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@ -20,7 +20,7 @@
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// AI缺陷分类模型开关：当前暂无分类模型(Class.engine)，分类流程代码先预留；
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// 待分类模型部署后，将 AI_CLASS_MODEL_ENABLE 置 1，即可恢复 AI_QX_Class_Thread 中的AI分类流程
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#define AI_CLASS_MODEL_ENABLE 1
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#define AI_CLASS_MODEL_ENABLE 0
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// 把手提取/判定参数（下面宏是“未配置 JSON 时”的默认值；现场可在参数 JSON 的 Handle_Detect.form.handle_param
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// 下按注释标注的键名配置同名参数覆盖它们，按产品/现场情况调整，无需改代码重编译）：
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@ -107,13 +107,25 @@ static std::string ClsTrimStr(const std::string &str)
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return str.substr(begin, end - begin + 1);
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}
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// 分类映射配置：分类label -> 缺陷名称
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// 组装程序内默认缺陷名称列表（AI_CLass_QX_NAME_Names）
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static std::vector<std::string> BuildDefaultAIQXNames()
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{
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std::vector<std::string> vecNames;
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for (int i = 0; i < AI_CLass_QX_NAME_count; i++)
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{
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vecNames.push_back(AI_CLass_QX_NAME_Names[i]);
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}
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return vecNames;
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}
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// 分类映射配置：缺陷名称列表(索引=行序) + 分类label->缺陷名称 映射
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struct ClsLabelConfig
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{
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std::vector<std::string> vecNames; // 缺陷名称列表
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std::map<int, std::string> mapLabelName; // 分类 label -> 缺陷名称
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};
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// 读取分类映射配置（每行：label索引:缺陷名称；# 后内容忽略）
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// 读取分类映射配置（每行：label索引:缺陷名称；冒号后名称按行序构成缺陷名称列表；# 后内容忽略）
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static ClsLabelConfig ReadClsLabelConfig(const std::string &strFilePath)
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{
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ClsLabelConfig config;
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@ -144,12 +156,13 @@ static ClsLabelConfig ReadClsLabelConfig(const std::string &strFilePath)
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}
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continue;
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}
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// label索引:缺陷名称
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// label索引:缺陷名称（冒号后名称按行序构成缺陷名称列表）
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std::string strKey = ClsTrimStr(strLine.substr(0, nPos));
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std::string strValue = ClsTrimStr(strLine.substr(nPos + 1));
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if (!strKey.empty() && !strValue.empty())
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{
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config.mapLabelName[atoi(strKey.c_str())] = strValue;
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config.vecNames.push_back(strValue); // 行序即缺陷类型索引
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}
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}
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return config;
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@ -162,16 +175,27 @@ static const ClsLabelConfig &GetClsLabelConfig()
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return config;
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}
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// 获取缺陷名称列表：优先取配置文件的名称列表，读不到用程序内默认列表
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static const std::vector<std::string> &GetAIQXNameList()
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{
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static const std::vector<std::string> vecDefaultNames = BuildDefaultAIQXNames();
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const ClsLabelConfig &config = GetClsLabelConfig();
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if (!config.vecNames.empty())
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{
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return config.vecNames;
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}
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return vecDefaultNames;
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}
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// 缺陷名称 -> AI_CLass 枚举（找不到返回 count 表示无效）
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// 注意：按程序内 AI_CLass_QX_NAME_Names 的枚举顺序匹配，而不是映射文件的行序，
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// 避免 BM_cls.txt 行数/顺序与枚举不一致时类别错位
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static int ClsNameToAItype(const std::string &strName)
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{
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for (int i = 0; i < AI_CLass_QX_NAME_count; i++)
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const std::vector<std::string> &vecNames = GetAIQXNameList();
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for (size_t i = 0; i < vecNames.size(); i++)
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{
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if (strName == AI_CLass_QX_NAME_Names[i])
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if (strName == vecNames[i])
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{
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return i;
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return (int)i;
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}
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}
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return AI_CLass_QX_NAME_count;
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@ -197,31 +221,32 @@ static std::map<int, int> BuildLabelTypeMap()
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return mapLabelType;
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}
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// 获取分类 label 对应的 AI 缺陷类型：优先配置文件(只读一次)，读不到按 label 顺序兜底
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// 规则：
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// ① label 超出 [0, AI_CLass_QX_NAME_count)（含推理失败返回的负值）→ 视为 TBMark；
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// ② DarkAoHen(暗场凹痕)只有暗场相机存在：明场相机分到该类时按 QiTa(其他)处理。
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static int GetClsTypeByLabel(int nLabel, bool bIsDark)
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// 获取分类 label 对应的 AI 缺陷类型：优先配置文件(只读一次)，读不到用默认顺序
