形态学基本操作与应用代码和结果展示剖析

1、#in elude #in elude #in elude using n amespace ev; /把灰度图像转化为二值图像 Mat ehangeToBinarylmage(Mat grayimage ) Mat binarylmage(graylmage .rows, graylmage .cols,CV 8UC1 Sealar (0); / 转化为二值图像 for (int i = 0; i graylmage .rows; i+) for (int j = 0; j 100) binarylmage.datai*graylmage .step + j = 255; else bina

2、rylmage.datai*graylmage .step + j = 0; imshow( binarylmage , binarylmage); returnbin arylmage; /创建结构元素| / 一般结构元素关于原点对称 /Mat createSE() / / int a33= 0,1,0, / 1,1,1, / 0,1,0; /Mat structureEleme nt(3, 3, CV_8UC1, a); / _ /二值图像腐蚀操作 Mat bin aryErosi on( Mat binarylmage ,Mat se) /二值图像移动 Mat window( se /定

3、义一个矩阵, .rows, se.cols, 存储腐蚀后的图像 CV_8UC1; Mat binaryErosionlmage(binarylmage .rows, binarylmage .cols, CV_8UC1 Scalar (0); for ( int i = (se.rows-1)/2; i binaryImage .rows-( se.rows-1)/2; i+) for (int j = ( se .cols - 1) / 2; j bin aryImage .cols - ( se .cols - 1) / 2; j+) /先设置第i行第j列像素值为255，即白色 bin a

4、ryErosi onl mage.datai*bin aryImage .step + j = 255; for ( int row = 0; row se.rows; row+) for ( int col = 0; col se.cols; col+) /把se对应的元素赋值到与se结构相同的矩阵中 wi ndow.datarow*wi ndow.step + col =bin arylmage .data(i + row - (win dow.rows - 1) / 2)*bin arylmage .step + (j + col - (win dow.cols - 1) / 2); /

5、 比较 se 与 window 中的像素值 int row, col; for (row = 0; row se.rows; row+) for (col = 0 ;col se.cols; col+) se.step + col) if (se.datarow* se.step + col != window.datarow* break ; if (row = se .rows imshow(bi naryErosio nlmage, bin aryErosi onl mage); returnbin aryErosi onl mage; /二值图像膨胀操作 Mat bin aryDila

6、tio n( Mat binaryImage ,Mat se) /二值图像移动 Mat window( se.rows, se.cols, CV_8UC1; /定义一个矩阵，存储膨胀后的图像 Mat binaryDilationlmage(binaryImage .rows,binaryImage .cols, CV_8UC1 Scalar (0); for ( int i = ( se.rows - 1) / 2; i binaryImage .rows - ( se.rows - 1) / 2; i+) for (int j = ( se .cols - 1) / 2; j bin ary

7、Image .cols - ( se .cols - 1) / 2; j+) /先设置第i行第j列像素值为 255，即白色 bin aryDilati onl mage.datai* bin aryImage .step + j = 255; for ( int row = 0; row se .rows; row+) for ( int col = 0; col se.cols; col+) /把se对应的元素赋值到与se结构相同的矩阵中 wi ndow.datarow*wi ndow.step + col =bin aryImage .data(i + row - (window.rows

8、 - 1) / 2)* bin aryImage .step + (j + col - (win dow.cols - 1) / 2); /比较se 与window中的像素值 /只要有 个相匹配就把像素值设为0，即置黑 int flag = 0;/标记是否有对应相等的像素值 :0表示没有，1表示有 int row, col; for (row = 0; row se.rows; row+) for (col = 0; col se.cols; col+) if (se.datarow* se.step + col = window.datarow* se.step + col) /如果有交集，

9、就设置为黑，即0 bin aryDilati onl mage.datai*bin arylmage .step + j = 0; | imshow(bi naryDilatio nlmage, bin aryDilati onl mage); return bi naryDilatio nl mage; /灰度图像腐蚀操作 Mat grayErosion(Mat graylmage , Mat se) 结构元素移动时所对应的源图像区域 Mat window( se.rows, se.cols, CV_8UC1; /定义一个矩阵，存储腐蚀后的图像 Mat grayErosionlmage( g

10、raylmage .rows, graylmage .cols, CV_8UC1 Scalar (0); for (int i = ( se.rows - 1) / 2; i graylmage .rows - ( se .rows - 1) / 2; i+) for (int j = ( se.cols - 1) / 2; j graylmage .cols - ( se.cols - 1) / 2; j+) /先设置第i行第j列像素值为255，即白色 grayErosi onl mage.datai* graylmage .step + . =255; for ( int row = 0;

11、 row se.rows; row+) for ( int col = 0; col se.cols; col+) /把se对应的元素赋值到与se结构相同的矩阵 window中 window.datarow*window.step + col =grayimage .data(i + row - (window.rows - 1) / 2)*grayimage .step + (j + col - (window.cols - 1) / 2); /比较se与window中的像素值 /在灰度图像中，腐蚀是取 window中最小的值赋值给原点所对用的像素 int min Pixel = 255;

12、int row, col; for (row = 0; row se.rows; row+) for (col = 0; col se.cols; col+) if (window.datarow* se.step + col minPixel) min Pixel = window.datarow*se .step + col; gray image .step + j = min Pixel; /*imshow(grayErosio ni mage, grayErosi oni mage);*/ return grayErosio nlmage; /灰度图像膨胀操作 Mat grayDil

