Application: gvSIG desktop

/* gvSIG. Sistema de Informaci?n Geogr?fica de la Generalitat Valenciana

 *

 * Copyright (C) 2006 Instituto de Desarrollo Regional and Generalitat Valenciana.

 *

 * This program is free software; you can redistribute it and/or

 * modify it under the terms of the GNU General Public License

 * as published by the Free Software Foundation; either version 2

 * of the License, or (at your option) any later version.

 *

 * This program is distributed in the hope that it will be useful,

 * but WITHOUT ANY WARRANTY; without even the implied warranty of

 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

 * GNU General Public License for more details.

 *

 * You should have received a copy of the GNU General Public License

 * along with this program; if not, write to the Free Software

 * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307,USA.

 *

 * For more information, contact:

 *

 *  Generalitat Valenciana

 *   Conselleria d'Infraestructures i Transport

 *   Av. Blasco Iba?ez, 50

 *   46010 VALENCIA

 *   SPAIN

 *

 *      +34 963862235

 *   gvsig@gva.es

 *      www.gvsig.gva.es

 *

 *    or

 *

 *   Instituto de Desarrollo Regional (Universidad de Castilla La-Mancha)

 *   Campus Universitario s/n

 *   02071 Alabacete

 *   Spain

 *

 *   +34 967 599 200

 */

package org.gvsig.remotesensing.processtest;

import java.util.ArrayList;

import junit.framework.TestCase;

import org.gvsig.fmap.raster.grid.roi.VectorialROI;

import org.gvsig.fmap.raster.layers.FLyrRasterSE;

import org.gvsig.raster.RasterLibrary;

import org.gvsig.raster.buffer.BufferFactory;

import org.gvsig.raster.buffer.RasterBufferInvalidException;

import org.gvsig.raster.dataset.IBuffer;

import org.gvsig.raster.grid.Grid;

import org.gvsig.raster.grid.GridException;

import org.gvsig.remotesensing.classification.ClassificationMaximumLikelihoodProcess;

import org.gvsig.remotesensing.classification.ClassificationMinimumDistanceProcess;

import com.iver.cit.gvsig.exceptions.layers.LoadLayerException;

import com.iver.cit.gvsig.fmap.core.GeneralPathX;

import com.iver.cit.gvsig.fmap.core.IGeometry;

import com.iver.cit.gvsig.fmap.core.ShapeFactory;

/**

* Este test prueba el proceso de clasificaci?n de una imagen de 4x4 con 

* tres bandas.El proceso de clasificaci?n se realiza por minima distancia, maxima probabilidad

* y paralelepipedos. Los resultados se comparan con el resultado te?rico v?lido

* 

* ** @author Alejandro Mu?oz Sanchez (alejandro.munoz@uclm.es)

* */

public class TClassificationProcessTest extends TestCase {

        private String baseDir = "./test-images/";

        private String path1 = baseDir + "classification_image_test.tif";

        private FLyrRasterSE lyr=null;

        private Grid dataGrid= null;

        GeneralPathX path, path2 = null;

        IGeometry geometry, geometry2 = null;

        static{

                RasterLibrary.wakeUp();        

        }

        public void start() {

                this.setUp();

                this.testStack();

        }

        public void setUp() {

                System.err.println("TClassificationProcessTest running...");

                try {

                        lyr = FLyrRasterSE.createLayer(

                                        path1,

                                        path1,

                                        null

                                        );

                        BufferFactory ds1 = new BufferFactory(lyr.getDataSource());

                        dataGrid= new Grid(ds1);

                } catch (LoadLayerException e) {

                        System.out.print("Error en la construcci?n de la capa");

                } catch (RasterBufferInvalidException e) {

                        System.out.print("Error en la carga del grid de datos");

                }

        }

        public void testStack() {

                // Definici?n de las clases (rois) para el proceso

                VectorialROI class1= new VectorialROI(dataGrid);

