Develop Reference

Calculate Haralick features using jfeaturelib

I want to calculate haralick features in android using jfeaturelib(which is basically for java) but I came to know that there is no implementation of ImageIO or BufferedImage in android as these are used in calculating haralick features in bellow code. These are only available in pure JAVA .
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"));
}
}
Is there a way to calculate haralick features in android. Any code example will be great help. Thanks in advance.

As you mentioned you cannot use jfeaturelib to calculate haralick features because this library use certain classes that are only implemented in pure java but not android.
You can use my code which I had taken from jfeaturelib and modified it to fit to use for android.
First you have to create a java class in your android project and name it what you want(in my case I name it as 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;
}
}
}
Now create object of that GLCM class in your main activity or in activity you want
GLCM glcm=new GLCM();
Next step is to copy past this function in your main activity or in activity you want. This function extract feature as you have to pass an image as bitmap and this function will return 14 haralick features in float array. Here is that function
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
}

Android Zipper Animation for unlock screen

I am currently working on zip animation to unlock android mobile screen. Changing background images is a expensive task and have not a smooth effect. I want a smooth effect in it. Any help please? Thanks

Try this:
The smooth effect makes use of Convolution Matrix:
Some image effects are better to implement using Convolution Matrix
method like: Gaussian Blur, Sharpening, Embossing, Smooth…
Check That Link to know more about Convolution Matrix or Another one
To do Convolution Matrix
import android.graphics.Bitmap;
import android.graphics.Color;
public class ConvolutionMatrix
{
public static final int SIZE = 3;
public double[][] Matrix;
public double Factor = 1;
public double Offset = 1;
public ConvolutionMatrix(int size) {
Matrix = new double[size][size];
}
public void setAll(double value) {
for (int x = 0; x < SIZE; ++x) {
for (int y = 0; y < SIZE; ++y) {
Matrix[x][y] = value;
}
}
}
public void applyConfig(double[][] config) {
for(int x = 0; x < SIZE; ++x) {
for(int y = 0; y < SIZE; ++y) {
Matrix[x][y] = config[x][y];
}
}
}
public static Bitmap computeConvolution3x3(Bitmap src, ConvolutionMatrix matrix) {
int width = src.getWidth();
int height = src.getHeight();
Bitmap result = Bitmap.createBitmap(width, height, src.getConfig());
int A, R, G, B;
int sumR, sumG, sumB;
int[][] pixels = new int[SIZE][SIZE];
for(int y = 0; y < height - 2; ++y) {
for(int x = 0; x < width - 2; ++x) {
// get pixel matrix
for(int i = 0; i < SIZE; ++i) {
for(int j = 0; j < SIZE; ++j) {
pixels[i][j] = src.getPixel(x + i, y + j);
}
}
// get alpha of center pixel
A = Color.alpha(pixels[1][1]);
// init color sum
sumR = sumG = sumB = 0;
// get sum of RGB on matrix
for(int i = 0; i < SIZE; ++i) {
for(int j = 0; j < SIZE; ++j) {
sumR += (Color.red(pixels[i][j]) * matrix.Matrix[i][j]);
sumG += (Color.green(pixels[i][j]) * matrix.Matrix[i][j]);
sumB += (Color.blue(pixels[i][j]) * matrix.Matrix[i][j]);
}
}
// get final Red
R = (int)(sumR / matrix.Factor + matrix.Offset);
if(R < 0) { R = 0; }
else if(R > 255) { R = 255; }
// get final Green
G = (int)(sumG / matrix.Factor + matrix.Offset);
if(G < 0) { G = 0; }
else if(G > 255) { G = 255; }
// get final Blue
B = (int)(sumB / matrix.Factor + matrix.Offset);
if(B < 0) { B = 0; }
else if(B > 255) { B = 255; }
// apply new pixel
result.setPixel(x + 1, y + 1, Color.argb(A, R, G, B));
}
}
// final image
return result;
}
}
Then to do Smooth effect
public static Bitmap smooth(Bitmap src, double value) {
ConvolutionMatrix convMatrix = new ConvolutionMatrix(3);
convMatrix.setAll(1);
convMatrix.Matrix[1][1] = value;
convMatrix.Factor = value + 8;
convMatrix.Offset = 1;
return ConvolutionMatrix.computeConvolution3x3(src, convMatrix);
}
You can change values and get the smooth effect as you want.
That tutorial it's found HERE

mosaic bitmap in android

Hi I need to make mosaic effect in android.
Convert this:
To this:
How can i do this?
Thank you

