xslandClaude Opus 4.8
ce95a508c1
医生在额头用马克笔画规划发际线 → 检测该线 → 生发。检测算法源自 /home/xsl/headmark。 - hairline/marker_detect.py: 黑帽响应图(MORPH_BLACKHAT)+鬓角锚点(MediaPipe 21/251吸附) +skimage route_through_array 最小路径检测画线；路径平均响应阈值拒识无画线 (headmark 调研：全局灰度阈值不可用，黑帽+Dijkstra 实测误差≤0.5px) - hairline/mask.py: 抽出 mask_from_curve(曲线+ROI闭合)，接口2/3共用 - hairline/service.py: generate_grow_b——检测→遮罩→原图重画干净线→ComfyUI生发 - app.py: /hair/grow-b 真实实现，marked+original各三选一+校验；输出 best_hairline_image_base64(=原图)/hair_growth_image_base64/hairline_type="custom"； 无人脸或未检测到画线→1001；重活进线程池 - requirements: scikit-image==0.24.0 (⚠️锁0.24，0.25+强依赖numpy>=2会顶掉mediapipe的numpy<2) - 文档: docs/接口3-B端生发-技术实现方案.md - 测试: test_marker.py(检测/拒识/辅助) + test_api grow-b(mock ComfyUI)，42全绿 实测(5090): grow-b ~6.4s，生发图把额头发际线补到医生画线、清除划线、人物保持。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
"""接口3 马克笔检测测试：辅助函数(纯numpy) + 真实样本检测/拒识。"""
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import cv2
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import numpy as np
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from conftest import fixture
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from hairline.marker_detect import (
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detect_marker_hairline, path_to_curve_mask, _snap_anchor,
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)
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from hairline.service import get_landmarker, get_parser
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def test_path_to_curve_mask():
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path = np.array([[10, 5], [10, 50], [10, 90]]) # 水平线 row=10
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m = path_to_curve_mask(path, 100, 100, thickness=3)
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assert m[10, 50] == 255
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assert m[90, 50] == 0
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def test_snap_anchor_moves_to_peak():
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bh = np.zeros((100, 100), np.float32)
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bh[40, 30] = 99.0 # 峰值在 (40,30)
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r, c = _snap_anchor(bh, x=33, y=42, w=1000) # 起点附近 → 应吸到峰值
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assert (r, c) == (40, 30)
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def _ctx(path_img):
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img = cv2.imread(path_img)
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rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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lm = get_landmarker().detect(rgb)
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pm = get_parser().parse(rgb)
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return img, lm, pm
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def test_detect_real_marked():
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img, lm, pm = _ctx(fixture("marked_hairline.jpg"))
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path = detect_marker_hairline(img, lm, pm)
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assert path is not None and len(path) > 50 # 检测到画线
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def test_reject_clean_photo():
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img, lm, pm = _ctx(fixture("frontal.jpg"))
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assert detect_marker_hairline(img, lm, pm) is None # 无画线 → 拒识
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