merge: T16 确定性视觉与 32 维诊断装配（T-P3-01/06）
This commit is contained in:
@@ -0,0 +1,737 @@
|
||||
"""诊断装配阶段（name="diagnosis"）：问卷 + 派生特征 + 规则结论 → 32 维画像。
|
||||
|
||||
T-P3-06 红线：**任何输入组合下 ``result["diagnosis"]["dimensions"]`` 恒 32 项**
|
||||
（number 1~32 全键在场）。输入不足的维度显式 ``status="skipped"`` 并把原因写进
|
||||
evidence——显式缺失优于编造，skipped 语义的本义正在于此。
|
||||
|
||||
四要素口径（对齐记忆系统 §3.5 的置信度分层）：
|
||||
|
||||
- ``confidence``：问卷自评 0.6（主观）、确定性视觉 0.85（mock 但稳定）、
|
||||
规则推导/知识库映射 0.9（硬结论）；skipped 恒 0.0。
|
||||
- ``source``：questionnaire / vision / rule / derived；skipped 用 "none"
|
||||
（没有数据来源，避免误标成"来自问卷"）。
|
||||
- ``evidence``：一句人话依据（将直接出现在报告里）；skipped 时即缺失原因。
|
||||
- ``rule_hits``：该维度聚合到的命中规则键（规则引擎硬结论的可解释锚点）。
|
||||
|
||||
规则引擎联动：构造 ``RuleContext``（body_shape/face_shape/features/goals…）→
|
||||
``RuleService`` 载入 published 规则 → ``evaluate()`` → verdict 的 recommend/avoid
|
||||
聚合进「核心诉求 #28 / 偏好与禁忌 #30」的 rule_hits 与 evidence；体型类
|
||||
（category=body_shape）与色彩类（category=color）命中分别回落到 #5/#19/#21。
|
||||
|
||||
色板维度（#19/#20/#21）：V1 从 ``kb_color`` 按季型代码读 base/accent/avoid；
|
||||
库空 → 该维度 skipped + 「色板未录入」（显式缺失优于编造）。
|
||||
|
||||
纯函数 ``evaluate_dimensions`` 与 ``build_rule_context`` 不碰 I/O——单测用
|
||||
内置最小规则集（engine.evaluate 直算）即可覆盖四要素齐全性，无需连库。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Mapping, Sequence
|
||||
from dataclasses import asdict, dataclass
|
||||
from typing import Any
|
||||
|
||||
from packages.kb import repo as kb_repo
|
||||
from packages.report.dimensions import (
|
||||
BASIC_FULL_KEYS,
|
||||
DIMENSION_BY_KEY,
|
||||
DimensionSpec,
|
||||
)
|
||||
from packages.report.pipeline import ReportContext
|
||||
from packages.report.vision import SEASON_CODE_CN, STYLE_TYPES
|
||||
from packages.rules import dsl, engine
|
||||
from packages.rules.service import RuleService
|
||||
from packages.shared.logging import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
#: 体型代码 → 中文名（证据文本用；值本身保留字母码，与 kb/记忆系统词汇一致）。
|
||||
_BODY_SHAPE_CN: Mapping[str, str] = {
|
||||
"A": "梨形",
|
||||
"O": "苹果形",
|
||||
"X": "沙漏形",
|
||||
"T": "倒三角",
|
||||
"H": "矩形",
|
||||
}
|
||||
|
||||
#: 中性色板的关键词（kb_color.role=base 且 name_cn 命中任一即视为中性色适配款）。
|
||||
_NEUTRAL_KEYWORDS: tuple[str, ...] = ("白", "黑", "灰", "米", "驼", "卡其")
|
||||
|
||||
#: 初级口径的色板条数上限（需求文档 §4：最佳色板 8 色、避雷色 3 色）。
|
||||
_BEST_COLORS_LIMIT = 8
|
||||
_AVOID_COLORS_LIMIT = 3
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class DimensionResult:
|
||||
"""单个维度的装配结果（四要素 + status，序列化形状即报告产物）。"""
|
||||
|
||||
number: int
|
||||
key: str
|
||||
value: Any # 结论值（枚举/数值/列表/字典）；skipped 时为 None
|
||||
confidence: float # 0~1，口径见模块 docstring
|
||||
source: str # questionnaire | vision | rule | derived | none
|
||||
evidence: str # 一句人话依据；skipped 时为缺失原因
|
||||
rule_hits: list[str] # 命中的 rule_key
|
||||
status: str # "ok" | "skipped"
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class ColorEntry:
|
||||
"""kb_color 行的纯数据投影（求值纯函数不吃 ORM 对象，测试可手工构造）。"""
|
||||
|
||||
season_code: str
|
||||
role: str # base / accent / avoid
|
||||
hex: str # 与 kb_color 列名保持一致
|
||||
name_cn: str
|
||||
|
||||
|
||||
# ── 小工具 ─────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _str(value: Any) -> str | None:
|
||||
return value if isinstance(value, str) and value else None
|
||||
|
||||
|
||||
def _str_list(value: Any) -> list[str]:
|
||||
if not isinstance(value, (list, tuple)):
|
||||
return []
|
||||
return [item for item in value if isinstance(item, str) and item]
|
||||
|
||||
|
||||
def _num(value: Any) -> float | None:
|
||||
if isinstance(value, bool) or not isinstance(value, (int, float)):
|
||||
return None
|
||||
return float(value)
|
||||
|
||||
|
||||
def _fmt(value: float) -> str:
|
||||
"""数值文本化：整数不带小数点（报告里 165cm 而不是 165.0cm）。"""
|
||||
return str(int(value)) if value == int(value) else str(round(value, 1))
|
||||
|
||||
|
||||
def _rule_keys(verdict: engine.RuleVerdict, *categories: str) -> list[str]:
|
||||
"""命中键提取：给 categories 就是类别过滤，不给就是全量（聚合进 #28/#30）。"""
|
||||
return [
|
||||
hit.rule_key for hit in verdict.rule_hits if not categories or hit.category in categories
|
||||
]
|
||||
|
||||
|
||||
def _rule_evidence(verdict: engine.RuleVerdict, *categories: str) -> str:
|
||||
"""命中规则的解释文本（≤3 条，报告证据链的人话部分）。"""
|
||||
explanations = [
|
||||
hit.explanation
|
||||
for hit in verdict.rule_hits
|
||||
if hit.explanation and (not categories or hit.category in categories)
|
||||
]
|
||||
return "；".join(explanations[:3])
|
||||
|
||||
|
||||
def _ok(
|
||||
spec: DimensionSpec,
|
||||
value: Any,
|
||||
source: str,
|
||||
confidence: float,
|
||||
evidence: str,
|
||||
rule_hits: list[str] | None = None,
|
||||
) -> DimensionResult:
|
||||
return DimensionResult(
|
||||
number=spec.number,
|
||||
key=spec.key,
|
||||
value=value,
|
||||
confidence=confidence,
|
||||
source=source,
|
||||
evidence=evidence,
|
||||
rule_hits=rule_hits or [],
|
||||
status="ok",
|
||||
)
|
||||
|
||||
|
||||
def _skip(spec: DimensionSpec, reason: str, rule_hits: list[str] | None = None) -> DimensionResult:
|
||||
# rule_hits 可选：skipped 维度仍可携带规则命中链（可解释性不因缺数据丢失）。
|
||||
return DimensionResult(
|
||||
number=spec.number,
|
||||
key=spec.key,
|
||||
value=None,
|
||||
confidence=0.0,
|
||||
source="none",
|
||||
evidence=reason,
|
||||
rule_hits=rule_hits or [],
|
||||
status="skipped",
|
||||
)
|
||||
|
||||
|
||||
def _spec(key: str) -> DimensionSpec:
|
||||
return DIMENSION_BY_KEY[key]
|
||||
|
||||
|
||||
# ── 纯函数：规则上下文构造与 32 维装配 ────────────────────────────────────
|
||||
|
||||
|
||||
def build_rule_context(
|
||||
questionnaire: Mapping[str, Any], derived_features: Mapping[str, Any]
|
||||
) -> dsl.RuleContext:
|
||||
"""问卷 + 派生特征 → 规则引擎画像（视觉侧优先，问卷补场景/风格/禁忌）。
|
||||
|
||||
occasion 取问卷场景权重中占比最高者（规则按单一主场景匹配；权重并列时
|
||||
按场景名排序取稳定的最大值，保证确定性）。
|
||||
"""
|
||||
occasion: str | None = None
|
||||
weights = questionnaire.get("occasion_weights")
|
||||
if isinstance(weights, dict) and weights:
|
||||
numeric = {
|
||||
str(k): v for k, v in weights.items() if isinstance(k, str) and _num(v) is not None
|
||||
}
|
||||
if numeric:
|
||||
occasion = max(numeric, key=lambda k: (float(numeric[k]), k))
|
||||
|
||||
return dsl.RuleContext(
|
||||
body_shape=_str(derived_features.get("body_shape")),
|
||||
season=_str(derived_features.get("season_code")),
|
||||
face_shape=_str(derived_features.get("face_shape")),
|
||||
features=tuple(_str_list(derived_features.get("body_features"))),
|
||||
goals=tuple(_str_list(questionnaire.get("core_goals"))),
|
||||
occasion=occasion,
|
||||
style=_str(questionnaire.get("psyche_style")),
|
||||
user_taboos=tuple(_str_list(questionnaire.get("taboos"))),
|
||||
)
|
||||
|
||||
|
||||
def evaluate_dimensions(
|
||||
questionnaire: Mapping[str, Any],
|
||||
derived_features: Mapping[str, Any],
|
||||
verdict: engine.RuleVerdict,
|
||||
kb_colors: Sequence[ColorEntry] = (),
|
||||
) -> list[DimensionResult]:
|
||||
"""32 维装配（纯函数）：按 DIMENSIONS 顺序恒输出 32 项，一个不缺。"""
|
||||
q, d = questionnaire, derived_features
|
||||
results: list[DimensionResult] = []
|
||||
|
||||
# -- 问卷直采（#1~#4） ---------------------------------------------------
|
||||
gender = _str(q.get("gender"))
|
||||
if gender:
|
||||
results.append(_ok(_spec("gender"), gender, "questionnaire", 0.6, f"问卷自评：{gender}"))
|
||||
else:
|
||||
results.append(_skip(_spec("gender"), "问卷未填写性别"))
|
||||
|
||||
height, weight = _num(q.get("height")), _num(q.get("weight"))
|
||||
parts: dict[str, float] = {}
|
||||
if height is not None:
|
||||
parts["height"] = height
|
||||
if weight is not None:
|
||||
parts["weight"] = weight
|
||||
if parts:
|
||||
desc = "、".join(
|
||||
f"{'身高' if k == 'height' else '体重'} {_fmt(v)}{'cm' if k == 'height' else 'kg'}"
|
||||
for k, v in parts.items()
|
||||
)
|
||||
results.append(
|
||||
_ok(_spec("height_weight"), parts, "questionnaire", 0.6, f"问卷自评：{desc}")
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("height_weight"), "问卷未填写身高/体重"))
|
||||
|
||||
birthday = _str(q.get("birthday"))
|
||||
if birthday:
|
||||
results.append(
|
||||
_ok(_spec("birthday"), birthday, "questionnaire", 0.6, f"问卷填写：{birthday}")
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("birthday"), "问卷未填写生日"))
|
||||
|
||||
occupation = _str(q.get("occupation"))
|
||||
if occupation:
|
||||
results.append(
|
||||
