update 初步优化把手检测

AlgorithmModule/include/CheckUtil.hpp

@@ -25,6 +25,26 @@
 #include <thread>
 #include <opencv2/opencv.hpp>
 using namespace std;
+
+// 掩膜主体（近似矩形）四周凸起（如把手）的描述
+struct Mask_Protrusion_
+{
+	int nSide;        // 所在边：0 左 1 右 2 上 3 下
+	cv::Rect roi;     // 凸起外接矩形（输入图坐标系，已裁剪到图像范围内）
+	float fLen;       // 沿该边的长度（像素）
+	float fDepth;     // 伸出主体的深度（像素）
+	float fArea;      // 凸起面积（roi 内掩膜像素数）
+	float fFillRatio; // roi 内掩膜像素占比（用于判定是否"类似矩形块"）
+	Mask_Protrusion_()
+	{
+		nSide = 0;
+		fLen = 0.0f;
+		fDepth = 0.0f;
+		fArea = 0.0f;
+		fFillRatio = 0.0f;
+	}
+};
+
 class CheckUtil
 {
 public:
@@ -58,6 +78,14 @@ public:
 	// 找图片的最大外轮廓
 	static std::vector<cv::Point> getLargestContourROI(const cv::Mat &binaryImg, bool &found);
 
+	// 抠出掩膜四周的凸起：主体近似矩形，凸起在该边表现为边界值的一次突变(进入)与一次反向突变(离开)
+	// binMask：掩膜（非零为前景，兼容 0/1 与 0/255）；protrusions：输出的凸起列表
+	// nJumpThresh：边界突变阈值(输入图像素)；nMinRun：凸起最小长度(输入图像素)；nMinDepth：凸起最小深度(输入图像素)
+	// nMaxRun：凸起最大长度(输入图像素)，<=0 时取 掩膜短边/3
+	// 返回：0 成功、1 掩膜为空、2 无有效前景
+	static int GetMaskProtrusions(const cv::Mat &binMask, std::vector<Mask_Protrusion_> &protrusions,
+								  int nJumpThresh = 40, int nMinRun = 40, int nMinDepth = 40, int nMaxRun = 0);
+
 	static std::string GetRectString(cv::Rect rect);
 	//创建目录
 	static int CreateDir(const std::string &dir);

AlgorithmModule/include/ImgCheckAnalysisy.hpp

@@ -49,6 +49,24 @@ enum AT_THRESHOLD_TYPE_
 
 // 全局静态变量， 记录图像灰度值异常累计数量
 static int g_nImgBrightnessErrorCount = 0;
+
+// 把手候选：bigmask 凸起 + 实测特征（后续增加面积/灰阶等判定时直接用这些实测值）
+struct Handle_Candidate_
+{
+	Mask_Protrusion_ protr; // 凸起几何信息（roi / 沿边长度 / 深度 / 面积 / 填充率）
+	int nLen;               // 沿边长度（像素）
+	int nDepth;             // 垂直深度（像素）
+	float fGrayDiff;        // 凸起区域与周边背景的平均灰度差（预留灰阶判定）
+	bool bHandle;           // 是否为把手
+	Handle_Candidate_()
+	{
+		nLen = 0;
+		nDepth = 0;
+		fGrayDiff = 0.0f;
+		bHandle = false;
+	}
+};
+
 class ImgCheckAnalysisy : public ImgCheckBase
 {
 
@@ -117,6 +135,13 @@ private:
     // 检测
     int CheckRun();
     int AI_Edge(const cv::Mat &img, cv::RotatedRect &outerRoi, cv::RotatedRect &innerRoi, std::vector<cv::RotatedRect> &tagroiList);
+    // 用 bigmask 检测把手（bigmask 主体近似矩形，把手是四周凸起的矩形块）
+    int CheckHandleByBigMask(const cv::Mat &img, const cv::Mat &bigmask);
+    // 判断凸起是否为把手：参数里每一项开启的判定条件（尺寸/填充率/面积/灰阶…）都满足才算把手
+    bool IsHandleProtrusion(const Handle_Candidate_ &cand, const Handle_Check_Param &param,
+                            const cv::Rect &handleBox, const cv::Size &maskSize);
+    // 计算凸起区域与周边背景的平均灰度差（灰阶判定的预留量测值，img 需为单通道）
+    float CalProtrusionGrayDiff(const cv::Mat &grayImg, const cv::Rect &roi);
     // 计算产品尺寸
     int CalProductSize();
     // 图片预处理
@@ -289,6 +314,14 @@ private:
     cv::RotatedRect m_outer_rroi;
 	cv::RotatedRect m_inner_rroi;
     std::vector<cv::RotatedRect> m_tag_roiList;
+
+	// 把手检测结果（bigmask 四周凸起）
+	std::vector<Handle_Candidate_> m_HandleCandList; // bigmask 四周抠出的凸起候选 + 实测特征
+	std::vector<cv::Rect> m_HandleRoiList;			// 判定为把手的凸起 roi（检测图坐标）
+	cv::Rect m_HandleBoxParamImg;					// 参数里绘制的把手区域（检测图坐标）
+	bool m_bHandleDetSucc;							// 本次是否完成把手检测
+	bool m_bHandleFound;							// 是否检测到把手
+	bool m_bHandleExpect;							// 参数中是否绘制了把手区域（有期望）
 };
 
