update 优化返回的物理坐标和宽高

AlgorithmModule/include/ImageAllResult.h

@@ -70,6 +70,9 @@ public:
     float fscale_detToresult_x = 0;
     float fscale_detToresult_y = 0;
 
+    // 产品左上角（rot_cur_roi.boundingRect）在检测图 detImg 中的偏移，缺陷坐标转换到产品坐标系时需减去该间隙
+    cv::Point ptRoiOffsetInDet = cv::Point(0, 0);
+
     int NG_num = 0;
     int YS_num = 0;
     int Ok_num = 0;

AlgorithmModule/include/ImgCheckAnalysisy.hpp

@@ -241,6 +241,8 @@ private:
 
     cv::Rect m_CutRoi;
     cv::Rect m_Crop_Roi_paramImg; // 裁切在参数模板图上的 位置。
+    cv::RotatedRect m_rot_cur_roi;  // 产品旋转外接矩形（原图坐标）
+    bool m_bRotCurRoiValid = false; // m_rot_cur_roi 是否有效
 
     std::string m_strCurDetChannel; // 当前处理的图片通道
 

AlgorithmModule/src/ImageResultJudge.cpp

@@ -119,7 +119,11 @@ int ImageResultJudge::GetAIDetImg(std::shared_ptr<ImageAllResult> pImageResult,
 QXImageResult ImageResultJudge::BuildDefectImage(std::shared_ptr<ImageAllResult> pImageResult, QX_ERROR_INFO_ *QX_info, 
                                                   int qxidx, cv::Point pCenter, float fs_resize_x, float fs_resize_y)
 {
-	cv::Rect roi = QX_info->roi;
+	cv::Rect roi = QX_info->roi; // 缺陷坐标（产品坐标系，已减去裁剪框与产品左上角的间隙）
+	// 还原检测图(detImg)坐标，用于缺陷小图裁剪等内部用途
+	cv::Rect roi_det = roi;
+	roi_det.x += pImageResult->ptRoiOffsetInDet.x;
+	roi_det.y += pImageResult->ptRoiOffsetInDet.y;
 	float JudgArea = QX_info->JudgArea;
 	float flen = QX_info->flen;
 	float fbreadth = QX_info->fbreadth;
@@ -130,7 +134,7 @@ QXImageResult ImageResultJudge::BuildDefectImage(std::shared_ptr<ImageAllResult>
 
 	QXImageResult tem;
 	tem.idx = qxidx;
-	cv::Rect CutRoi = GetCutRoi(roi, pImageResult->detImg);
+	cv::Rect CutRoi = GetCutRoi(roi_det, pImageResult->detImg);
 	cv::Size sz = cv::Size(QX_SAMLLIMG_WIDTH, QX_SAMLLIMG_HEIGHT);
 
 	if (pImageResult->detImg.channels() == 1)
@@ -151,25 +155,26 @@ QXImageResult ImageResultJudge::BuildDefectImage(std::shared_ptr<ImageAllResult>
 	tem.max_v = maxValue;
 	tem.strTypeName = QX_Result_Names[nqx_type];
 	tem.qx_Code = QX_Result_Code[nqx_type];
-	tem.srcImgroi = roi;
+	tem.srcImgroi = roi_det;
 	tem.len = flen;
 	tem.breadth = fbreadth;
 	tem.qx_type = 0;
 	tem.fScore = 0;
 	tem.density = 0;
 
-	tem.resizeImgroi.x = roi.x * fs_resize_x;
+	tem.resizeImgroi.x = roi_det.x * fs_resize_x;
-	tem.resizeImgroi.width = roi.width * fs_resize_x;
+	tem.resizeImgroi.width = roi_det.width * fs_resize_x;
-	tem.resizeImgroi.y = roi.y * fs_resize_y;
+	tem.resizeImgroi.y = roi_det.y * fs_resize_y;
-	tem.resizeImgroi.height = roi.height * fs_resize_y;
+	tem.resizeImgroi.height = roi_det.height * fs_resize_y;
 
+	// 缺陷中心：产品坐标系（像素/物理），相对产品左上角
 	tem.x_pixel = roi.x + roi.width * 0.5;
 	tem.y_pixel = roi.y + roi.height * 0.5;
 
 	tem.x_mm = tem.x_pixel * m_fImgage_Scale_X;
 	tem.y_mm = tem.y_pixel * m_fImgage_Scale_Y;
 
-	tem.CutImgroi = roi;
+	tem.CutImgroi = roi_det;
 	tem.CutImgroi.x -= CutRoi.x;
 	tem.CutImgroi.y -= CutRoi.y;
 

