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| package millie.plugins.core.transform;
import java.awt.image.BufferedImage;
import java.util.Arrays;
import millie.plugins.GenericPluginFilter;
import millie.plugins.parameters.IntSliderParameter;
/**
* Lanczos resampling of an image
*
* @author Xavier Philippeau
*/
public class Resample extends GenericPluginFilter {
/**
* inner class: Kernel
*/
private class Kernel1D {
int size, normalizer, coefs[];
Kernel1D(int size, int[] c, int n) {
this.size=size;
this.coefs=c;
this.normalizer=n;
}
}
private int minsupport = 0;
public Resample() {
setPluginName("Resampling");
setLongProcessing(true);
addParameter(new IntSliderParameter("scalex", "Redimensionnement horizontal (%)", 1,200,100));
addParameter(new IntSliderParameter("scaley", "Redimensionnement vertical (%)", 1,200,100));
addParameter(new IntSliderParameter("support", "taille minimum du support", 1,5,1));
}
// ---------------------------------------------------------------------------
// GenericPluginFilter interface
// ---------------------------------------------------------------------------
@Override
public BufferedImage filter() throws Exception {
BufferedImage input = getInputImage();
double xfactor = getIntValue("scalex")*0.01;
double yfactor = getIntValue("scaley")*0.01;
this.minsupport = getIntValue("support")*2+1;
// reset kernel cache
this.kernelsCacheX = new Kernel1D[100];
this.kernelsCacheY = new Kernel1D[100];
// create empty output image
int width = input.getWidth();
int height = input.getHeight();
int newwidth = (int)(0.5+width*xfactor);
int newheight = (int)(0.5+height*yfactor);
BufferedImage output = new BufferedImage(newwidth, newheight, BufferedImage.TYPE_INT_ARGB);
// for each pixel in output image
for (int y=0; y<newheight; y++) {
for (int x=0; x<newwidth; x++) {
// ideal sample point in the source image
double xo = x/xfactor;
double yo = y/yfactor;
// separate integer part and fractionnal part
int x_int = (int)xo; double x_frac = xo - x_int;
int y_int = (int)yo; double y_frac = yo - y_int;
// get/compute resampling Kernels
Kernel1D kx = getKernelX(xfactor, x_frac);
Kernel1D ky = getKernelY(yfactor, y_frac);
// compute resampled value
int rgb = fastconvolve(input, x_int, y_int, kx, ky);
// set to output image
output.setRGB(x,y,rgb);
}
}
return output;
}
// ---------------------------------------------------------------------------
// Compute and store Lanczos kernels
// ---------------------------------------------------------------------------
private Kernel1D[] kernelsCacheX = null;
private Kernel1D[] kernelsCacheY = null;
/**
* Lanczos function: sinc(d.pi)*sinc(d.pi/support)
*
* @param support,d input parameters
* @return value of the function
*/
private double lanczos(double support, double d) {
if (d==0) return 1.0;
if (d>=support) return 0.0;
double t = d * Math.PI;
return support*Math.sin(t)*Math.sin(t/support)/(t*t);
}
/**
* Compute a Lanczos resampling kernel
*
* @param scale scale to apply on the original image
* @param frac fractionnal part of ideal sample point
* @return the Lanczos resampling kernel
*/
private Kernel1D precompute(double scale, double frac) {
// compute support size = how many source pixels for 1 sampled pixel ?
int support=(int)(1+1.0/scale);
// minimum support
if (support<minsupport) support=minsupport;
// support size must be odd
if (support%2==0) support++;
// scale limiter (minimum unit = 1 pixel)
scale = Math.min(scale, 1.0);
// construct an empty kernel
Kernel1D kernel = new Kernel1D(support,new int[support],0);
int i=0;
int halfwindow = support/2;
for(int dx=-halfwindow;dx<=halfwindow;dx++) {
// ideal sample points (in the source image)
double x = scale*(dx+frac);
// corresponding weight (=contribution) of closest source pixel
double coef = lanczos( halfwindow , x );
// store to kernel
int c = (int)(1000*coef+0.5);
kernel.coefs[i++] = c;
kernel.normalizer += c;
}
return kernel;
}
private Kernel1D getKernel(Kernel1D[] cache,double scale, double frac) {
int kid = (int)(frac*100);
Kernel1D k = cache[kid];
if(k==null) {
k = precompute(scale, frac);
cache[kid]=k;
if (k.size>tmpbuffer_r.length) tmpbuffer_r = new int[k.size];
if (k.size>tmpbuffer_g.length) tmpbuffer_g = new int[k.size];
if (k.size>tmpbuffer_b.length) tmpbuffer_b = new int[k.size];
}
return k;
}
private Kernel1D getKernelX(double scale, double frac) {
return getKernel(kernelsCacheX,scale,frac);
}
private Kernel1D getKernelY(double scale, double frac) {
return getKernel(kernelsCacheY,scale,frac);
}
// ---------------------------------------------------------------------------
// Fast 2D convolution (separation of the 2 kernels)
// ---------------------------------------------------------------------------
// temporary buffer used for convolution
private int[] tmpbuffer_r = new int[0];
private int[] tmpbuffer_g = new int[0];
private int[] tmpbuffer_b = new int[0];
/**
* convolve an image with a kernel for one pixel
*
* @param c input image
* @param x,y coords of the pixel
* @param kernelx,kernely kernels to use
* @return new value of the pixel
*/
private int fastconvolve(BufferedImage img, int x, int y, Kernel1D kernelx, Kernel1D kernely) {
int halfwindowy = kernely.size/2; // assume a odd size
int halfwindowx = kernelx.size/2; // assume a odd size
// empty tmpbuffer
Arrays.fill(tmpbuffer_r, 0);
Arrays.fill(tmpbuffer_g, 0);
Arrays.fill(tmpbuffer_b, 0);
// pass 1 : horizontal convolution of image lines aree stored in tmpbuffer
for(int dy=-halfwindowy;dy<=halfwindowy;dy++) {
if (y+dy<0 || y+dy>=img.getHeight()) continue;
for(int dx=-halfwindowx;dx<=halfwindowx;dx++) {
if (x+dx<0 || x+dx>=img.getWidth()) continue;
int rgb = img.getRGB(x+dx, y+dy);
int r = (rgb>>16)&0xFF;
int g = (rgb>>8)&0xFF;
int b = (rgb)&0xFF;
tmpbuffer_r[halfwindowy+dy] += kernelx.coefs[halfwindowx-dx] * r;
tmpbuffer_g[halfwindowy+dy] += kernelx.coefs[halfwindowx-dx] * g;
tmpbuffer_b[halfwindowy+dy] += kernelx.coefs[halfwindowx-dx] * b;
}
}
// pass 2 : vertical convolution of values stored in tmpbuffer
double rc=0,gc=0,bc=0;
for(int dy=-halfwindowy;dy<=halfwindowy;dy++) {
rc += kernely.coefs[halfwindowy-dy] * tmpbuffer_r[halfwindowy+dy];
gc += kernely.coefs[halfwindowy-dy] * tmpbuffer_g[halfwindowy+dy];
bc += kernely.coefs[halfwindowy-dy] * tmpbuffer_b[halfwindowy+dy];
}
// normalization
double norm = kernelx.normalizer*kernely.normalizer;
rc/=norm; gc/=norm; bc/=norm;
// return in argb format
int r = (int)Math.min(255,Math.max(0,rc+0.5));
int g = (int)Math.min(255,Math.max(0,gc+0.5));
int b = (int)Math.min(255,Math.max(0,bc+0.5));
return 0xFF000000 + (r<<16) + (g<<8) + b;
}
} |
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