形态学基本操作与应用代码和结果展示

1、#include#include#includeusing namespace cv;/把灰度图像转化为二值图像Mat changeToBinaryImage(Mat grayImage)Mat binaryImage(grayImage.rows, grayImage.cols, CV_8UC1, Scalar(0);/转化为二值图像for (int i = 0; i grayImage.rows; i+)for (int j = 0; j 100)binaryImage.datai*grayImage.step + j = 255;elsebinaryImage.datai*grayIma

2、ge.step + j = 0;imshow(binaryImage, binaryImage);return binaryImage;/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 structureElement(3, 3, CV_8UC1, a);/二值图像腐蚀操作Mat binaryErosion(Mat binaryImage, Mat se)/二值图像移动Mat window(se.rows, se.cols, CV_8UC1);/定义一个矩阵，存储腐蚀后的图像Mat binaryEros

3、ionImage(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 binaryImage.cols - (se.cols - 1) / 2; j+)/先设置第i行第j列像素值为255，即白色binaryErosionImage.datai*binaryImage.step + j = 255;for (int row = 0; row s

4、e.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window.datarow*window.step + col = binaryImage.data(i + row - (window.rows - 1) / 2)*binaryImage.step + (j + col - (window.cols - 1) / 2);/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; c

5、ol+)if (se.datarow*se.step + col != window.datarow*se.step + col)break;if (col = se.cols)continue;elsebreak;if (row = se.rows&col = se.cols)binaryErosionImage.datai*binaryImage.step + j = 0;/imshow(binaryErosionImage, binaryErosionImage);return binaryErosionImage;/二值图像膨胀操作Mat binaryDilation(Mat bina

6、ryImage, 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 binaryDilationImage(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 binaryImage.cols - (se.cols - 1) /

7、2; j+)/先设置第i行第j列像素值为255，即白色binaryDilationImage.datai*binaryImage.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window.datarow*window.step + col = binaryImage.data(i + row - (window.rows - 1) / 2)*binaryImage.step + (j + col - (window.co

8、ls - 1) / 2);/比较se与window中的像素值/只要有一个相匹配 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)flag = 1;if (flag)/如果有交集，就设置为黑，即0binaryDilationImage.datai*binaryImage.s

9、tep + j = 0;/imshow(binaryDilationImage, binaryDilationImage);return binaryDilationImage;/灰度图像腐蚀操作Mat grayErosion(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 grayErosionImage(grayImage.rows, grayImage.cols, CV_8UC1, Scalar(0);for (int i = (se.rows

10、- 1) / 2; i grayImage.rows - (se.rows - 1) / 2; i+)for (int j = (se.cols - 1) / 2; j grayImage.cols - (se.cols - 1) / 2; j+)/先设置第i行第j列像素值为255，即白色grayErosionImage.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

11、.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 minPixel = 255;int row, col;for (row = 0; row se.rows; row+)for (col = 0; col se.cols; col+)if (window.datarow*se.ste

12、p + col minPixel)minPixel = window.datarow*se.step + col;grayErosionImage.datai*grayImage.step + j = minPixel;/*imshow(grayErosionImage, grayErosionImage);*/return grayErosionImage;/灰度图像膨胀操作Mat grayDilation(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M

13、at 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 grayImage.cols - (se.cols - 1) / 2; j+)/先设置第i行第j列像素值为255，即白色grayDilationImage.datai*grayImage.step + j = 255;for (int row

14、 = 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)*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,

15、col;for (row = 0; row se.rows; row+)for (col = 0; col maxPixel)maxPixel = window.datarow*se.step + col;grayDilationImage.datai*grayImage.step + j = maxPixel;/*imshow(grayDilationImage, grayDilationImage);*/return grayDilationImage;/二值图像开操作Mat binaryOpen(Mat binaryImage, Mat se)Mat openImage(binaryIm

16、age.rows,binaryImage.cols,CV_8UC1,Scalar(0);openImage = binaryDilation(binaryErosion(binaryImage, se), se);return openImage;/二值图像闭操作Mat binaryClose(Mat binaryImage, Mat se)Mat closeImage(binaryImage.rows, binaryImage.cols, CV_8UC1, Scalar(0);closeImage = binaryErosion(binaryDilation(binaryImage, se)

17、, se);return closeImage;/灰度图像开操作Mat grayOpen(Mat grayImage, Mat se)Mat openImage(grayImage.rows, grayImage.cols, CV_8UC1, Scalar(0);openImage = grayDilation(grayErosion(grayImage, se), se);return openImage;/灰度图像闭操作Mat grayClose(Mat grayImage, Mat se)Mat closeImage(grayImage.rows, grayImage.cols, CV_

18、8UC1, Scalar(0);closeImage = grayErosion(grayDilation(grayImage, se), se);return closeImage;/二值图像边界提取Mat binaryBorder(Mat binaryImage,Mat se)Mat borderImage(binaryImage.rows, binaryImage.cols, CV_8UC1, Scalar(0);Mat erosionImage(binaryImage.rows, binaryImage.cols, CV_8UC1, Scalar(0);erosionImage = b

19、inaryErosion(binaryImage,se);for (int i = 0; i erosionImage.rows; i+)for (int j = 0; j erosionImage.cols; j+)if (binaryImage.datai*erosionImage.step+j!=erosionImage.datai*erosionImage.step+j)borderImage.datai*erosionImage.step + j = 255;return borderImage;/灰度图像边界提取Mat grayBorder(Mat grayImage, Mat s

20、e)Mat borderImage(grayImage.rows, grayImage.cols, CV_8UC1, Scalar(0);borderImage = grayImage - grayErosion(grayImage, se);return borderImage;/灰度图像梯度Mat gradient(Mat grayImage, Mat se)Mat gradient(grayImage.rows, grayImage.cols, CV_8UC1, Scalar(0);gradient = grayDilation(grayImage, se) - grayErosion(

21、grayImage, se);return gradient;/灰度图像的顶帽运算 T（f）=f-fobMat topHat(Mat grayImage,Mat se)Mat topHatImage(grayImage.rows, grayImage.cols, CV_8UC1, Scalar(0);topHatImage = grayImage - grayOpen(grayImage,se);return topHatImage;/灰度图像的底帽运算 B（f）=fb-fMat bottomHat(Mat grayImage, Mat se)Mat bottomHatImage(grayIm

22、age.rows, grayImage.cols, CV_8UC1, Scalar(0);bottomHatImage = grayClose(grayImage, se)-grayImage;return 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 Ima

23、ge, grayImage);/转化为二值图像Mat binaryImage = changeToBinaryImage(grayImage);/创建模板 一般结构元素关于自身原点对称 /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 structureElement(3, 3, CV_8UC1, Scalar(0);/调用二值图像腐蚀函数/binaryErosion(binaryImage, structureElement);imshow(binaryErosionImage, binaryErosion(binaryImage, structureElement);/调

24、用二值图像膨胀函数/binaryDilation(binaryImage, structureElement);imshow(binaryDilationImage, binaryDilation(binaryImage, structureElement);/调用灰度图像腐蚀函数/grayErosion(grayImage, structureElement);imshow(grayErosionImage, grayErosion(grayImage, structureElement);/调用灰度图像膨胀函数/grayDilation(grayImage, structureElement);imshow(grayDilationImage, grayDilat