 #endif

AlgorithmModule/src/CheckUtil.cpp

@@ -693,3 +693,271 @@ cv::Point2f CheckUtil::transformPoint(const cv::Point2f &point, const cv::Mat &t
 
 	return cv::Point2f(result_mat.at<double>(0), result_mat.at<double>(1));
 }
+
+int CheckUtil::GetMaskProtrusions(const cv::Mat &binMask, std::vector<Mask_Protrusion_> &protrusions,
+								  int nJumpThresh, int nMinRun, int nMinDepth, int nMaxRun)
+{
+	protrusions.clear();
+	if (binMask.empty())
+	{
+		return 1;
+	}
+
+	// 1、二值化（兼容 0/1 与 0/255 掩膜）
+	cv::Mat gray;
+	if (binMask.channels() != 1)
+	{
+		cv::cvtColor(binMask, gray, cv::COLOR_BGR2GRAY);
+	}
+	else
+	{
+		gray = binMask;
+	}
+	cv::Mat maskFull;
+	cv::threshold(gray, maskFull, 0, 255, cv::THRESH_BINARY);
+
+	// 1.1 大掩膜先降采样再分析：凸起尺寸远大于降采样误差，可显著降低耗时
+	int nScale = 1;
+	while (nScale < 4 && std::max(maskFull.cols, maskFull.rows) / nScale > 2000)
+	{
+		nScale *= 2;
+	}
+	cv::Mat mask;
+	if (nScale > 1)
+	{
+		cv::resize(maskFull, mask, cv::Size(maskFull.cols / nScale, maskFull.rows / nScale), 0, 0, cv::INTER_NEAREST);
+	}
+	else
+	{
+		mask = maskFull;
+	}
+	// 阈值按降采样比例换算到工作图尺度
+	nJumpThresh = std::max(2, nJumpThresh / nScale);
+	nMinRun = std::max(3, nMinRun / nScale);
+	nMinDepth = std::max(2, nMinDepth / nScale);
+
+	// 2、只保留最大连通域，避免背景杂物参与边界统计
+	cv::Mat labels, stats, centroids;
+	int nLabels = cv::connectedComponentsWithStats(mask, labels, stats, centroids, 8, CV_32S);
+	if (nLabels <= 1)
+	{
+		return 2;
+	}
+	int nMainIdx = 1;
+	int nMainArea = 0;
+	for (int i = 1; i < nLabels; i++)
+	{
+		int nArea = stats.at<int>(i, cv::CC_STAT_AREA);
+		if (nArea > nMainArea)
+		{
+			nMainArea = nArea;
+			nMainIdx = i;
+		}
+	}
+
+	// 3、裁剪到主体外接矩形，缩小后续扫描范围
+	cv::Rect boxMain(stats.at<int>(nMainIdx, cv::CC_STAT_LEFT), stats.at<int>(nMainIdx, cv::CC_STAT_TOP),
+					 stats.at<int>(nMainIdx, cv::CC_STAT_WIDTH), stats.at<int>(nMainIdx, cv::CC_STAT_HEIGHT));
+	boxMain &= cv::Rect(0, 0, mask.cols, mask.rows);
+	if (boxMain.width <= 0 || boxMain.height <= 0)
+	{
+		return 2;
+	}
+	cv::Mat sub = (labels(boxMain) == nMainIdx); // 0/255
+	const int H = sub.rows;
+	const int W = sub.cols;
+	if (nMaxRun <= 0)
+	{
+		nMaxRun = std::max(100, std::min(H, W) / 3);
+	}
+
+	// 4、逐边抠凸起：
+	//    主体近似矩形，矩形边上的凸起会让该边的"边界值"先突变到外侧(进入凸起)、再突变回主体(离开凸起)；
+	//    区间内的边界值相对"首尾边界值线性插值"得到的基线持续外偏，即判定为一个凸起。
+	for (int nSide = 0; nSide < 4; nSide++)
+	{
+		const bool bRowScan = (nSide < 2);					  // 左右边按行扫描，上下边按列扫描
+		const int nLine = bRowScan ? H : W;					  // 扫描的行数/列数
+		const int nDir = (nSide == 0 || nSide == 2) ? 1 : -1; // 凸起内边界值变小(左侧/上侧)取 +1，变大取 -1
+
+		// 4.1 从每条 行/列 的外侧向内找第一个前景像素，得到该边的边界值 profile
+		std::vector<int> posList; // 有前景的行号/列号
+		std::vector<int> valList; // 对应的边界值
+		posList.reserve(nLine);
+		valList.reserve(nLine);
+		for (int i = 0; i < nLine; i++)
+		{
+			int nV = -1;
+			if (nSide == 0) // 左：自左向右
+			{
+				const uchar *p = sub.ptr<uchar>(i);
+				for (int x = 0; x < W; x++)
+				{
+					if (p[x])
+					{
+						nV = x;
+						break;
+					}
+				}
+			}
+			else if (nSide == 1) // 右：自右向左
+			{
+				const uchar *p = sub.ptr<uchar>(i);
+				for (int x = W - 1; x >= 0; x--)
+				{
+					if (p[x])
+					{
+						nV = x;
+						break;
+					}
+				}
+			}
+			else if (nSide == 2) // 上：自上向下
+			{
+				for (int y = 0; y < H; y++)
+				{
+					if (sub.ptr<uchar>(y)[i])
+					{
+						nV = y;
+						break;
+					}
+				}
+			}
+			else // 下：自下向上
+			{
+				for (int y = H - 1; y >= 0; y--)
+				{
+					if (sub.ptr<uchar>(y)[i])
+					{
+						nV = y;
+						break;
+					}
+				}
+			}
+			if (nV >= 0)
+			{
+				posList.push_back(i);
+				valList.push_back(nV);
+			}
+		}
+
+		const int n = (int)valList.size();
+		if (n < nMinRun + 2)
+		{
+			continue;
+		}
+
+		// 4.2 配对"进入/离开"突变，扣除该边上的凸起
+		int nUsedTo = -1; // 已统计过的凸起末端，避免同一个凸起重复输出
+		for (int s = 0; s + 1 < n; s++)
+		{
+			if (s <= nUsedTo)
+			{
+				continue;
+			}
+			// 进入凸起：边界值朝外侧突变
+			if ((valList[s + 1] - valList[s]) * nDir >= -nJumpThresh)
+			{
+				continue;
+			}
+			// 离开凸起：在其后 nMaxRun 范围内找第一个反向突变
+			int e = -1;
+			for (int j = s + nMinRun; j + 1 < n && j - s <= nMaxRun; j++)
+			{
+				if ((valList[j + 1] - valList[j]) * nDir > nJumpThresh)
+				{
+					e = j;
+					break;
+				}
+			}
+			if (e < 0)
+			{
+				continue;
+			}
+
+			// 4.3 区间内的边界值应持续超出"首尾边界值线性插值"得到的基线
+			const int kk = e - s;
+			int nDevOk = 0;
+			float fMaxDev = 0.0f;
+			int nLow = valList[s + 1];
+			int nHigh = valList[s + 1];
+			for (int i = 1; i <= kk; i++)
+			{
+				float fBase = valList[s] + (valList[e + 1] - valList[s]) * (float)i / (float)(kk + 1);
+				float fDev = (fBase - valList[s + i]) * nDir;
+				fMaxDev = std::max(fMaxDev, fDev);
+				if (fDev >= nMinDepth)
+				{
+					nDevOk++;
+				}
+				nLow = std::min(nLow, valList[s + i]);
+				nHigh = std::max(nHigh, valList[s + i]);
+			}
+			if (fMaxDev < nMinDepth)
+			{
+				continue;
+			}
+			// 凸起内大部分采样点都要明显外偏，避免把主体的斜边/圆角当成凸起
+			if (nDevOk < (int)(kk * 0.8f))
+			{
+				continue;
+			}
+
+			// 4.4 计算凸起外接矩形（先算在 sub 图上的位置）
+			const int nLen = posList[e] - posList[s + 1] + 1; // 沿该边的长度
+			cv::Rect roiSub;
+			if (nSide == 0 || nSide == 2) // 左/上：外侧取小值，主体边缘取大值
+			{
+				int nOut = nLow;
+				int nInner = std::max(valList[s], valList[e + 1]);
+				if (nSide == 0)
+				{
+					roiSub = cv::Rect(nOut, posList[s + 1], nInner - nOut + 1, nLen);
+				}
+				else
+				{
+					roiSub = cv::Rect(posList[s + 1], nOut, nLen, nInner - nOut + 1);
+				}
+			}
+			else // 右/下：外侧取大值，主体边缘取小值
+			{
+				int nOut = nHigh;
+				int nInner = std::min(valList[s], valList[e + 1]);
+				if (nSide == 1)
+				{
+					roiSub = cv::Rect(nInner, posList[s + 1], nOut - nInner + 1, nLen);
+				}
+				else
+				{
+					roiSub = cv::Rect(posList[s + 1], nInner, nLen, nOut - nInner + 1);
+				}
+			}
+			roiSub &= cv::Rect(0, 0, W, H);
+			if (roiSub.width <= 0 || roiSub.height <= 0)
+			{
+				continue;
+			}
+
+			Mask_Protrusion_ protr;
+			protr.nSide = nSide;
+			// 从工作图尺度还原到输入图尺度
+			protr.roi = cv::Rect((roiSub.x + boxMain.x) * nScale, (roiSub.y + boxMain.y) * nScale,
+								 roiSub.width * nScale, roiSub.height * nScale);
+			protr.roi &= cv::Rect(0, 0, binMask.cols, binMask.rows);
+			if (protr.roi.width <= 0 || protr.roi.height <= 0)
+			{
+				continue;
+			}
+			protr.fLen = (float)nLen * nScale;
+			protr.fDepth = fMaxDev * nScale;
+			int nMaskArea = cv::countNonZero(sub(roiSub));
+			protr.fArea = (float)nMaskArea * nScale * nScale;
+			protr.fFillRatio = (float)nMaskArea / (float)(roiSub.width * roiSub.height);
+
+			protrusions.push_back(protr);
+			nUsedTo = e; // 该凸起区间不再重复统计
+		}
+	}
+
+	return 0;
+}