                VectorialROI class2= new VectorialROI(dataGrid);

                path = new GeneralPathX();

                path2= new GeneralPathX();

                // class1-- Roi con los pixeles 1 y 2 de la primera fila de la imagen

                path.moveTo(dataGrid.getGridExtent().getULX(),dataGrid.getGridExtent().getULY());

                path.lineTo(dataGrid.getGridExtent().getULX()+(2*dataGrid.getCellSize())-0.3,dataGrid.getGridExtent().getULY());

                path.lineTo(dataGrid.getGridExtent().getULX()+(2*dataGrid.getCellSize())-0.3,dataGrid.getGridExtent().getULY()-dataGrid.getCellSize()+0.3);

                path.lineTo(dataGrid.getGridExtent().getULX(),dataGrid.getGridExtent().getULY()-dataGrid.getCellSize()+0.3);

                path.closePath();

                geometry=ShapeFactory.createPolygon2D(path);

                class1.addGeometry(geometry);

                //class2-- Roi con los pixeles 3 y 4 de la ultima fila

                path2.moveTo(dataGrid.getGridExtent().getLRX(),dataGrid.getGridExtent().getLRY());

                path2.lineTo(dataGrid.getGridExtent().getLRX(),dataGrid.getGridExtent().getLRY()+dataGrid.getCellSize()-0.1);

                path2.lineTo(dataGrid.getGridExtent().getLRX()-(2*dataGrid.getCellSize())+0.1,dataGrid.getGridExtent().getLRY()+dataGrid.getCellSize()-0.1);

                path2.lineTo(dataGrid.getGridExtent().getLRX()-(2*dataGrid.getCellSize())+0.1,dataGrid.getGridExtent().getLRY());

                geometry2=ShapeFactory.createPolygon2D(path2);

                class2.addGeometry(geometry2);

                ArrayList listRois=new ArrayList();

                listRois.add(class1);

                listRois.add(class2);

                // Clasificacion por el m?todo de m?nima distancia

                ClassificationMinimumDistanceProcess proceso = new ClassificationMinimumDistanceProcess();

                proceso.addParam("layer",lyr);

                proceso.addParam("bandList",new int[]{0,1,2});

                proceso.addParam("rois",listRois);

                proceso.run();

                IBuffer result= (IBuffer) proceso.getResult();

                assertEquals(result.getBandCount(),1);

                //Comparaci?n de valores

                assertEquals(result.getElemByte(0, 0,0),1);

                assertEquals(result.getElemByte(0, 1,0),0);

                assertEquals(result.getElemByte(0, 2,0),0);

                assertEquals(result.getElemByte(0, 3,0),0);        

                assertEquals(result.getElemByte(1, 0,0),1);

                assertEquals(result.getElemByte(1, 1,0),1);

                assertEquals(result.getElemByte(1, 2,0),1);

                assertEquals(result.getElemByte(1, 3,0),0);        

                assertEquals(result.getElemByte(2, 0,0),1);

                assertEquals(result.getElemByte(2, 1,0),1);

                assertEquals(result.getElemByte(2, 2,0),1);

                assertEquals(result.getElemByte(2, 3,0),0);        

                assertEquals(result.getElemByte(3, 0,0),0);

                assertEquals(result.getElemByte(3, 1,0),1);

                assertEquals(result.getElemByte(3, 2,0),1);

                assertEquals(result.getElemByte(3, 3,0),1);        

//                 Clasificacion por el m?todo de m?nima distancia

                ClassificationMaximumLikelihoodProcess proceso2 = new ClassificationMaximumLikelihoodProcess();

                proceso2.addParam("layer",lyr);

                proceso2.addParam("bandList",new int[]{0,1,2});

                proceso2.addParam("rois",listRois);

                proceso2.run();

                result= (IBuffer) proceso2.getResult();

                System.out.print("Proceso Completado..");

        }

}