prewitt算子的程序

Prewitt算子程序：

clc

clear all

close all

A = imread('tig.jpg') %读入图像

imshow(A)title('原图')

y_mask = [-1 -1 -10 0 01 1 1] %建立Y方向的模板

x_mask = y_mask' %建立X方向的模板

I = im2double(A) %将图像数据转化为双精度

dx = imfilter(I, x_mask) %计算X方向的梯度分量

dy = imfilter(I, y_mask) %计算Y方向的梯度分量

grad = sqrt(dx.*dx + dy.*dy) %计算梯度

grad = mat2gray(grad) %将梯度矩阵转换为灰度图像

level = graythresh(grad) %计算灰度阈值

BW = im2bw(grad,level) %用阈值分割梯度图像

figure, imshow(BW) %显示分割后的图像即边缘图像

title('Prewitt')

自己以前图像处理的时候写的,用的是C++, 不过处理流程一样的,可以参考一下

//Soble

void CBmp::RhSobel()

{

double temp[9]

DWORD m_Y=m_pInfo->bmiHeader.biHeight

DWORD m_X=WIDTH((m_pInfo->bmiHeader.biWidth)*(m_pInfo->bmiHeader.biBitCount))

BYTE *m_B=(BYTE *) new char[m_Y*m_X]

for(int d=0d<m_nPixelsd++)

{

m_B[d]=m_pPixels[d]

}

if((m_pInfo->bmiHeader.biBitCount)==24)

for(int i=1i<m_Y-1i++)

for(int j=3j<(m_X-2)j+=3)

{

for(int n=0n<9n+=3)

{

temp[n]=(m_B[(i-1+n/3)*m_X+j-3]+m_B[(i-1+n/3)*m_X+j-2]+m_B[(i-1+n/3)*m_X+j-1])/3

temp[n+1]=(m_B[(i-1+n/3)*m_X+j]+m_B[(i-1+n/3)*m_X+j+1]+m_B[(i-1+n/3)*m_X+j+2])/3

temp[n+2]=(m_B[(i-1+n/3)*m_X+j+3]+m_B[(i-1+n/3)*m_X+j+4]+m_B[(i-1+n/3)*m_X+j+5])/3

}

m_pPixels[i*m_X+j]=m_pPixels[i*m_X+j+1]=m_pPixels[i*m_X+j+2]=//

(BYTE((abs(temp[2]+2*temp[5]+temp[8]-//

temp[0]-2*temp[3]-temp[6])+

abs(temp[0]+2*temp[1]+temp[2]-//

temp[6]-2*temp[7]-temp[8]))))

}

else

for(int i=1i<(m_Y-1)i++)

{

for(int j=1j<(m_X-1)j++)

{

m_pPixels[i*m_X+j]=(abs(m_B[(i-1)*m_X+j+1]+(2*m_B[(i)*m_X+j+1])+m_B[(i+1)*m_X+j+1]-//

m_B[(i-1)*m_X+j-1]-(2*m_B[(i)*m_X+j-1])-m_B[(i+1)*m_X+j-1])+

abs(m_B[(i-1)*m_X+j-1]+(2*m_B[(i-1)*m_X+j])+m_B[(i-1)*m_X+j+1]-//

m_B[(i+1)*m_X+j-1]-(2*m_B[(i+1)*m_X+j])-m_B[(i+1)*m_X+j+1]))

}

}

}

//Prewitt

void CBmp::ByPrewitt()

{

double temp1,temp2

DWORD m_Y=m_pInfo->bmiHeader.biHeight

DWORD m_X=WIDTH((m_pInfo->bmiHeader.biWidth)*(m_pInfo->bmiHeader.biBitCount))

BYTE *m_B=(BYTE *) new char[m_Y*m_X]

for(int d=0d<m_nPixelsd++)

{

m_B[d]=m_pPixels[d]

}

if(m_pInfo->bmiHeader.biBitCount==8)

for(int i=1i<(m_Y-1)i++)

{

for(int j=1j<(m_X-1)j++)

{

temp1=abs(m_B[(i-1)*m_X+j+1]-m_B[(i-1)*m_X+j-1]+m_B[i*m_X+j+1]-//

m_B[i*m_X+j-1]+m_B[(i+1)*m_X+j+1]-m_B[(i+1)*m_X+j-1])

temp2=abs(m_B[(i-1)*m_X+j-1]+m_B[(i-1)*m_X+j]+m_B[(i-1)*m_X+j+1]-//

m_B[(i+1)*m_X+j-1]-m_B[(i+1)*m_X+j]-m_B[(i+1)*m_X+j+1])

m_pPixels[i*m_X+j]=(temp1>temp2?temp1:temp2)

}

}

else

{

Huidu()

for(int i=1i<(m_Y-1)i++)

{

for(int j=3j<(m_X-5)j+=3)

{

temp1=abs(m_B[(i-1)*m_X+j+3]-m_B[(i-1)*m_X+j-3]+m_B[i*m_X+j+3]-//

m_B[i*m_X+j-3]+m_B[(i+1)*m_X+j+3]-m_B[(i+1)*m_X+j-3])

temp2=abs(m_B[(i-1)*m_X+j-3]+m_B[(i-1)*m_X+j]+m_B[(i-1)*m_X+j+3]-//

m_B[(i+1)*m_X+j-3]-m_B[(i+1)*m_X+j]-m_B[(i+1)*m_X+j+3])

m_pPixels[i*m_X+j]=m_pPixels[i*m_X+j+1]=m_pPixels[i*m_X+j+2]=(temp1>temp2?temp1:temp2)

}

}

}

}

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