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| package millie.plugins.free;
import java.awt.color.ColorSpace;
import java.awt.image.BufferedImage;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.Map;
import millie.image.Kernel;
import millie.image.PredefinedKernel;
import millie.plugins.GenericPluginFilter;
import millie.plugins.parameters.ComboBoxEntry;
import millie.plugins.parameters.ComboBoxParameter;
import millie.plugins.parameters.IntSliderParameter;
import millie.se.operator.ConvolveOperator;
import millie.se.operator.extender.BorderExtenderCopy;
public class WaterShedPlugin extends GenericPluginFilter {
enum InputType {
Normal("image (fond sombre)"),
Invert("image (fond clair)"),
Variance("variance de l'image"),
LogVariance("log-variance de l'image");
private InputType(String txt) {this.label=txt;}
String label;
}
enum OutputType {
ImageShed("image + lignes de partage"),
Shed("lignes de partage seules"),
MosaicShed("mosaique + lignes de partage"),
Mosaic("mosaique seule");
private OutputType(String txt) {this.label=txt;}
String label;
}
// saved input parameters
private int savedRadius=0;
private InputType savedInputtype = null;
// saved sum images
private long[][] R,R2,G,G2,B,B2;
public WaterShedPlugin() {
setPluginName("Segmentation WaterShed (LPE)");
setRefreshable(false);
setLongProcessing(true);
setReinitializable(true);
ComboBoxEntry[] inputTypeParameter = new ComboBoxEntry[InputType.values().length];
for(InputType e : InputType.values())
inputTypeParameter[e.ordinal()] = new ComboBoxEntry(e.name(),e.label);
addParameter(new ComboBoxParameter("inputtype","Type d'entrée", inputTypeParameter));
addParameter(new IntSliderParameter("radius", "rayon (sampling)",1,10,3));
ComboBoxEntry[] outputTypeParameter = new ComboBoxEntry[OutputType.values().length];
for(OutputType e : OutputType.values())
outputTypeParameter[e.ordinal()] = new ComboBoxEntry(e.name(),e.label);
addParameter(new ComboBoxParameter("outputtype","Affichage", outputTypeParameter));
}
@Override
public BufferedImage filter() throws Exception {
BufferedImage original = getInputImage();
int radius = getParameter("radius").getIntValue();
OutputType outputtype = OutputType.valueOf(getParameter("outputtype").getStringValue());
InputType inputtype = InputType.valueOf(getParameter("inputtype").getStringValue());
boolean needCompute=false;
if (radius!=savedRadius) needCompute=true;
if (inputtype!=savedInputtype) needCompute=true;
boolean drawImage=false, drawMosaic=false, drawShed=true;
switch(outputtype) {
case ImageShed: drawImage=true;drawMosaic=false;drawShed=true; break;
case Shed: drawImage=false;drawMosaic=false;drawShed=true; break;
case MosaicShed: drawImage=false;drawMosaic=true;drawShed=true; break;
case Mosaic: drawImage=false;drawMosaic=true;drawShed=false; break;
}
if (needCompute) {
this.savedRadius = radius;
this.savedInputtype = inputtype;
this.image = null;
this.width = original.getWidth();
this.height = original.getHeight();
switch(inputtype) {
case Normal: this.image=BitmapToArrayGray(original,radius,true); break;
case Invert: this.image=BitmapToArrayGray(original,radius,false); break;
case Variance: this.image=variance(original,radius,false) ;break;
case LogVariance: this.image=variance(original,radius,true); break;
}
// compute ideal step
double stddev = stddev(this.image,this.width,this.height);
int step = (int)(0.50*stddev);
// watershed segmentation
process(step);
System.out.println("Region count : "+this.maxid);
}
return display(original,drawImage,drawMosaic,drawShed);
}
/* --------------------------------------------------------------------- */
/* INPUT PROCESSING */
/* --------------------------------------------------------------------- */
private static int[] logscale = new int[256];
static {
for(int i=0;i<256;i++)
logscale[i]=(int)(255*Math.pow(i/255.0,0.333));
}
private int getGrayLevel(BufferedImage input, int x, int y) {
// convert rgb to luminance
int rgb=input.getRGB(x,y);
int r = (rgb >>16 ) & 0xFF;
int g = (rgb >> 8 ) & 0xFF;
int b = rgb & 0xFF;
return (299*r + 587*g + 114*b)/1000;
}
private int[][] BitmapToArrayGray(BufferedImage input, int radius, boolean invert) {
// 1. bluring