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static int GetClsTypeByLabel(int nLabel)
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{
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// ① 超出程序的类别表（模型类别数与程序不一致）→ 统一按铜箔标记处理
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if (nLabel < 0 || nLabel >= AI_CLass_QX_NAME_count)
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{
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return AI_CLass_QX_NAME_TBMark;
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}
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// 未被映射文件覆盖时，按程序内 AI_CLass_QX_NAME_Names 的顺序与 label 对应
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int cls_num = nLabel;
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static const std::map<int, int> mapLabelType = BuildLabelTypeMap();
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std::map<int, int>::const_iterator it = mapLabelType.find(nLabel);
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if (it != mapLabelType.end())
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{
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cls_num = it->second;
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return it->second;
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}
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// ② 暗场凹痕只有暗场检出：明场分到该类时按"其他"处理
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if (cls_num == AI_CLass_QX_NAME_DarkAoHen && !bIsDark)
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// 映射文件未读到、或该 label 未在文件中配置时，按程序内默认顺序兜底（避免文件缺失/不全导致异常）
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int cls_num = AI_CLass_QX_NAME_count;
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switch (nLabel)
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{
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cls_num = AI_CLass_QX_NAME_QiTa;
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case 0:
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cls_num = AI_CLass_QX_NAME_CGMark;
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break;
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case 1:
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cls_num = AI_CLass_QX_NAME_BQBMark;
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break;
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case 2:
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cls_num = AI_CLass_QX_NAME_TBMark;
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break;
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default:
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cls_num = AI_CLass_QX_NAME_CGMark;
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break;
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}
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return cls_num;
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}
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@ -3495,34 +3520,8 @@ int ImgCheckAnalysisy::AI_QX_Class_Thread()
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pAIDet = AI_Factory->Class;
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}
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// 明/暗场判定：相机名含 "dark" 视为暗场（与 ALIGN_REUSE_*、暗场检测模型判定一致）
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std::string strProductName, strCameraName;
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GetProductCameraStr(strProductName, strCameraName);
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const bool bIsDark = !ReplaceDarkWithBright(strCameraName).empty();
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int deal_image_width = pAIDet ? pAIDet->input_0.width : 0;
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int deal_image_height = pAIDet ? pAIDet->input_0.height : 0;
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// 分类模型不可用(未加载/未初始化/输入尺寸异常)：全部按 TBMark 兑底（与无分类模型时行为一致）；
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// 否则裁剪/推理会失败，且 blob 的 AIclasstype 保持默认值 0 会被当成“支架偏移”上报
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if (!pAIDet || !pAIDet->m_bInitSuccess || deal_image_width <= 0 || deal_image_height <= 0)
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{
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int defaultqx = AIClassTypeToConfigType(AI_CLass_QX_NAME_TBMark);
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for (int i = 0; i < blobs.blobCount; i++)
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{
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blobs.blobTab[i].AIclasstype = defaultqx;
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}
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m_pdetlog->AddCheckstr(PrintLevel_0, DET_LOG_LEVEL_3, "QX_Class",
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" class model not ready(m_bInitSuccess %d, input [w %d h %d]), all %d qx -> TBMark(%d)",
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pAIDet ? (int)pAIDet->m_bInitSuccess : 0, deal_image_width, deal_image_height, blobs.blobCount, defaultqx);
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return 0;
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}
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// 先统一置为无效类型：分类未提交/失败(裁剪失败、label 非法等)的 blob 不会落到某个真实缺陷类型上
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for (int i = 0; i < blobs.blobCount; i++)
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{
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blobs.blobTab[i].AIclasstype = CONFIG_QX_NAME_count;
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}
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int deal_image_width = pAIDet->input_0.width;
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int deal_image_height = pAIDet->input_0.height;
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const int totalTasks = blobs.blobCount;
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int submitted = 0;
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@ -3539,8 +3538,8 @@ int ImgCheckAnalysisy::AI_QX_Class_Thread()
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ERROR_DOTS_BLOB_DATA *pblob = &blobs.blobTab[submitted];
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if (pblob->ErrType == ERR_TYPE_2)
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{
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// ERR_TYPE_2 不参与分类，按默认类别(铜箔标记)处理
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pblob->AIclasstype = AIClassTypeToConfigType(AI_CLass_QX_NAME_TBMark);
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// ERR_TYPE_2不参与分类，按默认AI类别(把手标记)处理
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pblob->AIclasstype = CONFIG_QX_NAME_cell_CGMark;
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submitted++;
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completed++;
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continue;
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@ -3568,9 +3567,6 @@ int ImgCheckAnalysisy::AI_QX_Class_Thread()
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}
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else
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{
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m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "QX_Class",