_ok(_spec("occupation"), occupation, "questionnaire", 0.6, f"问卷填写：{occupation}")
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("occupation"), "问卷未填写职业（全面报告维度）"))
|
||||
|
||||
# -- 视觉：体型与比例（#5~#7） -------------------------------------------
|
||||
body_shape = _str(d.get("body_shape"))
|
||||
if body_shape:
|
||||
hits = _rule_keys(verdict, "body_shape")
|
||||
evidence = (
|
||||
f"视觉分析：体型判定为 {body_shape}（{_BODY_SHAPE_CN.get(body_shape, body_shape)}）"
|
||||
)
|
||||
rule_text = _rule_evidence(verdict, "body_shape")
|
||||
if rule_text:
|
||||
evidence = f"{evidence}；{rule_text}"
|
||||
results.append(_ok(_spec("body_shape"), body_shape, "vision", 0.85, evidence, hits))
|
||||
else:
|
||||
results.append(_skip(_spec("body_shape"), "视觉分析未产出体型（无照片或视觉阶段被跳过）"))
|
||||
|
||||
body_features = _str_list(d.get("body_features"))
|
||||
if d.get("body_features") is not None:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("body_features"),
|
||||
body_features,
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉分析：身体局部特征 {('、'.join(body_features)) or '无显著特征'}",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("body_features"), "视觉分析未产出身体局部特征"))
|
||||
|
||||
body_ratio = _num(d.get("body_ratio")) if d.get("body_ratio") is not None else None
|
||||
if body_ratio is not None:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("body_ratio"),
|
||||
body_ratio,
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉测算：上下身比例约 {_fmt(body_ratio)}",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("body_ratio"), "视觉分析未产出上下身比例（全面报告维度）"))
|
||||
|
||||
# -- 视觉：面部（#8~#11） -------------------------------------------------
|
||||
face_shape = _str(d.get("face_shape"))
|
||||
if face_shape:
|
||||
results.append(
|
||||
_ok(_spec("face_shape"), face_shape, "vision", 0.85, f"视觉分析：脸型为{face_shape}")
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("face_shape"), "视觉分析未产出脸型"))
|
||||
|
||||
face_proportions = _str(d.get("face_proportions"))
|
||||
if face_proportions:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("face_proportions"),
|
||||
face_proportions,
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉分析：三庭五眼{face_proportions}",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("face_proportions"), "视觉分析未产出三庭五眼标签"))
|
||||
|
||||
facial_details = d.get("facial_details")
|
||||
if isinstance(facial_details, dict) and facial_details:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("facial_details"),
|
||||
dict(facial_details),
|
||||
"vision",
|
||||
0.85,
|
||||
"视觉分析：" + "、".join(f"{k}{v}" for k, v in facial_details.items()),
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("facial_details"), "视觉分析未产出五官细节标签"))
|
||||
|
||||
face_depth = _num(d.get("face_depth")) if d.get("face_depth") is not None else None
|
||||
if face_depth is not None:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("face_depth"),
|
||||
face_depth,
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉测算：面部立体度 {_fmt(face_depth)}/10",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("face_depth"), "视觉分析未产出面部立体度（全面报告维度）"))
|
||||
|
||||
# -- 色彩：肤档与季型（#12~#18） ------------------------------------------
|
||||
skin_tone = _str(d.get("skin_tone"))
|
||||
if skin_tone:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("skin_tone"), skin_tone, "vision", 0.85, f"视觉 Lab 采样：肤色档位{skin_tone}"
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("skin_tone"), "视觉分析未产出肤色档位"))
|
||||
|
||||
for key, label, axis in (
|
||||
("face_hue", "面部色相", "冷←→暖"),
|
||||
("face_lightness", "面部亮度", "暗沉←→白皙"),
|
||||
("face_chroma", "面部色度", "柔和←→鲜艳"),
|
||||
):
|
||||
axis_value = d.get(key)
|
||||
if isinstance(axis_value, dict) and axis_value.get("label"):
|
||||
results.append(
|
||||
_ok(
|
||||
_spec(key),
|
||||
dict(axis_value),
|
||||
"vision",
|
||||
0.85,
|
||||
f"色彩分析：{label}{axis_value['label']}（{axis} 连续值"
|
||||
f" {axis_value.get('value')}）",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec(key), f"色彩分析未产出{label}"))
|
||||
|
||||
face_contrast = _str(d.get("face_contrast"))
|
||||
if face_contrast:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("face_contrast"),
|
||||
face_contrast,
|
||||
"vision",
|
||||
0.85,
|
||||
f"色彩分析：面部对比度{face_contrast}",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("face_contrast"), "色彩分析未产出面部对比度（全面报告维度）"))
|
||||
|
||||
season = _str(d.get("season_code"))
|
||||
if season:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("season_code"),
|
||||
season,
|
||||
"vision",
|
||||
0.85,
|
||||
f"色彩分析：四季季型为{SEASON_CODE_CN.get(season, season)}（{season}）",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("season_code"), "色彩分析未产出四季季型"))
|
||||
|
||||
season_detailed = _str(d.get("season_detailed"))
|
||||
if season_detailed:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("season_detailed"),
|
||||
season_detailed,
|
||||
"vision",
|
||||
0.85,
|
||||
f"色彩分析：十二季细分为{season_detailed}",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("season_detailed"), "色彩分析未产出十二季细分（全面报告维度）"))
|
||||
|
||||
# -- 知识库映射：色板（#19~#21） -------------------------------------------
|
||||
color_hits = _rule_keys(verdict, "color")
|
||||
color_rule_text = _rule_evidence(verdict, "color")
|
||||
season_colors = [c for c in kb_colors if not season or c.season_code == season]
|
||||
|
||||
best = [c for c in season_colors if c.role in ("base", "accent")][:_BEST_COLORS_LIMIT]
|
||||
if best:
|
||||
evidence = (
|
||||
f"知识库色板映射（{SEASON_CODE_CN.get(season or '', season or '')}）：共 {len(best)} 色"
|
||||
)
|
||||
if color_rule_text:
|
||||
evidence = f"{evidence}；{color_rule_text}"
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("best_colors"),
|
||||
[{"hex": c.hex, "name_cn": c.name_cn, "role": c.role} for c in best],
|
||||
"derived",
|
||||
0.9,
|
||||
evidence,
|
||||
color_hits,
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(
|
||||
_skip(
|
||||
_spec("best_colors"), "色板未录入（kb_color 无该季型数据）", color_hits
|
||||
) # skipped 也保留色彩规则命中链（可解释性不因缺数据丢失）
|
||||
)
|
||||
|
||||
neutral = [
|
||||
c
|
||||
for c in season_colors
|
||||
if c.role == "base" and any(k in c.name_cn for k in _NEUTRAL_KEYWORDS)
|
||||
]
|
||||
if neutral:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("neutral_colors"),
|
||||
[{"hex": c.hex, "name_cn": c.name_cn} for c in neutral],
|
||||
"derived",
|
||||
0.9,
|
||||
f"知识库映射：中性色适配款 {('、'.join(c.name_cn for c in neutral))}",
|
||||
)
|
||||
)
|
||||
else:
|
||||
reason = "色板未录入" if not season_colors else "色板中无中性色适配款"
|
||||
results.append(_skip(_spec("neutral_colors"), f"{reason}（kb_color）"))
|
||||
|
||||
avoid = [c for c in season_colors if c.role == "avoid"][:_AVOID_COLORS_LIMIT]
|
||||
if avoid:
|
||||
evidence = f"知识库映射：避雷色 {('、'.join(c.name_cn for c in avoid))}"
|
||||
if color_rule_text:
|
||||
evidence = f"{evidence}；{color_rule_text}"
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("avoid_colors"),
|
||||
[{"hex": c.hex, "name_cn": c.name_cn} for c in avoid],
|
||||
"derived",
|
||||
0.9,
|
||||
evidence,
|
||||
color_hits,
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(
|
||||
_skip(_spec("avoid_colors"), "色板未录入（kb_color 无该季型数据）", color_hits)
|
||||
)
|
||||
|
||||
# -- 视觉：风格量感（#22~#25） ---------------------------------------------
|
||||
volume = d.get("volume")
|
||||
if isinstance(volume, dict) and volume.get("label"):
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("volume"),
|
||||
dict(volume),
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉分析：量感{volume['label']}（总分 {volume.get('score')}）",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("volume"), "视觉分析未产出量感粗判"))
|
||||
|
||||
curve = d.get("curve_straight")
|
||||
if isinstance(curve, dict) and curve.get("label"):
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("curve_straight"),
|
||||
dict(curve),
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉分析：轮廓{curve['label']}（直曲总分 {curve.get('score')}）",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("curve_straight"), "视觉分析未产出直曲粗判"))
|
||||
|
||||
motion = _str(d.get("motion"))
|
||||
if motion:
|
||||
results.append(
|
||||
_ok(_spec("motion"), motion, "vision", 0.85, f"视觉分析：动静判定为{motion}")
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("motion"), "视觉分析未产出动静判定（全面报告维度）"))
|
||||
|
||||
gene_scores = d.get("gene_style_scores")
|
||||
