prompt_prior' → 直接返回纯 medsam（门关，findings §3.7）。"""
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target_eff = ViewRect(0, 0, 100, 100)
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offset = (0, 0)
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medsam = [_circle_polygon((50.0, 50.0), 10.0, 64)]
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keyframes = [{"frame_index": 0, "mask": _disk_mask(100, 100, (50, 50), 10)}]
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out = fuse_prompt_prior_with_medsam(
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medsam,
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keyframes,
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offset,
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target_eff,
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_CONFIG.roi_shape.model_copy(update={"source": "medsam2"}),
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_CONFIG.smoothing,
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)
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assert out is medsam
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def test_fusion_keyframe_strength_one_yields_prior_shape() -> None:
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"""keyframe 帧 strength=1.0：融合结果几何上贴近绿框先验（逐顶点混合 s=1）。"""
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target_eff = ViewRect(0, 0, 200, 200)
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offset = (0, 0)
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# 绿框先验中心 (100, 100) 半径 30；medsam 偏移到 (130, 130) 半径 30
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keyframes = [{"frame_index": 0, "mask": _disk_mask(200, 200, (100, 100), 30)}]
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medsam = [_circle_polygon((130.0, 130.0), 30.0, 64)]
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fused = fuse_prompt_prior_with_medsam(
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medsam,
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keyframes,
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offset,
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target_eff,
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_CONFIG.roi_shape.model_copy(update={"keyframe_shape_strength": 1.0}),
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_CONFIG.smoothing,
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)
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assert len(fused) == 1
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center = fused[0].mean(axis=0)
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# s=1 → 结果应贴近先验中心 (100, 100) 而非 medsam 中心 (130, 130)
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assert abs(float(center[0]) - 100.0) < 8.0
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assert abs(float(center[1]) - 100.0) < 8.0
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def test_fusion_area_clamped_to_prior_band() -> None:
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"""融合面积超过 max_area_ratio_to_prompt 时按 sqrt 绕先验质心缩放回界（findings §3.7）。"""
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target_eff = ViewRect(0, 0, 400, 400)
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offset = (0, 0)
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# 关键帧只在 frame 0；frame 1 为传播帧（非 keyframe），用 propagation_shape_strength=0
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# 让 medsam 形状主导，从而触发面积收紧。先验半径 30，medsam 半径 80（面积远超 1.25×）。
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keyframes = [{"frame_index": 0, "mask": _disk_mask(400, 400, (200, 200), 30)}]
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medsam = [
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_circle_polygon((200.0, 200.0), 30.0, 64), # frame 0（keyframe，对照）
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_circle_polygon((200.0, 200.0), 80.0, 64), # frame 1（propagation，测收紧）
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]
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shape_cfg = _CONFIG.roi_shape.model_copy(
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update={
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"keyframe_shape_strength": 1.0,
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"propagation_shape_strength": 0.0, # frame 1 形状 = 纯 medsam（半径80，超界）
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"min_area_ratio_to_prompt": 0.70,
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"max_area_ratio_to_prompt": 1.25,
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}
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)
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fused = fuse_prompt_prior_with_medsam(
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medsam,
