怎样为网站设计关键词,wordpress配置域名,wordpress sae 4.4,湖南网站建设怎么样文章目录 get_domain 返回所有输入图像的定义域作为一个区域add_channels 给区域增加灰度值find_shape_model 发现匹配模板find_shape_models 发现最佳模板示例 get_domain 返回所有输入图像的定义域作为一个区域 Halcon 中的区域 get_domain(Image : Domain : : ) Image : … 文章目录 get_domain 返回所有输入图像的定义域作为一个区域add_channels 给区域增加灰度值find_shape_model 发现匹配模板find_shape_models 发现最佳模板示例 get_domain 返回所有输入图像的定义域作为一个区域 Halcon 中的区域 get_domain(Image : Domain : : ) Image : 图像 (input_object): 类型可以是 (多通道-) 图像数组对象类型为字节、方向、循环、int1、int2、uint2、int4、int8、实数、复数或矢量场。这是输入图像。Domain : 域 (output_object): 类型是区域数组对象类型。这是输入图像的定义域。add_channels 给区域增加灰度值 add_channels(Regions, Image : GrayRegions : : ) region(-array) → object: 这个参数是指输入的区域通常是一个数组描述了图像中的某些区域但不包含像素值。 Image (input_object) (multichannel-)image → object: 这个参数是指包含像素值的输入图像可能是多通道图像。 GrayRegions (output_object) image(-array) → object: 这个参数表示输出的图像或图像数组其中包含了具有像素值的区域每个输入区域对应一个输出图像。 find_shape_model 发现匹配模板 find_shape_model(Image : : //搜索图像ModelID, //模板句柄AngleStart, // 搜索时的起始角度AngleExtent, //搜索时的角度范围必须与创建模板时的有交集MinScore, //最小匹配值输出的匹配的得分Score 大于该值NumMatches, //定义要输出的匹配的最大个数MaxOverlap, //当找到的目标存在重叠时且重叠大于该值时选择一个好的输出 //如果MaxOverlap0, 找到的目标区域不能存在重叠, 如果MaxOverla p1所有找到的目标区域都要返回。SubPixel, //计算精度的设置五种模式多选23NumLevels, //搜索时金字塔的层数Greediness : //贪婪度搜索启发式一般都设为0.9越高速度快,容易出现找不到的情况//0安全慢1块不稳定其他就是介于中间值 Row,Column, Angle, Score) //输出匹配位置的行和列坐标、角度、得分。find_shape_models 发现最佳模板 find_shape_models(Image : : ModelIDs, AngleStart, AngleExtent, MinScore, NumMatches, MaxOverlap, SubPixel, NumLevels, Greediness : Row, Column, Angle, Score, Model)Image (input_object)这个参数表示输入图像数据这可能是多通道图像数组用于在其中找到模型。ModelIDs (input_control)这是模板或模型的ID数组用于指定哪一个或多个模型将在图像中寻找。AngleStart (input_control)指定模型可能的最小旋转角度单位是弧度。例如-0.39弧度表示模型可能有一些初始旋转。AngleExtent (input_control)模型的旋转角度范围单位为弧度。例如0.79弧度表示可以允许模型在指定角度范围内旋转。MinScore (input_control)找到模型实例的最小评分标准。0到1之间的值表示匹配得分的下限。NumMatches (input_control)要找到的模型实例的数量。如果为0表示找出所有匹配的实例。MaxOverlap (input_control)指定模型实例之间的最大重叠程度。值在0到1之间表示允许多大程度的重叠。SubPixel (input_control)选择是否使用子像素精度进行计算。如果不等于 none表示使用特定的子像素方法。NumLevels (input_control)用于匹配时的金字塔层级数量。更多的层级可能提供更快的匹配但可能牺牲准确性。Greediness (input_control)搜索算法的“贪心度”。0表示安全但慢1表示快速但可能错过匹配。值在0到1之间。Row (output_control)、Column (output_control)找到的模型实例的行坐标和列坐标。Angle (output_control)找到的模型实例的旋转角度。Score (output_control)找到的模型实例的匹配得分。Model (output_control)找到的模型实例的索引。示例 * This example program checks the right position of a label on * a shampoo bottle. * First, two shape models for the bottle and the label are created. * Then, the models are searched in a sequence of images, * and it is checked, if their relative position is within the * specified tolerances. * * init visualization dev_update_off () dev_set_draw (margin) dev_set_line_width (2) * * load reference image for model preparation * 0.读取图片 set_system (clip_region, false) read_image (Image, packaging/shampoo_01) dev_close_window () dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle) set_display_font (WindowHandle, 14, mono, true, false) dev_display (Image) * * prepare the shape model for matching * * generate first model region and search ROI * 1.产生第一个感兴趣的区域 BottleModelRow : 131 BottleModelColumn : 370 BottleModelLength1 : 350 BottleModelLength2 : 35 BottleModelPhi : -0.1093 * 绘制矩形 gen_rectangle2 (Rectangle1, BottleModelRow, BottleModelColumn, BottleModelPhi, BottleModelLength1, BottleModelLength2) gen_rectangle2 (Rectangle2, BottleModelRow 220, BottleModelColumn, -BottleModelPhi, BottleModelLength1, BottleModelLength2) * 将矩形联合 union2 (Rectangle1, Rectangle2, TemplateBottleRegion) * 获取整个图像的区域 get_domain (Image, Domain) * 计算区域的交集 intersection (TemplateBottleRegion, Domain, TemplateBottleRegion) * 获取相交的中心点区域和坐标 area_center (TemplateBottleRegion, Area, RowBottleRef, ColumnBottleRef) * 产生一个圆 gen_circle (SearchROIBottle, RowBottleRef, ColumnBottleRef, 40) * 将感兴趣的区域裁剪 reduce_domain (Image, TemplateBottleRegion, ImageReduced) * * create shape model * 2.