/* * Copyright (C) 2011-2012 Dr. John Lindsay <jlindsay@uoguelph.ca> * * 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 3 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, see <http://www.gnu.org/licenses/>. */ package plugins; import java.text.DecimalFormat; import java.util.Date; import java.util.Random; import whitebox.geospatialfiles.WhiteboxRaster; import whitebox.geospatialfiles.WhiteboxRasterBase.DataScale; import whitebox.geospatialfiles.WhiteboxRasterInfo; import whitebox.interfaces.WhiteboxPlugin; import whitebox.interfaces.WhiteboxPluginHost; /** * This tool can be used to perform classification, or clustering, in a group of images. * @author Dr. John Lindsay email: jlindsay@uoguelph.ca */ public class kMeansClassification implements WhiteboxPlugin { private WhiteboxPluginHost myHost = null; private String[] args; /** * Used to retrieve the plugin tool's name. This is a short, unique name containing no spaces. * @return String containing plugin name. */ @Override public String getName() { return "kMeansClassification"; } /** * Used to retrieve the plugin tool's descriptive name. This can be a longer name (containing spaces) and is used in the interface to list the tool. * @return String containing the plugin descriptive name. */ @Override public String getDescriptiveName() { return "k-Means Classification"; } /** * Used to retrieve a short description of what the plugin tool does. * @return String containing the plugin's description. */ @Override public String getToolDescription() { return "Performs a k-means classification on a multi-spectral dataset."; } /** * Used to identify which toolboxes this plugin tool should be listed in. * @return Array of Strings. */ @Override public String[] getToolbox() { String[] ret = {"ImageClass", "LandformClass"}; return ret; } /** * Sets the WhiteboxPluginHost to which the plugin tool is tied. This is the class * that the plugin will send all feedback messages, progress updates, and return objects. * @param host The WhiteboxPluginHost that called the plugin tool. */ @Override public void setPluginHost(WhiteboxPluginHost host) { myHost = host; } /** * Used to communicate feedback pop-up messages between a plugin tool and the main Whitebox user-interface. * @param feedback String containing the text to display. */ private void showFeedback(String message) { if (myHost != null) { myHost.showFeedback(message); } else { System.out.println(message); } } /** * Used to communicate a return object from a plugin tool to the main Whitebox user-interface. * @return Object, such as an output WhiteboxRaster. */ private void returnData(Object ret) { if (myHost != null) { myHost.returnData(ret); } } private int previousProgress = 0; private String previousProgressLabel = ""; /** * Used to communicate a progress update between a plugin tool and the main Whitebox user interface. * @param progressLabel A String to use for the progress label. * @param progress Float containing the progress value (between 0 and 100). */ private void updateProgress(String progressLabel, int progress) { if (myHost != null && ((progress != previousProgress) || (!progressLabel.equals(previousProgressLabel)))) { myHost.updateProgress(progressLabel, progress); } previousProgress = progress; previousProgressLabel = progressLabel; } /** * Used to communicate a progress update between a plugin tool and the main Whitebox user interface. * @param progress Float containing the progress value (between 0 and 100). */ private void updateProgress(int progress) { if (myHost != null && progress != previousProgress) { myHost.updateProgress(progress); } previousProgress = progress; } /** * Sets the arguments (parameters) used by the plugin. * @param args An array of string arguments. */ @Override public void setArgs(String[] args) { this.args = args.clone(); } private boolean cancelOp = false; /** * Used to communicate a cancel operation from the Whitebox GUI. * @param cancel Set to true if the plugin should be canceled. */ @Override public void setCancelOp(boolean cancel) { cancelOp = cancel; } private void cancelOperation() { showFeedback("Operation cancelled."); updateProgress("Progress: ", 0); } private boolean amIActive = false; /** * Used by the Whitebox GUI to tell if this plugin is still running. * @return a boolean describing whether or not the plugin is