if isinstance(gene_scores, dict) and gene_scores:
|
||||
# Top1 并列时按九型固定序取先者——输出必须完全确定。
|
||||
top1 = max(STYLE_TYPES, key=lambda s: float(gene_scores.get(s, 0)))
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("gene_style"),
|
||||
{"top1": top1, "scores": dict(gene_scores)},
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉分析：基因风格 Top1 为{top1}（{gene_scores.get(top1)} 分）",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("gene_style"), "视觉分析未产出基因风格评分"))
|
||||
|
||||
# -- 问卷偏好与规则命名（#26~#30） ------------------------------------------
|
||||
all_hits = _rule_keys(verdict)
|
||||
rule_text_all = _rule_evidence(verdict)
|
||||
|
||||
psyche = _str(q.get("psyche_style"))
|
||||
if psyche:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("psyche_style"), psyche, "questionnaire", 0.6, f"偏好问卷：心理风格为{psyche}"
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("psyche_style"), "问卷未完成心理风格测评"))
|
||||
|
||||
gene_top1 = (
|
||||
max(STYLE_TYPES, key=lambda s: float(gene_scores.get(s, 0)))
|
||||
if isinstance(gene_scores, dict) and gene_scores
|
||||
else None
|
||||
)
|
||||
if gene_top1 and psyche:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("fusion_style_name"),
|
||||
f"{gene_top1}底色 × {psyche}倾向",
|
||||
"rule",
|
||||
0.9,
|
||||
f"规则推导：视觉基因风格{gene_top1}与心理风格{psyche}融合命名",
|
||||
)
|
||||
)
|
||||
elif gene_top1 or psyche:
|
||||
only = gene_top1 or psyche
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("fusion_style_name"),
|
||||
f"以{only}为主导的风格",
|
||||
"rule",
|
||||
0.9,
|
||||
f"规则推导：仅有一侧输入，以{only}为主导命名",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(
|
||||
_skip(_spec("fusion_style_name"), "缺少基因风格与心理风格输入，无法命名融合风格")
|
||||
)
|
||||
|
||||
goals = _str_list(q.get("core_goals"))
|
||||
if goals:
|
||||
evidence = f"问卷勾选：{('、'.join(goals))}"
|
||||
if rule_text_all:
|
||||
evidence = f"{evidence}；规则建议：{rule_text_all}"
|
||||
# 规则命中链始终聚合（问卷没勾诉求时也不丢可解释性，见 skipped 分支）。
|
||||
results.append(_ok(_spec("core_goals"), goals, "questionnaire", 0.6, evidence, all_hits))
|
||||
else:
|
||||
results.append(_skip(_spec("core_goals"), "问卷未勾选核心诉求", all_hits))
|
||||
|
||||
weights = q.get("occasion_weights")
|
||||
if isinstance(weights, dict) and weights:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("occasion_weights"),
|
||||
dict(weights),
|
||||
"questionnaire",
|
||||
0.6,
|
||||
"问卷填写：场景权重 "
|
||||
+ "、".join(f"{k} {_fmt(float(v))}%" for k, v in weights.items()),
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("occasion_weights"), "问卷未填写场景权重（全面报告维度）"))
|
||||
|
||||
preferred_colors = _str_list(q.get("preferred_colors"))
|
||||
disliked_styles = _str_list(q.get("disliked_styles"))
|
||||
taboos = _str_list(q.get("taboos"))
|
||||
rule_avoids = [
|
||||
f"回避 {item.slot}（{dim}：{'、'.join(values)}）"
|
||||
for item in verdict.avoid
|
||||
for dim, values in item.attrs.items()
|
||||
]
|
||||
if preferred_colors or disliked_styles or taboos:
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("preferences_taboos"),
|
||||
{
|
||||
"preferred_colors": preferred_colors,
|
||||
"disliked_styles": disliked_styles,
|
||||
"taboos": taboos,
|
||||
},
|
||||
"questionnaire",
|
||||
0.6,
|
||||
"问卷填写："
|
||||
+ "；".join(
|
||||
filter(
|
||||
None,
|
||||
[
|
||||
f"偏好颜色{'、'.join(preferred_colors)}" if preferred_colors else "",
|
||||
f"不接受风格{'、'.join(disliked_styles)}" if disliked_styles else "",
|
||||
f"禁忌{'、'.join(taboos)}" if taboos else "",
|
||||
],
|
||||
)
|
||||
),
|
||||
all_hits,
|
||||
)
|
||||
)
|
||||
elif rule_avoids:
|
||||
hard_keys = [b.rule_key for b in verdict.hard_blocks]
|
||||
evidence = f"规则推导：{('；'.join(rule_avoids[:3]))}"
|
||||
if verdict.blocked:
|
||||
evidence = f"{evidence}（含硬约束 {('、'.join(hard_keys))}）"
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("preferences_taboos"),
|
||||
{"preferred_colors": [], "disliked_styles": [], "taboos": rule_avoids},
|
||||
"rule",
|
||||
0.9,
|
||||
evidence,
|
||||
all_hits,
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("preferences_taboos"), "问卷未填偏好与禁忌，且无规则回避结论"))
|
||||
|
||||
# -- 视觉：毛发现状（#31~#32） ----------------------------------------------
|
||||
hair = d.get("hair_status")
|
||||
if isinstance(hair, dict) and hair.get("length"):
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("hair_status"),
|
||||
dict(hair),
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉分析：发长{hair.get('length')}、卷度{hair.get('curl')}",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("hair_status"), "视觉分析未产出毛发现状（全面报告维度）"))
|
||||
|
||||
hair_color = d.get("hair_color")
|
||||
if isinstance(hair_color, dict) and hair_color.get("hex"):
|
||||
results.append(
|
||||
_ok(
|
||||
_spec("hair_color"),
|
||||
dict(hair_color),
|
||||
"vision",
|
||||
0.85,
|
||||
f"视觉分析：发色 {hair_color['hex']}（偏{hair_color.get('tone')}）",
|
||||
)
|
||||
)
|
||||
else:
|
||||
results.append(_skip(_spec("hair_color"), "视觉分析未产出发色（全面报告维度）"))
|
||||
|
||||
if len(results) != 32: # T-P3-06 红线：结构缺项是装配 bug，必须响亮失败。
|
||||
raise RuntimeError(f"32 维装配缺项：实际 {len(results)} 项")
|
||||
return results
|
||||
|
||||
|
||||
# ── 流水线阶段 ─────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class DiagnosisStage:
|
||||
"""流水线阶段：诊断装配。由 T15 的编排按 dotted path 动态装配（无参构造）。"""
|
||||
|
||||
name = "diagnosis"
|
||||
|
||||
async def run(self, ctx: ReportContext) -> None:
|
||||
questionnaire = dict(ctx.report_input.questionnaire)
|
||||
derived = dict(ctx.report_input.derived_features)
|
||||
|
||||
verdict = await RuleService(ctx.session).evaluate(
|
||||
build_rule_context(questionnaire, derived)
|
||||
)
|
||||
|
||||
season = _str(derived.get("season_code"))
|
||||
colors: list[ColorEntry] = []
|
||||
if season:
|
||||
rows = await kb_repo.list_colors(ctx.session, season_code=season)
|
||||
colors = [
|
||||
ColorEntry(
|
||||
season_code=row.season_code, role=row.role, hex=row.hex, name_cn=row.name_cn
|
||||
)
|
||||
for row in rows
|
||||
]
|
||||
|
||||
results = evaluate_dimensions(questionnaire, derived, verdict, colors)
|
||||
ok_basic_full = sum(1 for r in results if r.status == "ok" and r.key in BASIC_FULL_KEYS)
|
||||
# JSONB 列整体重赋值而非原地改：SQLAlchemy 对 in-place 变更不可见。
|
||||
ctx.report.result = {
|
||||
**(ctx.report.result or {}),
|
||||
"diagnosis": {
|
||||
"dimensions": [asdict(r) for r in results],
|
||||
"coverage": {"basic_full": ok_basic_full, "total": len(results)},
|
||||
},
|
||||
}
|
||||
# NFR-SEC-03：诊断结论不进日志，只记计数。
|
||||
logger.info(
|
||||
"diagnosis_stage_done",
|
||||
report_id=ctx.report.report_id,
|
||||
total=len(results),
|
||||
ok=sum(1 for r in results if r.status == "ok"),
|
||||
basic_full=ok_basic_full,
|
||||
rule_hits=len(verdict.rule_hits),
|
||||
)
|
||||
@@ -0,0 +1,122 @@
|
||||
"""诊断维度字典（需求文档《美学报告-需求文档》§4 的机器可读形态）。
|
||||
|
||||
32 个维度是报告产出的**事实核心**：diagnosis_stage 逐维装配四要素（T-P3-06），
|
||||
任何输入组合下都必须输出全部 32 个维度——缺失只能显式 skipped，不允许缺键。
|
||||
|
||||
逐行口径（与需求文档 §4 表格一一对应，2026-08-14 版）：
|
||||
|
||||
- ``source`` 按表「数据来源」列归类到六类：questionnaire（问卷）/ vision
|
||||
（视觉分析）/ color（色彩算法）/ kb（知识库映射）/ rule（规则）/ llm。
|
||||
「视觉 + 问卷」「规则 + LLM」这类复合来源取**主生产方**（视觉、规则），
|
||||
复合事实写进对应维度的 name_cn 与注释。
|
||||
- ``basic_coverage`` 按表「初级」列归类：✅→full（完整输出）、标签/Top1→label
|
||||
（仅标签）、部分/粗判/8 色/3 色→partial（粗判或子集）、❌→none（不输出）。
|
||||
|
||||
逐行统计（验收「初级 21/32」口径的来源，见需求文档 §4 表格下方统计）：
|
||||
|
||||
- full（10 项）：#1 gender、#2 height_weight、#3 birthday、#5 body_shape、
|
||||
#8 face_shape、#12 skin_tone、#17 season_code、#26 psyche_style、
|
||||
#27 fusion_style_name、#28 core_goals；
|
||||
- label（3 项）：#9 face_proportions、#10 facial_details、#25 gene_style；
|
||||
- partial（8 项）：#6 body_features、#13 face_hue、#14 face_lightness、
|
||||
#19 best_colors、#21 avoid_colors、#22 volume、#23 curve_straight、
|
||||
#30 preferences_taboos；
|
||||
- none（11 项）：#4 occupation、#7 body_ratio、#11 face_depth、#15 face_chroma、
|
||||
#16 face_contrast、#18 season_detailed、#20 neutral_colors、#24 motion、
|
||||
#29 occasion_weights、#31 hair_status、#32 hair_color。
|
||||
|
||||
合计 10 + 3 + 8 + 11 = 32；初级覆盖（非 none）= 10 + 3 + 8 = **21/32**。
|
||||
|
||||
编号 33（五行分布）/34（幸运色）已于 2026-08-13 随 TD-31 永久移除，**不复用**：
|
||||
避免历史数据与文档交叉引用错位。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
|
||||
#: basic_coverage 的合法取值（封闭枚举，防止拼写漂移出机器可读口径）。
|
||||
BASIC_COVERAGE_VALUES: frozenset[str] = frozenset({"full", "label", "partial", "none"})
