我发现Haralick的算法已经实现了。它用于在灰度级共生矩阵的帮助下获得一些特征。 现在我有问题让它工作。没有例外。代码很好。我不知道从哪里开始。有人可以帮助我完成第一步吗？如何获得纹理特征？
您可以在下面找到完整的Haralick源代码：
package de.lmu.dbs.jfeaturelib.features;
import Jama.Matrix;
import de.lmu.dbs.jfeaturelib.Progress;
import de.lmu.ifi.dbs.utilities.Arrays2;
import ij.plugin.filter.PlugInFilter;
import ij.process.ByteProcessor;
import ij.process.ImageProcessor;
import java.util.Arrays;
import java.util.EnumSet;
/**
* Haralick texture features
*
* http://makseq.com/materials/lib/Articles-Books/Filters/Texture/Co-occurence/haralick73.pdf
* <pre>
* @article{haralick1973textural,
* title={Textural features for image classification},
* author={Haralick, R.M. and Shanmugam, K. and Dinstein, I.},
* journal={Systems, Man and Cybernetics, IEEE Transactions on},
* volume={3},
* number={6},
* pages={610--621},
* year={1973},
* publisher={IEEE}
* }
* </pre>
*
* @author graf
*/
public class Haralick extends AbstractFeatureDescriptor {
/**
* The number of gray values for the textures
*/
private final int NUM_GRAY_VALUES = 32;
/**
* p_(x+y) statistics
*/
private double[] p_x_plus_y = new double[2 * NUM_GRAY_VALUES - 1];
/**
* p_(x-y) statistics
*/
private double[] p_x_minus_y = new double[NUM_GRAY_VALUES];
/**
* row mean value
*/
private double mu_x = 0;
/**
* column mean value
*/
private double mu_y = 0;
/**
* row variance
*/
private double var_x = 0;
/**
* column variance
*/
private double var_y = 0;
/**
* HXY1 statistics
*/
private double hx = 0;
/**
* HXY2 statistics
*/
private double hy = 0;
/**
* HXY1 statistics
*/
private double hxy1 = 0;
/**
* HXY2 statistics
*/
private double hxy2 = 0;
/**
* p_x statistics
*/
private double[] p_x = new double[NUM_GRAY_VALUES];
/**
* p_y statistics
*/
private double[] p_y = new double[NUM_GRAY_VALUES];
// -
private int haralickDist;
double[] features = null;
/**
* Constructs a haralick detector with default parameters.
*/
public Haralick() {
this.haralickDist = 1;
}
/**
* Constructs a haralick detector.
*
* @param haralickDist Integer for haralick distribution
*/
public Haralick(int haralickDist) {
this.haralickDist = haralickDist;
}
/**
* Defines the capability of the algorithm.
*
* @see PlugInFilter
* @see #supports()
*/
@Override
public EnumSet<Supports> supports() {
EnumSet set = EnumSet.of(
Supports.NoChanges,
Supports.DOES_8C,
Supports.DOES_8G,
Supports.DOES_RGB);
return set;
}
/**
* Starts the haralick detection.
*
* @param ip ImageProcessor of the source image
*/
@Override
public void run(ImageProcessor ip) {
if (!ByteProcessor.class.isAssignableFrom(ip.getClass())) {
ip = ip.convertToByte(true);
}
firePropertyChange(Progress.START);
process((ByteProcessor) ip);
addData(features);
firePropertyChange(Progress.END);
}
/**
* Returns information about the getFeature
*/
@Override
public String getDescription() {
StringBuilder sb = new StringBuilder();
sb.append("Haralick features: ");
sb.append("Angular 2nd moment, ");
sb.append("Contrast, ");
sb.append("Correlation, ");
sb.append("variance, ");
sb.append("Inverse Difference Moment, ");
sb.append("Sum Average, ");
sb.append("Sum Variance, ");
sb.append("Sum Entropy, ");
sb.append("Entropy, ");
sb.append("Difference Variance, ");
sb.append("Difference Entropy, ");
sb.append("Information Measures of Correlation, ");
sb.append("Information Measures of Correlation, ");
sb.append("Maximum Correlation COefficient");
return sb.toString();
}
private void process(ByteProcessor image) {
features = new double[14];
firePropertyChange(new Progress(1, "creating coocurrence matrix"));
Coocurrence coocurrence = new Coocurrence(image, NUM_GRAY_VALUES, this.haralickDist);
