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网站建设及发布的流程图,整站seo策略实施,浙江设计公司,佛山做外贸网站服务HALCON算子 get_metrology_object_result 全解析 一、算子核心定位 get_metrology_object_result 是HALCON 2D计量#xff08;2D Metrology#xff09;模块的核心结果读取算子#xff0c;也是获取最终测量数据的核心入口。其核心功能是读取经apply_metrology_model检测拟合后…HALCON算子get_metrology_object_result全解析一、算子核心定位get_metrology_object_result是HALCON 2D计量2D Metrology模块的核心结果读取算子也是获取最终测量数据的核心入口。其核心功能是读取经apply_metrology_model检测拟合后指定计量对象/实例的具体测量结果——包括几何参数如圆心、半径、矩形角度、拟合分数、所用边缘点坐标/振幅还可控制角度返回方向是从2D计量模型中提取精准测量数据的关键算子。二、算法核心原理参数校验验证计量模型句柄MetrologyHandle、计量对象索引Index、实例编号Instance的有效性确保参数匹配模型内的实际对象/实例结果类型解析根据GenParamName通用参数名和GenParamValue通用参数值的组合确定要读取的结果类型如全部几何参数、单个参数、拟合分数、所用边缘点结果提取若读取几何参数提取拟合后的计量对象实例参数像素/公制坐标取决于相机参数是否设置若读取分数计算“实际用于拟合的测量数/最大测量区域数”的比值若读取所用边缘点提取参与拟合的边缘点坐标/振幅若控制角度方向按指定旋转方向顺时针/逆时针转换角度值结果封装按“先第一个计量对象的所有实例再第二个计量对象的所有实例”的顺序将结果封装为数组输出到Parameter无修改逻辑仅读取检测拟合结果不改变模型/对象的任何属性。三、参数全详解一输入参数Input Parameters参数名类型功能说明默认值关键取值规则注意事项MetrologyHandlemetrology_model → (handle)指定要查询的2D计量模型句柄-必须为已有效创建/加载的计量模型句柄需通过create_metrology_model生成未被clear_metrology_model释放无效句柄抛异常Indexinteger(-array) → (integer / string)指定要查询的计量对象索引0可选值▪ ‘all’查询所有计量对象▪ 整数/整数数组查询指定索引如0、[0,1]整数索引需为get_metrology_object_indices返回的有效索引Instanceinteger(-array) → (string / integer)指定要查询的实例编号‘all’可选值▪ ‘all’查询指定计量对象的所有实例▪ 整数/整数数组查询指定实例如0、[0,1]实例编号从0开始需≤get_metrology_object_num_instances返回的实例数-1GenParamNameattribute.name-array → (string)通用参数名控制结果类型‘result_type’可选值▪ ‘result_type’读取几何参数/拟合分数▪ ‘used_edges’读取参与拟合的边缘点▪ ‘angle_direction’控制角度返回方向可传入数组如[‘result_type’,‘angle_direction’]需与GenParamValue长度一致GenParamValueattribute.value-array → (string / real)通用参数值匹配GenParamName‘all_param’核心组合规则1.GenParamNameresult_type时- ‘all_param’返回所有几何参数- ‘score’返回拟合分数- 单个参数名如’radius’/‘phi’返回指定参数2.GenParamNameused_edges时- ‘row’/‘column’/‘amplitude’返回边缘点行/列坐标/振幅3.GenParamNameangle_direction时- ‘positive’默认逆时针角度- ‘negative’顺时针角度多参数组合时GenParamName和GenParamValue需一一对应如[‘result_type’,‘angle_direction’], [‘radius’,‘negative’]二输出参数Output Parameters参数名类型功能说明关联说明Parameterreal(-array) → (real / integer / string)返回指定类型的测量结果数组1. 结果顺序先第一个计量对象的所有实例再第二个计量对象的所有实例2. 坐标类型未设置相机参数时返回像素坐标row/column设置后返回公制坐标x/y3. 角度单位弧度方向由angle_direction控制补充不同计量对象的all_param返回顺序计量对象类型像素坐标返回顺序公制坐标返回顺序圆Circle[row, column, radius][x, y, radius]椭圆Ellipse[row, column, phi, radius1, radius2][x, y, phi, radius1, radius2]线Line[row_begin, column_begin, row_end, column_end][x_begin, y_begin, x_end, y_end]矩形Rectangle2[row, column, phi, length1, length2][x, y, phi, length1, length2]四、使用关键注意事项执行时序约束必须在调用apply_metrology_model执行检测拟合后调用否则Parameter返回空数组坐标类型规则未设置相机参数/测量平面返回像素坐标row/column/nrow/ncolumn设置相机参数测量平面返回公制坐标x/y/nx/ny结果顺序规则批量查询时结果按“1st实例 of 1st对象 → 2nd实例 of 1st对象 → 1st实例 of 2nd对象 → …”排列角度相关角度单位为弧度默认逆时针positive可设为顺时针negative仅对椭圆/矩形的phi参数生效多线程特性多线程类型可重入能与非排他算子并行运行多线程范围全局可从任意线程调用无并行优化单线程读取数据拟合分数含义分数“用于拟合的测量数/最大测量区域数”值越高表示拟合质量越好越接近1返回值规则执行成功返回2H_MSG_TRUE参数无效如索引/实例错误时抛异常。五、算子调用链路一前置算子Possible Predecessorscreate_metrology_model创建空2D计量模型基础add_metrology_object_*如add_metrology_object_circle_measure添加计量对象set_metrology_object_param可选设置num_instances多实例检测apply_metrology_model执行检测拟合必要前置get_metrology_object_num_instances可选获取实例数用于遍历读取结果。二后置算子Possible Successorsclear_metrology_model释放计量模型句柄收尾dev_print_var打印测量结果验证/日志gen_contour_circle_xld根据圆参数生成XLD轮廓可视化gen_contour_rectangle2_xld根据矩形参数生成XLD轮廓可视化。六、与相似算子的核心差异算子名称核心区别适用场景get_metrology_object_result返回拟合后的最终测量结果几何参数/分数/所用边缘点获取目标的精准测量参数如圆心、半径、评估拟合质量get_metrology_object_measures返回原始边缘点坐标测量区域轮廓无拟合逻辑调试测量区域、验证原始边缘检测结果get_metrology_object_num_instances仅返回实例数量无具体测量数据统计检测到的目标数量、判断目标是否存在get_metrology_object_result_contour返回拟合后的目标轮廓XLD可视化用直观展示拟合后的目标形状/位置七、典型应用示例HDevelop 代码*This example program shows how to align images based on point-to-line*correspondences.HALCONs metrology model and point_line_to_hom_mat2d are used*to detect the position and angle of the print in the images.The found position*and angle are used to transform the images to the referenceposition(the*position of the print in the first image in this example).