|
||||
|
||||
#: source 的合法取值（按需求文档「数据来源」列归类的六类）。
|
||||
DIMENSION_SOURCES: frozenset[str] = frozenset(
|
||||
{"questionnaire", "vision", "color", "kb", "rule", "llm"}
|
||||
)
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class DimensionSpec:
|
||||
"""单个诊断维度的静态定义（字典行）。
|
||||
|
||||
只描述「这个维度是什么、数据从哪来、初级报告覆盖到什么程度」；
|
||||
具体取值与置信度由 diagnosis_stage 装配成 DimensionResult。
|
||||
"""
|
||||
|
||||
number: int # 1~32（33/34 已永久废弃，不复用）
|
||||
key: str # 稳定英文键，与 kb/记忆系统词汇对齐（body_shape/face_shape/season_code…）
|
||||
name_cn: str
|
||||
source: str # questionnaire | vision | color | kb | rule | llm
|
||||
basic_coverage: str # "full" | "label" | "partial" | "none"
|
||||
|
||||
|
||||
#: 32 个维度，按需求文档 §4 表格逐行编码；number 恰好 1~32 连续。
|
||||
DIMENSIONS: tuple[DimensionSpec, ...] = (
|
||||
# -- 问卷直采（基础信息） --------------------------------------------------
|
||||
DimensionSpec(1, "gender", "性别", "questionnaire", "full"),
|
||||
DimensionSpec(2, "height_weight", "身高/体重", "questionnaire", "full"),
|
||||
DimensionSpec(3, "birthday", "生日", "questionnaire", "full"),
|
||||
DimensionSpec(4, "occupation", "职业", "questionnaire", "none"),
|
||||
# -- 视觉分析（体型与比例） ------------------------------------------------
|
||||
DimensionSpec(5, "body_shape", "体型", "vision", "full"),
|
||||
DimensionSpec(6, "body_features", "身体局部特征", "vision", "partial"),
|
||||
DimensionSpec(7, "body_ratio", "上下身比例", "vision", "none"),
|
||||
# -- 视觉分析（面部） ------------------------------------------------------
|
||||
DimensionSpec(8, "face_shape", "脸型", "vision", "full"),
|
||||
DimensionSpec(9, "face_proportions", "三庭五眼", "vision", "label"),
|
||||
DimensionSpec(10, "facial_details", "五官细节", "vision", "label"),
|
||||
DimensionSpec(11, "face_depth", "面部立体度", "vision", "none"),
|
||||
# -- 色彩（肤档与季型，视觉 Lab 采样 + 色彩算法） --------------------------
|
||||
DimensionSpec(12, "skin_tone", "肤色档位", "vision", "full"),
|
||||
DimensionSpec(13, "face_hue", "面部色相", "color", "partial"),
|
||||
DimensionSpec(14, "face_lightness", "面部亮度", "color", "partial"),
|
||||
DimensionSpec(15, "face_chroma", "面部色度", "color", "none"),
|
||||
DimensionSpec(16, "face_contrast", "面部对比度", "color", "none"),
|
||||
DimensionSpec(17, "season_code", "四季季型", "color", "full"),
|
||||
DimensionSpec(18, "season_detailed", "十二季细分", "color", "none"),
|
||||
# -- 知识库映射（色板） ----------------------------------------------------
|
||||
DimensionSpec(19, "best_colors", "最佳色板", "kb", "partial"),
|
||||
DimensionSpec(20, "neutral_colors", "中性色板", "kb", "none"),
|
||||
DimensionSpec(21, "avoid_colors", "避雷色", "kb", "partial"),
|
||||
# -- 视觉分析（风格量感） --------------------------------------------------
|
||||
DimensionSpec(22, "volume", "量感", "vision", "partial"),
|
||||
DimensionSpec(23, "curve_straight", "直曲（轮廓）", "vision", "partial"),
|
||||
DimensionSpec(24, "motion", "动静", "vision", "none"), # 表列「视觉 + 问卷」，主生产方为视觉
|
||||
DimensionSpec(25, "gene_style", "基因风格", "vision", "label"), # 初级 Top1
|
||||
# -- 问卷偏好（心理侧） ----------------------------------------------------
|
||||
DimensionSpec(26, "psyche_style", "心理风格", "questionnaire", "full"),
|
||||
DimensionSpec(27, "fusion_style_name", "融合风格命名", "rule", "full"), # 表列「规则 + LLM」
|
||||
DimensionSpec(28, "core_goals", "核心诉求", "questionnaire", "full"), # 问卷 + 推导
|
||||
DimensionSpec(29, "occasion_weights", "场景权重", "questionnaire", "none"),
|
||||
DimensionSpec(30, "preferences_taboos", "偏好与禁忌", "questionnaire", "partial"),
|
||||
# -- 视觉分析（毛发现状） --------------------------------------------------
|
||||
DimensionSpec(31, "hair_status", "发长与卷度现状", "vision", "none"),
|
||||
DimensionSpec(32, "hair_color", "发色现状", "vision", "none"),
|
||||
)
|
||||
|
||||
#: 初级报告**完整输出**（basic_coverage == "full"）的维度键集合，共 10 项。
|
||||
#: 这是验收「初级 21/32」口径里 21 = 10 full + 3 label + 8 partial 的机器可读形态。
|
||||
BASIC_FULL_KEYS: frozenset[str] = frozenset(
|
||||
spec.key for spec in DIMENSIONS if spec.basic_coverage == "full"
|
||||
)
|
||||
|
||||
#: 初级报告**有产出**（full/label/partial，即 basic_coverage != "none"）的维度键集合。
|
||||
#: len == 21，对应需求文档 §4 统计表的「初级报告覆盖合计 21/32」。
|
||||
BASIC_COVERED_KEYS: frozenset[str] = frozenset(
|
||||
spec.key for spec in DIMENSIONS if spec.basic_coverage != "none"
|
||||
)
|
||||
|
||||
#: number → spec（诊断装配按编号稳定排序输出用）。
|
||||
DIMENSION_BY_NUMBER: dict[int, DimensionSpec] = {spec.number: spec for spec in DIMENSIONS}
|
||||
|
||||
#: key → spec（视觉/诊断阶段按维度键反查定义用）。
|
||||
DIMENSION_BY_KEY: dict[str, DimensionSpec] = {spec.key: spec for spec in DIMENSIONS}
|
||||
@@ -0,0 +1,304 @@
|
||||
"""视觉分析提供者：接口位 + V1 确定性（mock）实现。
|
||||
|
||||
**接口位**（T9 SPIKE 之后的正式契约）：真实 RTMPose/BiRefNet/Lab 采样实现后续
|
||||
按 ``VisionProvider`` Protocol 替换，流水线与其余阶段不感知差别。
|
||||
|
||||
**为什么 mock 要确定性**：黄金集与集成测试必须能对同一输入断言同一输出；
|
||||
概率性 mock 会让"偶现失败"混进 CI。因此 ``DeterministicVisionProvider``
|
||||
不读照片字节（V1 骨架没有照片下载步骤），只从 ``questionnaire`` +
|
||||
``photo_meta["object_key"]`` 的 sha256 派生特征——同输入恒同输出。
|
||||
|
||||
派生特征的形状（所有键即诊断维度键，两个例外见下）::
|
||||
|
||||
{
|
||||
"body_shape": "A" | "O" | "X" | "T" | "H", # 梨/苹果/沙漏/倒三角/矩形
|
||||
"body_features": ["溜肩", ...], # 多标签，可为空列表
|
||||
"body_ratio": float, # 上下身比例（0.58~0.68）
|
||||
"face_shape": "鹅蛋" | "圆" | "方" | "长" | "心形" | "菱形" | "梨形",
|
||||
"face_proportions": "均匀" | "上庭偏长" | "中庭偏长" | "下庭偏长",
|
||||
"facial_details": {"眉形": ..., "眼型": ..., "双眼皮": ..., "鼻型": ..., "唇形": ...},
|
||||
"face_depth": float, # 面部立体度 0~10
|
||||
"skin_lab": {"l": float, "a": float, "b": float}, # 中间量，不是维度键
|
||||
"skin_tone": "粉一白" | ... | "黄黑皮", # 六档
|
||||
"face_hue": {"value": float, "label": str}, # -1 冷 ←→ 1 暖
|
||||
"face_lightness": {"value": float, "label": str}, # -1 暗沉 ←→ 1 白皙
|
||||
"face_chroma": {"value": float, "label": str}, # -1 柔和 ←→ 1 鲜艳
|
||||
"face_contrast": "低" | "适中" | "高",
|
||||
"season_code": "spring" | "summer" | "autumn" | "winter",
|
||||
"season_detailed": "浅春" | ... | "深冬", # 十二季
|
||||
"volume": {"label": "小" | "中" | "大", "score": int},
|
||||
"curve_straight": {"label": "偏曲" | "适中" | "偏直", "score": float},
|
||||
"motion": "动态型" | "动静适中" | "静态型",
|
||||
"gene_style_scores": {"戏剧": int, ...}, # 9 型评分；例外键：维度键为 gene_style
|
||||
"hair_status": {"length": "短" | "中" | "长", "curl": "直" | "微卷" | "大卷"},
|
||||
"hair_color": {"hex": "#RRGGBB", "tone": "冷" | "暖"},
|
||||
}
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
from collections.abc import Mapping
|
||||
from typing import Any, Protocol, runtime_checkable
|
||||
|
||||
from packages.report.dimensions import DIMENSION_BY_KEY
|
||||
from packages.shared.config import Settings
|
||||
from packages.shared.errors import AppError, ErrorCode
|
||||
|
||||
#: 体型五型（需求文档 §4 #5 取值范围；字母码与表格一致）。
|
||||
BODY_SHAPES: tuple[str, ...] = ("A", "O", "X", "T", "H")
|
||||
|
||||
#: 脸型七型（需求文档 §4 #8）。
|
||||
FACE_SHAPES: tuple[str, ...] = ("鹅蛋", "圆", "方", "长", "心形", "菱形", "梨形")
|
||||
|
||||
#: 肤色六档（需求文档 §4 #12）。
|
||||
SKIN_TONES: tuple[str, ...] = ("粉一白", "粉二白", "粉三白", "黄一白", "黄二白", "黄黑皮")
|
||||
|
||||
#: 四季季型代码。kb_color.season_code 必须用同一套代码（色板维度的联表键）。
|
||||
SEASON_CODES: tuple[str, ...] = ("spring", "summer", "autumn", "winter")
|
||||
|
||||
SEASON_CODE_CN: Mapping[str, str] = {
|
||||
"spring": "春",
|
||||
"summer": "夏",
|
||||
"autumn": "秋",
|
||||
"winter": "冬",
|
||||
}
|
||||
|
||||
#: 基因/心理风格九型（需求文档 §4 #25/#26）。
|
||||
STYLE_TYPES: tuple[str, ...] = (
|
||||
"戏剧",
|
||||
"睿智",
|
||||
"自然",
|
||||
"古典",
|
||||
"优雅",
|
||||
"浪漫",
|
||||
"前卫",
|
||||
"少女",
|
||||
"少年",
|
||||
)
|
||||
|
||||
#: 派生特征键 → 诊断维度键的例外映射（其余键与维度键同名）。
|
||||
#: skin_lab 是色彩算法的中间量，不对应任何维度。
|
||||
FEATURE_TO_DIMENSION: Mapping[str, str] = {"gene_style_scores": "gene_style"}
|
||||
|
||||
|
||||
def dimensions_touched(features: Mapping[str, Any]) -> list[str]:
|
||||
"""派生特征覆盖到的诊断维度键（vision 阶段产物的 dimensions_touched）。"""
|
||||
touched: list[str] = []
|
||||
for key in features:
|
||||
dim = FEATURE_TO_DIMENSION.get(key, key)
|
||||
if dim in DIMENSION_BY_KEY and dim not in touched:
|
||||
touched.append(dim)
|
||||
return touched
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class VisionProvider(Protocol):
|
||||
"""视觉分析 SPI：照片元信息 + 问卷 → 派生特征 dict（形状见模块 docstring）。
|
||||
|
||||
实现必须**确定性**（同输入同输出），否则黄金集断言与失败重放都不成立。
|
||||
"""
|
||||
|
||||
async def analyze(
|
||||
self, photo_meta: dict[str, Any], questionnaire: dict[str, Any]
|
||||
) -> dict[str, Any]: ...