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -12,6 +12,19 @@
 #include <omp.h>
 #include "AICommonDefine.h"
 #include <algorithm>
+
+// 把手判定参数：
+// 1、有参数（参数里绘制了把手区域）时，凸起"沿边长度"与手绘区域对应边长度的比值范围
+#define HANDLE_LEN_MIN_RATIO 0.75f
+#define HANDLE_LEN_MAX_RATIO 1.5f
+// 2、凸起"垂直深度"与手绘区域对应边宽度的比值范围（凸起被图像边界截断时不校深度）
+#define HANDLE_DEPTH_MIN_RATIO 0.8f
+#define HANDLE_DEPTH_MAX_RATIO 1.5f
+// 3、无参数时，按掩膜短边推算沿边长度/垂直深度范围（最小/最大占比）
+#define HANDLE_DEFAULT_MIN_RATIO 0.03f
+#define HANDLE_DEFAULT_MAX_RATIO 0.40f
+// 4、凸起填充率下限、面积/灰阶等判定参数的默认值都在 Handle_Check_Param（CheckConfigDefine.h）里
+
 // 用于排序轮廓的比较函数
 static bool compareContourAreas(const vector<Point> &contour1, const vector<Point> &contour2)
 {
@@ -598,6 +611,60 @@ int ImgCheckAnalysisy::CheckRun()
 		}
 	}
 
