sauvola算法实现
2015-09-17 13:19
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实现了sauvola算法。
参数是:k, windowSize,自己调调看效果
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void sauvola(unsigned char * grayImage,unsigned char * biImage,int w,int h,int k,int windowSize)
{
int whalf = windowSize >> 1;
int i,j;
int IMAGE_WIDTH = w;
int IMAGE_HEIGHT = h;
// create the integral image
unsigned long * integralImg = (unsigned long*)malloc(IMAGE_WIDTH*IMAGE_HEIGHT*sizeof(unsigned long*));
unsigned long * integralImgSqrt = (unsigned long*)malloc(IMAGE_WIDTH*IMAGE_HEIGHT*sizeof(unsigned long*));
int sum = 0;
int sqrtsum = 0;
int index;
for (i=0; i<IMAGE_HEIGHT; i++)
{
// reset this column sum
sum = 0;
sqrtsum = 0;
for (j=0; j<IMAGE_WIDTH; j++)
{
index = i*IMAGE_WIDTH+j;
sum += grayImage[index];
sqrtsum += grayImage[index] * grayImage[index];
if (i==0)
{
integralImg[index] = sum;
integralImgSqrt[index] = sqrtsum;
}
else
{
integralImgSqrt[index] = integralImgSqrt[(i-1)*IMAGE_WIDTH+j] + sqrtsum;
integralImg[index] = integralImg[(i-1)*IMAGE_WIDTH+j] + sum;
}
}
}
//Calculate the mean and standard deviation using the integral image
int xmin,ymin,xmax,ymax;
double mean,std,threshold;
double diagsum,idiagsum,diff,sqdiagsum,sqidiagsum,sqdiff,area;
for (i=0; i<IMAGE_WIDTH; i++){
for (j=0; j<IMAGE_HEIGHT; j++){
xmin = max(0,i - whalf);
ymin = max(0,j - whalf);
xmax = min(IMAGE_WIDTH-1,i+whalf);
ymax = min(IMAGE_HEIGHT-1,j+whalf);
area = (xmax - xmin + 1) * (ymax - ymin + 1);
if(area <= 0)
{
biImage[i * IMAGE_WIDTH + j] = 255;
continue;
}
if(xmin == 0 && ymin == 0){
diff = integralImg[ymax * IMAGE_WIDTH + xmax];
sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax];
}else if(xmin > 0 && ymin == 0){
diff = integralImg[ymax * IMAGE_WIDTH + xmax] - integralImg[ymax * IMAGE_WIDTH + xmin - 1];
sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] - integralImgSqrt[ymax * IMAGE_WIDTH + xmin - 1];
}else if(xmin == 0 && ymin > 0){
diff = integralImg[ymax * IMAGE_WIDTH + xmax] - integralImg[(ymin - 1) * IMAGE_WIDTH + xmax];
sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] - integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmax];;
}else{
diagsum = integralImg[ymax * IMAGE_WIDTH + xmax] + integralImg[(ymin - 1) * IMAGE_WIDTH + xmin - 1];
idiagsum = integralImg[(ymin - 1) * IMAGE_WIDTH + xmax] + integralImg[ymax * IMAGE_WIDTH + xmin - 1];
diff = diagsum - idiagsum;
sqdiagsum = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] + integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmin - 1];
sqidiagsum = integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmax] + integralImgSqrt[ymax * IMAGE_WIDTH + xmin - 1];
sqdiff = sqdiagsum - sqidiagsum;
}
mean = diff/area;
std = sqrt((sqdiff - diff*diff/area)/(area-1));
threshold = mean*(1+k*((std/128)-1));
if(grayImage[j*IMAGE_WIDTH + i] < threshold)
biImage[j*IMAGE_WIDTH + i] = 0;
else
biImage[j*IMAGE_WIDTH + i] = 255;
}
}
free(integralImg);
free(integralImgSqrt);
}
![](http://img.blog.csdn.net/20150420203017746?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvY3hmNzM5NDM3Mw==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center)
