我想使用 jfeaturelib(基本上是用于 java)计算 android 中的 haralick 功能,但我发现没有ImageIO 或 BufferedImage 在 android 中的实现,因为这些用于计算波纹管代码中的 haralick 特征。这些仅在纯 JAVA 中可用。
public void haralickFeatures(){
InputStream stream = HaralickDemo.class.getClassLoader().getResourceAsStream("test.jpg");
ColorProcessor image = new ColorProcessor(ImageIO.read(stream));
// initialize the descriptor
Haralick descriptor = new Haralick();
// run the descriptor and extract the features
descriptor.run(image);
// obtain the features
List<double[]> features = descriptor.getFeatures();
// print the features to system out
for (double[] feature : features) {
System.out.println(Arrays2.join(feature, ", ", "%.5f"));
}
}
有没有办法计算 android 中的 haralick 特征。任何代码示例都会有很大帮助。提前致谢。
最佳答案
正如您提到的,您不能使用 jfeaturelib 来计算haralick 功能,因为该库使用某些类,这些类仅在纯 java 而不是 android 中实现。 您可以使用我从 jfeaturelib 中获取的代码,并对其进行修改以适合 android。
首先,您必须在您的 android 项目中创建一个 java 类,并将其命名为您想要的名称(在我的例子中,我将其命名为 GLCM)
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; } } }
现在在您的主要 Activity 或您想要的 Activity 中创建该 GLCM 类的对象
GLCM glcm=new GLCM();
下一步是在您的主要 Activity 或您想要的 Activity 中复制过去的此功能。此函数提取特征,因为您必须将图像作为位图传递,并且此函数将在 float 组中返回 14 haralick 特征。这是那个函数
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 }
关于android - 使用 jfeaturelib 计算 Haralick 特征,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/50659080/