Sobel 滤波算法 (C++) (无库)

Question

鉴于我访问图片像素的方法，我正在尝试将 sobel 滤镜算法应用于给定的图片(在这种情况下为灰度(。由于我以不使用库的方式访问它们，因此在这种方法下，我无法弄清楚如何应用算法。代码的第一部分只是访问像素数据：

篇一：

CKingimageDoc* pDoc = GetDocument();      // get picture
int iBitPerPixel = pDoc->_bmp->bitsperpixel;    // used to see if grayscale(8 bits) or RGB (24 bits)
int iWidth = pDoc->_bmp->width;
int iHeight = pDoc->_bmp->height;
BYTE *pImg = pDoc->_bmp->point;     // pointer used to point at pixels in the image
const int area = iWidth * iHeight;
int Wp = iWidth;
int intensity;
if (iBitPerPixel == 8)  ////Grayscale 8 bits image
{
int r = iWidth % 4;     // pixels leftover from width (remainder has to be factor of 8 or 24)
int p = (4-r) % 4;      // has to be a factor of number of bits in pixel, num leftover to take care of
Wp = iWidth + p;

第 2 部分(索贝尔滤波算法的实际应用(：

float kernelx[3][3] = { { -1, 0, 1 },
{ -2, 0, 2 },
{ -1, 0, 1 } };
float kernely[3][3] = { { -1, -2, -1 },
{ 0,  0,  0 },
{ 1,  2,  1 } };
double magX = 0.0; // this is your magnitude
for (int a = 0; a < 3; a++) {
for (int b = 0; b < 3; b++) {
magX += pImg[i*Wp + j] * kernelx[a][b];   // where i get confused
}
}
}

非常感谢任何和所有的帮助。

Answer 1

您必须使用来自中心像素邻域的适当像素来乘以内核条目：

//row, col - coordinates of central pixel for calculation
for (int row = 1; row < height - 1; row++) {
for (int col = 1; col < width - 1; col++) {
double magX = 0.0; // this is your magnitude
for (int a = 0; a < 3; a++) {
for (int b = 0; b < 3; b++) {
magX += pImg[(row - 1 + a) * Wp + col - 1 + b] * kernelx[a][b];   
}
}
resultImg[row * Wp + col] = magX;  
}
}

我省略了边框像素

Answer 2

CKingimageDoc* pDoc = GetDocument(); // get picture
int iBitPerPixel = pDoc->_bmp->bitsperpixel; // used to see if grayscale(8b) or RGB(24b)
int iWidth = pDoc->_bmp->width;
int iHeight = pDoc->_bmp->height;
BYTE *pImg = pDoc->_bmp->point; // pointer used to point at pixels in the image
const int area = iWidth * iHeight;
BYTE *pImg2 = new BYTE[area];
if (iBitPerPixel == 8) // Grayscale 8bit image
{
int pixel_x;
int pixel_y;
float sobel_x[3][3] =
{ { -1, 0, 1 },
{ -2, 0, 2 },
{ -1, 0, 1 } };
float sobel_y[3][3] =
{ { -1, -2, -1 },
{ 0,  0,  0 },
{ 1,  2,  1 } };
for (int x=1; x < iWidth-1; x++)
{
for (int y=1; y < iHeight-1; y++)
{
pixel_x = (sobel_x[0][0] * pImg[iWidth * (y-1) + (x-1)])
+ (sobel_x[0][1] * pImg[iWidth * (y-1) +  x   ])
+ (sobel_x[0][2] * pImg[iWidth * (y-1) + (x+1)])
+ (sobel_x[1][0] * pImg[iWidth *  y    + (x-1)])
+ (sobel_x[1][1] * pImg[iWidth *  y    +  x   ])
+ (sobel_x[1][2] * pImg[iWidth *  y    + (x+1)])
+ (sobel_x[2][0] * pImg[iWidth * (y+1) + (x-1)])
+ (sobel_x[2][1] * pImg[iWidth * (y+1) +  x   ])
+ (sobel_x[2][2] * pImg[iWidth * (y+1) + (x+1)]);
pixel_y = (sobel_y[0][0] * pImg[iWidth * (y-1) + (x-1)])
+ (sobel_y[0][1] * pImg[iWidth * (y-1) +  x   ])
+ (sobel_y[0][2] * pImg[iWidth * (y-1) + (x+1)])
+ (sobel_y[1][0] * pImg[iWidth *  y    + (x-1)])
+ (sobel_y[1][1] * pImg[iWidth *  y    +  x   ])
+ (sobel_y[1][2] * pImg[iWidth *  y    + (x+1)])
+ (sobel_y[2][0] * pImg[iWidth * (y+1) + (x-1)])
+ (sobel_y[2][1] * pImg[iWidth * (y+1) +  x   ])
+ (sobel_y[2][2] * pImg[iWidth * (y+1) + (x+1)]);
int val = (int)sqrt((pixel_x * pixel_x) + (pixel_y * pixel_y));
if(val < 0) val = 0;
if(val > 255) val = 255;
pImg2[iHeight * y + x] = val;
}
}
}