double[][] cooccurrenceMatrix = coocurrence.getCooccurrenceMatrix();
double meanGrayValue = coocurrence.getMeanGrayValue();
firePropertyChange(new Progress(25, "normalizing"));
normalize(cooccurrenceMatrix, coocurrence.getCooccurenceSums());
firePropertyChange(new Progress(50, "computing statistics"));
calculateStatistics(cooccurrenceMatrix);
firePropertyChange(new Progress(75, "computing features"));
double[][] p = cooccurrenceMatrix;
double[][] Q = new double[NUM_GRAY_VALUES][NUM_GRAY_VALUES];
for (int i = 0; i < NUM_GRAY_VALUES; i++) {
double sum_j_p_x_minus_y = 0;
for (int j = 0; j < NUM_GRAY_VALUES; j++) {
double p_ij = p[i][j];
sum_j_p_x_minus_y += j * p_x_minus_y[j];
features[0] += p_ij * p_ij;
features[2] += i * j * p_ij - mu_x * mu_y;
features[3] += (i - meanGrayValue) * (i - meanGrayValue) * p_ij;
features[4] += p_ij / (1 + (i - j) * (i - j));
features[8] += p_ij * log(p_ij);
// feature 13
if (p_ij != 0 && p_x[i] != 0) { // would result in 0
for (int k = 0; k < NUM_GRAY_VALUES; k++) {
if (p_y[k] != 0 && p[j][k] != 0) { // would result in NaN
Q[i][j] += (p_ij * p[j][k]) / (p_x[i] * p_y[k]);
}
}
}
}
features[1] += i * i * p_x_minus_y[i];
features[9] += (i - sum_j_p_x_minus_y) * (i - sum_j_p_x_minus_y) * p_x_minus_y[i];
features[10] += p_x_minus_y[i] * log(p_x_minus_y[i]);
}
// feature 13: Max Correlation Coefficient
double[] realEigenvaluesOfQ = new Matrix(Q).eig().getRealEigenvalues();
Arrays2.abs(realEigenvaluesOfQ);
Arrays.sort(realEigenvaluesOfQ);
features[13] = Math.sqrt(realEigenvaluesOfQ[realEigenvaluesOfQ.length - 2]);
features[2] /= Math.sqrt(var_x * var_y);
features[8] *= -1;
features[10] *= -1;
double maxhxhy = Math.max(hx, hy);
if (Math.signum(maxhxhy) == 0) {
features[11] = 0;
} else {
features[11] = (features[8] - hxy1) / maxhxhy;
}
features[12] = Math.sqrt(1 - Math.exp(-2 * (hxy2 - features[8])));
for (int i = 0; i < 2 * NUM_GRAY_VALUES - 1; i++) {
features[5] += i * p_x_plus_y[i];
features[7] += p_x_plus_y[i] * log(p_x_plus_y[i]);
double sum_j_p_x_plus_y = 0;
for (int j = 0; j < 2 * NUM_GRAY_VALUES - 1; j++) {
sum_j_p_x_plus_y += j * p_x_plus_y[j];
}
features[6] += (i - sum_j_p_x_plus_y) * (i - sum_j_p_x_plus_y) * p_x_plus_y[i];
}
features[7] *= -1;
}
/**
* Calculates the statistical properties.
*/
private void calculateStatistics(double[][] cooccurrenceMatrix) {
// p_x, p_y, p_x+y, p_x-y
for (int i = 0; i < NUM_GRAY_VALUES; i++) {
for (int j = 0; j < NUM_GRAY_VALUES; j++) {
double p_ij = cooccurrenceMatrix[i][j];
p_x[i] += p_ij;
p_y[j] += p_ij;
p_x_plus_y[i + j] += p_ij;
p_x_minus_y[Math.abs(i - j)] += p_ij;
}
}
// mean and variance values
double[] meanVar;
meanVar = meanVar(p_x);
mu_x = meanVar[0];
var_x = meanVar[1];
meanVar = meanVar(p_y);
mu_y = meanVar[0];
var_y = meanVar[1];
for (int i = 0; i < NUM_GRAY_VALUES; i++) {
// hx and hy
hx += p_x[i] * log(p_x[i]);
hy += p_y[i] * log(p_y[i]);
// hxy1 and hxy2
for (int j = 0; j < NUM_GRAY_VALUES; j++) {
double p_ij = cooccurrenceMatrix[i][j];
hxy1 += p_ij * log(p_x[i] * p_y[j]);
hxy2 += p_x[i] * p_y[j] * log(p_x[i] * p_y[j]);
}
}
hx *= -1;
hy *= -1;
hxy1 *= -1;
hxy2 *= -1;
}
/**
* Compute mean and variance of the given array
*
* @param a inut values
* @return array{mean, variance}
*/
private double[] meanVar(double[] a) {
// VAR(X) = E(X^2) - E(X)^2
double ex = 0, ex2 = 0; // E(X), E(X^2)
for (int i = 0; i < NUM_GRAY_VALUES; i++) {
ex += a[i];
ex2 += a[i] * a[i];
}
ex /= a.length;
ex2 /= a.length;
double var = ex2 - ex * ex;
return new double[]{ex, var};
}
/**
* Returns the logarithm of the specified value.