|
||||
|
||||
|
||||
def _seed(object_key: str, questionnaire: Mapping[str, Any], salt: str) -> bytes:
|
||||
"""确定性种子：canonical JSON 保证 dict 键序无关，salt 分组隔离特征域。"""
|
||||
canonical = json.dumps(questionnaire, sort_keys=True, ensure_ascii=False, separators=(",", ":"))
|
||||
return hashlib.sha256(f"{object_key}|{canonical}|{salt}".encode()).digest()
|
||||
|
||||
|
||||
def _num(value: Any) -> float | None:
|
||||
"""问卷数值字段的宽容读取：bool 不是数值（是 int 子类，须显式排除）。"""
|
||||
if isinstance(value, bool) or not isinstance(value, (int, float)):
|
||||
return None
|
||||
return float(value)
|
||||
|
||||
|
||||
def _bmi(questionnaire: Mapping[str, Any]) -> float | None:
|
||||
height = _num(questionnaire.get("height"))
|
||||
weight = _num(questionnaire.get("weight"))
|
||||
if height is None or weight is None or height <= 0:
|
||||
return None
|
||||
return weight / (height / 100.0) ** 2
|
||||
|
||||
|
||||
def _pick(digest: bytes, index: int, options: tuple[str, ...]) -> str:
|
||||
return options[digest[index] % len(options)]
|
||||
|
||||
|
||||
class DeterministicVisionProvider:
|
||||
"""V1 骨架的确定性假实现：真实视觉模型的占位（报告链路先跑通、可回归）。
|
||||
|
||||
派生规则（全部可复算，这里就是"模型"本身）：
|
||||
|
||||
- **问卷驱动**：
|
||||
- 身高/体重 → BMI：``<18.5`` → A/H 二选一；``18.5~24`` → X/H/T 三选一；
|
||||
``24~28`` → O/T 二选一；``≥28`` → O。缺失时按哈希在五型里均匀选。
|
||||
- 问卷自评（§3.6 降级路径的 body_shape_self / face_shape_self /
|
||||
skin_tone_self）**优先于哈希**——自评是真实信号，mock 噪声不该盖过它；
|
||||
skin_tone_self 同时反推 hue/lightness，保证季型/肤色档内部一致。
|
||||
- **哈希驱动**（sha256(object_key + canonical 问卷)，face/body 两组独立盐）：
|
||||
- face 组：d0/d1/d2 → 色相/亮度/色度（-1~1 连续值），季型=色相冷热 ×
|
||||
亮度深浅（暖+亮→春、冷+柔→夏、暖+深→秋、冷+亮→冬），十二季=季型 ×
|
||||
色度/亮度细分；d3~d10 → 脸型/三庭五眼/五官/立体度。
|
||||
- body 组：b0 → 体型枚举选择；b1~b3 → 身体局部特征多标签；
|
||||
b4 → 上下身比例（0.58~0.68）；b5/b6 → 量感(30~70)/直曲；b7 → 动静；
|
||||
b8~b21 → 发长/卷度/发色/九型基因风格评分（3~10 分）。
|
||||
"""
|
||||
|
||||
async def analyze(
|
||||
self, photo_meta: dict[str, Any], questionnaire: dict[str, Any]
|
||||
) -> dict[str, Any]:
|
||||
object_key = str(photo_meta.get("object_key", ""))
|
||||
face = _seed(object_key, questionnaire, "face")
|
||||
body = _seed(object_key, questionnaire, "body")
|
||||
|
||||
# -- 色彩三轴：连续值 + 三档标签（粗判即标签口径） --------------------
|
||||
hue = round(face[0] / 255 * 2 - 1, 3)
|
||||
lightness = round(face[1] / 255 * 2 - 1, 3)
|
||||
chroma = round(face[2] / 255 * 2 - 1, 3)
|
||||
|
||||
# 问卷肤色自评优先：粉系=冷（hue<0）、黄系=暖；白度档位反推亮度。
|
||||
skin_tone_self = questionnaire.get("skin_tone_self")
|
||||
if isinstance(skin_tone_self, str) and skin_tone_self in SKIN_TONES:
|
||||
skin_tone = skin_tone_self
|
||||
hue = -abs(hue) - 0.05 if skin_tone.startswith("粉") else abs(hue) + 0.05
|
||||
if skin_tone.endswith("一白"):
|
||||
lightness = 0.6
|
||||
elif skin_tone.endswith("二白"):
|
||||
lightness = 0.2
|
||||
elif skin_tone == "粉三白":
|
||||
lightness = -0.1
|
||||
else: # 黄黑皮
|
||||
lightness = -0.6
|
||||
else:
|
||||
if lightness > 0.35:
|
||||
skin_tone = "粉一白" if hue < 0 else "黄一白"
|
||||
elif lightness > 0.1:
|
||||
skin_tone = "粉二白" if hue < 0 else "黄二白"
|
||||
elif lightness > -0.2:
|
||||
skin_tone = "粉三白"
|
||||
else:
|
||||
skin_tone = "黄黑皮"
|
||||
|
||||
warm = hue >= 0
|
||||
season_code = (
|
||||
"spring"
|
||||
if warm and lightness >= 0
|
||||
else ("autumn" if warm else ("summer" if lightness >= 0 else "winter"))
|
||||
)
|
||||
if season_code == "spring":
|
||||
season_detailed = "亮春" if chroma > 0.25 else ("浅春" if lightness > 0.5 else "暖春")
|
||||
elif season_code == "summer":
|
||||
season_detailed = "浅夏" if lightness > 0.5 else ("柔夏" if chroma < -0.25 else "冷夏")
|
||||
elif season_code == "autumn":
|
||||
season_detailed = "柔秋" if chroma < -0.25 else ("深秋" if lightness < -0.5 else "暖秋")
|
||||
else:
|
||||
season_detailed = "亮冬" if chroma > 0.25 else ("深冬" if lightness < -0.5 else "冷冬")
|
||||
|
||||
# -- 体型（问卷 BMI 主驱动，自评覆盖） --------------------------------
|
||||
body_shape_self = questionnaire.get("body_shape_self")
|
||||
if isinstance(body_shape_self, str) and body_shape_self in BODY_SHAPES:
|
||||
body_shape = body_shape_self
|
||||
else:
|
||||
bmi = _bmi(questionnaire)
|
||||
if bmi is None:
|
||||
body_shape = _pick(body, 0, BODY_SHAPES)
|
||||
elif bmi < 18.5:
|
||||
body_shape = _pick(body, 0, ("A", "H"))
|
||||
elif bmi < 24:
|
||||
body_shape = _pick(body, 0, ("X", "H", "T"))
|
||||
elif bmi < 28:
|
||||
body_shape = _pick(body, 0, ("O", "T"))
|
||||
else:
|
||||
body_shape = "O"
|
||||
|
||||
feature_pool = ("溜肩", "平肩", "宽肩", "短颈", "长腰", "短腰", "粗腿")
|
||||
body_features = [_pick(body, 1, feature_pool)]
|
||||
extra = _pick(body, 2, feature_pool)
|
||||
if extra not in body_features and body[3] % 2 == 1:
|
||||
body_features.append(extra)
|
||||
|
||||
volume_score = 30 + body[5] % 41
|
||||
curve_score = round(body[6] / 255 * 2 - 1, 3)
|
||||
|
||||
face_shape_self = questionnaire.get("face_shape_self")
|
||||
face_shape = (
|
||||
face_shape_self
|
||||
if isinstance(face_shape_self, str) and face_shape_self in FACE_SHAPES
|
||||
else _pick(face, 3, FACE_SHAPES)
|
||||
)
|
||||
|
||||
gene_scores = {style: 3 + body[13 + i] % 8 for i, style in enumerate(STYLE_TYPES)}
|
||||
hair_hex = f"#{40 + body[10] % 80:02X}{20 + body[11] % 60:02X}{body[12] % 80:02X}"
|
||||
|
||||
return {
|
||||
"body_shape": body_shape,
|
||||
"body_features": body_features,
|
||||
"body_ratio": round(0.58 + body[4] / 255 * 0.10, 3),
|
||||
"face_shape": face_shape,
|
||||
"face_proportions": _pick(face, 4, ("均匀", "上庭偏长", "中庭偏长", "下庭偏长")),
|
||||
"facial_details": {
|
||||
"眉形": _pick(face, 5, ("平直眉", "弯眉", "挑眉")),
|
||||
"眼型": _pick(face, 6, ("杏仁眼", "圆眼", "细长眼")),
|
||||
"双眼皮": _pick(face, 7, ("双眼皮", "内双")),
|
||||
"鼻型": _pick(face, 8, ("直鼻", "翘鼻", "宽鼻")),
|
||||
"唇形": _pick(face, 9, ("薄唇", "标准唇", "厚唇")),
|
||||
},
|
||||
"face_depth": round(face[10] % 101 / 10, 1),
|
||||
"skin_lab": {
|
||||
"l": round(45 + (lightness + 1) * 20, 1),
|
||||
"a": round(hue * 12, 1),
|
||||
"b": round(20 + chroma * 15, 1),
|
||||
},
|
||||
"skin_tone": skin_tone,
|
||||
"face_hue": {
|
||||
"value": round(hue, 3),
|
||||
"label": "偏冷" if hue < -0.33 else ("偏暖" if hue > 0.33 else "中性"),
|
||||
},
|
||||
"face_lightness": {
|
||||
"value": round(lightness, 3),
|
||||
"label": "白皙" if lightness > 0.33 else ("暗沉" if lightness < -0.33 else "适中"),
|
||||
},
|
||||
"face_chroma": {
|
||||
"value": round(chroma, 3),
|
||||
"label": "鲜艳" if chroma > 0.33 else ("柔和" if chroma < -0.33 else "适中"),
|
||||
},
|
||||
"face_contrast": "高" if chroma > 0.2 else ("低" if chroma < -0.2 else "适中"),
|
||||
"season_code": season_code,
|
||||
"season_detailed": season_detailed,
|
||||
"volume": {
|
||||
"label": "小" if volume_score < 40 else ("中" if volume_score <= 55 else "大"),
|
||||
"score": volume_score,
|
||||
},
|
||||
"curve_straight": {
|
||||
"label": "偏曲"
|
||||
if curve_score < -0.33
|
||||
else ("偏直" if curve_score > 0.33 else "适中"),
|
||||
"score": curve_score,
|
||||
},
|
||||
"motion": _pick(body, 7, ("动态型", "动静适中", "静态型")),
|
||||
"gene_style_scores": gene_scores,
|
||||
"hair_status": {
|
||||
"length": _pick(body, 8, ("短", "中", "长")),
|
||||
"curl": _pick(body, 9, ("直", "微卷", "大卷")),
|
||||
},
|
||||
"hair_color": {"hex": hair_hex, "tone": "冷" if hue < 0 else "暖"},
|
||||
}
|
||||
|
||||
|
||||
def build_vision_provider(settings: Settings) -> VisionProvider:
|
||||
"""按配置装配视觉提供者。未知名直接 CONFIG_ERROR——静默回退到默认实现
|
||||
会让"配置了真实模型却跑着 mock"这种事故无人察觉。"""
|
||||
name = settings.report_vision_provider
|
||||
if name == "deterministic":
|
||||
return DeterministicVisionProvider()
|
||||
raise AppError(
|
||||
ErrorCode.CONFIG_ERROR,
|
||||
message="未知的报告视觉提供者",
|
||||
data={"report_vision_provider": name},
|
||||
)
|
||||
@@ -0,0 +1,70 @@
|
||||
"""视觉分析阶段（name="vision"）：照片元信息 + 问卷 → 派生特征中间态。
|
||||
|
||||
T-P3-09 的实现点：``rp_input.derived_features`` 已存在且非空时**跳过并标记
|
||||
reused**——LLM/渲染阶段失败重试时不重跑视觉分析，用户也不需要重新上传照片
|
||||
（这是报告链路"分步可恢复"的结构基础，见 packages/report/README.md）。
|
||||
|
||||
产物形状（``result["vision"]``）::
|
||||
|
||||
首跑： {"status": "ok", "dimensions_touched": [...], "quality_flags": [...]}
|
||||
复用： {"skipped": true, "reused": true}
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from packages.report.pipeline import ReportContext
|
||||
from packages.report.vision import VisionProvider, build_vision_provider, dimensions_touched
|
||||
from packages.shared.logging import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class VisionStage:
|
||||
"""流水线阶段：视觉分析。由 T15 的编排按 dotted path 动态装配。"""
|
||||
|
||||
name = "vision"
|
||||
|
||||
def __init__(self, provider: VisionProvider | None = None) -> None:
|
||||
# provider 可注入（单测/FakeProvider，参考 test_vertical_slice 的做法）；
|
||||
# 留空则每次 run 时按 settings 装配。编排层用无参构造（importlib 动态
|
||||
# 导入），因此该参数必须有默认值。
|
||||
self._provider = provider
|
||||
|
||||
async def run(self, ctx: ReportContext) -> None:
|
||||
if ctx.report_input.derived_features:
|
||||
# 中间态已落库：绝不重算（重算=同照片不同结论，报告可解释性破坏）。
|
||||
# JSONB 列整体重赋值而非原地改：SQLAlchemy 对 in-place 变更不可见。
|
||||
ctx.report.result = {
|
||||
**(ctx.report.result or {}),
|
||||
"vision": {"skipped": True, "reused": True},
|
||||
}
|
||||
logger.info("vision_stage_reused", report_id=ctx.report.report_id)