*From the aligned images of correct prints,a variation model is constructed.*This variation model is then used to check and classify images of correct and*incorrect prints.dev_update_off()read_image(Image,pen/pen-01)get_image_size(Image,Width,Height)dev_close_window()dev_open_window(0,Width12,Width,Height,black,WindowHandle)dev_open_window(0,0,Width,Height,black,WindowHandleAlign)set_display_font(WindowHandle,14,mono,true,false)set_display_font(WindowHandleAlign,14,mono,true,false)dev_set_color(red)dev_display(Image)*Note:the checking of the print will be restricted to the region of the clip.*Sometimes the print is also in an incorrect position of the clip.This will lead*to erroneous regions at the top or bottom border of the clip and hence can*be detected easily.threshold(Image,Region,100,255)fill_up(Region,RegionFillUp)erosion_rectangle1(RegionFillUp,RegionROI,1,15)*Set up the metrology model with four lines.Four lines are used since this is*the minimum number of point-to-line correspondences that results in a unique rigid*transformation.create_metrology_model(MetrologyHandle)set_metrology_model_image_size(MetrologyHandle,Width,Height)add_metrology_object_line_measure(MetrologyHandle,[208,137,213,90],[49,327,139,307],[100,201,214,89],[82,318,56,375],40,5,1,30,[measure_transition,min_score],[negative,0.5],Index)*Apply the metrology model to the reference image and read out the results.apply_metrology_model(Image,MetrologyHandle)get_metrology_object_result(MetrologyHandle,all,all,result_type,row_begin,RowBegin)get_metrology_object_result(MetrologyHandle,all,all,result_type,column_begin,ColBegin)get_metrology_object_result(MetrologyHandle,all,all,result_type,row_end,RowEnd)get_metrology_object_result(MetrologyHandle,all,all,result_type,column_end,ColEnd)*The reference points of the model are the center points of the detected line*segments.They will be used to compute the transformation from the current image*to the reference image using point_line_to_hom_mat2d below.RowRef:0.5*(RowBeginRowEnd)ColRef:0.5*(ColBeginColEnd)dev_set_window(WindowHandle)dev_display(Image)dev_set_window(WindowHandleAlign)dev_display(Image)get_metrology_object_measures(MeasureContours,MetrologyHandle,all,all,Row,Column)dev_set_color(green)dev_display(MeasureContours)dev_set_color(green)disp_message(WindowHandleAlign,[Measure objects of the four,metrology line objects],window,12,12,black,true)disp_continue_message(WindowHandle,black,true)stop()dev_set_window(WindowHandleAlign)dev_display(Image)get_metrology_object_result_contour(MeasuredLines,MetrologyHandle,all,all,1.5)gen_cross_contour_xld(RefPoints,RowRef,ColRef,16,rad(45))dev_set_line_width(2)dev_set_color(green)dev_display(MeasuredLines)dev_set_color(blue)dev_display(RefPoints)dev_set_line_width(1)disp_message(WindowHandleAlign,[Lines measured in the reference image,and reference points on the lines],window,12,12,black,true)disp_continue_message(WindowHandle,black,true)stop()**Create the variation model.create_variation_model(Width,Height,byte,standard,VariationModelID)forI:1to15by1read_image(Image,pen/pen-I$02d)*Apply the metrology model to the current image and read out the line segment*coordinates.apply_metrology_model(Image,MetrologyHandle)get_metrology_object_result(MetrologyHandle,all,all,result_type,row_begin,RowBegin)get_metrology_object_result(MetrologyHandle,all,all,result_type,column_begin,ColBegin)get_metrology_object_result(MetrologyHandle,all,all,result_type,row_end,RowEnd)get_metrology_object_result(MetrologyHandle,all,all,result_type,column_end,ColEnd)*Determine a rigid transformation based on the point-to-line correspondences*from the reference points to the extracted lines.Note that this determines*a transformation from the reference points to the lines in the current image.