+	/* 把手缺失检测：bigmask 的凸起本身不算 NG，只有"参数里画了把手区域但没检出把手"才算 NG */
+	{
+		if (m_bHandleDetSucc && m_bHandleExpect && !m_bHandleFound)
+		{
+			QX_ERROR_INFO_ temerror;
+			temerror.Idx = m_pDetResult->pQx_ErrorList->size();
+
+			// 上报参数里绘制的把手区域位置：原图坐标 -> 检测图(detImg)坐标
+			cv::Rect roi = m_HandleBoxParamImg;
+			roi.x -= m_outer_roi.x;
+			roi.y -= m_outer_roi.y;
+			roi &= cv::Rect(0, 0, m_pImageAllResult->detImg.cols, m_pImageAllResult->detImg.rows);
+			if (roi.width <= 0 || roi.height <= 0)
+			{
+				roi = cv::Rect(0, 0, m_pImageAllResult->detImg.cols, m_pImageAllResult->detImg.rows);
+			}
+			temerror.roi = roi;
+			temerror.area = roi.width * roi.height;
+			temerror.JudgArea = roi.width * m_fImgage_Scale_X * roi.height * m_fImgage_Scale_Y;
+			temerror.JudgArea_second = temerror.JudgArea;
+			float w = roi.width;
+			float h = roi.height;
+			temerror.flen = (w > h ? w : h) * m_fImgage_Scale_X;
+			temerror.fbreadth = (w > h ? h : w) * m_fImgage_Scale_Y;
+			temerror.nconfig_qx_type = CONFIG_QX_NAME_handle_loss;
+			temerror.qx_name = CONFIG_QX_NAME_Names[CONFIG_QX_NAME_handle_loss];
+			temerror.result = QX_RESULT_TYPE_NG;
+			temerror.result_name = QX_RESULT_TYPE_Names[QX_RESULT_TYPE_NG];
+
+			// 计算把手中心所在的检测区域
+			cv::Point pCenter;
+			pCenter.x = roi.x + roi.width * 0.5;
+			pCenter.y = roi.y + roi.height * 0.5;
+			int nmaxregionIdx = 0;
+			for (int iregion = 0; iregion < m_DetRoiList.roiList_Src.size(); iregion++)
+			{
+				const std::vector<cv::Point> &polygon = m_DetRoiList.roiList_Src[iregion];
+				double result = cv::pointPolygonTest(polygon, pCenter, false);
+				if (result < 0)
+				{
+					continue;
+				}
+				nmaxregionIdx = iregion;
+			}
+			temerror.detRegionidxList.push_back(nmaxregionIdx);
+
+			m_pDetResult->pQx_ErrorList->push_back(temerror);
+
+			m_pdetlog->AddCheckstr(PrintLevel_0, "把手检测", "%s %d roi [%d %d %d %d] handle param box [%d %d %d %d]",
+								   temerror.qx_name.c_str(), temerror.Idx, roi.x, roi.y, roi.width, roi.height,
+								   m_HandleBoxParamImg.x, m_HandleBoxParamImg.y, m_HandleBoxParamImg.width, m_HandleBoxParamImg.height);
+		}
+	}
+
 	/* Tag检测 */
 	{
 		for (const auto &tagRoi : m_tag_roiList)
@@ -2065,95 +2132,262 @@ int ImgCheckAnalysisy::AI_Edge(const cv::Mat &img, cv::RotatedRect &outerRoi, cv
 	}
 
 	/*使用m_pEdge_Align_Result->bigmask检测把手是否缺失*/
-	// 截取把手大致区域（使用把手模板完整 boundingRect，而非整图高度）
-	RotatedRect tpl_handle_rroi = m_pFuntion->function.f_supportDet.handleRect;
-	Rect handle_rect = tpl_handle_rroi.boundingRect() & Rect(0, 0, m_pEdge_Align_Result->bigmask.cols, m_pEdge_Align_Result->bigmask.rows);
-	if (handle_rect.width <= 0 || handle_rect.height <= 0)
 	{
+		long handle_s = CheckUtil::getcurTime();
+		CheckHandleByBigMask(img, m_pEdge_Align_Result->bigmask);
+		long handle_e = CheckUtil::getcurTime();
+		m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Handle", "handle detect use time %ld", handle_e - handle_s);
+	}
+	return re;
+}
+
+// 用 bigmask 检测把手：
+// 1、bigmask 主体近似矩形，把手是主体四周凸起的"类矩形块"
+// 2、把主体四周的凸起抠掉（凸起在该边的边界值上表现为一次突变进入 + 一次反向突变离开），剩下的就是矩形主体
+// 3、按参数里绘制的把手区域尺寸（沿边长度 + 垂直深度，带容差）逐个判定凸起是否为把手
+int ImgCheckAnalysisy::CheckHandleByBigMask(const cv::Mat &img, const cv::Mat &bigmask)
+{
+	m_bHandleDetSucc = false;
+	m_bHandleFound = false;
+	m_bHandleExpect = false;
+	m_HandleBoxParamImg = cv::Rect(0, 0, 0, 0);
+	m_HandleCandList.clear();
+	m_HandleRoiList.clear();
+
+	if (bigmask.empty())
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Handle", "bigmask is empty, skip handle detect");
+		return 1;
+	}
+
+	// 1、抠出 bigmask 四周凸起的矩形块
+	std::vector<Mask_Protrusion_> protrusionList;
+	int re = CheckUtil::GetMaskProtrusions(bigmask, protrusionList);
+	if (re != 0)
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Handle", "GetMaskProtrusions error %d", re);
 		return re;
 	}
-	Mat handle_roi = m_pEdge_Align_Result->bigmask(handle_rect).clone();
+	m_bHandleDetSucc = true;
 
-	// 对handle_roi做一下开运算
-	Mat element = getStructuringElement(MORPH_RECT, Size(std::max(1, tpl_handle_rroi.boundingRect().width / 3), std::max(1, tpl_handle_rroi.boundingRect().height / 3)));
-	morphologyEx(handle_roi, handle_roi, MORPH_OPEN, element);
+	// 2、把手参照尺寸 + 判定参数：都来自参数里绘制的把手区域（保留图像 x/y 方向）
+	Handle_Check_Param handleParam;
+	if (m_pFuntion != NULL)
+	{
+		const Function_Support_Det &supportDet = m_pFuntion->function.f_supportDet;
+		handleParam = supportDet.handleParam;
+		if (supportDet.handleRegion.size() > 0)
+		{
+			m_HandleBoxParamImg = cv::boundingRect(supportDet.handleRegion);
+		}
+	}
+	m_bHandleExpect = (handleParam.bOpen && m_HandleBoxParamImg.width > 1 && m_HandleBoxParamImg.height > 1);
+	m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Handle",
+						   "protrusion num %ld  handle param box [%d %d %d %d]  expect %s",
+						   protrusionList.size(), m_HandleBoxParamImg.x, m_HandleBoxParamImg.y,
+						   m_HandleBoxParamImg.width, m_HandleBoxParamImg.height, BOOL_TO_STR(m_bHandleExpect));
+	m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Handle", "%s", handleParam.GetInfo("judge").c_str());
 
