// Super Fast Blur v1.1
// by Mario Klingemann <http://incubator.quasimondo.com>
//
// Tip: Multiple invovations of this filter with a small 
// radius will approximate a gaussian blur quite well.
//
BImage a;
BImage b;

void setup()
{

  a=loadImage("dog.jpg");
  size(a.width, a.height);
  b=new BImage(a.width, a.height);
  fill(255);
  noStroke();
  framerate(25);

}

void loop()
{
  System.arraycopy(a.pixels,0,b.pixels,0,a.pixels.length);
  fastblur(b,mouseY/8);
  image(b, 0, 0);
}

void fastblur(BImage img,int radius){

  if (radius<1){
    return;
  }
  int w=img.width;
  int h=img.height;
  int wm=w-1;
  int hm=h-1;
  int wh=w*h;
  int div=radius+radius+1;
  int r[]=new int[wh];
  int g[]=new int[wh];
  int b[]=new int[wh];
  int rsum,gsum,bsum,x,y,i,p,p1,p2,yp,yi,yw;
  int vmin[] = new int[max(w,h)];
  int vmax[] = new int[max(w,h)];
  int[] pix=img.pixels;
  int dv[]=new int[256*div];
  for (i=0;i<256*div;i++){
     dv[i]=(i/div); 
  }
  
  yw=yi=0;
 
  for (y=0;y<h;y++){
    rsum=gsum=bsum=0;
    for(i=-radius;i<=radius;i++){
      p=pix[yi+min(wm,max(i,0))];
      rsum+=(p & 0xff0000)>>16;
      gsum+=(p & 0x00ff00)>>8;
      bsum+= p & 0x0000ff;
   }
    for (x=0;x<w;x++){
    
      r[yi]=dv[rsum];
      g[yi]=dv[gsum];
      b[yi]=dv[bsum];

      if(y==0){
        vmin[x]=min(x+radius+1,wm);
        vmax[x]=max(x-radius,0);
       } 
       p1=pix[yw+vmin[x]];
       p2=pix[yw+vmax[x]];

      rsum+=((p1 & 0xff0000)-(p2 & 0xff0000))>>16;
      gsum+=((p1 & 0x00ff00)-(p2 & 0x00ff00))>>8;
      bsum+= (p1 & 0x0000ff)-(p2 & 0x0000ff);
      yi++;
    }
    yw+=w;
  }
  
  for (x=0;x<w;x++){
    rsum=gsum=bsum=0;
    yp=-radius*w;
    for(i=-radius;i<=radius;i++){
      yi=max(0,yp)+x;
      rsum+=r[yi];
      gsum+=g[yi];
      bsum+=b[yi];
      yp+=w;
    }
    yi=x;
    for (y=0;y<h;y++){
      pix[yi]=0xff000000 | (dv[rsum]<<16) | (dv[gsum]<<8) | dv[bsum];
      if(x==0){
        vmin[y]=min(y+radius+1,hm)*w;
        vmax[y]=max(y-radius,0)*w;
      } 
      p1=x+vmin[y];
      p2=x+vmax[y];

      rsum+=r[p1]-r[p2];
      gsum+=g[p1]-g[p2];
      bsum+=b[p1]-b[p2];

      yi+=w;
    }
  }

}