org.jgrasstools.gears.modules.r.filter.OmsKernelFilter Maven / Gradle / Ivy
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package org.jgrasstools.gears.modules.r.filter;
import static org.jgrasstools.gears.libs.modules.JGTConstants.isNovalue;
import static org.jgrasstools.gears.libs.modules.Variables.BINARY;
import static org.jgrasstools.gears.libs.modules.Variables.COSINE;
import static org.jgrasstools.gears.libs.modules.Variables.DISTANCE;
import static org.jgrasstools.gears.libs.modules.Variables.EPANECHNIKOV;
import static org.jgrasstools.gears.libs.modules.Variables.GAUSSIAN;
import static org.jgrasstools.gears.libs.modules.Variables.INVERSE_DISTANCE;
import static org.jgrasstools.gears.libs.modules.Variables.QUARTIC;
import static org.jgrasstools.gears.libs.modules.Variables.TRIANGULAR;
import static org.jgrasstools.gears.libs.modules.Variables.TRIWEIGHT;
import java.awt.image.RenderedImage;
import java.awt.image.WritableRaster;
import javax.media.jai.KernelJAI;
import javax.media.jai.iterator.RandomIter;
import javax.media.jai.iterator.RandomIterFactory;
import javax.media.jai.iterator.WritableRandomIter;
import oms3.annotations.Author;
import oms3.annotations.Description;
import oms3.annotations.In;
import oms3.annotations.Keywords;
import oms3.annotations.Label;
import oms3.annotations.License;
import oms3.annotations.Name;
import oms3.annotations.Out;
import oms3.annotations.Status;
import oms3.annotations.UI;
import org.geotools.coverage.grid.GridCoverage2D;
import org.jaitools.media.jai.kernel.KernelFactory;
import org.jaitools.media.jai.kernel.KernelFactory.ValueType;
import org.jgrasstools.gears.libs.exceptions.ModelsIllegalargumentException;
import org.jgrasstools.gears.libs.modules.JGTConstants;
import org.jgrasstools.gears.libs.modules.JGTModel;
import org.jgrasstools.gears.utils.RegionMap;
import org.jgrasstools.gears.utils.coverage.CoverageUtilities;
@Description("A Kernel based filter.")
@Author(name = "Andrea Antonello, Silvia Franceschi", contact = "www.hydrologis.com")
@Keywords("kernel, filter, raster")
@Label(JGTConstants.RASTERPROCESSING)
@Name("kernelfilter")
@Status(Status.EXPERIMENTAL)
@License(JGTConstants.GPL3_LICENSE)
public class OmsKernelFilter extends JGTModel {
@Description("An input raster")
@In
public GridCoverage2D inRaster;
@Description("The kernel to use.")
@UI("combo:" + BINARY + "," + COSINE + "," + DISTANCE + "," //
+ EPANECHNIKOV + "," + GAUSSIAN + "," + INVERSE_DISTANCE + "," //
+ QUARTIC + "," + TRIANGULAR + "," + TRIWEIGHT)
@In
public String pKernel = EPANECHNIKOV;
@Description("The kernel radius to use in cells (default = 10).")
@In
public int pRadius = 10;
@Description("Filtered raster")
@Out
public GridCoverage2D outRaster;
public void process() throws Exception {
checkNull(inRaster);
RegionMap regionMap = CoverageUtilities.getRegionParamsFromGridCoverage(inRaster);
int cols = regionMap.getCols();
int rows = regionMap.getRows();
ValueType type = getKernelType(pKernel);
KernelJAI kernel = KernelFactory.createCircle(pRadius, type);
RenderedImage inImg = inRaster.getRenderedImage();
RandomIter inIter = RandomIterFactory.create(inImg, null);
WritableRaster outWR = CoverageUtilities.createDoubleWritableRaster(cols, rows, null, null, JGTConstants.doubleNovalue);
WritableRandomIter outIter = RandomIterFactory.createWritable(outWR, null);
float[] kernelData = kernel.getKernelData();
pm.beginTask("Processing...", cols - 2 * pRadius);
for( int c = pRadius; c < cols - pRadius; c++ ) {
for( int r = pRadius; r < rows - pRadius; r++ ) {
double kernelSum = 0.0;
int k = 0;
double outputValue = 0.0;
for( int kc = -pRadius; kc <= pRadius; kc++ ) {
for( int kr = -pRadius; kr <= pRadius; kr++, k++ ) {
double value = inIter.getSampleDouble(c + kc, r + kr, 0);