-	// imwrite("handle_roi.png", handle_roi);
+	// 灰阶判定是预留项：只有开启时才做灰度换算，避免无谓开销
+	cv::Mat grayImg;
+	if (handleParam.bJudgeGray && !img.empty())
+	{
+		if (img.channels() != 1)
+		{
+			cv::cvtColor(img, grayImg, cv::COLOR_BGR2GRAY);
+		}
+		else
+		{
+			grayImg = img;
+		}
+	}
 
-	// handle_roi找到最大连通域，和tpl_handle_rroi做面积形状比较，差异过大判为把手缺失，添加到缺陷并NG
+	// 3、逐个凸起：先算实测特征（沿边长度/深度/面积/灰度差），再做判定
+	cv::Size maskSize(bigmask.cols, bigmask.rows);
+	for (size_t i = 0; i < protrusionList.size(); i++)
 	{
-		Function_Support_Det &handleDet = m_pFuntion->function.f_supportDet;
-		if (handleDet.bOpen && tpl_handle_rroi.size.width > 0 && tpl_handle_rroi.size.height > 0 && !handle_roi.empty())
+		Handle_Candidate_ cand;
+		cand.protr = protrusionList[i];
+		// 左/右凸起：沿边为 y 方向，深度为 x 方向；上/下凸起相反
+		bool bHorz = (cand.protr.nSide == 0 || cand.protr.nSide == 1);
+		cand.nLen = bHorz ? cand.protr.roi.height : cand.protr.roi.width;
+		cand.nDepth = bHorz ? cand.protr.roi.width : cand.protr.roi.height;
+		if (!grayImg.empty())
 		{
-			std::vector<std::vector<cv::Point>> handle_contours;
-			cv::findContours(handle_roi, handle_contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
+			cand.fGrayDiff = CalProtrusionGrayDiff(grayImg, cand.protr.roi);
+		}
+		cand.bHandle = IsHandleProtrusion(cand, handleParam, m_HandleBoxParamImg, maskSize);
+		if (cand.bHandle)
+		{
+			m_HandleRoiList.push_back(cand.protr.roi);
+		}
+		m_HandleCandList.push_back(cand);
+		// grayDiff 打印 -1 表示本次未开启灰阶判定（未计算）
+		m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Handle",
+							   "protrusion %ld side %d roi [%d %d %d %d] len %d depth %d area %0.0f fill %0.2f grayDiff %0.1f -> %s",
+							   i, cand.protr.nSide, cand.protr.roi.x, cand.protr.roi.y, cand.protr.roi.width, cand.protr.roi.height,
+							   cand.nLen, cand.nDepth, cand.protr.fArea, cand.protr.fFillRatio,
+							   grayImg.empty() ? -1.0f : cand.fGrayDiff, cand.bHandle ? "handle" : "not handle");
+	}
+	m_bHandleFound = (!m_HandleRoiList.empty());
 
-			bool bHandleLoss = false;
-			if (handle_contours.empty())
-			{
-				bHandleLoss = true; // 没有连通域，把手完全缺失
-			}
-			else
-			{
-				// 取最大连通域
-				std::sort(handle_contours.begin(), handle_contours.end(), compareContourAreas);
-				cv::RotatedRect cur_handle_rrect = cv::minAreaRect(handle_contours[0]);
-				double curArea = cv::contourArea(handle_contours[0]);
-
-				// 面积比较
-				double tplArea = tpl_handle_rroi.size.width * tpl_handle_rroi.size.height;
-				double areaRatio = (tplArea > 0.0) ? (curArea / tplArea) : 0.0;
-
-				// 形状比较（长宽比）
-				float tplW = std::max(tpl_handle_rroi.size.width, tpl_handle_rroi.size.height);
-				float tplH = std::min(tpl_handle_rroi.size.width, tpl_handle_rroi.size.height);
-				float curW = std::max(cur_handle_rrect.size.width, cur_handle_rrect.size.height);
-				float curH = std::min(cur_handle_rrect.size.width, cur_handle_rrect.size.height);
-				float tplRatio = (tplH > 0.0f) ? (tplW / tplH) : 0.0f;
-				float curRatio = (curH > 0.0f) ? (curW / curH) : 0.0f;
-				float ratioDiff = (tplRatio > 0.0f) ? fabs(curRatio - tplRatio) / tplRatio : 0.0f;
-
-				// 面积差异过大 或 形状差异过大 判为把手缺失
-				if (areaRatio < 0.6f || ratioDiff > 0.6f)
-				{
-					bHandleLoss = true;
-				}
+	m_pdetlog->AddCheckstr(PrintLevel_0, DET_LOG_LEVEL_3, "Handle", "handle num %ld  found %s  use handle param %s",
+						   m_HandleRoiList.size(), BOOL_TO_STR(m_bHandleFound), BOOL_TO_STR(m_bHandleExpect));
 
-				m_pdetlog->AddCheckstr(PrintLevel_0, "把手检测", "areaRatio %f ratioDiff %f", areaRatio, ratioDiff);
-			}
+	// 参数里绘制了把手区域，但 bigmask 四周没有满足判定条件的凸起 -> 把手缺失（NG 由 CheckRun 上报）
+	if (m_bHandleExpect && !m_bHandleFound)
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_0, DET_LOG_LEVEL_3, "Handle", "handle is missing !!");
+		m_pdetlog->AddCheckstr(PrintLevel_0, DET_LOG_LEVEL_3, "Handle", "handle param box [%d %d %d %d]",
+							   m_HandleBoxParamImg.x, m_HandleBoxParamImg.y, m_HandleBoxParamImg.width, m_HandleBoxParamImg.height);
+	}
 
