Revision 19517 trunk/libraries/libRaster/src/org/gvsig/raster/grid/filter/enhancement/EcualizationFilter.java
| EcualizationFilter.java | ||
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* @author Nacho Brodin (nachobrodin@gmail.com) |
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*/ |
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public class EcualizationFilter extends RasterFilter {
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protected DatasetListStatistics stats = null; |
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protected double[] minBandValue = null; |
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protected double[] maxBandValue = null; |
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protected int nbands = 3; |
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protected int[] renderBands = null; |
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protected int[] k = null; |
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public static String[] names = new String[] {"ecualization"};
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protected Histogram histogram = null; |
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protected long nPixels = 0; |
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protected int[][] resValues = null; |
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protected long[][] lahe = null;
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protected long[][] laheNegative = null;
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protected int nElements = 0; |
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protected int[] ecualizedBands = null; |
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protected DatasetListStatistics stats = null;
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protected double[] minBandValue = null;
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protected double[] maxBandValue = null;
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protected int nbands = 3;
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protected int[] renderBands = null;
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protected int[] k = null;
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public static String[] names = new String[] {"ecualization"};
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protected Histogram histogram = null;
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protected long nPixels = 0;
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protected int[][] resValues = null;
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// protected long[][] lahe = null;
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// protected long[][] laheNegative = null;
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protected int nElements = 0;
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protected int[] ecualizedBands = null;
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protected double[][] aproximation = null; |
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protected double[][] aproximationNeg = null; |
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/** |
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* Construye un LinearEnhancementFilter |
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*/ |
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double[][] accumNormalize = Histogram.convertTableToNormalizeAccumulate(rgbHistogram.getTable()); |
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double[][] accumNormalizeNeg = Histogram.convertTableToNormalizeAccumulate(rgbHistogram.getNegativeTable()); |
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lahe = lahe(accumNormalize, 255); |
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laheNegative = lahe(accumNormalizeNeg, 255); |
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nElements = (laheNegative[0].length - 1); |
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double[][] probabilityDistrib = getProbabilityDistribution(raster.getBandCount(), 255); |
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aproximation = calcApproximation(accumNormalize, probabilityDistrib); |
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aproximationNeg = calcApproximation(accumNormalizeNeg, probabilityDistrib); |
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nElements = (aproximation[0].length - 1); |
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// lahe = lahe(accumNormalize, 255); |
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// laheNegative = lahe(accumNormalizeNeg, 255); |
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// nElements = (laheNegative[0].length - 1); |
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nbands = stats.getBandCount(); |
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rasterResult = RasterBuffer.getBuffer(IBuffer.TYPE_BYTE, raster.getWidth(), raster.getHeight(), raster.getBandCount(), true); |
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} |
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* @param value Valor m?ximo |
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* @return |
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*/ |
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private long[][] lahe(double[][] accumNorm, int value) {
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long[][] res = new long[accumNorm.length][accumNorm[0].length]; |
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for (int i = 0; i < res.length; i++) |
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for (int j = 0; j < res[i].length; j++) |
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res[i][j] = Math.round(accumNorm[i][j] * value); |
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return res; |
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} |
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// private long[][] lahe(double[][] accumNorm, int value) {
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// long[][] res = new long[accumNorm.length][accumNorm[0].length];
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// for (int i = 0; i < res.length; i++)
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// for (int j = 0; j < res[i].length; j++)
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// res[i][j] = Math.round(accumNorm[i][j] * value);
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// return res;
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// }
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/** |
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* Consulta si la ecualizaci?n est? activa para una banda o no |
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* @param nValues Valores a distribuir. Tipicamente 255 |
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* @return |
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*/ |
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/*private double[][] getProbabilityDistribution(int bands, int nValues) {
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double[][] res = new double[bands][nValues]; |
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private double[][] getProbabilityDistribution(int bands, double nValues) {
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double[][] res = new double[bands][(int)nValues];
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for (int i = 0; i < res.length; i++) |
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for (int j = 0; j < res[i].length; j++) |
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res[i][j] += (double)((double)j / 255D);
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res[i][j] += (double)((double)j / nValues);
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return res; |
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}*/
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} |
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/** |
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* Calcula un histograma aproximado ecualizado. |
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* Calcula un histograma aproximado ecualizado. Busca para cada valor del histograma acumulado |
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* normalizado el valor m?s cercano que hay en la distribuci?n de probabilidades. Con estos datos |
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* crea un array donde cada valor es el nuevo nivel de gris de esa posici?n. |
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* @param accumNorm |
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* @param distrib |
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* @return |
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*/ |
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/*private double[][] calcApproximation(double[][] accumNorm, double[][] distrib) {
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private double[][] calcApproximation(double[][] accumNorm, double[][] distrib) {
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double[][] res = new double[accumNorm.length][accumNorm[0].length]; |
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resValues = new int[accumNorm.length][accumNorm[0].length]; |
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for (int i = 0; i < accumNorm.length; i++) {
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for (int j = 0; j < accumNorm[i].length; j++) {
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double[] v = searchProbability(distrib[i], accumNorm[i][j]);
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if(v != null) {
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res[i][j] = v[0];
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resValues[i][j] = (int)v[1];
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}
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}
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}
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return res;
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}*/
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for (int i = 0; i < accumNorm.length; i++) {
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for (int j = 0; j < accumNorm[i].length; j++) {
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double[] v = searchProbability(distrib[i], accumNorm[i][j]); |
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if(v != null) {
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res[i][j] = v[0]; |
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resValues[i][j] = (int)v[1]; |
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} |
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} |
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} |
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return res; |
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} |
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/** |
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* En un array de distribuci?n de probabilidades busca la m?s cercana |
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* al valor pasado |
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* @param distrib Array de probabilidades |
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* @param value Valor a buscar en el array |
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* @return valor m?s cercano en el array |
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* @return valor m?s cercano en el array y posici?n del mismo
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*/ |
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/*private double[] searchProbability(double[] distrib, double value) {
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private double[] searchProbability(double[] distrib, double value) {
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double dif = Math.abs(distrib[0] - value); |
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for (int i = 1; i < distrib.length; i++) {
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double newDif = Math.abs(distrib[i] - value); |
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if(newDif < dif) |
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dif = newDif; |
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else |
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return new double[]{dif, i - 1};
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return new double[]{distrib[i - 1], i - 1};
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} |
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return null;
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}*/
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return new double[]{distrib[distrib.length - 1], distrib.length - 1};
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} |
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/* |
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* (non-Javadoc) |
Also available in: Unified diff