13、ation( Mat grayimage , Mat se) /结构元素移动时所对应的源图像区域 Mat window( se.rows, se.cols, CV_8UC1; /定义一个矩阵，存储腐蚀后的图像 Mat grayDilationimage( grayimage .rows, grayimage .cols, CV_8UC1 Scalar (0); for (int i = ( se.rows - 1) / 2; i grayimage .rows -( se .rows - 1) / 2; i+) for (int j = ( se.cols - 1) / 2; j grayim

14、age .cols - -(se.cols - 1) / 2; j+) /先设置第i行第j列像素值为255，即白色 grayDilati oni mage.datai* grayimage .step + j = 255; for ( int row = 0; row se.rows; row+) for ( int col = 0; col se.cols; col+) /把se对应的元素赋值到与se结构相同的矩阵 window中 window.datarow*window.step + col =grayimage .data(i + row - (window.rows - 1) / 2

15、)*grayimage .step + (j + col - (window.cols - 1) / 2); /比较se与window中的像素值 /在灰度图像中，膨胀是取 window中最大的值赋值给原点所对用的像素 int maxPixel = 0; int row, col; for (row = 0; row se.rows; row+) for (col = 0; col maxPixel) grayDilati oni mage.datai* grayimage .step + j = maxPixel; /*imshow(grayDilatio ni mage, grayDilat

16、i oni mage);*/ return grayDilati oni mage; / 二值图像开操作 Mat binaryOpen(Mat binaryimage Mat se) Mat openlmage( binarylmage .rows, binarylmage .cols, CV_8UC1 Scalar (0); openlmage = binaryDilation(binaryErosion(binaryImage , se), se); retur n ope nl mage; /二值图像闭操作 Mat binaryClose(Mat binarylmage ,Mat se)

17、 Mat closelmage(bin arylmage .rows,binarylmage .cols,CV_8UC1 Scalar (0); closelmage = binaryErosion(binaryDilation(binarylmage , se), se); return closelmage; /灰度图像开操作 Mat grayOpen( Mat graylmage , Mat se) Mat openlmage( graylmage .rows, graylmage .cols, CV_8UC1 Scalar (0); openlmage = grayDilation(g

18、rayErosion(graylmage , se), se); retur n ope nl mage; /灰度图像闭操作 Mat grayClose( Mat graylmage , Mat se) Mat closelmage( graylmage .rows, graylmage .cols, CV_8UC1 Scalar (0); closelmage = grayErosion(grayDilation(graylmage , se), se); return closelmage; /二值图像边界提取 Mat binaryBorder(Mat binarylmage , Mat

19、se) Mat borderlmage( binarylmage .rows,binarylmage .cols, CV_8UC1 Scalar (0); Mat erosi onl mage(bin arylmage .rows,bin arylmage .cols,CV 8UC1 Scalar (0); erosionlmage = binaryErosion(binarylmage , se); for ( int i = 0; i erosionlmage.rows; i+) for ( int j = 0; j erosionlmage.cols; j+) if (bin arylm

20、age .datai*erosio nlmage.step+j!=erosio nlmage.data i *erosio nl mage.step+j return borderlmage; /灰度图像边界提取 Mat grayBorder( Mat graylmage , Mat se) Mat borderlmage( graylmage .rows, graylmage .cols,CV_8UC1 Scalar (0); borderlmage = graylmage - grayErosion(graylmage , se); retur n borderlmage; /灰度图像梯度

21、 Mat gradient(Mat graylmage , Mat se) Mat gradient( graylmage .rows, graylmage .cols, CV_8UC1 Scalar (0); gradient = grayDilation(graylmage , se) - grayErosion( graylmage , se); return gradie nt; /灰度图像的顶帽运算T (f) =f-fob Mat topHat( Mat graylmage , Mat se) Mat topHatlmage( grayimage .rows, grayimage .

22、cols, CV_8UC1 Scalar (0); topHatimage = grayImage - grayOpen(grayImage , se); return topHatimage; /灰度图像的底帽运算 B (f) =f?b-f Mat bottomHat( Mat grayimage , Mat se) Mat bottomHatimage( grayimage .rows, grayimage .cols, CV_8UC1 Scalar (0); bottomHatimage = grayClose( grayimage , se)- grayimage ; retur n

23、bottomHatimage; int main() Mat src = imread( E:projectimages32.jpg); Mat grayimage(src.rows, src.cols,CV_8UC1; /转化为灰度图像 cvtColor(src, grayimage,CV_BGR2GRAY imshow( original image ,src); imshow( gray image , grayimage); /转化为二值图像 Mat bin aryimage = cha ngeToB in aryimage(grayimage); /创建模板 一般结构元素关于自身原点

24、对称 /也可以自定义结构元素 下面的变量是3*3的矩阵全部为0 Mat structureEleme nt(3, 3, CV_8UC1 Scalar (0); /调用二值图像腐蚀函数 /bin aryErosio n(bi narylmage, structureEleme nt); imshow( binaryErosionimage, binaryErosion(binarylmage, structureElement); /调用二值图像膨胀函数 /binaryDilation(binarylmage, structureElement); imshow( binaryDilationl

25、mage ,bin aryDilati on (bi narylmage, structureEleme nt); /调用灰度图像腐蚀函数 /grayErosio n(graylmage, structureEleme nt); imshow( grayErosionlmage , grayErosion(graylmage, structureElement); /调用灰度图像膨胀函数 /grayDilatio n(graylmage, structureEleme nt); imshow( grayDilationlmage , grayDilation(graylmage, structureElement); /调用二值图像开操作 imshow( binaryOpenlmage,bina