-			if (bHandleLoss)
-			{
-				QX_ERROR_INFO_ temerror;
-				temerror.Idx = m_pDetResult->pQx_ErrorList->size();
+	// 4、存过程图：凸起红框、把手绿框（仅调试时）
+	if (DetImgInfo_shareP->bsaveProcessImg)
+	{
+		cv::Mat show;
+		if (bigmask.channels() == 1)
+		{
+			cv::cvtColor(bigmask, show, cv::COLOR_GRAY2BGR);
+		}
+		else
+		{
+			show = bigmask.clone();
+		}
+		for (size_t i = 0; i < m_HandleCandList.size(); i++)
+		{
+			cv::rectangle(show, m_HandleCandList[i].protr.roi, cv::Scalar(0, 0, 255), 8);
+		}
+		for (size_t i = 0; i < m_HandleRoiList.size(); i++)
+		{
+			cv::rectangle(show, m_HandleRoiList[i], cv::Scalar(0, 255, 0), 12);
+		}
+		cv::Mat showSmall;
+		cv::resize(show, showSmall, cv::Size(show.cols / 4, show.rows / 4));
+		std::vector<int> paramJpg = {cv::IMWRITE_JPEG_QUALITY, 90};
+		cv::imwrite(DetImgInfo_shareP->strChannel + "_handle_det.jpg", showSmall, paramJpg);
+	}
 
-				// 把手模板 roi 在原图坐标系，转换为检测图(detImg)坐标系
-				cv::Rect roi = tpl_handle_rroi.boundingRect();
-				roi.x -= outerRoi.boundingRect().x;
-				roi.y -= outerRoi.boundingRect().y;
-				roi &= cv::Rect(0, 0, outerRoi.boundingRect().width, outerRoi.boundingRect().height);
-				temerror.roi = roi;
-				temerror.area = roi.width * roi.height;
-				temerror.JudgArea = roi.width * m_fImgage_Scale_X * roi.height * m_fImgage_Scale_Y;
-				temerror.JudgArea_second = temerror.JudgArea;
-				float w = tpl_handle_rroi.size.width;
-				float h = tpl_handle_rroi.size.height;
-				temerror.flen = (w > h ? w : h) * m_fImgage_Scale_X;
-				temerror.fbreadth = (w > h ? h : w) * m_fImgage_Scale_Y;
-				temerror.nconfig_qx_type = CONFIG_QX_NAME_support_loss;
-				temerror.qx_name = CONFIG_QX_NAME_Names[temerror.nconfig_qx_type];
-				temerror.result = QX_RESULT_TYPE_NG;
-				temerror.result_name = QX_RESULT_TYPE_Names[QX_RESULT_TYPE_NG];
-				temerror.detRegionidxList.push_back(0);
+	return 0;
+}
 
-				m_pDetResult->pQx_ErrorList->push_back(temerror);
+// 判断凸起是否为把手：参数里每一项"开启"的判定条件都满足才算把手
+// 尺寸参照参数里绘制的把手区域（handleBox，保留图像 x/y 方向）：
+//   沿边长度 对应 handleBox 在该边方向上的长度，垂直深度 对应 handleBox 的宽度；
+//   凸起外边缘被图像边界截断时，深度不可信，只校验沿边长度
+// 参数里没写显式像素范围时，按把手区域尺寸的比例推算（HANDLE_LEN_*_RATIO / HANDLE_DEPTH_*_RATIO）
+bool ImgCheckAnalysisy::IsHandleProtrusion(const Handle_Candidate_ &cand, const Handle_Check_Param &param,
+										   const cv::Rect &handleBox, const cv::Size &maskSize)
+{
+	const Mask_Protrusion_ &protr = cand.protr;
+	if (protr.roi.width <= 0 || protr.roi.height <= 0)
+	{
+		return false;
+	}
 
-				m_pdetlog->AddCheckstr(PrintLevel_0, "把手检测", "handle loss NG roi [%d %d %d %d]", roi.x, roi.y, roi.width, roi.height);
-			}
-		}
+	// 左/右凸起：沿边为 y 方向(长度=height)，深度为 x 方向(width)；上/下凸起相反
+	const bool bHorz = (protr.nSide == 0 || protr.nSide == 1);
+	// 凸起外边缘是否贴到图像边界（被截断）
+	bool bClip = bHorz ? (protr.roi.x <= 0 || protr.roi.x + protr.roi.width >= maskSize.width)
+					   : (protr.roi.y <= 0 || protr.roi.y + protr.roi.height >= maskSize.height);
+
+	// 该凸起对应的期望尺寸范围
+	int nMinLen = 0, nMaxLen = 0, nMinDepth = 0, nMaxDepth = 0;
+	if (handleBox.width > 1 && handleBox.height > 1)
+	{
+		int nExpLen = bHorz ? handleBox.height : handleBox.width;
+		int nExpDepth = bHorz ? handleBox.width : handleBox.height;
+		nMinLen = (int)(nExpLen * HANDLE_LEN_MIN_RATIO);
+		nMaxLen = (int)(nExpLen * HANDLE_LEN_MAX_RATIO);
+		nMinDepth = (int)(nExpDepth * HANDLE_DEPTH_MIN_RATIO);
+		nMaxDepth = (int)(nExpDepth * HANDLE_DEPTH_MAX_RATIO);
+	}
+	else // 参数里没画把手区域：按掩膜短边推算一个大致范围
+	{
+		int nMinSide = std::min(maskSize.width, maskSize.height);
+		nMinLen = nMinDepth = (int)(nMinSide * HANDLE_DEFAULT_MIN_RATIO);
+		nMaxLen = nMaxDepth = (int)(nMinSide * HANDLE_DEFAULT_MAX_RATIO);
+	}
+	// 参数里写了显式像素范围时以参数值为准
+	if (param.bJudgeLen && param.fLenMin > 0)
+	{
+		nMinLen = (int)param.fLenMin;
+	}
+	if (param.bJudgeLen && param.fLenMax > 0)
+	{
+		nMaxLen = (int)param.fLenMax;
+	}
+	if (param.bJudgeDepth && param.fDepthMin > 0)
+	{
+		nMinDepth = (int)param.fDepthMin;
+	}
+	if (param.bJudgeDepth && param.fDepthMax > 0)
+	{
+		nMaxDepth = (int)param.fDepthMax;
 	}
 
