feat: 报告页 V2.2 检测可视化 + 评分引擎审计 + 真值回归/e2e 闭环

- server.js: face_maps 改为白名单 5 图层完整 dataURL 透传(REPORT_MAPS,按魔数判 mime),marks 坐标随 result 原样透传;实测 return_marks 不支持毛孔/黑头等扩展标记 - skin-score.js: 按需求文档 V2.2 逐字审计修正 9 处(黑眼圈 dark_circle_severity、oil_desc/spot_flag §7.2 拼接、skintone 中文映射、轮廓预警档前缀、痘痘 heavy_note、50+精华全文、综合文案数值格式),契约引用 V2.0→V2.2 - app.js/index.html/css: 报告页 6 分类检测可视化(轮廓=正脸实拍、皱纹=正脸压暗+皱纹增强线、光滑=正脸+色斑/痣/痤疮标记、肤色=色沉图、敏感=红区图、痘痘=正脸+闭口/痤疮标记),数据驱动 VIZ_CAT 配置,优雅降级 - h5/test/: Excel 两组三视图真值回归 84/84(含案例2 AY 列扁平化数据重建 fixture) - h5/assets/mock/: 真实案例1 报告页 fixture(坐标与照片同比例缩放),?mock=1&screen=report 直渲 - e2e: Chrome 无头闭环 55/55(mock 全流程/直渲/真实 API 降级链路),截图存档 preview/e2e-20260926 - 自测: skin-score 83/83,fixture 冒烟 35/35
2026-09-26 22:35:02 +08:00
parent 71a090fd75
commit ba3b346fce
28 changed files with 2801 additions and 71 deletions
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# ═════ 真值 fixture 构建脚本（零第三方新依赖：openpyxl 环境已装，仅用于建 fixture）═════
+# 用法：python3 h5/test/build_fixtures.py
+# 产出：
+#   fixtures/case1.json  ← reference/facepp-skinanalyze/examples/ref_3view_raw.json
+#                          （案例1 完整原始返回，精简坐标数组与 face_maps base64）
+#   fixtures/case2.json  ← docs/皮肤检测测试结果_三视图_20260926.xlsx AY4 扁平化文本解析回嵌套
+#   fixtures/truth.json  ← 同表第 3/4 行真值（P/S/档位/文案等断言目标）
+# 两个 case fixture 均为服务端 /api/skin-analyze 响应形状：
+#   { result, face_rectangle, side: { left: left_side_result, right: right_side_result } }
+
+import json
+import os
+import re
+import sys
+
+import openpyxl
+
+ROOT = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..'))
+REF_JSON = os.path.join(ROOT, 'reference', 'facepp-skinanalyze', 'examples', 'ref_3view_raw.json')
+XLSX = os.path.join(ROOT, 'docs', '皮肤检测测试结果_三视图_20260926.xlsx')
+OUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'fixtures')
+
+
+# ─── 通用：递归精简（坐标数组→[]，face_maps→{}；评分只用标量与 count）───
+def slim(node, key=''):
+    if isinstance(node, dict):
+        if key == 'face_maps':  # base64 整图，评分不需要，整体置空对象
+            return {}
+        return {k: slim(v, k) for k, v in node.items()}
+    if isinstance(node, list):
+        # 元素为 dict 的坐标/标记数组（rectangle/polygon/*_mark/*_outline）→ 空数组；
+        # 纯数值数组（confidence 列表等）保留
+        if node and all(isinstance(x, dict) for x in node):
+            return []
+        return [slim(x, key) for x in node]
+    return node
+
+
+# ─── 案例1：原始 pro 返回 → 服务端响应形状 ───
+def build_case1():
+    with open(REF_JSON, encoding='utf-8') as f:
+        raw = json.load(f)
+    fixture = {
+        'request_id': raw.get('request_id'),
+        'time_used': raw.get('time_used'),
+        'result': slim(raw['result']),
+        'face_rectangle': raw.get('face_rectangle'),
+        'side': {
+            'left': slim(raw.get('left_side_result')),
+            'right': slim(raw.get('right_side_result'))
+        }
+    }
+    return fixture
+
+
+# ─── 案例2：AY4 扁平化「路径 = 值」文本 → 嵌套 JSON ───
+SEC_RE = re.compile(r'^——\s*(.+?)\s*——$')
+KV_RE = re.compile(r'^([^=]+?)\s*=\s*(.*)$')
+NUMLIST_RE = re.compile(r'^[\d\s,.\-eE+]+$')
+INT_RE = re.compile(r'^-?\d+$')
+FLOAT_RE = re.compile(r'^-?(?:\d+\.\d*|\.\d+)(?:[eE][-+]?\d+)?$|^-?\d+[eE][-+]?\d+$')
+MS_RE = re.compile(r'^(-?\d+(?:\.\d+)?)\s*ms$')
+
+skipped = []  # [摘要] 跳过的路径（坐标数组/base64/深层结构）
+
+
+def parse_value(path, s):
