形状匹配之detect

*这个例程演示了在图片数据库中寻找文章的页码 *第一步：不同页被训练，并模型内创建，之后，就在未知的图像中搜寻类似的文章。 *因为训练数据很大，所以需要一些内存

This example finds pages of articles in a picture database.In the first step different pages are trained and models are created.Afterwards unknown images are searched and the correct articlepages are detected.Please notice that this example needs some memory to train the models.

*滚啊比更新 dev_update_off () *关闭窗体 dev_close_window () *读取图像 read_image (Image, ‘brochure/brochure_page_01’) *获取图像尺寸 get_image_size (Image, Width, Height) *打开适合图像尺寸的窗体 dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle) *设置窗体字体，会影响窗体尺寸 set_display_font (WindowHandle, 14, ‘mono’, ‘true’, ‘false’) *设置填充方式 dev_set_draw (‘margin’) *显示图像 dev_display (Image)

清除所有创建的描述符模型数据Clear all already created descriptor models. ModelIDs := [] ModelsFound := 0 NumPoints := [] NumModels := 3 TotalTime := 0创建可视区域Create region for visualization purpose. RowRoi := [10,10,Height - 10,Height - 10] ColRoi := [10,Width - 10,Width - 10,10] *生成矩形 gen_rectangle1 (Rectangle, 10, 10, Height - 10, Width - 10) *显示信息 disp_message (WindowHandle, [‘Press ‘Run’ to start model creation …’,’(may take a few minutes)’], ‘window’, 10, 10, ‘black’, ‘true’) *右下角显示“PRESS F5 TO CONTINUE” disp_continue_message (WindowHandle, ‘black’, ‘true’) stop ()为每一页创建描述符模型For every page the descriptor model is created. for Index := 1 to NumModels by 1 *读取图像 read_image (Image, ‘brochure/brochure_page_’ + Index$’.2’) *转为灰度图像 rgb1_to_gray (Image, ImageGray) *获取图像尺寸 get_image_size (ImageGray, Width, Height) *裁剪区域 reduce_domain (ImageGray, Rectangle, ImageReduced) *清除窗体 dev_clear_window () *显示灰度图像 dev_display (ImageGray) *显示创建第几个模型的信息 disp_message (WindowHandle, ‘Creating model no. ’ + Index + ‘/’ + NumModels + ’ … please wait.’, ‘window’, 10, 10, ‘black’, ‘true’) 用默认设置参数创建基于描述符的模型Create the descriptor model with default parameters (except scaling) *为了更快检测，参数选用哈里斯二项式角点检测For a fast detection, the harris binomial point detector is chosen. *计时1开始 count_seconds (Seconds1) *创建描述符模型 create_uncalib_descriptor_model (ImageReduced, ‘harris_binomial’, [], [], [‘min_rot’,‘max_rot’,‘min_scale’,‘max_scale’], [-90,90,0.2,1.1], 42, ModelID) count_seconds (Seconds2) *计时2 *计算时间差 TotalTime := TotalTime + (Seconds2 - Seconds1)为了后续矩形的投影正确，modelID中心应该放在图像原点For the correct projection of the rectangles in a later step the originof the model has to be set to the image origin *设置模型原点 set_descriptor_model_origin (ModelID, -Height / 2, -Width / 2) *模型集合 ModelIDs := [ModelIDs,ModelID]存储从模型中提取出来的点位Store the points which are extracted from the model for later matching. *提取点位 get_descriptor_model_points (ModelID, ‘model’, ‘all’, Row_D, Col_D) *把点位存到数组中 NumPoints := [NumPoints,|Row_D|] endfor 创建模型完成Model creation finished. *显示灰度图像 dev_display (ImageGray) *显示创建了几个模型 disp_message (WindowHandle, NumModels + ’ models created in ’ + TotalTime$’.4’ + ’ seconds.’, ‘window’, 10, 10, ‘black’, ‘true’) *右下角显示“PRESS F5 TO CONTINUE” disp_continue_message (WindowHandle, ‘black’, ‘true’) stop ()由于图像尺寸有变化，所以需要再次初始化窗体Initialize the window again, because the image size has changed. *读取图像 read_image (Image, ‘brochure/brochure_01’) *打开适合图像尺寸的窗体 dev_resize_window_fit_image (Image, 0, 0, -1, -1) *设置窗体字体，会影响窗体尺寸 set_display_font (WindowHandle, 14, ‘mono’, ‘true’, ‘false’)主循环Main loop: *在所有图像中寻找模型Search the models in all images for Index1 := 1 to 12 by 1 OutputString := [] NumMsgs := 0 ModelsFound := 0 TotalTime := 0 *读取图像 read_image (Image, ‘brochure/brochure_’ + Index1$’.2’) *转灰度图像 rgb1_to_gray (Image, ImageGray) *显示图像 dev_display (Image) *显示信息，搜寻中… disp_message (WindowHandle, ‘Searching image …’, ‘window’, 10, 10, ‘black’, ‘true’)

