internal static Image makeTexturedColorizer(List <Color> colors, List <float> positions)
{
int count = colors.Count;
D.assert(count >= 2);
bool bottomHardStop = ScalarUtils.ScalarNearlyEqual(positions[0], positions[1]);
bool topHardStop =
ScalarUtils.ScalarNearlyEqual(positions[count - 2], positions[count - 1]);
int offset = 0;
if (bottomHardStop)
{
offset += 1;
count--;
}
if (topHardStop)
{
count--;
}
if (offset != 0 || count != colors.Count)
{
colors = colors.GetRange(offset, count);
positions = positions.GetRange(offset, count);
}
return(_cache.getGradient(colors, positions));
}
public static float[] get1DGaussianKernel(float gaussianSigma, int radius)
{
var width = 2 * radius + 1;
D.assert(width <= 25);
var key = new _GaussianKernelKey(gaussianSigma, radius);
return(_gaussianKernels.putIfAbsent(key, () => {
var kernel = new float[25];
float twoSigmaSqrd = 2.0f * gaussianSigma * gaussianSigma;
if (ScalarUtils.ScalarNearlyZero(twoSigmaSqrd))
{
for (int i = 0; i < width; ++i)
{
kernel[i] = 0.0f;
}
return kernel;
}
float denom = 1.0f / twoSigmaSqrd;
float sum = 0.0f;
for (int i = 0; i < width; ++i)
{
float x = i - radius;
// Note that the constant term (1/(sqrt(2*pi*sigma^2)) of the Gaussian
// is dropped here, since we renormalize the kernel below.
kernel[i] = Mathf.Exp(-x * x * denom);
sum += kernel[i];
}
// Normalize the kernel
float scale = 1.0f / sum;
for (int i = 0; i < width; ++i)
{
kernel[i] *= scale;
}
return kernel;
}));
}
static float[] calculateKernel(float _cur_gaussian_sigma, int _cur_width, int _cur_radius)
{
var kernel = new float[25];
float twoSigmaSqrd = 2.0f * _cur_gaussian_sigma * _cur_gaussian_sigma;
if (ScalarUtils.ScalarNearlyZero(twoSigmaSqrd))
{
for (int i = 0; i < _cur_width; ++i)
{
kernel[i] = 0.0f;
}
return(kernel);
}
float denom = 1.0f / twoSigmaSqrd;
float sum = 0.0f;
for (int i = 0; i < _cur_width; ++i)
{
float x = i - _cur_radius;
// Note that the constant term (1/(sqrt(2*pi*sigma^2)) of the Gaussian
// is dropped here, since we renormalize the kernel below.
kernel[i] = Mathf.Exp(-x * x * denom);
sum += kernel[i];
}
// Normalize the kernel
float scale = 1.0f / sum;
for (int i = 0; i < _cur_width; ++i)
{
kernel[i] *= scale;
}
_cur_gaussian_sigma = -1;
_cur_radius = -1;
return(kernel);
}
