Gabor内核参考wiki
使用实数Real的公式计算核函数代码：
Mat getGaborFilter(float lambda, float theta,
float sigma2,float gamma,
float psi = 0.0f){
if(abs(lambda-0.0f)<1e-6){
lambda = 1.0f;
}
float sigma_x = sigma2;
float sigma_y = sigma2/(gamma*gamma);
int nstds = 3;
float sqrt_sigma_x = sqrt(sigma_x);
float sqrt_sigma_y = sqrt(sigma_y);
int xmax = max(abs(nstds*sqrt_sigma_x*cos(theta)),abs(nstds*sqrt_sigma_y*sin(theta)));
int ymax = max(abs(nstds*sqrt_sigma_x*sin(theta)),abs(nstds*sqrt_sigma_y*cos(theta)));
int half_filter_size = xmax>ymax ? xmax:ymax;
int filter_size = 2*half_filter_size+1;
Mat gaber = Mat::zeros(filter_size,filter_size,CV_32F);
for(int i=0;i<filter_size;i++){
float* f = gaber.ptr<float>(i);
for(int j=0;j<filter_size;j++){
int x = j-half_filter_size;
int y = i-half_filter_size;
float x_theta=x*cos(theta)+y*sin(theta);
float y_theta=-x*sin(theta)+y*cos(theta);
f[x] = exp(-.5*(x_theta*x_theta/sigma_x+y_theta*y_theta/sigma_y));
f[x] = f[x]*cos(2*PI*x_theta/lambda+psi);
};
}
return gaber;
}
使用得到的Gabor核对一副图像进行卷积的函数：
Mat gaborFilter(Mat& img, Mat& filter){
int half_filter_size = (max(filter.rows,filter.cols)-1)/2;
Mat filtered_img(img.rows,img.cols,CV_32F);
for(int i=0;i<img.rows;i++){
uchar* img_p = img.ptr<uchar>(i);
float* img_f = filtered_img.ptr<float>(i);
for(int j=0;j<img.cols;j++){
float filter_value = 0.0f;
for(int fi=0;fi<filter.rows;fi++){
float* f = filter.ptr<float>(fi);
int img_i = i+fi-half_filter_size;
img_i = img_i < 0 ? 0 : img_i;
img_i = img_i >= img.rows ?
(img.rows-1) : img_i;
uchar* p = img.ptr<uchar>(img_i);
for(int fj=0;fj<filter.cols;fj++){
int img_j = j+fj-half_filter_size;
img_j = img_j < 0 ? 0 : img_j;
img_j = (img_j >= img.cols) ? (img.cols-1) : img_j;
float tmp = (float)p[img_j]*f[fj];
filter_value += tmp;
}
}
img_f[j] = filter_value;
}
}
return filtered_img;
}对一幅图使用例如以下核卷积：
Mat gaber = getGaborFilter(0.3,0,4,2);
效果例如以下：
Gabor算子卷积之后得到非常多负值（不知道有没有问题）。后面的图是归一化之后显示出来的。
Mat normalizeFilterShow(Mat gaber){
Mat gaber_show = Mat::zeros(gaber.rows,gaber.cols,CV_8UC1);
float gaber_max = FLT_MIN;
float gaber_min = FLT_MAX;
for(int i=0;i<gaber.rows;i++){
float* f = gaber.ptr<float>(i);
for(int j=0;j<gaber.cols;j++){
if(f[j]>gaber_max){
gaber_max = f[j];
}
if(f[j]<gaber_min){
gaber_min = f[j];
}
}
}
float gaber_max_min = gaber_max-gaber_min;
for(int i=0;i<gaber_show.rows;i++){
uchar* p = gaber_show.ptr<uchar>(i);
float* f = gaber.ptr<float>(i);
for(int j=0;j<gaber_show.cols;j++){
if(gaber_max_min!=0.0f){
float tmp = (f[j]-gaber_min)*255.0f/gaber_max_min;
p[j] = (uchar)tmp;
}
else{
p[j] = 255;
}
}
}
return gaber_show;
}（转载请注明作者和出处：http://blog.csdn.net/xiaowei_cqu 未经同意不用于商业用途）
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