-	return re;
+	// 以下条件逐条校验，任意一条不满足即不是把手
+	// 1、类似矩形块：凸起区域内掩膜填充率要高
+	if (param.bJudgeFill && protr.fFillRatio < param.fFillMin)
+	{
+		return false;
+	}
+	// 2、沿边长度
+	if (param.bJudgeLen && (cand.nLen < nMinLen || cand.nLen > nMaxLen))
+	{
+		return false;
+	}
+	// 3、垂直深度（凸起被图像边界截断时跳过）
+	if (param.bJudgeDepth && !bClip && (cand.nDepth < nMinDepth || cand.nDepth > nMaxDepth))
+	{
+		return false;
+	}
+	// 4、预留：凸起面积
+	if (param.bJudgeArea && param.fAreaMax > param.fAreaMin &&
+		(protr.fArea < param.fAreaMin || protr.fArea > param.fAreaMax))
+	{
+		return false;
+	}
+	// 5、预留：凸起与背景的灰度差
+	if (param.bJudgeGray && param.fGrayMax > param.fGrayMin &&
+		(cand.fGrayDiff < param.fGrayMin || cand.fGrayDiff > param.fGrayMax))
+	{
+		return false;
+	}
+	return true;
+}
+
+// 计算凸起区域与周边背景的平均灰度差（灰阶判定的预留量测值，grayImg 需为单通道）
+float ImgCheckAnalysisy::CalProtrusionGrayDiff(const cv::Mat &grayImg, const cv::Rect &roi)
+{
+	if (grayImg.empty() || grayImg.channels() != 1 || roi.width <= 0 || roi.height <= 0)
+	{
+		return 0.0f;
+	}
+	// 凸起区域通常整个都在掩膜内，向外扩一圈才能取到背景
+	int nExpand = std::max(20, std::min(roi.width, roi.height) / 2);
+	cv::Rect roiBg(roi.x - nExpand, roi.y - nExpand, roi.width + 2 * nExpand, roi.height + 2 * nExpand);
+	roiBg &= cv::Rect(0, 0, grayImg.cols, grayImg.rows);
+	if (roiBg.width <= 0 || roiBg.height <= 0)
+	{
+		return 0.0f;
+	}
+
+	cv::Mat grayRoi = grayImg(roiBg);
+	cv::Mat maskIn = cv::Mat::zeros(roiBg.size(), CV_8UC1);
+	cv::Rect inter = roi & roiBg;
+	cv::rectangle(maskIn, inter - roiBg.tl(), cv::Scalar(255), cv::FILLED);
+	cv::Mat maskOut;
+	cv::bitwise_not(maskIn, maskOut);
+	if (cv::countNonZero(maskIn) <= 0 || cv::countNonZero(maskOut) <= 0)
+	{
+		return 0.0f;
+	}
+
+	double fMeanIn = cv::mean(grayRoi, maskIn)[0];
+	double fMeanOut = cv::mean(grayRoi, maskOut)[0];
+	double fDiff = fMeanIn - fMeanOut;
+	return (float)(fDiff >= 0 ? fDiff : -fDiff);
 }
 
 int ImgCheckAnalysisy::CalProductSize()

ConfigModule/include/CheckConfigDefine.h

@@ -54,6 +54,7 @@ enum CONFIG_QX_NAME_
     CONFIG_QX_NAME_cell_tag,        // 分类 标签
     CONFIG_QX_NAME_support_offset,  // 支架偏移
     CONFIG_QX_NAME_support_loss,    // 支架缺失
+    CONFIG_QX_NAME_handle_loss,     // 把手缺失
 	CONFIG_QX_NAME_count,
 };
 // 缺陷项对应在参数中的名称
@@ -74,6 +75,7 @@ static std::vector<std::string> CONFIG_QX_NAME_Names =
         "tag",
         "support_offset",
         "support_loss",
+        "handle_loss",
 	};
 
 // 分析类型
@@ -932,6 +934,65 @@ struct Function_Image_Align
 		return str123;
 	}
 };
+// 把手缺失判定参数：
+// 当前用"沿边长度 + 垂直深度"判定，后续可继续添加面积、灰阶等参数，
+// 每一项都有独立的开关，所有开启的条件都满足才把凸起判为把手
+struct Handle_Check_Param
+{
+	bool bOpen;		 // 是否检测把手缺失（false 时不做把手判定，也不报 NG）
+	bool bJudgeFill;	 // 是否用"类矩形块"填充率判定
+	float fFillMin;	 // 填充率下限
+	bool bJudgeLen;	 // 是否用"沿边长度"判定
+	float fLenMin;	 // 沿边长度下限（像素，<=0 时按把手区域尺寸比例推算）
+	float fLenMax;	 // 沿边长度上限（像素，<=0 时按把手区域尺寸比例推算）
+	bool bJudgeDepth;  // 是否用"垂直深度"判定
+	float fDepthMin;	 // 垂直深度下限（像素，<=0 时按把手区域尺寸比例推算）
+	float fDepthMax;	 // 垂直深度上限（像素，<=0 时按把手区域尺寸比例推算）
+	bool bJudgeArea;	 // 预留：凸起面积判定
+	float fAreaMin;
+	float fAreaMax;
+	bool bJudgeGray;	 // 预留：凸起与背景灰度差判定
+	float fGrayMin;
+	float fGrayMax;
+
+	Handle_Check_Param()
+	{
+		Init();
+	}
+	void Init()
+	{
+		bOpen = true;
+		bJudgeFill = true;
+		fFillMin = 0.75f;
+		bJudgeLen = true;
+		fLenMin = 0;
+		fLenMax = 0;
+		bJudgeDepth = true;
+		fDepthMin = 0;
+		fDepthMax = 0;
+		bJudgeArea = false;
+		fAreaMin = 0;
+		fAreaMax = 0;
+		bJudgeGray = false;
+		fGrayMin = 0;
+		fGrayMax = 0;
+	}
+	void copy(Handle_Check_Param tem)
+	{
+		// 全为基本类型，直接赋值（避免后续加字段时漏拷）
+		*this = tem;
+	}
+	std::string GetInfo(std::string str)
+	{
+		char buffer[256];
+		sprintf(buffer, "%s>>bOpen %d fill[%d %0.2f] len[%d %0.0f %0.0f] depth[%d %0.0f %0.0f] area[%d %0.0f %0.0f] gray[%d %0.0f %0.0f]\n",
+				str.c_str(), bOpen, bJudgeFill, fFillMin, bJudgeLen, fLenMin, fLenMax,
+				bJudgeDepth, fDepthMin, fDepthMax, bJudgeArea, fAreaMin, fAreaMax, bJudgeGray, fGrayMin, fGrayMax);
+		std::string str123 = buffer;
+		return str123;
+	}
+};
+
 // 支架检测
 struct Function_Support_Det 
 {
@@ -943,6 +1004,7 @@ struct Function_Support_Det
 	float r_offset;
 	std::vector<cv::Point> handleRegion;
 	cv::RotatedRect handleRect;
+	Handle_Check_Param handleParam; // 把手缺失判定参数
 