在图像中搜寻每一个模型

Search every model in each image for Index2 := 0 to |ModelIDs| - 1 by 1

默认参数，寻找模型Find model (using default parameters) count_seconds (Seconds1) *寻找模型 find_uncalib_descriptor_model (ImageGray, ModelIDs[Index2], ‘threshold’, 600, [‘min_score_descr’,‘guided_matching’], [0.003,‘on’], 0.25, 1, ‘num_points’, HomMat2D, Score) count_seconds (Seconds2) Time := Seconds2 - Seconds1 TotalTime := TotalTime + Time找到的点的数量来判定找到结果是否正确Check if the found instance is to be considered as a possible right matchdepending on the number of points which were considered

if ((|HomMat2D| > 0) and (Score > NumPoints[Index2] / 4)) *获取找到的点 get_descriptor_model_points (ModelIDs[Index2], ‘search’, 0, Row, Col) *生成十字叉 gen_cross_contour_xld (Cross, Row, Col, 6, 0.785398) * 投影ROI和点 * Project the ROI rectangle and points *投影区域 projective_trans_region (Rectangle, TransRegion, HomMat2D, ‘bilinear’) *投影点 projective_trans_pixel (HomMat2D, RowRoi, ColRoi, RowTrans, ColTrans) *求两条线的夹角 angle_ll (RowTrans[2], ColTrans[2], RowTrans[1], ColTrans[1], RowTrans[1], ColTrans[1], RowTrans[0], ColTrans[0], Angle) Angle := deg(Angle) * * 找到的角度来判定找到结果是否正确 * Check if the projected rectangle is to be considered as a right match * depending on the angle in the right upper edge. *如果角度在70-110 if (Angle > 70 and Angle < 110) *求区域中心 area_center (TransRegion, Area, Row, Column) ModelsFound := ModelsFound + 1 *设置显示颜色 dev_set_color (‘green’) *社会i线宽 dev_set_line_width (4) *显示变换后的区域 dev_display (TransRegion) *设置多色显示 dev_set_colored (12) *设置线宽 dev_set_line_width (1) *显示十字叉 dev_display (Cross) *显示一些信息 disp_message (WindowHandle, ‘Page ’ + (Index2 + 1), ‘window’, Row, Column, ‘black’, ‘true’) OutputString := [OutputString,‘Page ’ + (Index2 + 1) + ’ found in ’ + (Time * 1000)KaTeX parse error: Undefined control sequence: \n at position 12: '.4' + ' ms\̲n̲'] endif …’.4’ + ’ ms’,OutputString] disp_message (WindowHandle, OutputString, ‘window’, 10, 10, ‘black’, ‘true’) disp_continue_message (WindowHandle, ‘black’, ‘true’) stop () endfor dev_display (ImageGray) disp_message (WindowHandle, ‘Program finished.\nPress ‘Run’ to clear all descriptor models.’, ‘window’, 10, 10, ‘black’, ‘true’) stop ()

清除所有模型，释放内存 for Index := 0 to |ModelIDs| - 1 by 1 clear_descriptor_model (ModelIDs[Index]) endfor