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 vec2FieldMagnitude(Field field, AffineTransform ftoi) {
AffineTransform itof = null;
try {
itof = ftoi.createInverse();
} catch (NoninvertibleTransformException niv) {
TDebug.println(0, "NoninvertibleTransformException: " + niv);
}
Vector3d v = new Vector3d();
Point2D.Double p = new Point2D.Double();
for (int j = 0, k = 0; j < height; ++j)
for (int i = 0; i < width; ++i, ++k) {
p.x = i;
p.y = j;
itof.transform(p, p);
v = field.get(p.x, p.y, 0.0);
f[k] = (float) Math.sqrt(v.x * v.x + v.y * v.y);
}
}
public BufferedImage getBufferedImage(int type, Color c) {
BufferedImage image = null;
float[] colComp = new float[3];
c.getRGBColorComponents(colComp);
double red = (double) colComp[0];
double green = (double) colComp[1];
double blue = (double) colComp[2];
// System.out.println("blue, green, red = "+ blue +", " + green + ", " + red);
double x = 0.0;
double x2;
switch (type) {
case ScalarImage.TYPE_BYTE_RANDOM:
{
int numCol = 256;
byte[] bBuf = new byte[numCol * 3];
blue *= 255 * 4.;
green *= 255 * 4.;
red *= 255 * 4.;
double delta = 1.0 / (double) (numCol + 1);
int j = 0;
for (int i = 0; i < numCol; i++) {
if (i % 5 == 0) x = 0.7 * Math.random() + 0.3 * x;
x2 = x * x;
bBuf[j++] = (byte) clamp((510 - red) * x2 + (red - 255) * x);
bBuf[j++] = (byte) clamp((510 - green) * x2 + (green - 255) * x);
bBuf[j++] = (byte) clamp((510 - blue) * x2 + (blue - 255) * x);
// x += delta;
}
IndexColorModel cm = new IndexColorModel(8, numCol, bBuf, 0, false);
// image = new
// BufferedImage(width,height,BufferedImage.TYPE_BYTE_INDEXED,cm);
byte[] idxBuffer = new byte[size];
for (int i = 0; i < size; i++) {
idxBuffer[i] = (byte) (clamp(f[i] * 255.));
}
DataBufferByte dataBuffer = new DataBufferByte(idxBuffer, size);
int idxOffset[] = {0};
int idxBits[] = {8};
try {
ComponentSampleModel idxSampleModel =
new ComponentSampleModel(DataBuffer.TYPE_BYTE, width, height, 1, width, idxOffset);
WritableRaster rasterIdx =
java.awt.image.Raster.createWritableRaster(
idxSampleModel, dataBuffer, new Point(0, 0));
image = new BufferedImage(cm, rasterIdx, false, null);
} catch (Exception e) {
System.out.println("Exception caught while acquiring image:");
System.out.println(e.getMessage());
}
}
break;
case BufferedImage.TYPE_BYTE_INDEXED:
{
int numCol = 256;
byte[] bBuf = new byte[numCol * 3];
blue *= 255 * 4.;
green *= 255 * 4.;
red *= 255 * 4.;
double delta = 1.0 / (double) (numCol + 1);
int j = 0;
for (int i = 0; i < numCol; i++) {
x2 = x * x;
bBuf[j++] = (byte) clamp((510 - red) * x2 + (red - 255) * x);
bBuf[j++] = (byte) clamp((510 - green) * x2 + (green - 255) * x);
bBuf[j++] = (byte) clamp((510 - blue) * x2 + (blue - 255) * x);
x += delta;
}
IndexColorModel cm = new IndexColorModel(8, numCol, bBuf, 0, false);
// image = new
// BufferedImage(width,height,BufferedImage.TYPE_BYTE_INDEXED,cm);
byte[] idxBuffer = new byte[size];
for (int i = 0; i < size; i++) {
idxBuffer[i] = (byte) (clamp(f[i] * 255.));
}
DataBufferByte dataBuffer = new DataBufferByte(idxBuffer, size);
int idxOffset[] = {0};
int idxBits[] = {8};
try {
ComponentSampleModel idxSampleModel =
new ComponentSampleModel(DataBuffer.TYPE_BYTE, width, height, 1, width, idxOffset);
WritableRaster rasterIdx =
java.awt.image.Raster.createWritableRaster(
idxSampleModel, dataBuffer, new Point(0, 0));
image = new BufferedImage(cm, rasterIdx, false, null);
} catch (Exception e) {
System.out.println("Exception caught while acquiring image:");
System.out.println(e.getMessage());
}
}
break;
case BufferedImage.TYPE_BYTE_GRAY:
break;
case BufferedImage.TYPE_3BYTE_BGR:
default:
byte[] byteBuffer = new byte[size * 3];
