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FastGuidedFilter.java
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import java.util.ArrayList;
import org.opencv.core.*;
import org.opencv.imgproc.Imgproc;
public class FastGuidedFilter {
ArrayList<Mat> Ichannels;
ArrayList<Mat> Isubchannels;
int Idepth;
int r;
double eps;
double s;
double r_sub;
Mat mean_I_r;
Mat mean_I_g;
Mat mean_I_b;
Mat invrr;
Mat invrg;
Mat invrb;
Mat invgg;
Mat invgb;
Mat invbb;
public FastGuidedFilter(){
Ichannels = new ArrayList<Mat>();
Isubchannels = new ArrayList<Mat>();
invrr = new Mat();
invrg = new Mat();
invrb = new Mat();
invgg = new Mat();
invgb = new Mat();
invbb = new Mat();
}
public static Mat boxfilter(Mat I, int r){
Mat result = new Mat();
Imgproc.blur(I, result, new Size(r, r));
return result;
}
public static Mat convertTo(Mat mat, int depth){
if(mat.depth() == depth){
return mat;
}
Mat result = new Mat();
mat.convertTo(result, depth);
return result;
}
public Mat filterSingleChannel(Mat p, double s){
Mat p_sub = new Mat();
Imgproc.resize(p, p_sub, new Size(p.cols()/s, p.rows()/s), 0.0, 0.0, Imgproc.INTER_NEAREST);
Mat mean_p = boxfilter(p_sub, (int)r_sub);
Mat mean_Ip_r = boxfilter(Isubchannels.get(0).mul(p_sub), (int)r_sub);
Mat mean_Ip_g = boxfilter(Isubchannels.get(1).mul(p_sub), (int)r_sub);
Mat mean_Ip_b = boxfilter(Isubchannels.get(2).mul(p_sub), (int)r_sub);
// convariance of (I, p) in each local patch
Mat cov_Ip_r = new Mat();
Mat cov_Ip_g = new Mat();
Mat cov_Ip_b = new Mat();
Core.subtract(mean_Ip_r, mean_I_r.mul(mean_p), cov_Ip_r);
Core.subtract(mean_Ip_g, mean_I_g.mul(mean_p), cov_Ip_g);
Core.subtract(mean_Ip_b, mean_I_b.mul(mean_p), cov_Ip_b);
Mat temp1 = new Mat();
Mat a_r = new Mat();
Mat a_g = new Mat();
Mat a_b = new Mat();
Core.add(invrr.mul(cov_Ip_r), invrg.mul(cov_Ip_g), temp1);
Core.add(temp1, invrb.mul(cov_Ip_b), a_r);
Core.add(invrg.mul(cov_Ip_r), invgg.mul(cov_Ip_g), temp1);
Core.add(temp1, invgb.mul(cov_Ip_b), a_g);
Core.add(invrb.mul(cov_Ip_r), invgb.mul(cov_Ip_g), temp1);
Core.add(temp1, invbb.mul(cov_Ip_b), a_b);
Mat b = new Mat();
Core.subtract(mean_p, a_r.mul(mean_I_r), b);
Core.subtract(b, a_g.mul(mean_I_g), b);
Core.subtract(b, a_b.mul(mean_I_b), b);
Mat mean_a_r = boxfilter(a_r, (int)r_sub);
Mat mean_a_g = boxfilter(a_g, (int)r_sub);
Mat mean_a_b = boxfilter(a_b, (int)r_sub);
Mat mean_b = boxfilter(b, (int)r_sub);
Imgproc.resize(mean_a_r, mean_a_r, new Size(Ichannels.get(0).cols(), Ichannels.get(0).rows()), 0.0, 0.0, Imgproc.INTER_LINEAR);
Imgproc.resize(mean_a_g, mean_a_g, new Size(Ichannels.get(0).cols(), Ichannels.get(0).rows()), 0.0, 0.0, Imgproc.INTER_LINEAR);
Imgproc.resize(mean_a_b, mean_a_b, new Size(Ichannels.get(0).cols(), Ichannels.get(0).rows()), 0.0, 0.0, Imgproc.INTER_LINEAR);
Imgproc.resize(mean_b, mean_b, new Size(Ichannels.get(0).cols(), Ichannels.get(0).rows()), 0.0, 0.0, Imgproc.INTER_LINEAR);
Mat result = new Mat();
Core.add(mean_a_r.mul(Ichannels.get(0)), mean_a_g.mul(Ichannels.get(1)), temp1);
Core.add(temp1, mean_a_b.mul(Ichannels.get(2)), temp1);
Core.add(temp1, mean_b, result);
return result;
}
public Mat filter(Mat origI, Mat p, int r, double eps, double s, int depth){
Mat I;
if(origI.depth() == CvType.CV_32F || origI.depth() == CvType.CV_64F){
I = origI.clone();
}
else{
I = convertTo(origI, CvType.CV_32F);
}
Idepth = I.depth();
Core.split(I, Ichannels);
Mat I_sub = new Mat();
Imgproc.resize(I, I_sub, new Size(I.cols()/s, I.rows()/s), 0.0, 0.0, Imgproc.INTER_NEAREST);
Core.split(I_sub, Isubchannels);
r_sub = r / s;
mean_I_r = boxfilter(Isubchannels.get(0), (int)r_sub);
mean_I_g = boxfilter(Isubchannels.get(1), (int)r_sub);
mean_I_b = boxfilter(Isubchannels.get(2), (int)r_sub);
// variance of I in each local patch: the matrix Sigma in Eqn (14).
