@Test
public void performLearning() {
float interp_factor = 0.075f;
ImageFloat32 a = new ImageFloat32(20, 25);
ImageFloat32 b = new ImageFloat32(20, 25);
ImageMiscOps.fill(a, 100);
ImageMiscOps.fill(b, 200);
CirculantTracker<ImageFloat32> alg =
new CirculantTracker<ImageFloat32>(1f / 16, 0.2, 1e-2, 0.075, 1.0, 64, 255, interp);
alg.initialize(a, 0, 0, 20, 25);
// copy its internal value
ImageFloat64 templateC = new ImageFloat64(alg.template.width, alg.template.height);
templateC.setTo(alg.template);
// give it two images
alg.performLearning(b);
// make sure the images aren't full of zero
assertTrue(Math.abs(ImageStatistics.sum(templateC)) > 0.1);
assertTrue(Math.abs(ImageStatistics.sum(alg.template)) > 0.1);
int numNotSame = 0;
// the result should be an average of the two
for (int i = 0; i < a.data.length; i++) {
if (Math.abs(a.data[i] - alg.templateNew.data[i]) > 1e-4) numNotSame++;
// should be more like the original one than the new one
double expected =
templateC.data[i] * (1 - interp_factor) + interp_factor * alg.templateNew.data[i];
double found = alg.template.data[i];
assertEquals(expected, found, 1e-4);
}
// make sure it is actually different
assertTrue(numNotSame > 100);
}
