@Test
public void testSymetricGaussian() {
int width = 200;
int height = 200;
final int nspots = 10;
long[] dimensions = new long[] {width, height};
ArrayImg<UnsignedByteType, ByteArray> img = ArrayImgs.unsignedBytes(dimensions);
Collection<Localizable> peaks = new HashSet<Localizable>(nspots);
Map<Localizable, double[]> groundTruth = new HashMap<Localizable, double[]>(nspots);
for (int i = 1; i < nspots; i++) {
for (int j = 1; j < nspots; j++) {
double A = 100;
double x0 = width / (double) nspots * i * 1.02d;
double y0 = width / (double) nspots * j * 1.02d;
double sigma = 2 - (double) i / nspots + (double) i / nspots;
Localizable peak = new Point((long) x0, (long) y0);
peaks.add(peak);
double[] params = new double[] {x0, y0, A, 1 / sigma / sigma};
LocalizationUtils.addGaussianSpotToImage(img, params);
groundTruth.put(peak, params);
}
}
// Instantiate fitter once
PeakFitter<UnsignedByteType> fitter =
new PeakFitter<UnsignedByteType>(
img,
peaks,
new LevenbergMarquardtSolver(),
new Gaussian(),
new MLGaussianEstimator(2d, 2));
if (!fitter.checkInput() || !fitter.process()) {
fail("Problem with peak fitting: " + fitter.getErrorMessage());
return;
}
Map<Localizable, double[]> results = fitter.getResult();
for (Localizable peak : peaks) {
double[] params = results.get(peak);
double[] truth = groundTruth.get(peak);
// Pedestrian assertion
assertEquals(
"Bad accuracy on amplitude parameter A: ", truth[2], params[2], TOLERANCE * truth[2]);
assertEquals(
"Bad accuracy on peak location x0: ", truth[0], params[0], LOCALIZATION_TOLERANCE);
assertEquals(
"Bad accuracy on peak location y0: ", truth[1], params[1], LOCALIZATION_TOLERANCE);
assertEquals("Bad accuracy on peak paramter b: ", truth[3], params[3], TOLERANCE * truth[3]);
}
}
@Test
public void testEllipticGaussian() {
int width = 200;
int height = 200;
final int nspots = 10;
long[] dimensions = new long[] {width, height};
ArrayImg<UnsignedByteType, ByteArray> img = ArrayImgs.unsignedBytes(dimensions);
Collection<Localizable> peaks = new HashSet<Localizable>(nspots);
Map<Localizable, double[]> groundTruth = new HashMap<Localizable, double[]>(nspots);
for (int i = 1; i < nspots; i++) {
for (int j = 1; j < nspots; j++) {
double A = 100;
double x0 = width / (double) nspots * i * 1.02d;
double y0 = width / (double) nspots * j * 1.02d;
double sigma_x = 2 - (double) i / nspots;
double sigma_y = 2 - (double) j / nspots;
Localizable peak = new Point((long) x0, (long) y0);
peaks.add(peak);
double[] params = new double[] {x0, y0, A, 1 / sigma_x / sigma_x, 1 / sigma_y / sigma_y};
LocalizationUtils.addEllipticGaussianSpotToImage(img, params);
groundTruth.put(peak, params);
}
}
// Instantiate fitter once
PeakFitter<UnsignedByteType> fitter =
new PeakFitter<UnsignedByteType>(
img,
peaks,
new LevenbergMarquardtSolver(),
new EllipticGaussianOrtho(),
new MLEllipticGaussianEstimator(new double[] {2d, 2d}));
if (!fitter.checkInput() || !fitter.process()) {
fail("Problem with peak fitting: " + fitter.getErrorMessage());
return;
}
Map<Localizable, double[]> results = fitter.getResult();
for (Localizable peak : peaks) {
double[] params = results.get(peak);
double[] truth = groundTruth.get(peak);
// Pedestrian assertion
assertEquals(
"Bad accuracy on amplitude parameter A: ", truth[2], params[2], TOLERANCE * truth[2]);
assertEquals(
"Bad accuracy on peak location x0: ", truth[0], params[0], LOCALIZATION_TOLERANCE);
assertEquals(
"Bad accuracy on peak location y0: ", truth[1], params[1], LOCALIZATION_TOLERANCE);
assertEquals("Bad accuracy on peak paramter bx: ", truth[3], params[3], TOLERANCE * truth[3]);
assertEquals("Bad accuracy on peak paramter by: ", truth[4], params[4], TOLERANCE * truth[4]);
// System.out.println(String.format("- For " + peak + "\n - Found : " +
// "A = %6.2f, x0 = %6.2f, y0 = %6.2f, sx = %5.2f, sy = %5.2f",
// params[0], params[1], params[2], 1 / Math.sqrt(params[3]), 1/Math.sqrt(params[4])));
// System.out.println(String.format(" - Real values: " +
// "A = %6.2f, x0 = %6.2f, y0 = %6.2f, sx = %5.2f, sy = %5.2f",
// truth[0], truth[1], truth[2], 1 / Math.sqrt(truth[3]), 1 / Math.sqrt(truth[4]) ));
}
}