/*
* 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();
//
// }
}