|
||||
return
|
||||
|
||||
provider = self._provider or build_vision_provider(ctx.settings)
|
||||
features = await provider.analyze(
|
||||
dict(ctx.report_input.photo_meta), dict(ctx.report_input.questionnaire)
|
||||
)
|
||||
|
||||
# 中间态立即 flush：后续阶段（或流水线重试）可见，T-P3-09 的落点。
|
||||
ctx.report_input.derived_features = features
|
||||
await ctx.session.flush()
|
||||
|
||||
# 质量标记（T-P3-01）：photo_meta 可选 quality_flags（模糊/背光等），
|
||||
# mock 骨架下默认空列表；透传进产物供报告页以"参考"语气呈现。
|
||||
quality_flags = list(ctx.report_input.photo_meta.get("quality_flags") or [])
|
||||
touched = dimensions_touched(features)
|
||||
ctx.report.result = {
|
||||
**(ctx.report.result or {}),
|
||||
"vision": {
|
||||
"status": "ok",
|
||||
"dimensions_touched": touched,
|
||||
"quality_flags": quality_flags,
|
||||
},
|
||||
}
|
||||
logger.info(
|
||||
"vision_stage_done",
|
||||
report_id=ctx.report.report_id,
|
||||
touched=len(touched),
|
||||
quality_flags=len(quality_flags),
|
||||
)
|
||||
@@ -0,0 +1,223 @@
|
||||
"""报告流水线视觉/诊断两阶段集成用例（真实 PostgreSQL）——只测 T16 的两个阶段。
|
||||
|
||||
覆盖任务契约：种 2 条 published 规则 → VisionStage → DiagnosisStage，断言
|
||||
derived_features 落库、32 维全键、规则 rule_hits 出现、kb_color 为空时
|
||||
色板维度 skipped。整条流水线的编排（装配/失败退款）归 T15 的集成测试。
|
||||
|
||||
确定性保证：问卷带 skin_tone_self=黄一白 → 暖+亮 → 季型恒为 spring，
|
||||
因此色板断言不依赖哈希；body 规则的条件值先用 provider 离线算出再种入。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import uuid
|
||||
from collections.abc import AsyncIterator
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
def _infra_available() -> bool:
|
||||
return bool(os.getenv("MEIDA_RUN_INTEGRATION"))
|
||||
|
||||
|
||||
pytestmark = [
|
||||
pytest.mark.integration,
|
||||
pytest.mark.skipif(
|
||||
not _infra_available(),
|
||||
reason="set MEIDA_RUN_INTEGRATION=1 with PostgreSQL and Redis running",
|
||||
),
|
||||
]
|
||||
|
||||
_PREFIX = "R-IT16-"
|
||||
#: 问卷肤色自评钉死的季型（黄一白 → 暖+亮 → spring），见 vision.py 派生规则。
|
||||
_SEASON = "spring"
|
||||
|
||||
_QUESTIONNAIRE: dict[str, Any] = {
|
||||
"gender": "女",
|
||||
"height": 165,
|
||||
"weight": 50,
|
||||
"birthday": "1998-06-01",
|
||||
"occupation": "设计师",
|
||||
"core_goals": ["显高", "显瘦"],
|
||||
"occasion_weights": {"通勤": 0.7, "休闲": 0.3},
|
||||
"psyche_style": "自然",
|
||||
"preferred_colors": ["白色"],
|
||||
"taboos": ["不接受深V"],
|
||||
"skin_tone_self": "黄一白",
|
||||
}
|
||||
|
||||
|
||||
def _key(suffix: str) -> str:
|
||||
return f"{_PREFIX}{suffix}-{uuid.uuid4().hex[:6]}"
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def _session() -> AsyncIterator[Any]:
|
||||
"""每个用例独立 engine：pytest-asyncio 每用例一个事件循环（同 test_rules_engine）。"""
|
||||
from sqlalchemy.ext.asyncio import async_sessionmaker
|
||||
|
||||
from packages.shared.config import get_settings
|
||||
from packages.shared.db import create_engine
|
||||
|
||||
engine = create_engine(get_settings())
|
||||
factory = async_sessionmaker(bind=engine, expire_on_commit=False, autoflush=False)
|
||||
try:
|
||||
async with factory() as session:
|
||||
try:
|
||||
yield session
|
||||
await session.commit()
|
||||
except Exception:
|
||||
await session.rollback()
|
||||
raise
|
||||
finally:
|
||||
await engine.dispose()
|
||||
|
||||
|
||||
async def _cleanup(report_id: int, input_id: int) -> None:
|
||||
from sqlalchemy import delete
|
||||
|
||||
from packages.rules import service as rules_service
|
||||
from packages.shared.models import KbColor, KbRule, RpInput, RpReport
|
||||
|
||||
rules_service.invalidate_published_cache()
|
||||
async with _session() as session:
|
||||
await session.execute(delete(KbRule).where(KbRule.rule_key.like(f"{_PREFIX}%")))
|
||||
await session.execute(delete(RpInput).where(RpInput.input_id == input_id))
|
||||
await session.execute(delete(RpReport).where(RpReport.report_id == report_id))
|
||||
await session.execute(delete(KbColor).where(KbColor.season_code == _SEASON))
|
||||
|
||||
|
||||
def _body_payload(rule_key: str, body_shape: str) -> dict[str, Any]:
|
||||
return {
|
||||
"rule_key": rule_key,
|
||||
"category": "body_shape",
|
||||
"priority": 90,
|
||||
"confidence": 0.95,
|
||||
"conditions": {"body_shape": body_shape},
|
||||
"effects": {
|
||||
"recommend": [{"slot": "top", "attrs": {"shoulder": ["IT16垫肩"]}}],
|
||||
"explanation": "IT16 体型策略",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _color_payload(rule_key: str, season: str) -> dict[str, Any]:
|
||||
return {
|
||||
"rule_key": rule_key,
|
||||
"category": "color",
|
||||
"priority": 85,
|
||||
"confidence": 0.9,
|
||||
"conditions": {"season": season},
|
||||
"effects": {
|
||||
"avoid": [{"slot": "dress", "attrs": {"color": ["IT16荧光绿"]}}],
|
||||
"explanation": "IT16 季型避雷",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
async def test_vision_then_diagnosis_with_published_rules() -> None:
|
||||
from packages.domain.ids import next_id
|
||||
from packages.report.diagnosis_stage import DiagnosisStage
|
||||
from packages.report.pipeline import ReportContext
|
||||
from packages.report.vision import DeterministicVisionProvider
|
||||
from packages.report.vision_stage import VisionStage
|
||||
from packages.rules.service import RuleService
|
||||
from packages.shared.config import get_settings
|
||||
from packages.shared.models import RpInput, RpReport
|
||||
|
||||
report_id, input_id = next_id(), next_id()
|
||||
await _cleanup(report_id, input_id)
|
||||
body_key, color_key = _key("BODY"), _key("COLOR")
|
||||
try:
|
||||
# 视觉结果是确定性的：先离线算出派生特征，用它的值种规则条件。
|
||||
photo_meta = {
|
||||
"object_key": f"{_PREFIX}photo.jpg",
|
||||
"quality_flags": ["backlit"],
|
||||
}
|
||||
derived = await DeterministicVisionProvider().analyze(photo_meta, _QUESTIONNAIRE)
|
||||
assert derived["season_code"] == _SEASON # 问卷自评钉死季型，断言不依赖哈希
|
||||
|
||||
async with _session() as session:
|
||||
svc = RuleService(session)
|
||||
await svc.create(_body_payload(body_key, derived["body_shape"]), author="it16")
|
||||
await svc.create(_color_payload(color_key, derived["season_code"]), author="it16")
|
||||
await svc.publish(body_key)
|
||||
await svc.publish(color_key)
|
||||
|
||||
async with _session() as session:
|
||||
report = RpReport(
|
||||
report_id=report_id,
|
||||
user_id=next_id(),
|
||||
tier="basic",
|
||||
status="generating",
|
||||
result={},
|
||||
)
|
||||
session.add(report)
|
||||
await session.flush() # rp_input.report_id 有外键，先落主表
|
||||
rp_input = RpInput(
|
||||
input_id=input_id,
|
||||
report_id=report_id,
|
||||
questionnaire=_QUESTIONNAIRE,
|
||||
photo_meta=photo_meta,
|
||||
derived_features={},
|
||||
)
|
||||
session.add(rp_input)
|
||||
await session.flush()
|
||||
|
||||
ctx = ReportContext(
|
||||
report=report,
|
||||
report_input=rp_input,
|
||||
session=session,
|
||||
settings=get_settings(),
|
||||
)
|
||||
|
||||
# -- VisionStage：首跑产出中间态 + 产物 --------------------------
|
||||
await VisionStage().run(ctx)
|
||||
assert rp_input.derived_features == derived
|
||||
assert report.result["vision"]["status"] == "ok"
|
||||
assert report.result["vision"]["quality_flags"] == ["backlit"] # T-P3-01 透传
|
||||
assert "body_shape" in report.result["vision"]["dimensions_touched"]
|
||||
|
||||
# -- VisionStage：中间态已存在 → skipped/reused（T-P3-09） --------
|
||||
await VisionStage().run(ctx)
|
||||
assert report.result["vision"] == {"skipped": True, "reused": True}
|
||||
|
||||
# -- DiagnosisStage：32 维全键 + 规则联动 + 空色板 skipped --------
|
||||
await DiagnosisStage().run(ctx)
|
||||
diagnosis = report.result["diagnosis"]
|
||||
dimensions = diagnosis["dimensions"]
|
||||
assert [d["number"] for d in dimensions] == list(range(1, 33))
|
||||
by_key = {d["key"]: d for d in dimensions}
|
||||
for dim in dimensions:
|
||||
for field in ("value", "confidence", "source", "evidence", "rule_hits", "status"):
|
||||
assert field in dim
|
||||
|
||||
# 体型规则 → #5；色彩规则 → #21/#28/#30（聚合口径见 diagnosis_stage）
|
||||
assert body_key in by_key["body_shape"]["rule_hits"]
|
||||
assert color_key in by_key["avoid_colors"]["rule_hits"]
|
||||
for key in ("core_goals", "preferences_taboos"):
|
||||
assert {body_key, color_key} <= set(by_key[key]["rule_hits"])
|
||||
|
||||
# kb_color 为空 → 色板维度显式 skipped + 原因（显式缺失优于编造）
|
||||
assert by_key["best_colors"]["status"] == "skipped"
|
||||
assert "色板未录入" in by_key["best_colors"]["evidence"]
|
||||
assert by_key["neutral_colors"]["status"] == "skipped"
|
||||
|
||||
# 本场景问卷完整 + 视觉已跑：初级 full 10/10。
|
||||
assert diagnosis["coverage"] == {"basic_full": 10, "total": 32}
|
||||
|
||||
# -- 落库验证：新会话读回中间态与产物 -------------------------------
|
||||
async with _session() as session:
|
||||
saved_input = await session.get(RpInput, input_id)
|
||||
assert saved_input is not None
|
||||
assert saved_input.derived_features == derived
|
||||
saved_report = await session.get(RpReport, report_id)
|
||||
assert saved_report is not None
|
||||
assert set(saved_report.result) == {"vision", "diagnosis"}
|
||||
assert saved_report.result["vision"]["reused"] is True
|
||||
assert saved_report.result["diagnosis"]["coverage"]["total"] == 32
|
||||
finally:
|
||||
await _cleanup(report_id, input_id)
|
||||
@@ -0,0 +1,262 @@
|
||||
"""诊断装配：32 维四要素齐全、缺失显式 skipped、规则命中进 rule_hits（T-P3-06）。
|
||||
|
||||