// Note the variance in each local patch is a 3x3 symmetric matrix:
// rr, rg, rb
// Sigma = rg, gg, gb
// rb, gb, bb
Mat var_I_rr = new Mat();
Mat var_I_rg = new Mat();
Mat var_I_rb = new Mat();
Mat var_I_gg = new Mat();
Mat var_I_gb = new Mat();
Mat var_I_bb = new Mat();
Mat temp1 = new Mat();
Core.subtract(boxfilter(Isubchannels.get(0).mul(Isubchannels.get(0)), (int)r_sub), mean_I_r.mul(mean_I_r), temp1);
Core.add(temp1, new Scalar(eps), var_I_rr);
Core.subtract(boxfilter(Isubchannels.get(0).mul(Isubchannels.get(1)), (int)r_sub), mean_I_r.mul(mean_I_g), var_I_rg);
Core.subtract(boxfilter(Isubchannels.get(0).mul(Isubchannels.get(2)), (int)r_sub), mean_I_r.mul(mean_I_b), var_I_rb);
Core.subtract(boxfilter(Isubchannels.get(1).mul(Isubchannels.get(1)), (int)r_sub), mean_I_g.mul(mean_I_g), temp1);
Core.add(temp1, new Scalar(eps), var_I_gg);
Core.subtract(boxfilter(Isubchannels.get(1).mul(Isubchannels.get(2)), (int)r_sub), mean_I_g.mul(mean_I_b), var_I_gb);
Core.subtract(boxfilter(Isubchannels.get(2).mul(Isubchannels.get(2)), (int)r_sub), mean_I_b.mul(mean_I_b), temp1);
Core.add(temp1, new Scalar(eps), var_I_bb);
// Inverse of Sigma + eps * I
Core.subtract(var_I_gg.mul(var_I_bb), var_I_gb.mul(var_I_gb), invrr);
Core.subtract(var_I_gb.mul(var_I_rb), var_I_rg.mul(var_I_bb), invrg);
Core.subtract(var_I_rg.mul(var_I_gb), var_I_gg.mul(var_I_rb), invrb);
Core.subtract(var_I_rr.mul(var_I_bb), var_I_rb.mul(var_I_rb), invgg);
Core.subtract(var_I_rb.mul(var_I_rg), var_I_rr.mul(var_I_gb), invgb);
Core.subtract(var_I_rr.mul(var_I_gg), var_I_rg.mul(var_I_rg), invbb);
Mat covDet = new Mat();
Core.add(invrr.mul(var_I_rr), invrg.mul(var_I_rg), temp1);
Core.add(temp1, invrb.mul(var_I_rb), covDet);
Core.divide(invrr, covDet, invrr);
Core.divide(invrg, covDet, invrg);
Core.divide(invrb, covDet, invrb);
Core.divide(invgg, covDet, invgg);
Core.divide(invgb, covDet, invgb);
Core.divide(invbb, covDet, invbb);
Mat p2 = convertTo(p, Idepth);
Mat result = new Mat();
if(p.channels() == 1){
result = filterSingleChannel(p2, s);
}else{
ArrayList<Mat> pc = new ArrayList<Mat>();
Core.split(p2, pc);
for(int i = 0; i < pc.size(); i++){
pc.set(i, filterSingleChannel(pc.get(i), s));
}
Core.merge(pc, result);
}
return convertTo(result, depth == -1 ? p.depth() : depth);
}
}