xslandClaude Opus 4.8
8d3b145111
worker 侧从 Mock 替换为真实算法： - face_analysis 包：detector(MediaPipe 478点) / pose(solvePnP 姿态) / calibration(虹膜直径法) / hair_segmenter+bisenet_model(方案B 头发分割) / measure(方案A兜底+B/A决策+七眼+换算) / annotation(numpy渐变线+中文标注) - app.py：/api/v1/face/measure 接真实实现，返回 annotated_image_base64 （不落盘不拼URL，落盘由网关做）；加 X-Internal-Token 鉴权、/health 就绪态、 可配置分辨率门槛、异常兜底 - 部署：start.sh/run_worker.sh/hair-worker.service 监听 8187；worker_config 示例 - 测试 tests/：Tier1合成真值<1e-6 + Tier2缩放不变 + Tier3叠加 + 错误码集成 + 数值回归，pytest 24 项全绿 - 文档补实测基线表 + RTX5090/torch 说明 注：worker 为 RTX 5090(sm_120)，pinned torch 2.2.2(cu121) 只到 sm_90， BiSeNet 已自动回退 CPU（方案B 正常）；要用 GPU 需换 torch cu128(≥2.7)。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
65 lines
2.2 KiB
Python
65 lines
2.2 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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