![](http://img.blog.csdn.net/20150420203024954?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvY3hmNzM5NDM3Mw==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center)
本文转载于:/article/1465571.html
实现了sauvola算法。
参数是:k, windowSize,自己调调看效果
[cpp] view
plaincopy
![](https://code.csdn.net/assets/CODE_ico.png)
void sauvola(unsigned char * grayImage,unsigned char * biImage,int w,int h,int k,int windowSize)
{
int whalf = windowSize >> 1;
int i,j;
int IMAGE_WIDTH = w;
int IMAGE_HEIGHT = h;
// create the integral image
unsigned long * integralImg = (unsigned long*)malloc(IMAGE_WIDTH*IMAGE_HEIGHT*sizeof(unsigned long*));
unsigned long * integralImgSqrt = (unsigned long*)malloc(IMAGE_WIDTH*IMAGE_HEIGHT*sizeof(unsigned long*));
int sum = 0;
int sqrtsum = 0;
int index;
for (i=0; i<IMAGE_HEIGHT; i++)
{
// reset this column sum
sum = 0;
sqrtsum = 0;
for (j=0; j<IMAGE_WIDTH; j++)
{
index = i*IMAGE_WIDTH+j;
sum += grayImage[index];
sqrtsum += grayImage[index] * grayImage[index];
if (i==0)
{
integralImg[index] = sum;
integralImgSqrt[index] = sqrtsum;
}
else
{
integralImgSqrt[index] = integralImgSqrt[(i-1)*IMAGE_WIDTH+j] + sqrtsum;
integralImg[index] = integralImg[(i-1)*IMAGE_WIDTH+j] + sum;
}
}
}
//Calculate the mean and standard deviation using the integral image
int xmin,ymin,xmax,ymax;
double mean,std,threshold;
double diagsum,idiagsum,diff,sqdiagsum,sqidiagsum,sqdiff,area;
for (i=0; i<IMAGE_WIDTH; i++){
for (j=0; j<IMAGE_HEIGHT; j++){
xmin = max(0,i - whalf);
ymin = max(0,j - whalf);
xmax = min(IMAGE_WIDTH-1,i+whalf);
ymax = min(IMAGE_HEIGHT-1,j+whalf);
area = (xmax - xmin + 1) * (ymax - ymin + 1);
if(area <= 0)
{
biImage[i * IMAGE_WIDTH + j] = 255;
continue;
}
if(xmin == 0 && ymin == 0){
diff = integralImg[ymax * IMAGE_WIDTH + xmax];
sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax];
}else if(xmin > 0 && ymin == 0){
diff = integralImg[ymax * IMAGE_WIDTH + xmax] - integralImg[ymax * IMAGE_WIDTH + xmin - 1];
sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] - integralImgSqrt[ymax * IMAGE_WIDTH + xmin - 1];
}else if(xmin == 0 && ymin > 0){
diff = integralImg[ymax * IMAGE_WIDTH + xmax] - integralImg[(ymin - 1) * IMAGE_WIDTH + xmax];
sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] - integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmax];;
}else{
diagsum = integralImg[ymax * IMAGE_WIDTH + xmax] + integralImg[(ymin - 1) * IMAGE_WIDTH + xmin - 1];
idiagsum = integralImg[(ymin - 1) * IMAGE_WIDTH + xmax] + integralImg[ymax * IMAGE_WIDTH + xmin - 1];
diff = diagsum - idiagsum;
sqdiagsum = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] + integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmin - 1];
sqidiagsum = integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmax] + integralImgSqrt[ymax * IMAGE_WIDTH + xmin - 1];
sqdiff = sqdiagsum - sqidiagsum;
}
mean = diff/area;
std = sqrt((sqdiff - diff*diff/area)/(area-1));
threshold = mean*(1+k*((std/128)-1));
if(grayImage[j*IMAGE_WIDTH + i] < threshold)
biImage[j*IMAGE_WIDTH + i] = 0;
else
biImage[j*IMAGE_WIDTH + i] = 255;
}
}
free(integralImg);
free(integralImgSqrt);
}
本文转载于:/article/1465571.html
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