+    s = s.strip()
+    if s == '':
+        return None, False
+    if s.startswith('{'):  # 行内 JSON（face_rectangle）
+        try:
+            return json.loads(s), True
+        except ValueError:
+            skipped.append(path + '  [行内JSON解析失败]')
+            return None, False
+    if s.startswith('['):
+        if s == '[]':
+            return [], True
+        inner = s[1:-1].strip() if s.endswith(']') else ''
+        if inner and NUMLIST_RE.match(inner):  # 纯数值数组（confidence 列表）
+            out = []
+            for tok in inner.split(','):
+                tok = tok.strip()
+                if tok == '':
+                    continue
+                out.append(float(tok) if ('.' in tok or 'e' in tok or 'E' in tok) else int(tok))
+            return out, True
+        skipped.append(path + '  ' + s[:40])  # [N组坐标]/[N项]/[base64图,…]/[深层结构省略]
+        return None, False
+    m = MS_RE.match(s)  # time_used 的「751 ms」
+    if m:
+        return float(m.group(1)) if '.' in m.group(1) else int(m.group(1)), True
+    if INT_RE.match(s):
+        return int(s), True
+    if FLOAT_RE.match(s):
+        return float(s), True
+    return s, True  # 字符串（request_id 等）
+
+
+def set_path(root, path, value):
+    segs = path.split('.')
+    cur = root
+    for i, seg in enumerate(segs[:-1]):
+        nxt = segs[i + 1]
+        if seg.isdigit():  # 数字段：上级应是 list（本数据仅 skin_type.details.N，均被摘要跳过）
+            idx = int(seg)
+            while len(cur) <= idx:
+                cur.append({} if not nxt.isdigit() else [])
+            if cur[idx] is None:
+                cur[idx] = [] if nxt.isdigit() else {}
+            cur = cur[idx]
+        else:
+            if seg not in cur or not isinstance(cur[seg], (dict, list)):
+                cur[seg] = [] if nxt.isdigit() else {}
+            cur = cur[seg]
+    last = segs[-1]
+    if last.isdigit():
+        idx = int(last)
+        while len(cur) <= idx:
+            cur.append(None)
+        cur[idx] = value
+    else:
+        cur[last] = value
+
+
+def build_case2(flat_text):
+    doc = {}
+    section = None  # 当前 —— section —— 名；None=顶层
+    for line in flat_text.split('\n'):
+        line = line.strip()
+        if not line:
+            continue
+        m = SEC_RE.match(line)
+        if m:
+            section = m.group(1)
+            continue
+        m = KV_RE.match(line)
+        if not m:
+            continue
+        path, raw_v = m.group(1).strip(), m.group(2)
+        full_path = (section + '.' + path) if section else path
+        value, ok = parse_value(full_path, raw_v)
+        if not ok:
+            continue
+        target = doc
+        if section:
+            if section not in doc or not isinstance(doc[section], dict):
+                doc[section] = {}
+            target = doc[section]
+        set_path(target, path, value)
+    fixture = {
+        'request_id': doc.get('request_id'),
+        'time_used': doc.get('time_used'),
+        'result': slim(doc.get('result', {})),
+        'face_rectangle': doc.get('face_rectangle'),
+        'side': {
+            'left': slim(doc.get('left_side_result')),
+            'right': slim(doc.get('right_side_result'))
+        }
+    }
+    return fixture
+
+