if (!isNovalue(value)) {
outputValue = outputValue + value * kernelData[k];
kernelSum = kernelSum + kernelData[k];
}
}
}
outIter.setSample(c, r, 0, outputValue / kernelSum);
}
pm.worked(1);
}
pm.done();
outRaster = CoverageUtilities.buildCoverage("filtered", outWR, regionMap, inRaster.getCoordinateReferenceSystem());
}
private static ValueType getKernelType( String pKernel2 ) {
ValueType type = null;
pKernel2 = pKernel2.trim();
if (pKernel2.equals(BINARY)) {
type = KernelFactory.ValueType.BINARY;
} else if (pKernel2.equals(COSINE)) {
type = KernelFactory.ValueType.COSINE;
} else if (pKernel2.equals(DISTANCE)) {
type = KernelFactory.ValueType.DISTANCE;
} else if (pKernel2.equals(GAUSSIAN)) {
type = KernelFactory.ValueType.GAUSSIAN;
} else if (pKernel2.equals(INVERSE_DISTANCE)) {
type = KernelFactory.ValueType.INVERSE_DISTANCE;
} else if (pKernel2.equals(QUARTIC)) {
type = KernelFactory.ValueType.QUARTIC;
} else if (pKernel2.equals(TRIANGULAR)) {
type = KernelFactory.ValueType.TRIANGULAR;
} else if (pKernel2.equals(TRIWEIGHT)) {
type = KernelFactory.ValueType.TRIWEIGHT;
} else if (pKernel2.equals(EPANECHNIKOV)) {
type = KernelFactory.ValueType.EPANECHNIKOV;
} else {
throw new ModelsIllegalargumentException("Kernel type not recognised: " + pKernel2, "OmsKernelFilter");
}
return type;
}
/**
* Smooth an array of values with a gaussian blur.
*
* @param values the values to smooth.
* @param kernelRadius the radius of the kernel to use.
* @return the smoothed values.
* @throws Exception
*/
public static double[] gaussianSmooth( double[] values, int kernelRadius ) throws Exception {
int size = values.length;
double[] newValues = new double[values.length];
double[] kernelData2D = makeGaussianKernel(kernelRadius);
for( int i = 0; i < kernelRadius; i++ ) {
newValues[i] = values[i];
}
for( int r = kernelRadius; r < size - kernelRadius; r++ ) {
double kernelSum = 0.0;
double outputValue = 0.0;
int k = 0;
for( int kc = -kernelRadius; kc <= kernelRadius; kc++, k++ ) {
double value = values[r + kc];
if (!isNovalue(value)) {
outputValue = outputValue + value * kernelData2D[k];
kernelSum = kernelSum + kernelData2D[k];
}
}
newValues[r] = outputValue / kernelSum;
}
for( int i = size - kernelRadius; i < size; i++ ) {
newValues[i] = values[i];
}
return newValues;
}
/**
* Smooth an array of values with an averaging moving window.
*
* @param values the values to smooth.
* @param lookAhead the size of half of the window.
* @return the smoothed values.
* @throws Exception
*/
public static double[] averageSmooth( double[] values, int lookAhead ) throws Exception {
int size = values.length;
double[] newValues = new double[values.length];
for( int i = 0; i < lookAhead; i++ ) {
newValues[i] = values[i];
}
for( int i = lookAhead; i < size - lookAhead; i++ ) {
double sum = 0.0;
int k = 0;
for( int l = -lookAhead; l <= lookAhead; l++ ) {
double value = values[i + l];
if (!isNovalue(value)) {
sum = sum + value;
k++;
}
}
newValues[i] = sum / k;
}
for( int i = size - lookAhead; i < size; i++ ) {
newValues[i] = values[i];
}
return newValues;
}
/**
* Make a Gaussian blur kernel.
*/
public static double[] makeGaussianKernel( int radius ) {
int rows = radius * 2 + 1;
double r = (double) radius;
double[] matrix = new double[rows];
double sigma = r / 3;
double sigma22 = 2 * sigma * sigma;
double sigmaPi2 = 2 * Math.PI * sigma;
double sqrtSigmaPi2 = Math.sqrt(sigmaPi2);
double radius2 = r * r;
double total = 0;
int index = 0;
for( int row = -radius; row <= radius; row++ ) {
double distance = row * row;
if (distance > radius2)
matrix[index] = 0;
else
matrix[index] = (double) Math.exp(-(distance) / sigma22) / sqrtSigmaPi2;
total += matrix[index];
index++;
}
for( int i = 0; i < rows; i++ )
matrix[i] /= total;
return matrix;
}
}