xslandCursor
d4aae9722c
ITERATIVE 偶发收敛到相机后方(tz<0)，roll≈±180° 超阈值， 把正面照误判为非正面。检测到负深度时回退 SQPNP 重解正深度解。 补充回归测试。 Co-authored-by: Cursor <cursoragent@cursor.com>
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
"""姿态校验测试：真实正面图通过 + 合成大 yaw 拒绝 + 阈值门控。"""
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import cv2
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import numpy as np
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from conftest import fixture, _LM
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from face_analysis import pose
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from face_analysis.detector import detector
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from face_analysis.pose import (
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estimate_head_pose, check_frontal_face, _MODEL_POINTS,
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)
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from face_analysis.face_mesh_landmarks import PNP_INDICES
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def _project_model_with_yaw(yaw_deg, W=1000, H=1000, tz=1000.0):
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"""把 _MODEL_POINTS 绕 Y 轴旋转 yaw 后投影回像素，构造伪 landmarks。
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与 pose.estimate_head_pose 使用同一相机模型，故应能近似反解出该 yaw。
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"""
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a = np.radians(yaw_deg)
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Ry = np.array([[np.cos(a), 0, np.sin(a)],
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[0, 1, 0],
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[-np.sin(a), 0, np.cos(a)]])
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focal = float(W)
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cx, cy = W / 2, H / 2
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lm = [_LM(0.5, 0.5) for _ in range(478)]
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for idx, X in zip(PNP_INDICES, _MODEL_POINTS):
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Xc = Ry @ X + np.array([0, 0, tz])
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u = focal * Xc[0] / Xc[2] + cx
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v = focal * Xc[1] / Xc[2] + cy
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lm[idx] = _LM(u / W, v / H)
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class _Holder:
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landmark = lm
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return _Holder()
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def test_frontal_image_passes():
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img = cv2.imread(fixture("frontal.jpg"))
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h, w = img.shape[:2]
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lms = detector.detect(img)
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assert lms is not None
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assert check_frontal_face(lms, w, h) is True
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def test_synthetic_large_yaw_recovered_and_rejected():
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holder = _project_model_with_yaw(40.0)
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yaw, pitch, roll = estimate_head_pose(holder, 1000, 1000)
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# 反解出的 yaw 量级应接近 40°（符号取决于约定）
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assert abs(yaw) > 30
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# 默认阈值（30°）下应判为非正面
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assert check_frontal_face(holder, 1000, 1000) is False
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def test_threshold_gating_rejects_when_zeroed():
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"""阈值门控逻辑：阈值压到 0，则任何非零角度都应被拒。"""
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img = cv2.imread(fixture("frontal.jpg"))
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h, w = img.shape[:2]
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lms = detector.detect(img)
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assert check_frontal_face(lms, w, h, yaw_thr=0, pitch_thr=0, roll_thr=0) is False
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def test_pose_none_is_not_blocked():
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"""solvePnP 失败（返回 None）时不拦截，check_frontal_face 返回 True。"""
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assert pose.estimate_head_pose.__doc__ # 占位，确保导入
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def test_iterative_flipped_solution_falls_back_to_sqpnp():
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"""回归：部分正面照上 ITERATIVE 会解出 tz<0、roll≈±180°，应回退 SQPNP。
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像素点取自一张真实正面短发照（720×945）；裸跑 ITERATIVE 会得到负深度。
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"""
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W, H = 720, 945
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# 鼻尖 / 下巴 / 左眼外 / 右眼外 / 左嘴角 / 右嘴角（像素）
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px = [
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(358.32715988, 600.98652095),
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(346.83344364, 779.63507116),
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(242.94779778, 466.76155195),
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(478.43703747, 480.10321766),
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(284.13277388, 679.95527387),
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(422.19510555, 684.31899190),
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]
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lm = [_LM(0.5, 0.5) for _ in range(478)]
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for idx, (u, v) in zip(PNP_INDICES, px):
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lm[idx] = _LM(u / W, v / H)
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class _Holder:
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landmark = lm
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holder = _Holder()
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# 确认裸 ITERATIVE 确实是翻转解（否则本回归失去意义）
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image_points = np.array(px, dtype=np.float64)
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cam = np.array([[float(W), 0, W / 2], [0, float(W), H / 2], [0, 0, 1]],
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dtype=np.float64)
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ok, rvec, tvec = cv2.solvePnP(
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_MODEL_POINTS, image_points, cam, np.zeros((4, 1)),
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flags=cv2.SOLVEPNP_ITERATIVE,
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)
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assert ok and float(tvec[2, 0]) < 0
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yaw, pitch, roll = estimate_head_pose(holder, W, H)
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assert abs(roll) < 30, f"roll 应被纠正，实际 roll={roll}"
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assert check_frontal_face(holder, W, H) is True
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