规则求值走纯函数 ``engine.evaluate``（内置最小规则集，不连库）——
|
||||
``evaluate_dimensions`` 本身不碰 I/O，这正是它能被穷举输入组合的原因。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from dataclasses import asdict
|
||||
from typing import Any
|
||||
|
||||
from packages.report.diagnosis_stage import (
|
||||
ColorEntry,
|
||||
build_rule_context,
|
||||
evaluate_dimensions,
|
||||
)
|
||||
from packages.report.dimensions import DIMENSION_BY_KEY, DIMENSIONS
|
||||
from packages.report.vision import DeterministicVisionProvider
|
||||
from packages.rules import dsl, engine
|
||||
|
||||
_QUESTIONNAIRE = {
|
||||
"gender": "女",
|
||||
"height": 165,
|
||||
"weight": 50,
|
||||
"birthday": "1998-06-01 10:00",
|
||||
"occupation": "设计师",
|
||||
"core_goals": ["显高", "显瘦", "显白"],
|
||||
"occasion_weights": {"通勤": 0.6, "休闲": 0.2, "约会": 0.2},
|
||||
"psyche_style": "自然",
|
||||
"preferred_colors": ["白色", "蓝色"],
|
||||
"disliked_styles": ["前卫"],
|
||||
"taboos": ["不接受深V"],
|
||||
}
|
||||
|
||||
|
||||
def _kb_colors_for(season: str) -> list[ColorEntry]:
|
||||
"""给定季型的完整色板（base 含中性色命名，覆盖 #19/#20/#21 三个维度）。"""
|
||||
return [
|
||||
ColorEntry(season_code=season, role="base", hex="#FFFFFF", name_cn="柔白"),
|
||||
ColorEntry(season_code=season, role="base", hex="#2B2B2B", name_cn="炭黑"),
|
||||
ColorEntry(season_code=season, role="base", hex="#6B6B6B", name_cn="中灰"),
|
||||
ColorEntry(season_code=season, role="accent", hex="#0F4C81", name_cn="宝蓝"),
|
||||
ColorEntry(season_code=season, role="avoid", hex="#E4E444", name_cn="芥末黄"),
|
||||
]
|
||||
|
||||
|
||||
def _rule(rule_key: str, category: str, conditions: dict[str, Any]) -> dsl.Rule:
|
||||
return dsl.Rule.model_validate(
|
||||
{
|
||||
"rule_key": rule_key,
|
||||
"category": category,
|
||||
"priority": 90,
|
||||
"conditions": conditions,
|
||||
"effects": {
|
||||
"recommend": [{"slot": "top", "attrs": {"shoulder": ["垫肩"]}}],
|
||||
"avoid": [{"slot": "dress", "attrs": {"color": ["荧光绿"]}}],
|
||||
"explanation": f"{rule_key} 命中",
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def _derived() -> dict[str, Any]:
|
||||
return await DeterministicVisionProvider().analyze(
|
||||
{"object_key": "unit/report-vision.jpg"}, _QUESTIONNAIRE
|
||||
)
|
||||
|
||||
|
||||
async def _verdict(derived: dict[str, Any]) -> engine.RuleVerdict:
|
||||
rules = [
|
||||
_rule("T16-BODY", "body_shape", {"body_shape": derived["body_shape"]}),
|
||||
_rule("T16-COLOR", "color", {"season": derived["season_code"]}),
|
||||
]
|
||||
return engine.evaluate(rules, build_rule_context(_QUESTIONNAIRE, derived))
|
||||
|
||||
|
||||
def _by_key(results: list[Any]) -> dict[str, Any]:
|
||||
return {r.key: r for r in results}
|
||||
|
||||
|
||||
def _assert_four_elements(result: Any) -> None:
|
||||
# 四要素 + 定位字段：一个都不能少（T-P3-06 验收原文）。
|
||||
for field in ("value", "confidence", "source", "evidence", "rule_hits", "status"):
|
||||
assert hasattr(result, field)
|
||||
assert 0.0 <= result.confidence <= 1.0
|
||||
assert result.evidence, f"{result.key} 的 evidence 不能为空"
|
||||
assert isinstance(result.rule_hits, list)
|
||||
|
||||
|
||||
# ── 32 键恒在场（红线） ─────────────────────────────────────────────────────
|
||||
|
||||
|
||||
async def test_full_inputs_produce_all_32_dimensions() -> None:
|
||||
derived = await _derived()
|
||||
results = evaluate_dimensions(
|
||||
_QUESTIONNAIRE, derived, await _verdict(derived), _kb_colors_for(derived["season_code"])
|
||||
)
|
||||
assert [r.number for r in results] == list(range(1, 33))
|
||||
assert [r.key for r in results] == [spec.key for spec in DIMENSIONS]
|
||||
for result in results:
|
||||
_assert_four_elements(result)
|
||||
assert result.status == "ok", f"{result.key} 应有产出：{result.evidence}"
|
||||
|
||||
|
||||
async def test_missing_everything_still_returns_32_keys_all_skipped() -> None:
|
||||
# 故意缺问卷 + 缺视觉：32 键全在、全部显式 skipped 且带原因（不允许缺键）。
|
||||
verdict = engine.evaluate((), dsl.RuleContext())
|
||||
results = evaluate_dimensions({}, {}, verdict)
|
||||
assert len(results) == 32
|
||||
for result in results:
|
||||
_assert_four_elements(result)
|
||||
assert result.status == "skipped"
|
||||
assert result.value is None
|
||||
assert result.confidence == 0.0
|
||||
assert result.source == "none"
|
||||
assert result.evidence # 缺失原因必须显式给出
|
||||
|
||||
|
||||
async def test_questionnaire_only_keeps_32_keys_with_vision_skipped() -> None:
|
||||
# 问卷版轻画像（§3.6）：视觉维度 skipped，问卷维度照常输出。
|
||||
verdict = engine.evaluate((), dsl.RuleContext())
|
||||
results = evaluate_dimensions(_QUESTIONNAIRE, {}, verdict)
|
||||
by_key = _by_key(results)
|
||||
assert len(results) == 32
|
||||
for key in ("body_shape", "face_shape", "skin_tone", "season_code", "hair_color"):
|
||||
assert by_key[key].status == "skipped"
|
||||
for key in ("gender", "birthday", "occupation", "psyche_style", "core_goals"):
|
||||
assert by_key[key].status == "ok"
|
||||
assert by_key[key].source == "questionnaire"
|
||||
assert by_key[key].confidence == 0.6
|
||||
|
||||
|
||||
async def test_assembly_is_deterministic() -> None:
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
colors = _kb_colors_for(derived["season_code"])
|
||||
first = evaluate_dimensions(_QUESTIONNAIRE, derived, verdict, colors)
|
||||
second = evaluate_dimensions(_QUESTIONNAIRE, derived, verdict, colors)
|
||||
assert [asdict(r) for r in first] == [asdict(r) for r in second]
|
||||
|
||||
|
||||
# ── 规则引擎联动（rule_hits / evidence） ───────────────────────────────────
|
||||
|
||||
|
||||
async def test_rule_hits_land_on_related_dimensions() -> None:
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
by_key = _by_key(evaluate_dimensions(_QUESTIONNAIRE, derived, verdict))
|
||||
|
||||
# 体型类命中 → #5 体型维度。
|
||||
assert "T16-BODY" in by_key["body_shape"].rule_hits
|
||||
assert "T16-BODY" in by_key["body_shape"].evidence
|
||||
|
||||
# 核心诉求/偏好与禁忌聚合全部命中（recommend/avoid 服务于诉求与禁忌）。
|
||||
for key in ("core_goals", "preferences_taboos"):
|
||||
assert set(by_key[key].rule_hits) >= {"T16-BODY", "T16-COLOR"}
|
||||
|
||||
|
||||
async def test_color_rules_carry_hits_even_when_kb_empty() -> None:
|
||||
# kb_color 为空 → 色板 skipped，但色彩规则命中链仍在（可解释性不丢）。
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
by_key = _by_key(evaluate_dimensions(_QUESTIONNAIRE, derived, verdict))
|
||||
for key in ("best_colors", "avoid_colors"):
|
||||
assert by_key[key].status == "skipped"
|
||||
assert "色板未录入" in by_key[key].evidence
|
||||
assert "T16-COLOR" in by_key[key].rule_hits
|
||||
|
||||
|
||||
async def test_rule_derived_taboos_when_questionnaire_empty() -> None:
|
||||
# 问卷没有偏好禁忌、但规则有 avoid → #30 以规则为源产出。
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
by_key = _by_key(evaluate_dimensions({}, derived, verdict))
|
||||
prefs = by_key["preferences_taboos"]
|
||||
assert prefs.status == "ok"
|
||||
assert prefs.source == "rule"
|
||||
assert prefs.confidence == 0.9
|
||||
assert any("回避" in taboo for taboo in prefs.value["taboos"])
|
||||
|
||||
|
||||
# ── 色板（kb_color）与知识库映射 ───────────────────────────────────────────
|
||||
|
||||
|
||||
async def test_color_palettes_from_kb_entries() -> None:
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
colors = _kb_colors_for(derived["season_code"])
|
||||
by_key = _by_key(evaluate_dimensions(_QUESTIONNAIRE, derived, verdict, colors))
|
||||
|
||||
best = by_key["best_colors"]
|
||||
assert best.status == "ok"
|
||||
assert len(best.value) <= 8 # 初级口径：最佳色板 8 色
|
||||
assert best.source == "derived"
|
||||
assert {c.hex for c in colors if c.role != "avoid"} >= {c["hex"] for c in best.value}
|
||||
|
||||
avoid = by_key["avoid_colors"]
|
||||
assert avoid.status == "ok"
|
||||
assert len(avoid.value) == 1 # 初级口径：避雷色 3 色为上限
|
||||
assert avoid.value[0]["name_cn"] == "芥末黄"
|
||||
|
||||
neutral = by_key["neutral_colors"]
|
||||
assert neutral.status == "ok"
|
||||
assert {c["name_cn"] for c in neutral.value} == {"柔白", "炭黑", "中灰"}
|
||||
|
||||
|
||||
async def test_other_season_colors_are_not_mixed_in() -> None:
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
# 只录他季色板：本季必须 skipped 而不是混入他季颜色（色板映射按季型联表）。
|
||||
other = "summer" if derived["season_code"] != "summer" else "winter"
|
||||
by_key = _by_key(evaluate_dimensions(_QUESTIONNAIRE, derived, verdict, _kb_colors_for(other)))
|
||||
assert by_key["best_colors"].status == "skipped"
|
||||
assert "色板未录入" in by_key["best_colors"].evidence
|
||||
assert by_key["neutral_colors"].status == "skipped"
|
||||
|
||||
|
||||
# ── 融合风格命名与序列化 ───────────────────────────────────────────────────
|
||||
|
||||
|
||||
async def test_fusion_style_naming_degrades_gracefully() -> None:
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
both = _by_key(evaluate_dimensions(_QUESTIONNAIRE, derived, verdict))["fusion_style_name"]
|
||||
assert both.status == "ok"
|
||||
assert both.source == "rule"
|
||||
assert both.confidence == 0.9
|
||||
assert "自然" in both.value # 心理风格侧参与了命名
|
||||
|
||||
only_questionnaire = evaluate_dimensions(
|
||||
_QUESTIONNAIRE, {}, engine.evaluate((), dsl.RuleContext())
|
||||
)
|
||||
fused = _by_key(only_questionnaire)["fusion_style_name"]
|
||||
assert fused.status == "ok"
|
||||
assert "自然" in fused.value
|
||||
|
||||
|
||||
