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>
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
"""Tier 2 缩放不变性 + Tier 3 落点可视化 + 数值回归（均走方案 A，torch 无关、确定性）。"""
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import os
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import cv2
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import numpy as np
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from conftest import fixture, OUTPUT
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from face_analysis.detector import detector
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from face_analysis.measure import measure_face
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def _run(img):
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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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# 固定走方案 A（mask=None），保证确定性与 torch 无关
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return measure_face(lms, None, w, h)
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def test_scale_invariance():
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"""等比放大 2×：占比几乎不变（±0.5%），cm 近似不变（±2%）。"""
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img = cv2.imread(fixture("frontal.jpg"))
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big = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
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r1, r2 = _run(img), _run(big)
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cm1 = {"top": r1.top_cm, "upper": r1.upper_cm, "middle": r1.middle_cm, "lower": r1.lower_cm}
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cm2 = {"top": r2.top_cm, "upper": r2.upper_cm, "middle": r2.middle_cm, "lower": r2.lower_cm}
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d1, d2 = r1.to_response(), r2.to_response()
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for k in ["top_court", "upper_court", "middle_court", "lower_court"]:
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a = d1["four_courts"]["ratios"][k]
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b = d2["four_courts"]["ratios"][k]
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assert abs(a - b) < 0.005, f"ratio {k} 漂移过大: {a} vs {b}"
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for k in ["top", "upper", "middle", "lower"]:
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assert abs(cm1[k] - cm2[k]) / cm1[k] < 0.02, f"cm {k} 漂移过大: {cm1[k]} vs {cm2[k]}"
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def test_landmark_overlay():
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"""生成 5 纵向点叠加图供人工核验，并断言坐标在图内且自上而下有序。"""
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img = cv2.imread(fixture("frontal.jpg"))
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h, w = img.shape[:2]
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r = _run(img)
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pts = r.to_response()["landmarks"]
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order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
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ys = [pts[n]["y"] for n in order]
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# 坐标在图像范围内
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for n in order:
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assert 0 <= pts[n]["x"] <= w
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assert 0 <= pts[n]["y"] <= h
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# 自上而下严格递增
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assert ys == sorted(ys), f"纵向点未自上而下有序: {ys}"
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# dump 叠加图
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canvas = img.copy()
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for n in order:
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cv2.circle(canvas, (pts[n]["x"], pts[n]["y"]), 4, (0, 0, 255), -1)
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cv2.line(canvas, (0, pts[n]["y"]), (w, pts[n]["y"]), (0, 255, 0), 1)
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cv2.imwrite(os.path.join(OUTPUT, "frontal_landmarks.png"), canvas)
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# 数值回归基线：frontal.jpg 方案A 首次实测值，防重构回归（容差 1%）。
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_BASELINE = {
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"face_total_height_cm": 24.01,
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"top_court_cm": 5.06, "upper_court_cm": 6.07,
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"middle_court_cm": 7.23, "lower_court_cm": 5.64,
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"eye_width_cm": 2.52, "face_width_cm": 12.49, "inter_eye_distance_cm": 3.24,
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}
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def test_numeric_regression():
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img = cv2.imread(fixture("frontal.jpg"))
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d = _run(img).to_response()
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got = {
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"face_total_height_cm": d["face_total_height_cm"],
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**{k: d["four_courts"][k] for k in
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["top_court_cm", "upper_court_cm", "middle_court_cm", "lower_court_cm"]},
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**{k: d["seven_eyes"][k] for k in
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["eye_width_cm", "face_width_cm", "inter_eye_distance_cm"]},
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}
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for k, base in _BASELINE.items():
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assert abs(got[k] - base) / base < 0.01, f"{k} 回归: 基线 {base}, 实测 {got[k]}"
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