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keyframes,
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offset,
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target_eff,
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shape_cfg,
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_CONFIG.smoothing,
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)
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# 真实先验面积取自 frame 0（keyframe，s=1 → 形状=离散化绿框先验），避免用解析圆面积失真
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prior_area = polygon_area(fused[0])
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fused_area = polygon_area(fused[1])
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# frame 1 s=0 → 形状=medsam（半径80，面积远超界），应被收紧到 ≈ max_ratio × prior_area
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assert fused_area <= prior_area * 1.25 + 50.0, "超界面积应被收紧到 max_area_ratio。"
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assert fused_area >= prior_area * 1.25 - 100.0, "应恰好收到上界附近，而非过度收缩。"
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# Phase 4b: fuse_prompt_prior_with_medsam —— 关键证伪路径
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def test_fusion_propagation_frame_linear_blend_without_tightening() -> None:
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"""非 keyframe 帧 + 0<s<1：未触发面积收紧时逐顶点 ≈ medsam·(1−s) + prior·s。
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构造 medsam 与 prior 半径相近（面积比落在 [min, max] 带内，不触发收紧），中心同位，
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使逐顶点线性混合的几何结果可被精确预测（绕开起点对齐 / 收紧的扰动）。
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"""
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target_eff = ViewRect(0, 0, 400, 400)
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offset = (0, 0)
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# 单关键帧（frame 0），frame 1 为传播帧；先验半径 50，medsam 半径 60（面积比≈1.44，
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# 但 max_ratio 放宽到 2.0 → 不触发收紧），中心同在 (200, 200)
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keyframes = [{"frame_index": 0, "mask": _disk_mask(400, 400, (200, 200), 50)}]
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medsam = [
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_circle_polygon((200.0, 200.0), 50.0, 64), # frame 0（keyframe，对照）
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_circle_polygon((200.0, 200.0), 60.0, 64), # frame 1（传播帧，测线性混合）
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]
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strength = 0.5
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shape_cfg = _RawShapeCfg(
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keyframe_shape_strength=1.0,
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propagation_shape_strength=strength,
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min_area_ratio_to_prompt=0.10,
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max_area_ratio_to_prompt=2.00, # 放宽上界，确保 frame 1 不触发面积收紧
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)
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fused = fuse_prompt_prior_with_medsam(
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medsam,
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keyframes,
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offset,
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target_eff,
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shape_cfg,
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_CONFIG.smoothing,
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)
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# frame 1 半径 ≈ medsam·(1−s) + prior·s。prior 取自 frame 0（离散化绿框，s=1）。
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# 用面积反推等效半径，校验落在 medsam(60) 与 prior(≈50) 之间且约为 0.5 混合。
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prior_area = polygon_area(fused[0])
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prior_radius = float(np.sqrt(prior_area / np.pi))
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fused_radius = float(np.sqrt(polygon_area(fused[1]) / np.pi))
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expected_radius = 60.0 * (1.0 - strength) + prior_radius * strength
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assert abs(fused_radius - expected_radius) < 3.0, (
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f"传播帧应为线性混合半径 ≈ {expected_radius:.1f}，实得 {fused_radius:.1f}。"
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)
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# 逐顶点直接证伪（非聚合面积）：每个融合顶点到圆心的半径都应 ≈ 混合半径，
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# 而不仅是整体面积匹配——bbox 整体插值或部分混合会在个别顶点暴露偏差。
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center = np.array([200.0, 200.0], dtype=np.float64)
