public static void horizontal3(
Kernel1D_I32 kernel, ImageSInt16 image, ImageInt16 dest, int divisor, boolean includeBorder) {
final short[] dataSrc = image.data;
final short[] dataDst = dest.data;
final int k1 = kernel.data[0];
final int k2 = kernel.data[1];
final int k3 = kernel.data[2];
final int radius = kernel.getRadius();
final int yBorder = includeBorder ? 0 : radius;
final int width = image.getWidth();
final int height = image.getHeight() - yBorder;
final int halfDivisor = divisor / 2;
for (int i = yBorder; i < height; i++) {
int indexDst = dest.startIndex + i * dest.stride + radius;
int j = image.startIndex + i * image.stride - radius;
final int jEnd = j + width - radius;
for (j += radius; j < jEnd; j++) {
int indexSrc = j;
int total = (dataSrc[indexSrc++]) * k1;
total += (dataSrc[indexSrc++]) * k2;
total += (dataSrc[indexSrc]) * k3;
dataDst[indexDst++] = (short) ((total + halfDivisor) / divisor);
}
}
}
public static void convolve3(Kernel2D_I32 kernel, ImageSInt16 src, ImageInt16 dest, int divisor) {
final short[] dataSrc = src.data;
final short[] dataDst = dest.data;
final int width = src.getWidth();
final int height = src.getHeight();
final int halfDivisor = divisor / 2;
final int kernelRadius = kernel.getRadius();
final int totalRow[] = new int[width];
for (int y = kernelRadius; y < height - kernelRadius; y++) {
// first time through the value needs to be set
int k1 = kernel.data[0];
int k2 = kernel.data[1];
int k3 = kernel.data[2];
int indexSrcRow = src.startIndex + (y - kernelRadius) * src.stride - kernelRadius;
for (int x = kernelRadius; x < width - kernelRadius; x++) {
int indexSrc = indexSrcRow + x;
int total = 0;
total += (dataSrc[indexSrc++]) * k1;
total += (dataSrc[indexSrc++]) * k2;
total += (dataSrc[indexSrc]) * k3;
totalRow[x] = total;
}
// rest of the convolution rows are an addition
for (int i = 1; i < 3; i++) {
indexSrcRow = src.startIndex + (y + i - kernelRadius) * src.stride - kernelRadius;
k1 = kernel.data[i * 3 + 0];
k2 = kernel.data[i * 3 + 1];
k3 = kernel.data[i * 3 + 2];
for (int x = kernelRadius; x < width - kernelRadius; x++) {
int indexSrc = indexSrcRow + x;
int total = 0;
total += (dataSrc[indexSrc++]) * k1;
total += (dataSrc[indexSrc++]) * k2;
total += (dataSrc[indexSrc]) * k3;
totalRow[x] += total;
}
}
int indexDst = dest.startIndex + y * dest.stride + kernelRadius;
for (int x = kernelRadius; x < width - kernelRadius; x++) {
dataDst[indexDst++] = (short) ((totalRow[x] + halfDivisor) / divisor);
}
}
}
/** Adds Gaussian/normal i.i.d noise to each pixel in the image. */
public static void addGaussian(ImageSInt16 img, Random rand, double sigma, int min, int max) {
final int h = img.getHeight();
final int w = img.getWidth();
short[] data = img.data;
for (int y = 0; y < h; y++) {
int index = img.getStartIndex() + y * img.getStride();
for (int x = 0; x < w; x++) {
int value = (data[index]) + (int) (rand.nextGaussian() * sigma);
if (value < min) value = min;
if (value > max) value = max;
data[index++] = (short) value;
}
}
}
/** Adds uniform i.i.d noise to each pixel in the image. Noise range is min <= X < max. */
public static void addUniform(ImageSInt16 img, Random rand, int min, int max) {
final int h = img.getHeight();
final int w = img.getWidth();
int range = max - min;
short[] data = img.data;
for (int y = 0; y < h; y++) {
int index = img.getStartIndex() + y * img.getStride();
for (int x = 0; x < w; x++) {
int value = (data[index]) + rand.nextInt(range) + min;
if (value < -32768) value = -32768;
if (value > 32767) value = 32767;
data[index++] = (short) value;
}
}
}