/**
* 效果能实现，但是是个耗时的操作
*
* #param bmp
* #param precent 马赛克的程度（0-1）
* 300*300 precent=1 time=57ms
* #return
*/
public static Bitmap getMosaicsBitmap(Bitmap bmp, double precent) {
long start = System.currentTimeMillis();
int bmpW = bmp.getWidth();
int bmpH = bmp.getHeight();
Bitmap resultBmp = Bitmap.createBitmap(bmpW, bmpH, Bitmap.Config.ARGB_8888);
Canvas canvas = new Canvas(resultBmp);
Paint paint = new Paint();
double unit;
if (precent == 0) {
unit = bmpW;
} else {
unit = 1 / precent;
}
double resultBmpW = bmpW / unit;
double resultBmpH = bmpH / unit;
for (int i = 0; i < resultBmpH; i++) {
for (int j = 0; j < resultBmpW; j++) {
int pickPointX = (int) (unit * (j + 0.5));
int pickPointY = (int) (unit * (i + 0.5));
int color;
if (pickPointX >= bmpW || pickPointY >= bmpH) {
color = bmp.getPixel(bmpW / 2, bmpH / 2);
} else {
color = bmp.getPixel(pickPointX, pickPointY);
}
paint.setColor(color);
canvas.drawRect((int) (unit * j), (int) (unit * i), (int) (unit * (j + 1)), (int) (unit * (i + 1)), paint);
}
}
canvas.setBitmap(null);
long end = System.currentTimeMillis();
Log.v(TAG, "DrawTime:" + (end - start));
return resultBmp;
}
/**
* 和上面的函数同样的功能，效率远高于上面
*
* #param bmp
* #param precent
* #return
*/
public static Bitmap getMosaicsBitmaps(Bitmap bmp, double precent) {
long start = System.currentTimeMillis();
int bmpW = bmp.getWidth();
int bmpH = bmp.getHeight();
int[] pixels = new int[bmpH * bmpW];
bmp.getPixels(pixels, 0, bmpW, 0, 0, bmpW, bmpH);
int raw = (int) (bmpW * precent);
int unit;
if (raw == 0) {
unit = bmpW;
} else {
unit = bmpW / raw; //原来的unit*unit像素点合成一个，使用原左上角的值
}
if (unit >= bmpW || unit >= bmpH) {
return getMosaicsBitmap(bmp, precent);
}
for (int i = 0; i < bmpH; ) {
for (int j = 0; j < bmpW; ) {
int leftTopPoint = i * bmpW + j;
for (int k = 0; k < unit; k++) {
for (int m = 0; m < unit; m++) {
int point = (i + k) * bmpW + (j + m);
if (point < pixels.length) {
pixels[point] = pixels[leftTopPoint];
}
}
}
j += unit;
}
i += unit;
}
long end = System.currentTimeMillis();
Log.v(TAG, "DrawTime:" + (end - start));
return Bitmap.createBitmap(pixels, bmpW, bmpH, Bitmap.Config.ARGB_8888);
}
when you want change a bitmap to mosaic bitmap,just invoke function
getMosaicsBitmaps(bmp,0.1)

Android: Adaptive Thresholding

I'm trying to implement adaptive thresholding algorithm by Derek Bradley using Android. But it is returning black pixels all the time. Here is my code snippet. Please suggest me about what should I do. Thanks in advance.
public static Bitmap GrayscaleToBin(Bitmap bm2)
{
Bitmap bm;
bm=bm2.copy(Config.ARGB_8888, true);
final int width = bm.getWidth();
final int height = bm.getHeight();
int[] pixels;
pixels = new int[width*height];
bm.getPixels(pixels,0,width,0,0,width,height);
//Bradley AdaptiveThrsholdging
int []intImg= new int[width*height];
int sum=0;
for(int i=0;i<width;++i){
sum=0;
for(int j=0;j<height;++j)
{
sum=sum+pixels[i+j*width];
if(i==0){intImg[i+j*width]=sum;}
else
{
intImg[i+j*width]= intImg[i-1+j*width]+sum;
}
}
}
int x1,x2,y1,y2=0,count=0;
int s=width >> 3;
int t=15;
for(int i=0;i<width;++i)
{
for(int j=0;j<height;++j)
{
x1=i-s/2;
x2=i+s/2;
y1=j-s/2;
y2=j+s/2;
if (x1 <0) x1 = 0;
if (x2>= width) x2 = width-1;
if (y1 <0) y1 = 0;
if (y2>= height) y2 = height-1;
count = (x2-x1) * (y2-y1);
sum = intImg [y2 * width + x2] -
intImg [y1 * width + x2] -
intImg [y2 * width + x1] +
intImg [y1 * width + x1];
if((pixels[i+j*width]*count)<=(sum*(100-t)/100))
{
pixels[i+j*width]=0;
}
else
{
pixels[i+j*width]=255;
}
}
}
/*---------------------------------------------------------------------------*/
bm.setPixels(pixels,0,width,0,0,width,height);
// Log.d("cdsfss","afterloop");
return bm;
}