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"blob idx %d crop class img failed(re %d) roi[%d %d %d %d] -> skip",
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submitted, re, roi.x, roi.y, roi.width, roi.height);
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completed++;
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}
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@ -3586,24 +3582,8 @@ int ImgCheckAnalysisy::AI_QX_Class_Thread()
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int temClass = result->cls_label;
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// label 越界(含推理失败返回的负值)：按规则统一视为 TBMark
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if (temClass < 0 || temClass >= AI_CLass_QX_NAME_count)
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{
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m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "QX_Class",
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"blob idx %d invalid class label %d (valid 0~%d), use TBMark",
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result->id, temClass, AI_CLass_QX_NAME_count - 1);
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}
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// 分类 label 映射：优先读映射配置文件（BM_cls.txt），读不到按 label 顺序；
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// 明场分到暗场凹痕(DarkAoHen)时按 QiTa 处理
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int cls_num = GetClsTypeByLabel(temClass, bIsDark);
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// 明细日志：原始 label -> 最终类别（仅 -s / 详细日志级别输出）
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m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "QX_Class",
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"blob idx %d label %d -> %s score %.3f (dark %d)",
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result->id, temClass,
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(cls_num >= 0 && cls_num < AI_CLass_QX_NAME_count) ? AI_CLass_QX_NAME_Names[cls_num].c_str() : "invalid",
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result->cls_score, (int)bIsDark);
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// 分类 label 映射：优先读映射配置文件（BM_cls.txt），读不到用默认顺序
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int cls_num = GetClsTypeByLabel(temClass);
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// std::string strclassName = AI_CLass_QX_NAME_Names[cls_num];
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// printf("AI Class num %d = %s %f\n", cls_num, strclassName.c_str(), result->cls_score);
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@ -3876,25 +3856,21 @@ int ImgCheckAnalysisy::DrawResult_Step_1()
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int ImgCheckAnalysisy::AIClassTypeToConfigType(int nAIQXType)
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{
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// AI 分类类别 -> 缺陷类型；支架偏移/支架缺失/把手缺失为传统算法检出，不在本表内
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// 未知类别（含 AI_CLass_QX_NAME_count 及越界值）统一按铜箔标记处理
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int resultError_type = CONFIG_QX_NAME_cell_TBMark;
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// 只有主检测+分类的缺陷在映射表中；支架偏移/支架缺失/把手缺失(传统检出)、铜箔标签(标签检测)不走AI分类
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int resultError_type = CONFIG_QX_NAME_cell_CGMark;
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switch (nAIQXType)
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{
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case AI_CLass_QX_NAME_DarkAoHen:
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resultError_type = CONFIG_QX_NAME_cell_DarkAoHen;
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case AI_CLass_QX_NAME_CGMark:
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resultError_type = CONFIG_QX_NAME_cell_CGMark;
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break;
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case AI_CLass_QX_NAME_BQBMark:
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resultError_type = CONFIG_QX_NAME_cell_BQBMark;
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break;
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case AI_CLass_QX_NAME_TBMark:
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resultError_type = CONFIG_QX_NAME_cell_TBMark;
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break;
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case AI_CLass_QX_NAME_QiPao:
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resultError_type = CONFIG_QX_NAME_cell_QiPao;
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break;
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case AI_CLass_QX_NAME_TBBQ:
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resultError_type = CONFIG_QX_NAME_cell_TBBQ;
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break;
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case AI_CLass_QX_NAME_QiTa:
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resultError_type = CONFIG_QX_NAME_cell_QiTa;
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case AI_CLass_QX_NAME_count: // 无效类别（分类映射配置中不存在）：不参与缺陷分析
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resultError_type = CONFIG_QX_NAME_count;
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break;
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default:
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break;
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@ -3934,20 +3910,12 @@ int ImgCheckAnalysisy::BLobToDetResult()
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int config_qx_type = blobs.blobTab[i].AIclasstype;
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if (config_qx_type < 0 || config_qx_type >= CONFIG_QX_NAME_count)
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{
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// AI 分类无对应缺陷类型（无效类别/分类失败）时，不参与缺陷结果分析
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// AI 分类无对应缺陷类型（无效类别）时，不参与缺陷结果分析
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m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "BLobToDetResult",
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"blob idx %d invalid AIclasstype %d skip", i, config_qx_type);
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continue;
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}
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// 暗场凹痕目前不做检测：分到该类的 blob 直接按 OK 处理（不产生缺陷条目，仅记日志）
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if (config_qx_type == CONFIG_QX_NAME_cell_DarkAoHen)
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{
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m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "BLobToDetResult",
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"blob idx %d AIclasstype DarkAoHen -> OK (not detected yet), skip", i);
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continue;
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|
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}
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float JudgArea = blobs.blobTab[i].JudgArea;
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float fsecondArea = JudgArea;
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std::string qx_name = CONFIG_QX_NAME_Names[config_qx_type];
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