async def test_results_serialize_to_json() -> None:
|
||||
derived = await _derived()
|
||||
verdict = await _verdict(derived)
|
||||
payload = [asdict(r) for r in evaluate_dimensions(_QUESTIONNAIRE, derived, verdict)]
|
||||
assert len(json.loads(json.dumps(payload, ensure_ascii=False))) == 32
|
||||
|
||||
|
||||
def test_build_rule_context_maps_fields() -> None:
|
||||
derived = {
|
||||
"body_shape": "A",
|
||||
"season_code": "autumn",
|
||||
"face_shape": "圆",
|
||||
"body_features": ["溜肩", "短颈"],
|
||||
}
|
||||
ctx = build_rule_context(_QUESTIONNAIRE, derived)
|
||||
assert ctx.body_shape == "A"
|
||||
assert ctx.season == "autumn"
|
||||
assert ctx.face_shape == "圆"
|
||||
assert ctx.features == ("溜肩", "短颈")
|
||||
assert ctx.goals == ("显高", "显瘦", "显白")
|
||||
assert ctx.style == "自然"
|
||||
assert ctx.user_taboos == ("不接受深V",)
|
||||
assert ctx.occasion == "通勤" # 占比最高的场景
|
||||
assert DIMENSION_BY_KEY["body_shape"].number == 5
|
||||
@@ -0,0 +1,87 @@
|
||||
"""维度字典：32 项完整性、编号连续、33/34 废弃不复用、初级口径机器可读（T-P3-06）。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from packages.report.dimensions import (
|
||||
BASIC_COVERAGE_VALUES,
|
||||
BASIC_COVERED_KEYS,
|
||||
BASIC_FULL_KEYS,
|
||||
DIMENSION_BY_KEY,
|
||||
DIMENSION_BY_NUMBER,
|
||||
DIMENSION_SOURCES,
|
||||
DIMENSIONS,
|
||||
)
|
||||
|
||||
#: 需求文档 §4 逐行统计出的初级 full 覆盖集（10 项）——本文件用硬编码预期
|
||||
#: 对照 BASIC_FULL_KEYS，任何一边漂移都会立刻暴露。
|
||||
_EXPECTED_BASIC_FULL = {
|
||||
"gender",
|
||||
"height_weight",
|
||||
"birthday",
|
||||
"body_shape",
|
||||
"face_shape",
|
||||
"skin_tone",
|
||||
"season_code",
|
||||
"psyche_style",
|
||||
"fusion_style_name",
|
||||
"core_goals",
|
||||
}
|
||||
|
||||
#: 初级有产出（full + label + partial）= 21 项，即验收「初级 21/32」口径。
|
||||
_EXPECTED_BASIC_COVERED_COUNT = 21
|
||||
|
||||
|
||||
def test_exactly_32_dimensions_with_consecutive_numbers() -> None:
|
||||
assert len(DIMENSIONS) == 32
|
||||
assert [spec.number for spec in DIMENSIONS] == list(range(1, 33))
|
||||
assert len(DIMENSION_BY_NUMBER) == 32
|
||||
assert len(DIMENSION_BY_KEY) == 32
|
||||
|
||||
|
||||
def test_keys_unique_and_stable() -> None:
|
||||
keys = [spec.key for spec in DIMENSIONS]
|
||||
assert len(set(keys)) == 32
|
||||
# 字典键与 kb/记忆系统词汇对齐的锚点抽查。
|
||||
for anchor in ("body_shape", "face_shape", "season_code"):
|
||||
assert anchor in DIMENSION_BY_KEY
|
||||
|
||||
|
||||
def test_retired_numbers_33_34_never_reused() -> None:
|
||||
# TD-31（2026-08-13）移除五行分布/幸运色，编号永久废弃。
|
||||
assert 33 not in DIMENSION_BY_NUMBER
|
||||
assert 34 not in DIMENSION_BY_NUMBER
|
||||
all_names = "".join(spec.name_cn for spec in DIMENSIONS)
|
||||
assert "五行" not in all_names
|
||||
assert "幸运色" not in all_names
|
||||
|
||||
|
||||
def test_enums_legal() -> None:
|
||||
for spec in DIMENSIONS:
|
||||
assert spec.basic_coverage in BASIC_COVERAGE_VALUES
|
||||
assert spec.source in DIMENSION_SOURCES
|
||||
assert spec.name_cn, spec.key
|
||||
|
||||
|
||||
def test_basic_full_keys_match_row_by_row_count() -> None:
|
||||
# 机器可读口径 vs 逐行统计：两边必须同时成立。
|
||||
assert BASIC_FULL_KEYS == _EXPECTED_BASIC_FULL
|
||||
assert (
|
||||
frozenset(spec.key for spec in DIMENSIONS if spec.basic_coverage == "full")
|
||||
== BASIC_FULL_KEYS
|
||||
)
|
||||
assert len(BASIC_FULL_KEYS) == 10
|
||||
|
||||
|
||||
def test_basic_covered_keys_is_21_of_32() -> None:
|
||||
derived = frozenset(spec.key for spec in DIMENSIONS if spec.basic_coverage != "none")
|
||||
assert derived == BASIC_COVERED_KEYS
|
||||
assert len(BASIC_COVERED_KEYS) == _EXPECTED_BASIC_COVERED_COUNT
|
||||
assert BASIC_FULL_KEYS <= BASIC_COVERED_KEYS
|
||||
|
||||
|
||||
def test_coverage_split_matches_requirement_stats() -> None:
|
||||
# 需求文档 §4 统计表：初级 full 10 / label+partial 11 / none 11，合计 32。
|
||||
counts = {"full": 0, "label": 0, "partial": 0, "none": 0}
|
||||
for spec in DIMENSIONS:
|
||||
counts[spec.basic_coverage] += 1
|
||||
assert counts == {"full": 10, "label": 3, "partial": 8, "none": 11}
|
||||
@@ -0,0 +1,142 @@
|
||||
"""确定性视觉提供者：同输入同输出、问卷驱动、枚举合法、工厂拒绝未知名。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from packages.report.dimensions import DIMENSION_BY_KEY
|
||||
from packages.report.vision import (
|
||||
BODY_SHAPES,
|
||||
FACE_SHAPES,
|
||||
SEASON_CODES,
|
||||
SKIN_TONES,
|
||||
STYLE_TYPES,
|
||||
DeterministicVisionProvider,
|
||||
VisionProvider,
|
||||
build_vision_provider,
|
||||
dimensions_touched,
|
||||
)
|
||||
from packages.shared.config import Settings
|
||||
from packages.shared.errors import AppError, ErrorCode
|
||||
|
||||
_PHOTO = {"object_key": "upload/report/photo-001.jpg"}
|
||||
|
||||
_QUESTIONNAIRE = {
|
||||
"gender": "女",
|
||||
"height": 165,
|
||||
"weight": 50,
|
||||
"birthday": "1998-06-01",
|
||||
}
|
||||
|
||||
|
||||
async def test_same_input_same_output() -> None:
|
||||
provider = DeterministicVisionProvider()
|
||||
first = await provider.analyze(_PHOTO, _QUESTIONNAIRE)
|
||||
second = await provider.analyze(dict(_PHOTO), dict(_QUESTIONNAIRE))
|
||||
assert first == second
|
||||
|
||||
|
||||
async def test_satisfies_protocol() -> None:
|
||||
assert isinstance(DeterministicVisionProvider(), VisionProvider)
|
||||
|
||||
|
||||
async def test_shape_contract_and_enum_values() -> None:
|
||||
features = await DeterministicVisionProvider().analyze(_PHOTO, _QUESTIONNAIRE)
|
||||
expected_keys = {
|
||||
"body_shape",
|
||||
"body_features",
|
||||
"body_ratio",
|
||||
"face_shape",
|
||||
"face_proportions",
|
||||
"facial_details",
|
||||
"face_depth",
|
||||
"skin_lab",
|
||||
"skin_tone",
|
||||
"face_hue",
|
||||
"face_lightness",
|
||||
"face_chroma",
|
||||
"face_contrast",
|
||||
"season_code",
|
||||
"season_detailed",
|
||||
"volume",
|
||||
"curve_straight",
|
||||
"motion",
|
||||
"gene_style_scores",
|
||||
"hair_status",
|
||||
"hair_color",
|
||||
}
|
||||
assert set(features) == expected_keys
|
||||
assert features["body_shape"] in BODY_SHAPES
|
||||
assert features["face_shape"] in FACE_SHAPES
|
||||
assert features["skin_tone"] in SKIN_TONES
|
||||
assert features["season_code"] in SEASON_CODES
|
||||
assert set(features["gene_style_scores"]) == set(STYLE_TYPES)
|
||||
# 色彩轴连续值域与标签三档。
|
||||
for axis in ("face_hue", "face_lightness", "face_chroma"):
|
||||
assert -1 <= features[axis]["value"] <= 1
|
||||
assert features[axis]["label"]
|
||||
assert 0 <= features["face_depth"] <= 10
|
||||
assert 0.58 <= features["body_ratio"] <= 0.68
|
||||
|
||||
|
||||
async def test_different_questionnaire_drives_different_body_shape() -> None:
|
||||
provider = DeterministicVisionProvider()
|
||||
slim = await provider.analyze(_PHOTO, {"height": 165, "weight": 45}) # BMI≈16.5
|
||||
heavy = await provider.analyze(_PHOTO, {"height": 165, "weight": 95}) # BMI≈34.9
|
||||
assert slim["body_shape"] in ("A", "H") # 过瘦区间：梨/矩形
|
||||
assert heavy["body_shape"] == "O" # 高 BMI：苹果形
|
||||
assert slim["body_shape"] != heavy["body_shape"]
|
||||
|
||||
|
||||
async def test_questionnaire_self_report_overrides_hash() -> None:
|
||||
# §3.6 降级路径：问卷自评是真实信号，优先于 mock 哈希。
|
||||
provider = DeterministicVisionProvider()
|
||||
plain = await provider.analyze(_PHOTO, {})
|
||||
self_reported = await provider.analyze(
|
||||
_PHOTO,
|
||||
{"body_shape_self": "X", "face_shape_self": "圆", "skin_tone_self": "粉一白"},
|
||||
)
|
||||
assert self_reported["body_shape"] == "X"
|
||||
assert self_reported["face_shape"] == "圆"
|
||||
assert self_reported["skin_tone"] == "粉一白"
|
||||
# 自评肤色反推冷轴：粉系=冷 → 季型落在冷季（夏/冬）。
|
||||
assert self_reported["season_code"] in ("summer", "winter")
|
||||
assert plain["season_code"] in SEASON_CODES
|
||||
|
||||
|
||||
async def test_different_photo_changes_features() -> None:
|
||||
provider = DeterministicVisionProvider()
|
||||
a = await provider.analyze({"object_key": "a.jpg"}, {})
|
||||
b = await provider.analyze({"object_key": "b.jpg"}, {})
|
||||
assert a != b
|
||||
|
||||
|
||||
async def test_missing_object_key_still_deterministic() -> None:
|
||||
provider = DeterministicVisionProvider()
|
||||
empty_meta: dict[str, str] = {}
|
||||
assert await provider.analyze(empty_meta, {}) == await provider.analyze({}, {})
|
||||
|
||||
|
||||
def test_dimensions_touched_maps_to_dimension_keys() -> None:
|
||||
features = {
|
||||
"body_shape": "A",
|
||||
"skin_lab": {"l": 60.0, "a": 1.0, "b": 20.0}, # 中间量，不是维度
|
||||
"gene_style_scores": {"自然": 9},
|
||||
}
|
||||
touched = dimensions_touched(features)
|
||||
assert touched == ["body_shape", "gene_style"]
|
||||
assert all(key in DIMENSION_BY_KEY for key in touched)
|
||||
|
||||
|
||||
def test_factory_default_is_deterministic() -> None:
|
||||
settings = Settings(_env_file=None)
|
||||
provider = build_vision_provider(settings)
|
||||
assert isinstance(provider, DeterministicVisionProvider)
|
||||
|
||||
|
||||
def test_factory_rejects_unknown_provider() -> None:
|
||||
settings = Settings(_env_file=None, report_vision_provider="rtmpose")
|
||||
with pytest.raises(AppError) as excinfo:
|
||||
build_vision_provider(settings)
|
||||
assert excinfo.value.code == ErrorCode.CONFIG_ERROR
|
||||
assert excinfo.value.data["report_vision_provider"] == "rtmpose"
|
||||
Reference in New Issue
Block a user