double sigma = radius*0.333;
Kernel kernel = PredefinedKernel.getGaussianKernel(radius, sigma);
ConvolveOperator op = new ConvolveOperator(kernel,new BorderExtenderCopy());
BufferedImage blured = op.compute(input);
// 2. convert to graylevel array
int width = blured.getWidth();
int height = blured.getHeight();
int[][] c2 = new int[width][height];
for (int y=0; y<height; y++)
for (int x=0; x<width; x++) {
c2[x][y]=getGrayLevel(blured,x,y);
if (invert) c2[x][y]=255-c2[x][y];
}
return c2;
}
// compute the sum-image of the given image
public long[][] sumImage(long[][] image, int width, int height) {
long[][] sum = new long[width][height];
sum[0][0] = image[0][0];
// 2. first column
for (int y=1; y<height; y++)
sum[0][y] = image[0][y] + sum[0][y-1];
// 3. first line
for (int x=1; x<width; x++)
sum[x][0] = image[x][0] + sum[x-1][0];
// 4. remaining pixels
for (int y=1; y<height; y++)
for (int x=1; x<width; x++)
sum[x][y] = image[x][y] + sum[x-1][y] + sum[x][y-1] - sum[x-1][y-1];
return sum;
}
private int[][] variance(BufferedImage input, int radius, boolean log) {
int width = input.getWidth();
int height = input.getHeight();
long[][] layer = new long[width][height];
// RED sum-image
if (this.R==null || this.R2==null) {
for (int y=0; y<height; y++) for (int x=0; x<width; x++) layer[x][y]=((input.getRGB(x,y)>>16) & 0xFF);
this.R = sumImage(layer,width,height);
for (int y=0; y<height; y++) for (int x=0; x<width; x++) layer[x][y]*=layer[x][y];
this.R2 = sumImage(layer,width,height);
}
// GREEN sum-image
if (this.G==null || this.G2==null) {
for (int y=0; y<height; y++) for (int x=0; x<width; x++) layer[x][y]=((input.getRGB(x,y)>>8) & 0xFF);
this.G = sumImage(layer,width,height);
for (int y=0; y<height; y++) for (int x=0; x<width; x++) layer[x][y]*=layer[x][y];
this.G2 = sumImage(layer,width,height);
}
// BLUE sum-image
if (this.B==null || this.B2==null) {
for (int y=0; y<height; y++) for (int x=0; x<width; x++) layer[x][y]=(input.getRGB(x,y) & 0xFF);
this.B = sumImage(layer,width,height);
for (int y=0; y<height; y++) for (int x=0; x<width; x++) layer[x][y]*=layer[x][y];
this.B2 = sumImage(layer,width,height);
}
// compute the local variance
int[][] c2 = new int[width][height];
for (int y=0; y<height; y++) {
for (int x=0; x<width; x++) {
// zone coords
int xmin = Math.max(x-radius,0);
int xmax = Math.min(x+radius,width-1);
int ymin = Math.max(y-radius,0);
int ymax = Math.min(y+radius,height-1);
int area = (xmax-xmin)*(ymax-ymin);
// red variance
double XR = R[xmax][ymax] - R[xmin][ymax] - R[xmax][ymin] + R[xmin][ymin];
double XR2 = R2[xmax][ymax] - R2[xmin][ymax] - R2[xmax][ymin] + R2[xmin][ymin];
double var_R = XR2/area - (XR*XR)/(area*area);
// green variance
double XG = G[xmax][ymax] - G[xmin][ymax] - G[xmax][ymin] + G[xmin][ymin];
double XG2 = G2[xmax][ymax] - G2[xmin][ymax] - G2[xmax][ymin] + G2[xmin][ymin];
double var_G = XG2/area - (XG*XG)/(area*area);
// blue variance
double XB = B[xmax][ymax] - B[xmin][ymax] - B[xmax][ymin] + B[xmin][ymin];
double XB2 = B2[xmax][ymax] - B2[xmin][ymax] - B2[xmax][ymin] + B2[xmin][ymin];
double var_B = XB2/area - (XB*XB)/(area*area);
// weighted sum of the 3 variances
double v = 0.299*var_R + 0.587*var_G + 0.114*var_B;
// convert to range [0-255]
v = Math.sqrt(v)*2;
if (v>255) v=255;
if (log) v = logscale[(int)v];
c2[x][y]=(int)v;
}
}
return c2;
}
private static double stddev(int[][] image,int W,int H) {
double X=0,X2=0,COUNT=0;
for (int y = 0; y < H; y++) {
for (int x = 0; x < W; x++) {
X+=image[x][y];
X2+=image[x][y]*image[x][y];
COUNT++;
}
}
double variance = (X2/COUNT) - ((X*X)/(COUNT*COUNT));
double stddev = Math.sqrt(variance);
return stddev;
}
/* --------------------------------------------------------------------- */
/* OUTPUT PROCESSING */
/* --------------------------------------------------------------------- */
public BufferedImage display(BufferedImage original, boolean drawImage, boolean drawMosaic, boolean drawShed) {
Map<Integer, int[]> regions = new HashMap<Integer, int[]>();
// Compute mean region color
if (drawMosaic) {
for (int y = 0; y < this.height; y++) {
for (int x = 0; x < this.width; x++) {
int rid = this.rmap[x][y];
int[] stat = regions.get(rid);