创建形状匹配模型 ModelIDBottle create_shape_model (ImageReduced, 5, -rad(3), rad(6), 0, auto, use_polarity, 25, 3, ModelIDBottle) * * display shape model dev_display (Image) dev_set_color (forest green) dev_display (TemplateBottleRegion) dev_set_color (slate blue) disp_message (WindowHandle, bottle shape model, window, 12, 12, black, true) disp_continue_message (WindowHandle, black, true) stop () * * generate second model region and search ROI * 3.产生第二个感兴趣的区域 LabelRow1 : 180 LabelRow2 : 310 LabelColumn1 : 50 LabelColumn2 : 470 * 产生一个矩形 gen_rectangle1 (TemplateLabelRegion, LabelRow1, LabelColumn1, LabelRow2, LabelColumn2) * 获取区域的中心点以及坐标 area_center (TemplateLabelRegion, Area1, RowLabelRef, ColumnLabelRef) * 产生一个圆 gen_circle (SearchROILabel, RowLabelRef, ColumnLabelRef, 60) * 将感兴趣的区域裁剪 reduce_domain (Image, TemplateLabelRegion, ImageReduced) * 4.检查已创建的形状模型并获得其属性信息 inspect_shape_model (ImageReduced, ModelImages, ModelRegions, 1, 25) * * create shape model * 创建模板 ModelIDLabel1 create_shape_model (ImageReduced, 5, rad(-3), rad(6), 0, auto, use_polarity, 25, 5, ModelIDLabel1) * 将匹配模板选择180度创建模板 ModelIDLabel2 create_shape_model (ImageReduced, 5, rad(180 - 3), rad(6), 0, auto, use_polarity, 25, 5, ModelIDLabel2) ModelIDsLabel : [ModelIDLabel1,ModelIDLabel2] * * display label model dev_display (Image) dev_set_color (forest green) dev_display (TemplateLabelRegion) dev_set_color (slate blue) dev_display (ModelRegions) disp_message (WindowHandle, create label shape model, window, 12, 12, black, true) disp_continue_message (WindowHandle, black, true) stop () * * MainLoop * * check relative positions of label and bottle in all images * 5.检测位置 for Index : 1 to 13 by 1read_image (Image, packaging/shampoo_ Index$.2)dev_display (Image)disp_message (WindowHandle, check label position, window, 12, 12, black, true)count_seconds (s1)* 裁剪第一次的搜索圆reduce_domain (Image, SearchROIBottle, ImageReduced)* 通过ModelIDBottle寻找搜索圆获取ScoreBottlefind_shape_model (ImageReduced, ModelIDBottle, -rad(3), rad(6), 0.7, 1, 0.5, least_squares, 0, 0.9, RowBottle, ColumnBottle, AngleBottle, ScoreBottle)* prepare search ROIs in search image* 将两个搜索标记合并concat_obj (SearchROILabel, SearchROILabel, SearchROIs)add_channels (SearchROIs, Image, GrayRegions)* * search shape models* 通过ModelIDsLabel 查找匹配模板获取Score得分注意角度范围并且获取FoundModelfind_shape_models (GrayRegions, ModelIDsLabel, [rad(-3),rad(180 - 3)], [rad(6),rad(6)], 0.6, 1, 1, interpolation, 0, 0.9, Row, Column, Angle, Score, FoundModel)count_seconds (s2)if (|Score| ! 1 or |ScoreBottle| ! 1)disp_message (WindowHandle, Model not found, window, 40, 12, red, true)else* 如果模板if (ModelIDsLabel[FoundModel] ModelIDLabel2)* 显示图标选择 180°disp_message (WindowHandle, Label rotated by 180°, window, 40, 12, red, true)else* calculate y deviation* 计算y的偏移量Diffy : (RowBottle - Row) - (RowBottleRef - RowLabelRef)* calculate x deviation* 计算x的偏移量Diffx : (ColumnBottle - Column) - (ColumnBottleRef - ColumnLabelRef)* * check the rotation angle of the bottle label* 计算旋转角度Diffa : deg(AngleBottle - Angle)* * calculate the time elapsedTime : s2 - s1* * display results* 显示结果Color : blackModelColor : forest greenif (abs(Diffx) 3)Color : [Color,red]ModelColor : redelseColor : [Color,forest green]endifif (abs(Diffy) 1)Color : [Color,red]ModelColor : redelseColor : [Color,forest green]endifif (abs(Diffa) 1)Color : [Color,red]ModelColor : redelseColor : [Color,forest green]endif* 显示匹配结果dev_display_shape_matching_results (ModelIDsLabel, [slate blue,ModelColor], Row, Column, Angle, 1, 1, FoundModel)* 提示消息disp_message (WindowHandle, [Time (Time * 1000)$ .2 ms,Diff x Diffx$ .2,Diff y Diffy$ .2,Diff angle Diffa$ .2], window, 35, 12, Color, true)endif* endifif (Index 13)disp_continue_message (WindowHandle, black, true)stop ()elsedisp_message (WindowHandle, Program finished, window, 450, 430, Color, true)endif endfor stop () clear_shape_model (ModelIDLabel1) clear_shape_model (ModelIDLabel2) clear_shape_model (ModelIDBottle) set_system (clip_region, true)