*Therefore,we must invert this transformation to obtain the transformation*from the current image to the rerefence image.point_line_to_hom_mat2d(rigid,RowRef0.5,ColRef0.5,RowBegin0.5,ColBegin0.5,RowEnd0.5,ColEnd0.5,HomMat2D)hom_mat2d_invert(HomMat2D,HomMat2DInvert)affine_trans_image(Image,ImageTrans,HomMat2DInvert,constant,false)*Train the variation model with the transformed,i.e.,aligned image.train_variation_model(ImageTrans,VariationModelID)dev_set_window(WindowHandleAlign)dev_display(Image)get_metrology_object_result_contour(MeasuredLines,MetrologyHandle,all,all,1.5)dev_set_line_width(2)dev_set_color(green)dev_display(MeasuredLines)dev_set_color(blue)dev_display(RefPoints)dev_set_line_width(1)disp_message(WindowHandleAlign,[Lines measured in the current,image and reference points],window,12,12,black,true)dev_set_window(WindowHandle)dev_clear_window()dev_display(ImageTrans)affine_trans_contour_xld(MeasuredLines,MeasuredLinesTrans,HomMat2DInvert)dev_set_line_width(2)dev_set_color(green)dev_display(MeasuredLinesTrans)dev_set_color(blue)dev_display(RefPoints)dev_set_line_width(1)disp_message(WindowHandle,[Measured lines transformed to the,reference image pose and reference points],window,12,12,black,true)disp_continue_message(WindowHandle,black,true)stop()endforget_variation_model(MeanImage,VarImage,VariationModelID)prepare_variation_model(VariationModelID,20,3)*We can now free the training data to save some memory.clear_train_data_variation_model(VariationModelID)dev_set_window(WindowHandleAlign)dev_display(MeanImage)disp_message(WindowHandleAlign,Reference image,window,12,12,black,true)dev_set_window(WindowHandle)dev_display(VarImage)disp_message(WindowHandle,Variation image,window,12,12,black,true)disp_continue_message(WindowHandle,black,true)stop()**Check and classify images of correct and incorrect prints.NumImages:30forI:1to30by1read_image(Image,pen/pen-I$02d)apply_metrology_model(Image,MetrologyHandle)get_metrology_object_result(MetrologyHandle,all,all,result_type,row_begin,RowBegin)get_metrology_object_result(MetrologyHandle,all,all,result_type,column_begin,ColBegin)get_metrology_object_result(MetrologyHandle,all,all,result_type,row_end,RowEnd)get_metrology_object_result(MetrologyHandle,all,all,result_type,column_end,ColEnd)point_line_to_hom_mat2d(rigid,RowRef0.5,ColRef0.5,RowBegin0.5,ColBegin0.5,RowEnd0.5,ColEnd0.5,HomMat2D)hom_mat2d_invert(HomMat2D,HomMat2DInvert)affine_trans_image(Image,ImageTrans,HomMat2DInvert,constant,false)reduce_domain(ImageTrans,RegionROI,ImageReduced)compare_variation_model(ImageReduced,RegionDiff,VariationModelID)connection(RegionDiff,ConnectedRegions)select_shape(ConnectedRegions,RegionsError,area,and,20,1000000)count_obj(RegionsError,NumError)dev_set_window(WindowHandle)dev_clear_window()dev_display(ImageTrans)dev_set_color(red)dev_set_draw(margin)dev_display(RegionsError)disp_message(WindowHandle,Aligned image,window,12,12,black,true)if(NumError0)disp_message(WindowHandle,Print OK,window,12,300,green,false)elsedisp_message(WindowHandle,Print not OK,window,12,300,red,false)endifdev_set_window(WindowHandleAlign)dev_display(Image)get_metrology_object_result_contour(MeasuredLines,MetrologyHandle,all,all,1.5)dev_set_line_width(2)dev_set_color(green)dev_display(MeasuredLines)dev_set_color(blue)dev_display(RefPoints)dev_set_line_width(1)disp_message(WindowHandleAlign,[Lines measured in the current,image and reference points],window,12,12,black,true)if(INumImages)disp_continue_message(WindowHandle,black,true)stop()endif endforclear_variation_model(VariationModelID)clear_metrology_model(MetrologyHandle)八、总结关键点回顾get_metrology_object_result是2D计量模块获取最终拟合测量结果的核心算子支持读取几何参数、拟合分数、所用边缘点GenParamName和GenParamValue的组合决定结果类型all_param可批量返回目标的所有几何参数且坐标类型像素/公制取决于相机参数是否设置结果顺序遵循“先对象内实例后多个对象”的规则角度方向可通过angle_direction控制默认逆时针单位弧度。