blue *= 255 * 4.;
green *= 255 * 4.;
red *= 255 * 4.;
int j = 0;
for (int i = 0; i < size; i++) {
x = f[i];
x2 = x * x;
/*
byteBuffer[j++] = (byte)clamp( ( 2 * 255 - 4 * red ) * x * x + ( 4 * red - 255 ) * x);
byteBuffer[j++] = (byte)clamp( ( 2 * 255 - 4 * green ) * x * x + ( 4 * green - 255 ) * x);
byteBuffer[j++] = (byte)clamp( ( 2 * 255 - 4 * blue ) * x * x + ( 4 * blue - 255 ) * x);
*/
byteBuffer[j++] = (byte) clamp((510 - red) * x2 + (red - 255) * x);
byteBuffer[j++] = (byte) clamp((510 - green) * x2 + (green - 255) * x);
byteBuffer[j++] = (byte) clamp((510 - blue) * x2 + (blue - 255) * x);
}
DataBufferByte dataBuffer = new DataBufferByte(byteBuffer, size * 3);
int componentOffset[] = {0, 1, 2};
int componentBits[] = {8, 8, 8};
try {
WritableRaster raster =
java.awt.image.Raster.createWritableRaster(
new PixelInterleavedSampleModel(
DataBuffer.TYPE_BYTE, width, height, 3, width * 3, componentOffset),
dataBuffer,
new Point(0, 0));
image =
new BufferedImage(
new ComponentColorModel(
ColorSpace.getInstance(ColorSpace.CS_LINEAR_RGB),
componentBits,
false,
false,
ColorModel.OPAQUE,
DataBuffer.TYPE_BYTE),
raster,
false,
null);
} catch (Exception e) {
System.out.println("Exception caught while acquiring image:");
System.out.println(e.getMessage());
}
break;
}
return image;
}
public void normalize(double range) {
double scale = range;
float min = 1000f;
float max = -1000f;
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]);
}
// System.out.println();
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 (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 + " after normalization.");
f[k] = 0f;
continue;
}
// System.out.print(k +"="+f[k]+ ", ");
min = Math.min(min, f[k]);
max = Math.max(max, f[k]);
}
System.out.println("After normalization: min= " + min + " max= " + max);
}
public Tuple4f getDynamics() {
Tuple4f dynamics = new Vector4f();
long count = 0;
double total = 0;
float min = Float.POSITIVE_INFINITY;
float max = Float.NEGATIVE_INFINITY;
for (int k = 0; k < size; k++) {
// Do not include NAN or isInfinite in dynamics
if (!((Float.isNaN(f[k])) || (Float.isInfinite(f[k])))) {
// System.out.print(k +"="+f[k]+ ", ");
min = Math.min(min, f[k]);
max = Math.max(max, f[k]);
total += (double) f[k];
count++;
}
}
dynamics.w = min;
dynamics.x = max;
dynamics.y = (float) (total / (double) count);
float range = max - min;
int N = 256;
System.out.println("min: " + min + " Max: " + max + " Range: " + range);
int[] histogram = new int[N];
int level = 0;
float quant = N * 0.999f;
for (int k = 0; k < size; k++) {
if (!((Float.isNaN(f[k])) || (Float.isInfinite(f[k])))) {
level = (int) (quant * ((f[k] - min) / range));
try {
histogram[level]++;
} catch (ArrayIndexOutOfBoundsException e) {
System.out.println("ArrayIndexOutOfBoundsException in ScalarImage.analyze(double) [A].");
}
}
}
for (int j = 0; j < N; j++) {
System.out.println(
"Level: "
+ j
+ " count: "
+ histogram[j]
+ " value: "
+ (float) ((j / quant * (max - min)) + min));
}
int target = (int) (count * 0.9);
int cum = 0;
for (int i = 0; i < N; i++) {
cum += histogram[i];
System.out.println(
"target: "
+ target
+ " cum: "
+ cum
+ " Level: "
+ i
+ " value: "
+ ((i / quant * (max - min)) + min));
if (cum >= target) {
System.out.println(
"target: " + target + " Level: " + i + " value: " + ((i * (max - min)) + min));
dynamics.z = (float) ((i / quant * (max - min)) + min);
break;
}
}
return dynamics;
}
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);
*/
}
public void power(double exp)
/* Transforms all the scalar values in 'this' by the rule:
* f' = f^exp */
{
for (int k = 0; k < size; ++k) f[k] = (float) Math.pow(f[k], exp);
}