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radii = np.linalg.norm(fused[1].astype(np.float64) - center, axis=1)
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assert radii.shape == (64,)
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assert np.all(np.abs(radii - expected_radius) < 3.0), (
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f"逐顶点半径应均 ≈ {expected_radius:.1f}（顶点级线性混合），"
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f"实得范围 [{radii.min():.1f}, {radii.max():.1f}]。"
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)
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def test_fusion_lower_bound_tightening_expands_area() -> None:
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"""下界收紧：medsam 面积 < min_ratio × prior 面积 → 被放大到 ≈ min_ratio × prior。"""
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target_eff = ViewRect(0, 0, 400, 400)
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offset = (0, 0)
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# 先验半径 60；medsam 传播帧半径 20（面积比≈0.11 < min_ratio 0.70 → 触发下界放大）
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keyframes = [{"frame_index": 0, "mask": _disk_mask(400, 400, (200, 200), 60)}]
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medsam = [
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_circle_polygon((200.0, 200.0), 60.0, 64), # frame 0（keyframe，对照）
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_circle_polygon((200.0, 200.0), 20.0, 64), # frame 1（传播帧，面积过小）
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]
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shape_cfg = _RawShapeCfg(
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keyframe_shape_strength=1.0,
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propagation_shape_strength=0.0, # frame 1 形状 = 纯 medsam（半径20，欠界）
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min_area_ratio_to_prompt=0.70,
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max_area_ratio_to_prompt=1.25,
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)
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fused = fuse_prompt_prior_with_medsam(
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medsam,
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keyframes,
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offset,
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target_eff,
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shape_cfg,
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_CONFIG.smoothing,
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)
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prior_area = polygon_area(fused[0])
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fused_area = polygon_area(fused[1])
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# 欠界面积应被放大到 ≈ min_ratio × prior_area（绕先验质心 sqrt 缩放）
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assert fused_area >= prior_area * 0.70 - 100.0, "欠界面积应被放大到 min_area_ratio。"
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assert fused_area <= prior_area * 0.70 + 200.0, "应恰好放大到下界附近，而非过度膨胀。"
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def test_fusion_start_point_misalignment_does_not_distort_shape() -> None:
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"""medsam 与 prior 起点错位但形状相同：融合后不因起点错位畸变（输出仍贴近该形状）。
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把 medsam 顶点循环移位（起点错位）但形状 / 中心与 prior 完全一致，s=0.5：
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若不先对齐起点，逐顶点混合会把不同角度的点平均出来导致半径塌缩；align 后应保形。
|
||
"""
|
||
target_eff = ViewRect(0, 0, 400, 400)
|
||
offset = (0, 0)
|
||
|
||
radius = 50.0
|
||
keyframes = [{"frame_index": 0, "mask": _disk_mask(400, 400, (200, 200), int(radius))}]
|
||
base_circle = _circle_polygon((200.0, 200.0), radius, 64)
|
||
shifted_circle = np.roll(base_circle, 17, axis=0) # 起点错位 17 个顶点
|
||
medsam = [
|
||
_circle_polygon((200.0, 200.0), radius, 64), # frame 0（keyframe，对照）
|
||
shifted_circle, # frame 1（传播帧，起点错位）
|
||
]
|
||
|
||
shape_cfg = _RawShapeCfg(
|
||
keyframe_shape_strength=1.0,
|
||
propagation_shape_strength=0.5,
|
||
min_area_ratio_to_prompt=0.10,
|
||
max_area_ratio_to_prompt=2.00, # 放宽上界，孤立起点对齐效应
|
||
)
|
||
|
||
fused = fuse_prompt_prior_with_medsam(
|
||
medsam,
|
||
keyframes,
|
||
offset,
|
||
target_eff,
|
||
shape_cfg,
|
||
_CONFIG.smoothing,
|
||
)
|
||
|
||
prior_area = polygon_area(fused[0])
|
||
fused_area = polygon_area(fused[1])
|
||
# 起点对齐后形状不塌缩：融合面积应与先验面积相当（错位若未对齐会显著缩小）
|
||
assert fused_area == pytest.approx(prior_area, rel=0.10), (
|
||
f"起点错位经 align 后应保形（≈{prior_area:.0f}），实得 {fused_area:.0f}。"
|
||
)
|
||
|
||
|
||
def test_fusion_propagation_strength_clipped_to_unit_interval() -> None:
|
||
"""传播权重越界（1.5 / −0.5）经函数内部 np.clip → 行为等价 1.0 / 0.0。"""
|
||
target_eff = ViewRect(0, 0, 400, 400)