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -802,7 +802,16 @@ int ImgCheckAnalysisy::CheckRun()
 	/*自适应更新参数*/
 	long time_adapt_s = CheckUtil::getcurTime();
 	cv::RotatedRect rot_cur_roi;
-	Adapt_Config(m_CheckResult_shareP->in_shareImage->img, m_CutRoi, rot_cur_roi, m_pbaseCheckFunction->markLine.badapt_region);
+	int nAdaptRe = Adapt_Config(m_CheckResult_shareP->in_shareImage->img, m_CutRoi, rot_cur_roi, m_pbaseCheckFunction->markLine.badapt_region);
+	if (nAdaptRe == 0)
+	{
+		m_rot_cur_roi = rot_cur_roi;
+		m_bRotCurRoiValid = true;
+	}
+	else
+	{
+		m_bRotCurRoiValid = false;
+	}
 	long time_adapt_e = CheckUtil::getcurTime();
 	m_pdetlog->AddCheckstr(PrintLevel_0, "Adapt_Config", "time = %ld", time_adapt_e - time_adapt_s);
 
@@ -842,6 +851,18 @@ int ImgCheckAnalysisy::CheckRun()
 	m_pdetlog->AddCheckstr(PrintLevel_0, "2、pre detect", "-------------------------AI_Edge------------succ---time %ld----\n", time_edge_e - time_edge_s);
 
 	m_Crop_Roi_paramImg = m_CutRoi;
+	// 计算产品左上角在检测图(detImg)中的偏移（间隙），用于缺陷坐标转换到产品坐标系
+	if (m_bRotCurRoiValid)
+	{
+		// 取旋转矩形实际的左上角顶点（非轴对齐外接框 boundingRect，后者在带旋转角时会偏大）
+		std::vector<cv::Point2f> rroi_vertices = sort_vertices(m_rot_cur_roi);
+		m_pImageAllResult->ptRoiOffsetInDet.x = cvRound(rroi_vertices[0].x) - m_CutRoi.x;
+		m_pImageAllResult->ptRoiOffsetInDet.y = cvRound(rroi_vertices[0].y) - m_CutRoi.y;
+	}
+	else
+	{
+		m_pImageAllResult->ptRoiOffsetInDet = cv::Point(0, 0);
+	}
 	m_pImageAllResult->pDetResult->CutRoi = m_CutRoi;
 	m_pImageAllResult->pDetResult->Param_CropRoi = m_Crop_Roi_paramImg;
 
@@ -2532,6 +2553,12 @@ int ImgCheckAnalysisy::Edge_Qx_Det(const cv::Mat &img)
 			// 	}
 			// }
 
+			// 缺陷坐标转换到产品坐标系：减去裁剪框与产品左上角的间隙
+			if (m_bRotCurRoiValid)
+			{
+				temerror.roi.x -= m_pImageAllResult->ptRoiOffsetInDet.x;
+				temerror.roi.y -= m_pImageAllResult->ptRoiOffsetInDet.y;
+			}
 			m_pDetResult->pQx_ErrorList->push_back(temerror);
 		}
 
@@ -2604,6 +2631,12 @@ int ImgCheckAnalysisy::BLobToDetResult()
 		{
 			QX_ERROR_INFO_ temerror;
 			temerror.roi = roi;
+			// 缺陷坐标转换到产品坐标系：减去裁剪框与产品左上角的间隙
+			if (m_bRotCurRoiValid)
+			{
+				temerror.roi.x -= m_pImageAllResult->ptRoiOffsetInDet.x;
+				temerror.roi.y -= m_pImageAllResult->ptRoiOffsetInDet.y;
+			}
 			temerror.Idx = m_pDetResult->pQx_ErrorList->size();
 			temerror.area = blobs.blobTab[i].area;
 			temerror.JudgArea = JudgArea;
@@ -2695,8 +2728,25 @@ int ImgCheckAnalysisy::AI_Edge(const cv::Mat &img, cv::Rect &cutRoi)
 
 int ImgCheckAnalysisy::CalProductSize()
 {
-	float fw = m_CutRoi.width * m_fImgage_Scale_X;
+	float fw, fh;
-	float fh = m_CutRoi.height * m_fImgage_Scale_Y;
+	if (m_bRotCurRoiValid)
+	{
+		// 产品存在旋转角：用旋转矩形四个顶点的实际边长换算物理尺寸
+		// 宽 = (左上-右上 + 左下-右下) / 2，高 = (左上-左下 + 右上-右下) / 2
+		std::vector<cv::Point2f> v = sort_vertices(m_rot_cur_roi);
+		auto edgeLenPhys = [&](const cv::Point2f &a, const cv::Point2f &b) -> double {
+			double dx = (a.x - b.x) * m_fImgage_Scale_X;
+			double dy = (a.y - b.y) * m_fImgage_Scale_Y;
+			return std::sqrt(dx * dx + dy * dy);
+		};
+		fw = (float)((edgeLenPhys(v[0], v[1]) + edgeLenPhys(v[2], v[3])) * 0.5); // 上边+下边平均
+		fh = (float)((edgeLenPhys(v[0], v[2]) + edgeLenPhys(v[1], v[3])) * 0.5); // 左边+右边平均
+	}
+	else
+	{
+		fw = m_CutRoi.width * m_fImgage_Scale_X;
+		fh = m_CutRoi.height * m_fImgage_Scale_Y;
+	}
 	fw = std::ceil(fw * 10) / 10;
 	fh = std::ceil(fh * 10) / 10;
 	m_CheckResult_shareP->productWidht_mm = fw;