 	Function_Support_Det ()
 	{
@@ -959,6 +1021,7 @@ struct Function_Support_Det
 		r_offset = 0;
 		handleRegion.clear();
 		handleRect = cv::RotatedRect();
+		handleParam.Init();
 	}
 	void copy(Function_Support_Det  tem)
 	{
@@ -970,18 +1033,21 @@ struct Function_Support_Det
 		this->r_offset = tem.r_offset;
 		this->handleRegion.assign(tem.handleRegion.begin(), tem.handleRegion.end());
 		this->handleRect = tem.handleRect;
+		this->handleParam.copy(tem.handleParam);
 	}
 	void print(std::string str)
 	{
 		printf("%s>>bOpen %d  x_offset %f y_offset %f r_offset %f\n", str.c_str(),
 			   bOpen, x_offset, y_offset, r_offset);
+		printf("%s", handleParam.GetInfo("handleParam").c_str());
 	}
 	std::string GetInfo(std::string str)
 	{
 		char buffer[256];
 		sprintf(buffer, "%s>>bOpen %d  x_offset %f y_offset %f r_offset %f\n", str.c_str(),
-				bOpen, x_offset, y_offset, r_offset);
+			   bOpen, x_offset, y_offset, r_offset);
 		std::string str123 = buffer;
+		str123 += handleParam.GetInfo("handleParam");
 		return str123;
 	}
 	

ConfigModule/src/JsonConfig.cpp

@@ -470,6 +470,72 @@ int ChannelFuntonConfigJson::GetFunction(Json::Value value, CheckFunction &funct
                             }
                         }
                     }
+
+                    // 把手缺失判定参数（参数缺失时保留默认值：长度/深度按把手区域尺寸比例推算）
+                    {
+                        auto handle_p = value_f["form"]["handle_param"];
+                        Handle_Check_Param &handleParam = function.f_supportDet.handleParam;
+                        if (handle_p["handle_disabled"].isBool())
+                        {
+                            handleParam.bOpen = !handle_p["handle_disabled"].asBool();
+                        }
+                        if (handle_p["handle_fill_disabled"].isBool())
+                        {
+                            handleParam.bJudgeFill = !handle_p["handle_fill_disabled"].asBool();
+                        }
+                        if (handle_p["handle_fill_min"].isNumeric())
+                        {
+                            handleParam.fFillMin = handle_p["handle_fill_min"].asFloat();
+                        }
+                        if (handle_p["handle_len_disabled"].isBool())
+                        {
+                            handleParam.bJudgeLen = !handle_p["handle_len_disabled"].asBool();
+                        }
+                        if (handle_p["handle_len_min"].isNumeric())
+                        {
+                            handleParam.fLenMin = handle_p["handle_len_min"].asFloat();
+                        }
+                        if (handle_p["handle_len_max"].isNumeric())
+                        {
+                            handleParam.fLenMax = handle_p["handle_len_max"].asFloat();
+                        }
+                        if (handle_p["handle_depth_disabled"].isBool())
+                        {
+                            handleParam.bJudgeDepth = !handle_p["handle_depth_disabled"].asBool();
+                        }
+                        if (handle_p["handle_depth_min"].isNumeric())
+                        {
+                            handleParam.fDepthMin = handle_p["handle_depth_min"].asFloat();
+                        }
+                        if (handle_p["handle_depth_max"].isNumeric())
+                        {
+                            handleParam.fDepthMax = handle_p["handle_depth_max"].asFloat();
+                        }
+                        if (handle_p["handle_area_disabled"].isBool())
+                        {
+                            handleParam.bJudgeArea = !handle_p["handle_area_disabled"].asBool();
+                        }
+                        if (handle_p["handle_area_min"].isNumeric())
+                        {
+                            handleParam.fAreaMin = handle_p["handle_area_min"].asFloat();
+                        }
+                        if (handle_p["handle_area_max"].isNumeric())
+                        {
+                            handleParam.fAreaMax = handle_p["handle_area_max"].asFloat();
+                        }
+                        if (handle_p["handle_gray_disabled"].isBool())
+                        {
+                            handleParam.bJudgeGray = !handle_p["handle_gray_disabled"].asBool();
+                        }
+                        if (handle_p["handle_gray_min"].isNumeric())
+                        {
+                            handleParam.fGrayMin = handle_p["handle_gray_min"].asFloat();
+                        }
+                        if (handle_p["handle_gray_max"].isNumeric())
+                        {
+                            handleParam.fGrayMax = handle_p["handle_gray_max"].asFloat();
+                        }
+                    }
                 }
                 else
                 {