actively being used. */ @Override public boolean isActive() { return amIActive; } /** * Used to execute this plugin tool. */ @Override public void run() { amIActive = true; String inputFilesString = null; String[] imageFiles = null; String outputHeader = null; WhiteboxRasterInfo[] images = null; WhiteboxRaster ouptut = null; int nCols = 0; int nRows = 0; double z; int numClasses; int numImages; int progress = 0; int col, row; int a, i, j; double[][] data; double noData = -32768; double[][] classCentres; double[][] imageMetaData; long[] numPixelsInEachClass; int maxIterations = 100; double dist, minDist; int whichClass; double minAdjustment = 10; byte initializationMode = 0; // maximum dispersion along diagonal long numCellsChanged = 0; long totalNumCells = 0; boolean totalNumCellsCounted = false; double percentChanged = 0; double percentChangedThreshold = 1.0; if (args.length <= 0) { showFeedback("Plugin parameters have not been set."); return; } // read the input parameters inputFilesString = args[0]; outputHeader = args[1]; numClasses = Integer.parseInt(args[2]); maxIterations = Integer.parseInt(args[3]); percentChangedThreshold = Double.parseDouble(args[4]); if (args[5].toLowerCase().contains("random")) { initializationMode = 1; //random positioning } else { initializationMode = 0; //maximum dispersion along multi-dimensional diagonal } try { // deal with the input images imageFiles = inputFilesString.split(";"); numImages = imageFiles.length; images = new WhiteboxRasterInfo[numImages]; imageMetaData = new double[numImages][3]; for (i = 0; i < numImages; i++) { images[i] = new WhiteboxRasterInfo(imageFiles[i]); if (i == 0) { nCols = images[i].getNumberColumns(); nRows = images[i].getNumberRows(); noData = images[i].getNoDataValue(); } else { if (images[i].getNumberColumns() != nCols || images[i].getNumberRows() != nRows) { showFeedback("All input images must have the same dimensions (rows and columns)."); return; } } imageMetaData[i][0] = images[i].getNoDataValue(); imageMetaData[i][1] = images[i].getMinimumValue(); imageMetaData[i][2] = images[i].getMaximumValue(); } data = new double[numImages][]; numPixelsInEachClass = new long[numImages]; // now set up the output image WhiteboxRaster output = new WhiteboxRaster(outputHeader, "rw", imageFiles[0], WhiteboxRaster.DataType.INTEGER, 0); output.setDataScale(DataScale.CATEGORICAL); output.setPreferredPalette("qual.pal"); if (initializationMode == 1) { // initialize the class centres randomly Random generator = new Random(); double range; classCentres = new double[numClasses][numImages]; for (a = 0; a < numClasses; a++) { for (i = 0; i < numImages; i++) { range = imageMetaData[i][2] - imageMetaData[i][1]; classCentres[a][i] = imageMetaData[i][1] + generator.nextDouble() * range; } } } else { double range, spacing; classCentres = new double[numClasses][numImages]; for (a = 0; a < numClasses; a++) { for (i = 0; i < numImages; i++) { range = imageMetaData[i][2] - imageMetaData[i][1]; spacing = range / numClasses; classCentres[a][i] = imageMetaData[i][1] + spacing * a; } } } j = 0; whichClass = 0; do { j++; // assign each pixel to a class updateProgress("Loop " + j, 1); double[][] classCentreData = new double[numClasses][numImages]; numPixelsInEachClass = new long[numClasses]; numCellsChanged = 0; for (row = 0; row < nRows; row++) { for (i = 0; i < numImages; i++) { data[i] = images[i].getRowValues(row); } for (col = 0; col < nCols; col++) { if (data[0][col] != noData) { if (!totalNumCellsCounted) { totalNumCells++; } // calculate the squared distance to each of the centroids // and assign the pixel the value of the nearest centroid. minDist = Double.POSITIVE_INFINITY; for (a = 0; a < numClasses; a++) { dist = 0; for (i = 0; i < numImages; i++) { dist += (data[i][col] - classCentres[a][i]) * (data[i][col] - classCentres[a][i]); } if (dist < minDist) { minDist = dist; whichClass = a; } } z = output.getValue(row, col); if ((int)z != whichClass) { numCellsChanged++; } output.setValue(row, col, whichClass); numPixelsInEachClass[whichClass]++; for (i = 0; i < numImages; i++) { classCentreData[whichClass][i] += data[i][col]; } } else { output.setValue(row, col, noData); } } if (cancelOp) { cancelOperation(); return; } progress = (int) (100f * row / (nRows - 1)); updateProgress("Loop " + j, progress); } totalNumCellsCounted = true; // Update the class centroids for (a = 0; a < numClasses; a++) { if (numPixelsInEachClass[a] > 0) { double[] newClassCentre = new double[numImages]; for (i = 0; i < numImages; i++) { newClassCentre[i] = classCentreData[a][i] / numPixelsInEachClass[a]; } for (i = 0; i < numImages; i++) { classCentres[a][i] = newClassCentre[i]; } } } percentChanged = (double)numCellsChanged / totalNumCells * 100; } while ((percentChanged > percentChangedThreshold) && (j < maxIterations)); // prepare the report double[] totalDeviations = new double[numClasses]; for (row = 0; row < nRows; row++) { for (i = 0; i < numImages; i++) { data[i] = images[i].getRowValues(row); } for (col = 0; col < nCols; col++) { if (data[0][col] != noData) { whichClass = (int)(output.getValue(row, col)); dist = 0; for (i = 0; i < numImages; i++) { dist += (data[i][col] - classCentres[whichClass][i]) * (data[i][col] - classCentres[whichClass][i]); } totalDeviations[whichClass] += dist; } } if (cancelOp) { cancelOperation(); return; } progress = (int) (100f * row / (nRows - 1)); updateProgress("Loop " + j, progress); } double[] standardDeviations = new double[numClasses]; for (a = 0; a < numClasses; a++) { standardDeviations[a] = Math.sqrt(totalDeviations[a] / (numPixelsInEachClass[a] - 1)); } DecimalFormat df; df = new DecimalFormat("0.00"); String retStr = "k-Means Classification Report\n\n"; retStr += " \tCentroid Vector\n"; retStr += " \t"; for (i = 0; i < numImages; i++) { retStr += "Image" + (i + 1) + "\t"; } retStr += "SD\tPixels\t% Area\n"; for (a = 0; a < numClasses; a++) { String str = ""; for (i = 0; i < numImages; i++) { str += df.format(classCentres[a][i]) + "\t"; } retStr += "Cluster " + a + "\t" + str + df.format(standardDeviations[a]) + "\t" + numPixelsInEachClass[a] + "\t" + df.format((double)numPixelsInEachClass[a] / totalNumCells * 100) + "\n"; } retStr += "\n"; for (i = 0; i < numImages; i++) { retStr += "Image" + (i + 1) + " = " + images[i].getShortHeaderFile() + "\n"; } retStr += "\nCluster Centroid Distance Analysis:\n"; for (a = 0; a < numClasses; a++) { retStr += "\tClus. " + a; } retStr += "\n"; //double[][] centroidDistances = new double[numClasses][numClasses]; for (a = 0; a < numClasses; a++) { retStr += "Cluster " + a; for (int b = 0; b < numClasses; b++) { if (b >= a) { dist = 0; for (i = 0; i < numImages; i++) { dist += (classCentres[a][i] - classCentres[b][i]) * (classCentres[a][i] - classCentres[b][i]); } retStr += "\t" + df.format(Math.sqrt(dist)); } else { retStr += "\t"; } } retStr += "\n"; } returnData(retStr); Dendrogram plot = new Dendrogram(classCentres, numPixelsInEachClass); returnData(plot); for (i = 0; i < numImages; i++) { images[i].close(); } output.addMetadataEntry("Created by the " + getDescriptiveName() + " tool."); output.addMetadataEntry("Created on " + new Date()); output.close(); // returning a header file string displays the image. returnData(outputHeader); } catch (OutOfMemoryError oe) { myHost.showFeedback("An out-of-memory error has occurred during operation."); } catch (Exception e) { myHost.showFeedback("An error has occurred during operation. See log file for details."); myHost.logException("Error in " + getDescriptiveName(), e); } finally { updateProgress("Progress: ", 0); // tells the main application that this process is completed. amIActive = false; myHost.pluginComplete(); } } // // this is only used for debugging the tool // public static void main(String[] args) { // kMeansClassification kmc = new kMeansClassification(); // args = new String[5]; // args[0] = "/Users/johnlindsay/Documents/Teaching/GEOG3420/Winter 2012/Labs/Lab1/Data/LE70180302002142EDC00/band1 clipped.dep;/Users/johnlindsay/Documents/Teaching/GEOG3420/Winter 2012/Labs/Lab1/Data/LE70180302002142EDC00/band2 clipped.dep;/Users/johnlindsay/Documents/Teaching/GEOG3420/Winter 2012/Labs/Lab1/Data/LE70180302002142EDC00/band3 clipped.dep;/Users/johnlindsay/Documents/Teaching/GEOG3420/Winter 2012/Labs/Lab1/Data/LE70180302002142EDC00/band4 clipped.dep;/Users/johnlindsay/Documents/Teaching/GEOG3420/Winter 2012/Labs/Lab1/Data/LE70180302002142EDC00/band5 clipped.dep;"; // args[1] = "/Users/johnlindsay/Documents/Teaching/GEOG3420/Winter 2012/Labs/Lab1/Data/LE70180302002142EDC00/tmp1.dep"; // args[2] = "10"; // args[3] = "25"; // args[4] = "3"; // // kmc.setArgs(args); // kmc.run(); // // } }