static {
g = new float[3][3];
sum = 0;
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
int x = i - 1;
int y = j - 1;
g[i][j] = (float) Math.exp(-(x * x + y * y) / (2.0 * 0.8));
sum += g[i][j];
}
}
sum1 = g[0][0] + g[0][1] + g[0][2] + g[1][0] + g[1][1] + g[1][2];
}
/**
* Make a Gaussian blur kernel.
*
* @param radius the blur radius
* @return the kernel
*/
public static Kernel makeKernel(float radius) {
int r = (int) Math.ceil(radius);
int rows = r * 2 + 1;
float[] matrix = new float[rows];
float sigma = radius / 3;
float sigma22 = 2 * sigma * sigma;
float sigmaPi2 = 2 * ImageMath.PI * sigma;
float sqrtSigmaPi2 = (float) Math.sqrt(sigmaPi2);
float radius2 = radius * radius;
float total = 0;
int index = 0;
for (int row = -r; row <= r; row++) {
float distance = row * row;
if (distance > radius2) matrix[index] = 0;
else matrix[index] = (float) Math.exp(-(distance) / sigma22) / sqrtSigmaPi2;
total += matrix[index];
index++;
}
for (int i = 0; i < rows; i++) matrix[i] /= total;
return new Kernel(rows, 1, matrix);
}
public static WritableRaster imgblur(WritableRaster img, int rad, double var) {
int h = img.getHeight(), w = img.getWidth();
double[] gk = new double[(rad * 2) + 1];
for (int i = 0; i <= rad; i++)
gk[rad + i] = gk[rad - i] = Math.exp(-0.5 * Math.pow(i / var, 2.0));
double s = 0;
for (double cw : gk) s += cw;
s = 1.0 / s;
for (int i = 0; i <= rad * 2; i++) gk[i] *= s;
int[] buf = new int[w * h];
for (int band = 0; band < img.getNumBands(); band++) {
int o;
o = 0;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
double v = 0;
int l = Math.max(0, x - rad), r = Math.min(w - 1, x + rad);
for (int x2 = l, ks = l - (x - rad); x2 <= r; x2++, ks++)
v += img.getSample(x2, y, band) * gk[ks];
buf[o++] = (int) v;
}
}
img.setSamples(0, 0, w, h, band, buf);
o = 0;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
double v = 0;
int u = Math.max(0, y - rad), b = Math.min(h - 1, y + rad);
for (int y2 = u, ks = u - (y - rad); y2 <= b; y2++, ks++)
v += img.getSample(x, y2, band) * gk[ks];
buf[o++] = (int) v;
}
}
img.setSamples(0, 0, w, h, band, buf);
}
return (img);
}
/**
* Applies a gaussian blur filter to the given image. Apart from the filter radius, you can also
* specify an alpha factor which will be multiplied with the filter's result. Also, you can
* specify whether the blurred image should be rendered into a newly created BufferedImage
* instance or into the original image. If you request a new image instance, the result will be
* larger than the original one as a (2*filterradius) pixel wide padding will be applied.
*
* @param image the image to be blurred.
* @param filterRadius the radius of the gaussian filter to apply. The corresponding kernel will
* be sized 2 * filterRadius + 1;
* @param alphaFactor a factor which will be multiplied with the filtered image. You can use this
* parameter to weaken or strengthen the colors in the blurred image.
* @param useOriginalImageAsDestination Determines whether the blur result should be rendered into
* the original image or into a new image instance. If you choose to create a new image
* instance, the result will be larger than the original image to provide the required padding
* for the blur effect.
* @return An image instance containing a blurred version of the given image.
*/
public static BufferedImage applyGaussianBlur(
final BufferedImage image,
final int filterRadius,
final float alphaFactor,
final boolean useOriginalImageAsDestination) {
if (filterRadius < 1) {
throw new IllegalArgumentException(
"Illegal filter radius: expected to be >= 1, was " + filterRadius);
}
float[] kernel = new float[2 * filterRadius + 1];
final float sigma = filterRadius / 3f;
final float alpha = 2f * sigma * sigma;
final float rootAlphaPI = (float) Math.sqrt(alpha * Math.PI);
float sum = 0;
for (int i = -0; i < kernel.length; i++) {
final int d = -((i - filterRadius) * (i - filterRadius));
kernel[i] = (float) (Math.exp(d / alpha) / rootAlphaPI);
sum += kernel[i];
}
for (int i = 0; i < kernel.length; i++) {
kernel[i] /= sum;
kernel[i] *= alphaFactor;
}
final Kernel horizontalKernel = new Kernel(kernel.length, 1, kernel);
final Kernel verticalKernel = new Kernel(1, kernel.length, kernel);
synchronized (BlurUtils.class) {
final int blurredWidth =
useOriginalImageAsDestination ? image.getWidth() : image.getWidth() + 4 * filterRadius;
final int blurredHeight =
useOriginalImageAsDestination ? image.getHeight() : image.getHeight() + 4 * filterRadius;
final BufferedImage img0 = ensureBuffer0Capacity(blurredWidth, blurredHeight);
final Graphics2D graphics0 = img0.createGraphics();
graphics0.drawImage(
image,
null,
useOriginalImageAsDestination ? 0 : 2 * filterRadius,
useOriginalImageAsDestination ? 0 : 2 * filterRadius);
graphics0.dispose();
final BufferedImage img1 = ensureBuffer1Capacity(blurredWidth, blurredHeight);
final Graphics2D graphics1 = img1.createGraphics();
graphics1.drawImage(
img0, new ConvolveOp(horizontalKernel, ConvolveOp.EDGE_NO_OP, null), 0, 0);
graphics1.dispose();
BufferedImage destination =
useOriginalImageAsDestination
? image
: new BufferedImage(blurredWidth, blurredHeight, BufferedImage.TYPE_INT_ARGB);
final Graphics2D destGraphics = destination.createGraphics();
destGraphics.drawImage(
img1, new ConvolveOp(verticalKernel, ConvolveOp.EDGE_NO_OP, null), 0, 0);
destGraphics.dispose();
return destination;
}
}