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"""
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ML配置加载器 — 解析 /usr/local/Models/ml.json
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直接翻译 C++ UrineCheck14::initconfig() + UrineCheck_Class::initconfig()
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Author: 小月
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Date: 2026-05-13
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"""
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import json
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import logging
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from collections import defaultdict
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from typing import Dict, List, Optional, Callable, Tuple
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import cv2
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import numpy as np
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logger = logging.getLogger("ml_config")
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# 常量（与C++一致）
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MAX_PAPER_TYPE = 21
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MAX_PAPER_BLOCK = 15
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MAX_ALG_TAG = 8
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# 各检测项在 ml.json 中的 name → 结果索引函数类型映射
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# algorithm=0 → Red, algorithm=1 → Green/Blue (原始)
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ITEM_FUNC_MAP = {
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"uro": "uro",
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"glu": "glu",
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"ket": "ket",
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"bil": "bil",
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"pro": "pro",
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"nit": "nit",
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"ph": "ph",
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"bld": "bld",
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"sg": "sg",
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"wbc": "wbc",
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"vc": "vc",
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"cre": "cre",
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"uca": "uca",
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"mau": "mau",
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}
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class MLConfigLoader:
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"""ML模型配置加载器 — 全局单例"""
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def __init__(self, config_path: str = "/usr/local/Models/ml.json"):
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self.config_path = config_path
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# 三维查表: [paperType][position][rawIndex] → outputIndex
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self.itemsResultIdxMapTbl: List[List[List[int]]] = [
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[[-1] * MAX_ALG_TAG for _ in range(MAX_PAPER_BLOCK)]
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for _ in range(MAX_PAPER_TYPE)
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]
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# 二维: [paperType][position] → 输出id
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self.itemsStatIds: List[List[int]] = [
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[-1] * MAX_PAPER_BLOCK for _ in range(MAX_PAPER_TYPE)
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]
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# [paperType][position] → 已加载的 OpenCV RTrees 模型
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self.models: List[Dict[int, cv2.ml.RTrees]] = [
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{} for _ in range(MAX_PAPER_TYPE)
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]
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# [paperType][position] → 结果索引函数类型名
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# "uro"|"glu"|...|"red_uro"|"red_glu"|...|"pet_glu"|"pet_sg"
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self.result_funcs: List[Dict[int, str]] = [
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{} for _ in range(MAX_PAPER_TYPE)
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]
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# PRO/MAU/SG/pH 在各 paperType 上的 position
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self.positionsPro: List[int] = [-1] * MAX_PAPER_TYPE
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self.positionsMau: List[int] = [-1] * MAX_PAPER_TYPE
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self.positionsSg: List[int] = [-1] * MAX_PAPER_TYPE
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self.positionsPh: List[int] = [-1] * MAX_PAPER_TYPE
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# 文件名缓存（避免重复加载同一个 .data 文件）
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self._file_cache: Dict[str, cv2.ml.RTrees] = {}
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self._loaded = False
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def load(self) -> bool:
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"""加载 ml.json 并初始化所有配置"""
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if self._loaded:
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return True
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try:
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with open(self.config_path, 'r') as f:
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config = json.load(f)
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except Exception as e:
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logger.error(f"无法加载 {self.config_path}: {e}")
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return False
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ml = config.get("ml", [])
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if not ml:
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logger.error("ml.json 中未找到 'ml' 数组")
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return False
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for item in ml:
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name = item.get("name", "")
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models_list = item.get("model", [])
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if name not in ITEM_FUNC_MAP:
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logger.warning(f"未知检测项目: {name}")
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continue
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for paper_type, block in enumerate(models_list):
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if paper_type >= MAX_PAPER_TYPE:
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break
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position = block.get("positon", -1)
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if position < 0:
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continue
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item_id = block.get("id", 0)
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filepath = block.get("filepath", "")
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algorithm = block.get("algorithm", 1)
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indexmap_str = block.get("indexmap", "0")
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# 记录 PRO/MAU/SG/pH 的 position
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if name == "pro":
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self.positionsPro[paper_type] = position
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elif name == "mau":
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self.positionsMau[paper_type] = position
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elif name == "sg":
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self.positionsSg[paper_type] = position
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elif name == "ph":
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self.positionsPh[paper_type] = position
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# 记录 output id
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self.itemsStatIds[paper_type][position] = item_id
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# 解析 indexmap
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index_map = [int(x.strip()) for x in indexmap_str.split(",")]
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for k, val in enumerate(index_map):
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if k < MAX_ALG_TAG:
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self.itemsResultIdxMapTbl[paper_type][position][k] = val
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# 加载模型文件（带缓存）
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if filepath not in self._file_cache:
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try:
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model = cv2.ml.RTrees_load(filepath)
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if model is None or model.empty():
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logger.error(f"模型加载失败: {filepath}")
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continue
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self._file_cache[filepath] = model
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except Exception as e:
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logger.error(f"模型加载异常 {filepath}: {e}")
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continue
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self.models[paper_type][position] = self._file_cache[filepath]
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# 确定结果索引函数
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func_type = self._resolve_func_type(name, algorithm)
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self.result_funcs[paper_type][position] = func_type
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self._loaded = True
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count = sum(len(m) for m in self.models)
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logger.info(f"ML配置加载完成: {count} 个模型, {len(self._file_cache)} 个唯一文件")
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return True
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def _resolve_func_type(self, name: str, algorithm: int) -> str:
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"""根据试纸类型确定结果索引函数名"""
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base = ITEM_FUNC_MAP[name]
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if algorithm == 0:
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# 红色试纸
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return f"red_{base}"
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elif algorithm == 2:
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# PET 试纸 (仅 glu/sg 有)
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return f"pet_{base}"
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else:
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# 绿色/蓝色试纸（原始）
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return base
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# ---- 便捷查询方法 ----
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def get_model(self, paper_type: int, position: int) -> Optional[cv2.ml.RTrees]:
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if paper_type < 0 or paper_type >= MAX_PAPER_TYPE:
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return None
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return self.models[paper_type].get(position)
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def get_func_type(self, paper_type: int, position: int) -> Optional[str]:
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if paper_type < 0 or paper_type >= MAX_PAPER_TYPE:
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return None
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return self.result_funcs[paper_type].get(position)
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def get_stat_id(self, paper_type: int, position: int) -> int:
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if paper_type < 0 or paper_type >= MAX_PAPER_TYPE:
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return -1
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return self.itemsStatIds[paper_type][position]
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def get_output_index(self, paper_type: int, position: int, raw_index: int) -> int:
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"""通过 indexmap 将算法输出index映射为最终输出index"""
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if paper_type < 0 or paper_type >= MAX_PAPER_TYPE:
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return 0
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if position < 0 or position >= MAX_PAPER_BLOCK:
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return 0
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if raw_index < 0 or raw_index >= MAX_ALG_TAG:
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return 0
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mapped = self.itemsResultIdxMapTbl[paper_type][position][raw_index]
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return mapped if mapped >= 0 else 0
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def get_max_mau(self, paper_type: int) -> int:
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"""MAU最大等级: red=3, others=2"""
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return 2 if paper_type == 0 else 3
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def get_max_sg(self, paper_type: int) -> int:
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"""SG最大等级: red=6, others=5"""
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return 5 if paper_type == 0 else 6
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def has_model_at(self, paper_type: int, position: int) -> bool:
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if paper_type < 0 or paper_type >= MAX_PAPER_TYPE:
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return False
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return position in self.models[paper_type]
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