选择轮廓(select_shape)
选择轮廓(select_shape)
Halcon是一款运用广泛的图像识别和处理软件。在肤浅的接触中,它的轮廓选择算子(select_shape)给予我很深的印象。结果是往往几行代码就能够产生很好的效果:
比如要得到这样的结果
只需要
read_image (Image1, 'F:/未来项目/钢管识别/FindTube/FindTube/1.jpg') rgb1_to_gray (Image1, GrayImage) threshold (GrayImage, Regions, 43, 111) connection (Regions, ConnectedRegions) select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 150, 666) select_shape (SelectedRegions, SelectedRegions1, 'circularity', 'and', 0. 45, 1)
当然Halcon是在背后做了许多工作的。
几行代码中,比较重要的是算子就是"select_shape"。这个算子的参数很多,我也就比较熟悉这两种。
如果我想在Opencv中也要这样的结果,就需要自己动手尝试实现。实现过程中我采用了类似的函数名表示敬意。
// selectshape.cpp : 选择轮廓 // by: jsxyhelu(1755311380) # include "stdafx.h" # include <iostream > # include "opencv2/core/core.hpp" # include "opencv2/highgui/highgui.hpp" # include "opencv2/imgproc/imgproc.hpp" using namespace std; using namespace cv; # define VP vector <Point > //用VP符号代替 vector<point> RNG rng( 12345 ); //带有上下限的threshold void threshold2(Mat gray,Mat & thresh, int minvalue, int maxvalue) { Mat thresh1; Mat thresh2; threshold(gray,thresh1, 43, 255, THRESH_BINARY); threshold(gray,thresh2, 111, 255,THRESH_BINARY_INV); thresh = thresh1 & thresh2; } //寻找并绘制出联通区域 vector <VP > connection2(Mat src,Mat & draw) { draw = Mat : :zeros(src.rows,src.cols,CV_8UC3); vector <VP >contours; findContours(src,contours,CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE); for ( int i = 0;i <contours.size();i ++) { Scalar color = Scalar(rng.uniform( 0, 255),rng.uniform( 0, 255),rng.uniform( 0, 255)); drawContours(draw,contours,i,color, - 1); } return contours; } //select_shape vector <VP > selectShapeArea(Mat src,Mat & draw,vector <VP > contours, int minvalue, int maxvalue) { vector <VP > result_contours; draw = Mat : :zeros(src.rows,src.cols,CV_8UC3); for ( int i = 0;i <contours.size();i ++) { int countour_area = contourArea(contours[i]); if (countour_area >minvalue && countour_area <maxvalue) { result_contours.push_back(contours[i]); } } for ( int i = 0;i <result_contours.size();i ++) { Scalar color = Scalar(rng.uniform( 0, 255),rng.uniform( 0, 255),rng.uniform( 0, 255)); drawContours(draw,result_contours,i,color, - 1); } return result_contours; } //计算轮廓的圆的特性 float calculateCircularity(VP contour) { Point2f center; float radius = 0; minEnclosingCircle((Mat)contour,center,radius); //以最小外接圆半径作为数学期望,计算轮廓上各点到圆心距离的标准差 float fsum = 0; float fcompare = 0; for ( int i = 0;i <contour.size();i ++) { Point2f ptmp = contour[i]; float fdistenct = sqrt(( float)((ptmp.x - center.x) *(ptmp.x - center.x) +(ptmp.y - center.y) *(ptmp.y -center.y))); float fdiff = abs(fdistenct - radius); fsum = fsum + fdiff; } fcompare = fsum /( float)contour.size(); return fcompare; } //select_shape vector <VP > selectShapeCircularity(Mat src,Mat & draw,vector <VP > contours, float minvalue, float maxvalue) { vector <VP > result_contours; draw = Mat : :zeros(src.rows,src.cols,CV_8UC3); for ( int i = 0;i <contours.size();i ++) { float fcompare = calculateCircularity(contours[i]); if (fcompare > =minvalue && fcompare < =maxvalue) { result_contours.push_back(contours[i]); } } for ( int i = 0;i <result_contours.size();i ++) { Scalar color = Scalar(rng.uniform( 0, 255),rng.uniform( 0, 255),rng.uniform( 0, 255)); drawContours(draw,result_contours,i,color, - 1); } return result_contours; } int _tmain( int argc, _TCHAR * argv[]) { Mat src; Mat gray; Mat thresh; Mat draw_connection; Mat draw_area; Mat draw_circle; vector <VP >contours_connection; vector <VP >contours_area; vector <VP >contours_circle; vector <VP >contours_tmp; //read_image (Image1, 'F:/未来项目/钢管识别/FindTube/FindTube/1.jpg') src = imread( "1.jpg"); //rgb1_to_gray (Image1, GrayImage) cvtColor(src,gray,COLOR_BGR2GRAY); //threshold (GrayImage, Regions, 43, 111) threshold2(gray,thresh, 43, 111); //connection (Regions, ConnectedRegions) contours_connection = connection2(thresh.clone(),draw_connection); //select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 150, 666) contours_area = selectShapeArea(thresh.clone(),draw_area,contours_connection, 150, 666); //select_shape (SelectedRegions, SelectedRegions1, 'circularity', 'and', 0.45, 1) contours_circle = selectShapeCircularity(thresh.clone(),draw_circle,contours_area, 1, 6); //显示结果 imshow( "src",src); imshow( "thresh",thresh); imshow( "draw_connection",draw_connection); imshow( "draw_area",draw_area); imshow( "draw_circle",draw_circle); waitKey(); }
结果如下,这段代码中还有一个问题,就是计算轮廓圆的性质的方法,我这里采用的是自己想出来的方法,似乎不是很完善,需要进一步找到资料才修正。