这是可以计算 GLCM 并为您提供 14 个 Haralick 特征（纹理特征）的类
public class GLCM {
static int totalPixels=0;
/**
* The number of gray values for the textures
*/
private final int NUM_GRAY_VALUES = 32;
/**
* p_(x+y) statistics
*/
private final double[] p_x_plus_y = new double[2 * NUM_GRAY_VALUES - 1];
/**
* p_(x-y) statistics
*/
private final 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 final double[] p_x = new double[NUM_GRAY_VALUES];
/**
* p_y statistics
*/
private final double[] p_y = new double[NUM_GRAY_VALUES];
// -
public List<double[]> data;
public int haralickDist;
double[] features = null;
static byte[] imageArray;
public void addData(double[] data) {
this.data.add(data);
}
public List<double[]> getFeatures() {
return data;
}
public void process(Bitmap b) {
features = new double[14];
Coocurrence coocurrence = new Coocurrence(b, NUM_GRAY_VALUES, this.haralickDist);
coocurrence.calculate();
double[][] cooccurrenceMatrix = coocurrence.getCooccurrenceMatrix();
double meanGrayValue = coocurrence.getMeanGrayValue();
normalize(cooccurrenceMatrix, coocurrence.getCooccurenceSums());
calculateStatistics(cooccurrenceMatrix);
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
// two-pass is numerically stable.
double ex = 0;
for (int i = 0; i < NUM_GRAY_VALUES; i++) {
ex += a[i];
}
ex /= a.length;
double var = 0;
for (int i = 0; i < NUM_GRAY_VALUES; i++) {
var += (a[i] - ex) * (a[i] - ex);
}
var /= (a.length - 1);
return new double[]{ex, var};
}
/**
* Returns the bound logarithm of the specified value.
*
* If Math.log would be Double.NEGATIVE_INFINITY, 0 is returned
*
* @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;
}
/**
* Normalizes the array by the given sum. by dividing each 2nd dimension
* array componentwise by the sum.
*
* @param A
* @param sum
*/
private void normalize(double[][] A, double sum) {
for (double[] A1 : A) {
Arrays2.div(A1, 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 (must be >= 1)
*/
public void setHaralickDist(int haralickDist) {
if (haralickDist <= 0) {
throw new IllegalArgumentException("the distance for haralick must be >= 1 but was " + haralickDist);
}
this.haralickDist = haralickDist;
}
//</editor-fold>
static class Coocurrence {
/**
* The number of gray values for the textures
*/
private final int NUM_GRAY_VALUES;
/**
* The number of gray levels in an image
*/
int GRAY_RANGES = 256;
/**
* The scale for the gray values for conversion rgb to gray values.
*/
double GRAY_SCALE;
/**
* gray histogram of the image.
*/
double[] grayHistogram;
/**
* Quantized gray values of each pixel of the image.
*
* Use int instead of byte as there is no unsigned byte in Java.
* Otherwise you'll have a hard time using white = 255. Alternative:
* replace with ImageJ ByteProcessor.
*/
private final int[] 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 Bitmap image;
public Coocurrence(Bitmap b, int numGrayValues, int haralickDist) {
this.NUM_GRAY_VALUES = numGrayValues;
this.HARALICK_DIST = haralickDist;
this.cooccurrenceMatrices = new double[NUM_GRAY_VALUES][NUM_GRAY_VALUES];
this.image = b;
totalPixels=b.getHeight()*b.getWidth();
this.grayValue = new int[totalPixels];
}
void calculate() {
this.GRAY_SCALE = (double) GRAY_RANGES / (double) NUM_GRAY_VALUES;
this.grayHistogram = new double[GRAY_RANGES];
calculateGreyValues();
final int imageWidth = image.getWidth();
final int imageHeight = image.getHeight();
final int d = HARALICK_DIST;
final int yOffset = d * imageWidth;
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;
if (i >= 0) {
increment(grayValue[pos], grayValue[pos - d]);
}
// vertical neighbor: 90 degree
j = y - d;
if (j >= 0) {
increment(grayValue[pos], grayValue[pos - yOffset]);
}
// 45 degree diagonal neigbor
i = x + d;
j = y - d;
if (i < imageWidth && j >= 0) {
increment(grayValue[pos], grayValue[pos + d - yOffset]);
}
// 135 vertical neighbor
i = x - d;
j = y - d;
if (i >= 0 && j >= 0) {
increment(grayValue[pos], grayValue[pos - d - yOffset]);
}
}
}
}
private void calculateGreyValues() {
final int size = grayValue.length;
double graySum = 0;
for (int pos = 0; pos < size; pos++) {
int gray = imageArray[pos]&0xff;
graySum += gray;
grayValue[pos] = (int) (gray / GRAY_SCALE); // quantized for texture analysis
assert grayValue[pos] >= 0 : grayValue[pos] + " > 0 violated";
grayHistogram[gray]++;
}
Arrays2.div(grayHistogram, size);
meanGrayValue = Math.floor(graySum / size / GRAY_SCALE)*GRAY_SCALE;
}
/**
* Incremets the coocurrence matrix at the specified positions (g1,g2)
* and (g2,g1) if g1 and g2 are in range.
*
* @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]++;
}
public double getMeanGrayValue() {
return this.meanGrayValue;
}
public double[][] getCooccurrenceMatrix() {
return this.cooccurrenceMatrices;
}
public double getCooccurenceSums() {
// divide by R=8 neighbours
// see p.613, §2 of Haralick paper
return totalPixels * 8;
}
}
}
创建 GLCM 类的对象为
GLCM glcm=new GLCM();
现在要提取特征，您必须编写一个函数，该函数在位图中获取图像并在浮点数组中返回 14 个 haralick 特征。这是那个功能
public void haralickFeatures(Bitmap b) throws IOException {
glcm.haralickDist=1;
ByteArrayOutputStream stream = new ByteArrayOutputStream();
b.compress(Bitmap.CompressFormat.PNG, 90, stream); // what 90 does ??
GLCM.imageArray=new byte[]{};
GLCM.imageArray = stream.toByteArray();
glcm.process(b);
glcm.data = new ArrayList<>(1);
glcm.addData(glcm.features);
List<double[]> featuresHar=glcm.getFeatures();
for (double[] feature : featuresHar) {
featureString=Arrays2.join(feature, ",", "%.5f");
}
String[] featureStr=featureString.split(Pattern.quote(","));
float[] featureFlot = new float[featureStr.length];
for (int i=0;i<featureStr.length;i++){
featureFlot[i]=Float.parseFloat(featureStr[i]);
}
//featureFlot is array that contain all 14 haralick features
}