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);
}
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 process(ColorProcessor ip) {
double[] feature = new double[binX * binY * binZ];
int processedPixels = 0;
ImageProcessor mask = ip.getMask();
int numpixels = ip.getPixelCount();
float[] hsbvals = new float[3]; // Conversion buffer
for (int i = 0; i < numpixels; i++) {
if (mask == null || mask.get(i) != 0) {
if (type == TYPE.HSB) {
feature[getBinForHSB(ip.get(i), hsbvals)]++;
} else if (type == TYPE.RGB) {
feature[getBinForRGB(ip.get(i))]++;
}
processedPixels++;
}
}
Arrays2.div(feature, processedPixels);
addData(feature);
}
/**
* 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 (int i = 0; i < A.length; i++) {
Arrays2.div(A[i], sum);
}
}
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;
}
