形态学基本操作与应用代码和结果展示.doc

#include<opencv.hpp>
#include<highgui.h>
#include<imgproc.hpp>
using 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 < grayImage.cols; j++)
{
if (grayImage.data[i*grayImage.step + j]>100)
{
binaryImage.data[i*grayImage.step + j] = 255;
}
else
{
binaryImage.data[i*grayImage.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 a[3][3]={ 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 binaryErosionImage(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.data[i*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.data[row*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; col++)
{
if (se.data[row*se.step + col] != window.data[row*se.step + col])
{
break;
}
}
if (col == se.cols)
{
continue;
}
else
{
break;
}
}
if (row == se.rows&&col == se.cols)
{
binaryErosionImage.data[i*binaryImage.step + j] = 0;
}
}
}

//imshow("binaryErosionImage", binaryErosionImage);

return binaryErosionImage;
}

//二值图像膨胀操作
Mat binaryDilation(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 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) / 2; j++)
{
//先设置第i行第j列像素值为255，即白色
binaryDilationImage.data[i*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.data[row*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
//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.data[row*se.step + col] == window.data[row*se.step + col])
{
flag = 1;
}
}
}
if (flag)
{
//如果有交集，就设置为黑，即0
binaryDilationImage.data[i*binaryImage.step + 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 - 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.data[i*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.data[row*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.data[row*se.step + col] < minPixel)
{
minPixel = window.data[row*se.step + col];
}
}
}

grayErosionImage.data[i*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
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 < 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.data[i*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.data[row*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, col;
for (row = 0; row < se.rows; row++)
{
for (col = 0; col < se.cols; col++)
{
if (window.data[row*se.step + col] > maxPixel)
{
maxPixel = window.data[row*se.step + col];
}
}
}
grayDilationImage.data[i*grayImage.step + j] = maxPixel;
}
}

/*imshow("grayDilationImage", grayDilationImage);*/

return grayDilationImage;
}

//二值图像开操作
Mat binaryOpen(Mat binaryImage, Mat se)
{
Mat openImage(binaryImage.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), 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_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 = binaryErosion(binaryImage,se);

for (int i = 0; i < erosionImage.rows; i++)
{
for (int j = 0; j < erosionImage.cols; j++)
{
if (binaryImage.data[i*erosionImage.step+j]!=erosionImage.data[i*erosionImage.step+j])
{
borderImage.data[i*erosionImage.step + j] = 255;
}
}
}

return borderImage;
}

//灰度图像边界提取
Mat grayBorder(Mat grayImage, Mat se)
{
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(grayImage, se);

return gradient;
}

//灰度图像的顶帽运算 T（f）=f-fob
Mat 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）=f⋅b-f
Mat bottomHat(Mat grayImage, Mat se)
{
Mat bottomHatImage(grayImage.rows, grayImage.cols, CV_8UC1, Scalar(0));

bottomHatImage = grayClose(grayImage, se)-grayImage;

return bottomHatImage;
}

int main()
{
Mat src = imread("E:\\project\\images\\32.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 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", grayDilation(grayImage, structureElement));

//调用二值图像开操作
imshow("binaryOpenImage",binaryOpen(binaryImage,structureElement));

//调用二值图像闭操作
imshow("binaryCloseImage", binaryClose(binaryImage, structureElement));

//调用灰度图像开操作
imshow("grayOpenImage", grayOpen(grayImage, structureElement));

//调用灰度图像闭操作
imshow("grayCloseImage", grayClose(grayImage, structureElement));

//二值图像边界提取
imshow("binaryBorderImage",binaryBorder(binaryImage,structureElement));
//灰度图像边界提取
imshow("grayBorderImage",grayBorder(grayImage,structureElement));

//调用灰度梯度函数
imshow("Gradient", gradient(binaryImage, structureElement));

//调用顶帽函数
imshow("topHat",topHat(grayImage,structureElement));

//调用底帽函数
imshow("bottomHat", bottomHat(grayImage, structureElement));

cvWaitKey(0);

return 0;
}

结果展示： 原图

二值图像

二值图像腐蚀操作

二值图像膨胀操作

二值图像开操作

二值图像闭操作

灰度图像

灰度图像腐蚀操作

灰度图像膨胀操作

灰度图像开操作

灰度图像闭操作

二值图像--边界提取

灰度图像--边界提取

灰度图像--梯度提取

灰度图像--顶帽运算

灰度图像--底帽运算