|
||
offset = (0, 0)
|
||
|
||
keyframes = [{"frame_index": 0, "mask": _disk_mask(400, 400, (200, 200), 40)}]
|
||
medsam = [
|
||
_circle_polygon((200.0, 200.0), 40.0, 64), # frame 0（keyframe）
|
||
_circle_polygon((260.0, 260.0), 40.0, 64), # frame 1（传播帧，偏移到右下）
|
||
]
|
||
|
||
def _fuse(propagation_strength: float) -> np.ndarray:
|
||
shape_cfg = _RawShapeCfg(
|
||
keyframe_shape_strength=1.0,
|
||
propagation_shape_strength=propagation_strength,
|
||
min_area_ratio_to_prompt=0.10,
|
||
max_area_ratio_to_prompt=2.00,
|
||
)
|
||
return fuse_prompt_prior_with_medsam(
|
||
medsam,
|
||
keyframes,
|
||
offset,
|
||
target_eff,
|
||
shape_cfg,
|
||
_CONFIG.smoothing,
|
||
)[1]
|
||
|
||
# s=1.5 被 clip 到 1.0 → 形状=纯 prior（中心贴近 (200,200)）；与显式 1.0 等价
|
||
over = _fuse(1.5).mean(axis=0)
|
||
one = _fuse(1.0).mean(axis=0)
|
||
assert np.allclose(over, one, atol=1.0), "s=1.5 应被 clip 到 1.0（行为等价）。"
|
||
# s=−0.5 被 clip 到 0.0 → 形状=纯 medsam（中心贴近 (260,260)）；与显式 0.0 等价
|
||
neg = _fuse(-0.5).mean(axis=0)
|
||
zero = _fuse(0.0).mean(axis=0)
|
||
assert np.allclose(neg, zero, atol=1.0), "s=−0.5 应被 clip 到 0.0（行为等价）。"
|
||
|
||
|
||
# Phase 5: 时间平滑 + 位置约束补测
|
||
|
||
|
||
def test_temporal_smoothing_damps_per_vertex_jitter_along_time() -> None:
|
||
"""逐帧抖动的顶点序列经时间轴三角平滑后抖动被削弱、形状趋稳。
|
||
|
||
构造一段「真值恒定 + 逐帧高频抖动」的多边形序列（顶点已逐点对应），
|
||
masks_to_smoothed_polygons 内的时间平滑应显著降低逐帧位移幅度。
|
||
"""
|
||
frame_count = 9
|
||
base_center = (200, 200)
|
||
radius = 30
|
||
rng = np.random.default_rng(7)
|
||
|
||
# 每帧在固定圆心附近抖动 ±4px（mask 级别抖动 → 平滑后中心轨迹应更平稳）
|
||
masks_by_frame: dict[int, np.ndarray] = {}
|
||
for frame_idx in range(frame_count):
|
||
jitter = rng.integers(-4, 5, size=2)
|
||
center = (int(base_center[0] + jitter[0]), int(base_center[1] + jitter[1]))
|
||
masks_by_frame[frame_idx] = _disk_mask(400, 400, center, radius)
|
||
|
||
polygons = masks_to_smoothed_polygons(
|
||
masks_by_frame, frame_count, _CONFIG.smoothing.model_copy(update={"temporal_window": 5})
|
||
)
|
||
|
||
# 平滑后逐帧质心位移幅度应小于原始抖动尺度（抖动被削弱）
|
||
centers = np.array([poly.mean(axis=0) for poly in polygons])
|
||
frame_to_frame_shift = np.linalg.norm(np.diff(centers, axis=0), axis=1)
|
||
assert frame_to_frame_shift.max() < 4.0, "时间平滑应把逐帧质心抖动削弱到原始 ±4px 尺度以下。"
|
||
# 形状趋稳：各帧面积方差小（半径恒定 → 平滑不应制造面积剧烈起伏）
|
||
areas = np.array([polygon_area(poly) for poly in polygons])
|
||
assert areas.std() / areas.mean() < 0.10, "恒定半径下平滑后面积应稳定。"
|
||
|
||
|
||
def test_temporal_smoothing_no_vertex_crossing_distortion() -> None:
|
||
"""相邻帧轮廓起点对齐使逐点对应，时间平滑不产生顶点错位畸变（形状保真）。
|
||
|
||
两帧同形同位的圆（仅起点天然可能错位），平滑后每帧仍是完整圆而非塌缩折线。
|
||
"""
|
||
frame_count = 3
|
||
masks_by_frame = {idx: _disk_mask(400, 400, (200, 200), 40) for idx in range(frame_count)}
|
||
|
||
polygons = masks_to_smoothed_polygons(
|
||
masks_by_frame, frame_count, _CONFIG.smoothing.model_copy(update={"temporal_window": 3})
|
||
)
|
||
|
||
# 起点对齐 + 平滑后：每帧面积应接近真实圆面积，未因顶点错位塌缩
|
||
analytic_area = np.pi * 40.0**2
|
||
for poly in polygons:
|
||
assert polygon_area(poly) == pytest.approx(analytic_area, rel=0.10), (
|
||
"起点对齐后时间平滑应保形，不出现顶点错位导致的面积塌缩。"
|
||
)
|
||
|
||
|
||
def test_position_constraint_invalid_mode_raises() -> None:
|
||
"""mode != 'center_interpolation' → RuntimeError（当前仅支持该模式，纵深防御）。"""
|
||
target_eff = ViewRect(0, 0, 200, 200)
|
||
offset = (0, 0)
|
||
polygons = [_circle_polygon((100.0, 100.0), 20.0, 64)]
|
||
keyframes = [{"frame_index": 0, "mask": _disk_mask(200, 200, (100, 100), 20)}]
|
||
|
||
with pytest.raises(RuntimeError):
|
||
apply_prompt_frame_position_constraints(
|
||
polygons,
|
||
keyframes,
|
||
offset,
|
||
target_eff,
|
||
_RawConstraintCfg(mode="affine"),
|
||
)
|
||
|
||
|
||
def test_position_constraint_strength_clipped_to_unit_interval() -> None:
|
||
"""strength 越界（1.5 / −0.5）经函数内部 np.clip → 行为等价 1.0 / 0.0。"""
|
||
target_eff = ViewRect(0, 0, 200, 200)
|
||
offset = (0, 0)
|
||
keyframes = [
|
||
{"frame_index": 0, "mask": _disk_mask(200, 200, (40, 100), 15)},
|
||
{"frame_index": 2, "mask": _disk_mask(200, 200, (160, 100), 15)},
|
||
]
|
||
base_polys = [_circle_polygon((100.0, 30.0), 12.0, 64) for _ in range(3)]
|
||
|
||
def _correct(strength: float) -> np.ndarray:
|
||
corrected = apply_prompt_frame_position_constraints(
|
||
[poly.copy() for poly in base_polys],
|
||
keyframes,
|
||
offset,
|
||
target_eff,
|
||
_RawConstraintCfg(strength=strength),
|
||
)
|
||
return np.array([poly.mean(axis=0) for poly in corrected])
|
||
|
||
# s=1.5 → clip 到 1.0（全量拉到 keyframe 轨迹）；与显式 1.0 等价
|
||
assert np.allclose(_correct(1.5), _correct(1.0), atol=0.5)
|
||
# s=−0.5 → clip 到 0.0（不平移，质心保持原 (100, 30)）；与显式 0.0 等价
|
||
over_zero = _correct(-0.5)
|
||
explicit_zero = _correct(0.0)
|
||
assert np.allclose(over_zero, explicit_zero, atol=0.5)
|
||
assert np.allclose(over_zero[:, 1], 30.0, atol=2.0), "s=0 不平移，质心 y 应保持 30。"
|