public void accumulateBilinear(double x, double y, double s)
/* Bilinearly accumulates 's' to the four integer grid points surrounding
* the continuous coordinate (x, y). */
{
double xpf = Math.floor(x);
int xi = (int) xpf;
double xf = x - xpf;
double ypf = Math.floor(y);
int yi = (int) ypf;
double yf = y - ypf;
double b;
b = (1.0 - xf) * (1.0 - yf);
accumulate(xi, yi, s * b);
b = xf * (1.0 - yf);
accumulate(xi + 1, yi, s * b);
b = (1.0 - xf) * yf;
accumulate(xi, yi + 1, s * b);
b = xf * yf;
accumulate(xi + 1, yi + 1, s * b);
}
public double getBilinear(double x, double y)
/* Returns: the bilinearly-interpolated value of the continuous field
* at (x, y).
* Requires: (x, y) is inside the domain of the field */
{
if (!inBounds(x, y))
throw new RuntimeException(
"ScalarImage.getBilinear: RuntimeException at (" + x + "," + y + ")");
int xi, yi;
double xf, yf;
if (x == (double) (width - 1)) {
xi = width - 2;
xf = 1.0;
} else {
double xpf = Math.floor(x);
xi = (int) xpf;
xf = x - xpf;
}
if (y == (double) (height - 1)) {
yi = height - 2;
yf = 1.0;
} else {
double ypf = Math.floor(y);
yi = (int) ypf;
yf = y - ypf;
}
double b1 = get(xi, yi);
double b2 = get(xi + 1, yi);
double b3 = get(xi, yi + 1);
double b4 = get(xi + 1, yi + 1);
double bb1 = b1 + xf * (b2 - b1);
double bb2 = b3 + xf * (b4 - b3);
return bb1 + yf * (bb2 - bb1);
}
public void analyze(boolean doit) {
float min = Float.POSITIVE_INFINITY;
float max = Float.NEGATIVE_INFINITY;
for (int k = 0; k < size; k++) {
// System.out.println(f[k]);
/*
* if (f[k] == Float.NaN) { System.out.println("NaN at k= "+k);
* f[k] = 0f; }
*/
if (Float.isNaN(f[k])) {
System.out.println("NaN at k= " + k);
f[k] = 0f;
continue;
}
if (Float.isInfinite(f[k])) {
if (f[k] < 0) {
System.out.println("-Infinity at k= " + k);
f[k] = 0f; // -1000f;
} else {
System.out.println("+Infinity at k= " + k);
f[k] = 0f; // +1000f;
}
continue;
}
// System.out.print(k +"="+f[k]+ ", ");
min = Math.min(min, f[k]);
max = Math.max(max, f[k]);
}
int N = 256;
float[] histogram = new float[N];
for (int k = 0; k < size; k++) {
int level = (int) (0.999f * (float) N * (f[k] - min) / (max - min));
try {
histogram[level]++;
} catch (ArrayIndexOutOfBoundsException e) {
System.out.println("ArrayIndexOutOfBoundsException in ScalarImage.analyze(double) [A].");
}
}
float[] cumulative = new float[N];
if (histogram[0] > 0) {
if (doit) {
cumulative[0] = histogram[0];
} else {
cumulative[0] = 1; // histogram[0];
}
}
for (int i = 1; i < N; i++) {
if (histogram[i] > 0) {
if (doit) {
cumulative[i] = cumulative[i - 1] + histogram[i];
} else {
cumulative[i] = cumulative[i - 1] + 1; // histogram[i];
}
} else {
cumulative[i] = cumulative[i - 1];
}
}
/* for(int k=0; k<size; k++) {
int level = (int) ( 0.999f*(float)N*(f[k]-min)/(max-min) );
try {
f[k]=(cumulative[level]-cumulative[0])/(cumulative[N-1]-cumulative[0]);
} catch( ArrayIndexOutOfBoundsException e) {
System.out.println("ArrayIndexOutOfBoundsException in ScalarImage.analyze(double) [B].");
}
}
*/
for (int k = 0; k < size; k++) {
float x, x1, x2, f1, f2;
try {
x = Math.abs((f[k] - min) / (max - min));
x1 = (float) Math.floor((float) (N - 1) * x * 0.999f) / (float) (N - 1);
x2 = (float) Math.ceil((float) (N - 1) * x * 0.999f) / (float) (N - 1);
f1 =
(cumulative[(int) Math.floor((float) (N - 1) * x * 0.999f)] - cumulative[0])
/ (cumulative[N - 1] - cumulative[0]);
f2 =
(cumulative[(int) Math.ceil((float) (N - 1) * x * 0.999f)] - cumulative[0])
/ (cumulative[N - 1] - cumulative[0]);
f[k] = (f2 - f1) * (x - x1) / (x2 - x1) + f1;
} catch (ArrayIndexOutOfBoundsException e) {
System.out.println(
"ArrayIndexOutOfBoundsException in ScalarImage.analyze(double) [C]. " + e.getMessage());
}
}
return;
/*
double offset = 0. - min;
if ((max - min) != 0f) {
scale /= (max - min);
offset *= scale;
rescale(scale, offset);
}
System.out.println("Normalizing using: min= " + min + " max= " + max
+ ", through scaleAdd(" + scale + ", " + offset + ").");
min = 1000f;
max = -1000f;
for (int k = 0; k < size; k++) {
if (Float.isNaN(f[k])) {
System.out.println("NaN at k= " + k + " after normalization.");
f[k] = 0f;
continue;
}
min = Math.min(min, f[k]);
max = Math.max(max, f[k]);
}
System.out.println("After normalization: min= " + min + " max= " + max);
*/
}