+# ─── 真值表：Excel 第 3/4 行 → truth.json ───
+def col(ws, row, letter):
+    return ws['%s%d' % (letter, row)].value
+
+
+def build_truth(ws, row):
+    def cat(p_col, s_col, tier_col, copy_col):
+        return {
+            'P': col(ws, row, p_col), 'S': col(ws, row, s_col),
+            'tier': col(ws, row, tier_col), 'copy': col(ws, row, copy_col)
+        }
+    def dim(p_col, s_col, tier_col):
+        return {'P': col(ws, row, p_col), 'S': col(ws, row, s_col), 'tier': col(ws, row, tier_col)}
+    return {
+        'profile': {'realAge': col(ws, row, 'AS'), 'skinType': '混合性'},  # 综合文案实测输出「混合性」
+        'skinAge': col(ws, row, 'E'),
+        'totalScore': col(ws, row, 'F'),
+        'cats': {
+            'contour': cat('I', 'J', 'K', 'L'),
+            'wrinkles': cat('M', 'N', 'O', 'P'),
+            'smooth': cat('Q', 'R', 'S', 'T'),
+            'tone': cat('U', 'V', 'W', 'X'),
+            'sens': cat('Y', 'Z', 'AA', 'AB'),
+            'acne': cat('AC', 'AD', 'AE', 'AF')
+        },
+        'dims': {
+            'firm': dim('AG', 'AH', 'AI'),
+            'vita': dim('AJ', 'AK', 'AL'),
+            'stab': dim('AM', 'AN', 'AO')
+        },
+        'jawline': {'left': col(ws, row, 'AP'), 'right': col(ws, row, 'AQ')},
+        'group': col(ws, row, 'AT'),
+        'essence': col(ws, row, 'AU'),
+        'avgS': col(ws, row, 'AV'),
+        'summary': col(ws, row, 'AW'),
+        'lifestyle': col(ws, row, 'AX')
+    }
+
+
+def dump(name, obj):
+    path = os.path.join(OUT_DIR, name)
+    with open(path, 'w', encoding='utf-8') as f:
+        json.dump(obj, f, ensure_ascii=False, indent=1)
+    print('  写出 %s（%d 字节）' % (os.path.relpath(path, ROOT), os.path.getsize(path)))
+
+
+def main():
+    os.makedirs(OUT_DIR, exist_ok=True)
+    print('== 案例1 fixture ← ref_3view_raw.json ==')
+    dump('case1.json', build_case1())
+
+    print('== 案例2 fixture ← Excel AY4 扁平化解析 ==')
+    wb = openpyxl.load_workbook(XLSX, data_only=True)
+    ws = wb['三视图检测汇总']
+    case2 = build_case2(ws['AY4'].value)
+    dump('case2.json', case2)
+    print('  [摘要] 跳过 %d 个路径（坐标数组/base64/深层结构，评分只用标量，不影响）：' % len(skipped))
+    for s in skipped:
+        print('    - ' + s)
+
+    print('== 真值表 ← Excel 第 3/4 行 ==')
+    dump('truth.json', {'case1': build_truth(ws, 3), 'case2': build_truth(ws, 4)})
+
+    # 构建期自检：fixture 关键标量 vs 真值
+    c1 = json.load(open(os.path.join(OUT_DIR, 'case1.json'), encoding='utf-8'))
+    c2 = json.load(open(os.path.join(OUT_DIR, 'case2.json'), encoding='utf-8'))
+    t = json.load(open(os.path.join(OUT_DIR, 'truth.json'), encoding='utf-8'))
+    for tag, fx, tr in (('案例1', c1, t['case1']), ('案例2', c2, t['case2'])):
+        la = fx['side']['left']['left_jawline_info']['left_jawline_angle']
+        ra = fx['side']['right']['right_jawline_info']['right_jawline_angle']
+        sa = fx['result']['skin_age']['value']
+        assert abs(la - tr['jawline']['left']) < 1e-9, (tag, '左下颌角', la, tr['jawline']['left'])
+        assert abs(ra - tr['jawline']['right']) < 1e-9, (tag, '右下颌角', ra, tr['jawline']['right'])
+        assert sa == tr['skinAge'], (tag, '肤龄', sa, tr['skinAge'])
+        print('  %s 自检通过：下颌角 %.2f/%.2f、肤龄 %d 与真值一致' % (tag, la, ra, sa))
+
+
+if __name__ == '__main__':
+    sys.exit(main())