if (stat==null) {
stat = new int[4];
regions.put(rid, stat);
}
int rgb = original.getRGB(x, y);
int r=(rgb>>16) & 0xFF, g=(rgb>>8 ) & 0xFF, b=rgb & 0xFF;
stat[0]+=r; stat[1]+=g; stat[2]+=b; stat[3]++;
}
}
for(int[] rgb:regions.values()) {
// mean
rgb[0]=(int)(0.5+rgb[0]/rgb[3]);
rgb[1]=(int)(0.5+rgb[1]/rgb[3]);
rgb[2]=(int)(0.5+rgb[2]/rgb[3]);
// rgb format
int r = rgb[0] & 0xFF;
int g = rgb[1] & 0xFF;
int b = rgb[2] & 0xFF;
rgb[3] = (r<<16)+(g<<8)+b;
}
}
// create output image
BufferedImage out = new BufferedImage(this.width,this.height,ColorSpace.TYPE_RGB);
for (int y = 0; y < this.height; y++) {
for (int x = 0; x < this.width; x++) {
int rid = this.rmap[x][y];
if (rid>0) {
if (drawImage) // copy original pixel
out.setRGB(x, y, original.getRGB(x, y));
if (drawMosaic) // copy mean value of region
out.setRGB(x, y, regions.get(rid)[3]);
}
if (rid<0) {
if (drawShed) // draw watershed
out.setRGB(x, y, 0xFF0000);
else // copy mean value of an adjacent region
out.setRGB(x, y, regions.get(-rid)[3]);
}
}
}
return out;
}
/* --------------------------------------------------------------------- */
/* WATERSHED PROCESSING */
/* --------------------------------------------------------------------- */
// original gray-level image
private int width,height;
private int[][] image = null;
private final int GRAYLEVEL = 256;
// region map
private int[][] rmap = null;
private int maxid=0;
// pixel and list of pixel structure
class Pixel {
int x,y,level;
public Pixel(int x,int y,int l) {
this.x=x; this.y=y; this.level=l;
}
}
class ListOfPixels extends LinkedList<Pixel> {}
// list of pixels (one per level) to process
private ListOfPixels[] explorelist;
// offsets of the 8 neighbors
private int[] dx8 = new int[] {-1, 0, 1, 1, 1, 0,-1,-1};
private int[] dy8 = new int[] {-1,-1,-1, 0, 1, 1, 1, 0};
// ---------------------------------------------------
// gray-level watershed algorithm : return the boolean watershep map
public void process(int step) {
// allocate memory
this.maxid=0;
this.rmap = new int[this.width][this.height];
this.explorelist = new ListOfPixels[GRAYLEVEL];
for(int i=0;i<GRAYLEVEL;i++)
this.explorelist[i]=new ListOfPixels();
// flooding level by level
int level=0,yoffset=0;
while(level<GRAYLEVEL) {
// extend region by exploring neighbors of known pixels
while(true) {
Pixel p = nextPixel(level,step);
if (p==null) break;
extend(p);
}
// find a new seed for this level
Pixel seed = findSeed(level,yoffset);
if (seed!=null) {
// create and assign a new region to this seed
this.rmap[seed.x][seed.y]=(++maxid);
yoffset=seed.y;
// add this seed to the list of pixel to explore
explorelist[level].add(seed);
} else {
// no more seed for this level -> next level
level++;
yoffset=0;
}
}
// clean up
this.explorelist = null;
}
// find a seed ( = unassigned pixel ) at the specified level
private Pixel findSeed(int level,int yoffset) {
for (int y = yoffset; y < this.height; y++)
for (int x = 0; x < this.width; x++)
if (this.image[x][y]==level && this.rmap[x][y]==0)
return new Pixel(x,y,level);
return null;
}
// return the next pixel to explore
private Pixel nextPixel(int level, int step) {
// return the first pixel found in the explorelist
for(int i=level;i<level+step && i<GRAYLEVEL;i++) {
if (!explorelist[i].isEmpty())
return explorelist[i].remove(0);
}
return null;
}
// explore the 8 neighbors of a pixel and set the region
private void extend(Pixel p) {
int region=this.rmap[p.x][p.y];
// this pixel is a watershed => cannot extend it
if (region<0) return;
// for each neighbor pixel
for(int k=0;k<8;k++) {
int xk = p.x+dx8[k];
int yk = p.y+dy8[k];
if (xk<0 || xk>=this.width) continue;
if (yk<0 || yk>=this.height) continue;
// level and region of neighbor
int vk = this.image[xk][yk];
int rk = this.rmap[xk][yk];
// neighbor is a watershed => ignore
if (rk<0) continue;
// neighbor as no region assigned => set it
if (rk==0) {
this.rmap[xk][yk]=region;
this.explorelist[vk].add(new Pixel(xk,yk,vk));
continue;
}
// neighbor is assigned to the same region => nothing to do
if (rk==region) continue;
// neighbor is assigned to another region => it's a watershed
this.rmap[xk][yk]=-rk;
}
}
} |
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