After a Long struggle I have solved the issue with the following code.
public static Bitmap GrayscaleToBin(Bitmap bm2)
{
Bitmap bm;
bm=bm2.copy(Config.RGB_565, true);
final int width = bm.getWidth();
final int height = bm.getHeight();
int pixel1,pixel2,pixel3,pixel4,A,R;
int[] pixels;
pixels = new int[width*height];
bm.getPixels(pixels,0,width,0,0,width,height);
int size=width*height;
int s=width/8;
int s2=s>>1;
double t=0.15;
double it=1.0-t;
int []integral= new int[size];
int []threshold=new int[size];
int i,j,diff,x1,y1,x2,y2,ind1,ind2,ind3;
int sum=0;
int ind=0;
while(ind<size)
{
sum+=pixels[ind] & 0xFF;
integral[ind]=sum;
ind+=width;
}
x1=0;
for(i=1;i<width;++i)
{
sum=0;
ind=i;
ind3=ind-s2;
if(i>s)
{
x1=i-s;
}
diff=i-x1;
for(j=0;j<height;++j)
{
sum+=pixels[ind] & 0xFF;
integral[ind]=integral[(int)(ind-1)]+sum;
ind+=width;
if(i<s2)continue;
if(j<s2)continue;
y1=(j<s ? 0 : j-s);
ind1=y1*width;
ind2=j*width;
if (((pixels[ind3]&0xFF)*(diff * (j - y1))) < ((integral[(int)(ind2 + i)] - integral[(int)(ind1 + i)] - integral[(int)(ind2 + x1)] + integral[(int)(ind1 + x1)])*it)) {
threshold[ind3] = 0x00;
} else {
threshold[ind3] = 0xFFFFFF;
}
ind3 += width;
}
}
y1 = 0;
for( j = 0; j < height; ++j )
{
i = 0;
y2 =height- 1;
if( j <height- s2 )
{
i = width - s2;
y2 = j + s2;
}
ind = j * width + i;
if( j > s2 ) y1 = j - s2;
ind1 = y1 * width;
ind2 = y2 * width;
diff = y2 - y1;
for( ; i < width; ++i, ++ind )
{
x1 = ( i < s2 ? 0 : i - s2);
x2 = i + s2;
// check the border
if (x2 >= width) x2 = width - 1;
if (((pixels[ind]&0xFF)*((x2 - x1) * diff)) < ((integral[(int)(ind2 + x2)] - integral[(int)(ind1 + x2)] - integral[(int)(ind2 + x1)] + integral[(int)(ind1 + x1)])*it)) {
threshold[ind] = 0x00;
} else {
threshold[ind] = 0xFFFFFF;
}
}
}
/*-------------------------------
* --------------------------------------------*/
bm.setPixels(threshold,0,width,0,0,width,height);
return bm;
}

You can use Catalano Framework. There's an example using Bradley for Android in samples folder.
FastBitmap fb = new FastBitmap(bitmap);
fb.toGrayscale();
BradleyLocalThreshold bradley = new BradleyLocalThreshold();
bradley.applyInPlace(fb);
bitmap = fb.toBitmap();

Android Renderscript String Functions?

Are there any string functions in Renderscript? Like vsprintf, for example?
Specifically, I'd like to convert a float to a string. Do I have to write that from scratch?
Thanks!

Sorry, here's a better one. It'll work for integers as well but they have ".000" added on.
char stringBuffer[50];
static const int MAX_STRING_LENGTH = sizeof(stringBuffer) - 1;
void drawFloat(float value, int x, int y) {
int index = 0;
int scaledValue = (int)(value * 1000);
index = MAX_STRING_LENGTH;
stringBuffer[index] = 0;
while(scaledValue > 0 || index > MAX_STRING_LENGTH - 4) {
index--;
if(index == MAX_STRING_LENGTH - 4) {
stringBuffer[index--] = '.';
}
int digitValue = scaledValue % 10;
stringBuffer[index] = '0' + digitValue;
scaledValue /= 10;
}
if(value < 0) {
stringBuffer[index--] = '-';
}
rsgDrawText(&stringBuffer[index], x - 10, y + 5);
}

Couldn't find a simple way so...
void drawInteger(int value, int x, int y) {
char text[50] = "0";
int index = 0;
if(value != 0) {
index = 49;
text[index] = 0;
while(value > 0) {
index--;
int digitValue = value % 10;
text[index] = '0' + digitValue;
value /= 10;
}
if(value < 0) {
text[index--] = '-';
}
}
rsgDrawText(&text[index], x - 10, y + 5);
}
void drawFloat(float value, int x, int y) {
char text[50] = "0.000";
int index = 0;
if(value != 0) {
int integerPart = (int)(value * 1000);
index = 49;
text[index] = 0;
while(integerPart > 0) {
index--;
if(index == 45) {
text[index--] = '.';
}
int digitValue = integerPart % 10;
text[index] = '0' + digitValue;
integerPart /= 10;
}
if(value < 0) {
text[index--] = '-';
}
}
rsgDrawText(&text[index], x - 10, y + 5);
}