*
* @param value the value for which the logarithm should be returned
* @return the logarithm of the specified value
*/
private double log(double value) {
double log = Math.log(value);
if (log == Double.NEGATIVE_INFINITY) {
log = 0;
}
return log;
}
private void normalize(double[][] A, double sum) {
for (int i = 0; i < A.length; i++) {
Arrays2.div(A[i], sum);
}
}
//<editor-fold defaultstate="collapsed" desc="getter/Setter">
/**
* Getter for haralick distributions
*
* @return haralick distributions
*/
public int getHaralickDist() {
return haralickDist;
}
/**
* Setter for haralick distributions
*
* @param haralickDist int for haralick distributions
*/
public void setHaralickDist(int haralickDist) {
this.haralickDist = haralickDist;
}
//</editor-fold>
}
//<editor-fold defaultstate="collapsed" desc="Coocurrence Matrix">
/**
* http://makseq.com/materials/lib/Articles-Books/Filters/Texture/Co-occurence/haralick73.pdf
*/
class Coocurrence {
/**
* The number of gray values for the textures
*/
private final int NUM_GRAY_VALUES;
/**
* The number of gray levels in an image
*/
private final int GRAY_RANGES = 256;
/**
* The scale for the gray values for conversion rgb to gray values.
*/
private final double GRAY_SCALE;
/**
* gray histogram of the image.
*/
private final double[] grayHistogram;
/**
* quantized gray values of each pixel of the image.
*/
private final byte[] grayValue;
/**
* mean gray value
*/
private double meanGrayValue = 0;
/**
* The cooccurrence matrix
*/
private final double[][] cooccurrenceMatrices;
/**
* The value for one increment in the gray/color histograms.
*/
private final int HARALICK_DIST;
private final ByteProcessor image;
public Coocurrence(ByteProcessor b, int numGrayValues, int haralickDist) {
this.NUM_GRAY_VALUES = numGrayValues;
this.image = b;
this.GRAY_SCALE = (double) GRAY_RANGES / (double) NUM_GRAY_VALUES;
this.cooccurrenceMatrices = new double[NUM_GRAY_VALUES][NUM_GRAY_VALUES];
this.grayValue = new byte[image.getPixelCount()];
this.grayHistogram = new double[GRAY_RANGES];
this.HARALICK_DIST = haralickDist;
calculate();
}
public double getMeanGrayValue() {
return this.meanGrayValue;
}
public double[][] getCooccurrenceMatrix() {
return this.cooccurrenceMatrices;
}
public double getCooccurenceSums() {
return image.getPixelCount() * 8;
}
private void calculate() {
calculateGreyValues();
final int imageWidth = image.getWidth();
final int imageHeight = image.getHeight();
final int d = HARALICK_DIST;
int i, j, pos;
// image is not empty per default
for (int y = 0; y < imageHeight; y++) {
for (int x = 0; x < imageWidth; x++) {
pos = imageWidth * y + x;
// horizontal neighbor: 0 degrees
i = x - d;
// j = y;
if (!(i < 0)) {
increment(grayValue[pos], grayValue[pos - d]);
}
// vertical neighbor: 90 degree
// i = x;
j = y - d;
if (!(j < 0)) {
increment(grayValue[pos], grayValue[pos - d * imageWidth]);
}
// 45 degree diagonal neigbor
i = x + d;
j = y - d;
if (i < imageWidth && !(j < 0)) {
increment(grayValue[pos], grayValue[pos + d - d * imageWidth]);
}
// 135 vertical neighbor
i = x - d;
j = y - d;
if (!(i < 0) && !(j < 0)) {
increment(grayValue[pos], grayValue[pos - d - d * imageWidth]);
}
}
}
meanGrayValue = Arrays2.sum(grayValue);
}
private void calculateGreyValues() {
int size = image.getPixelCount();
int gray;
for (int pos = 0; pos < size; pos++) {
gray = image.get(pos);
grayValue[pos] = (byte) (gray / GRAY_SCALE); // quantized for texture analysis
grayHistogram[gray]++;
}
Arrays2.div(grayHistogram, size);
}
/**
* Incremets the coocurrence matrix at the specified positions (g1,g2) and
* (g2,g1).
*
* @param g1 the gray value of the first pixel
* @param g2 the gray value of the second pixel
*/
private void increment(int g1, int g2) {
cooccurrenceMatrices[g1][g2]++;
cooccurrenceMatrices[g2][g1]++;
}
}
//</editor-fold>
Here你有来源。
提前致谢=）