/** Test against perfectly matched and a little bit of noise */
@Test
public void test2D() {
for (double noise = 0; noise <= 0.01; noise += 0.01) {
// can only correct small changes
Se2_F64 tran = new Se2_F64(0.01, -0.02, 0.01);
List<Point2D_F64> srcPts = UtilPoint2D_F64.random(-10, 10, 30, rand);
List<Point2D_F64> srcOrig = UtilPoint2D_F64.copy(srcPts);
List<Point2D_F64> modelPts = new ArrayList<Point2D_F64>();
for (Point2D_F64 p : srcPts) {
modelPts.add(SePointOps_F64.transform(tran, p, null));
}
// add noise
UtilPoint2D_F64.noiseNormal(modelPts, noise, rand);
PointModel<Point2D_F64> model = new PointModel<Point2D_F64>(modelPts);
StoppingCondition stop = new StoppingCondition(10, 0.1 * noise / srcPts.size() + 1e-8);
IterativeClosestPoint<Se2_F64, Point2D_F64> alg =
new IterativeClosestPoint<Se2_F64, Point2D_F64>(stop, new MotionSe2PointSVD_F64());
alg.setModel(model);
alg.process(srcPts);
Se2_F64 foundTran = alg.getPointsToModel();
checkTransform(srcOrig, modelPts, foundTran, noise * 10 + 1e-8);
}
}
/**
* Returns the closest point on the contour to the provided point in space
*
* @return index of closest point
*/
int closestPoint(Point2D_F64 target) {
double bestDistance = Double.MAX_VALUE;
int bestIndex = -1;
for (int i = 0; i < contour.size(); i++) {
Point2D_I32 c = contour.get(i);
double d = UtilPoint2D_F64.distanceSq(target.x, target.y, c.x, c.y);
if (d < bestDistance) {
bestDistance = d;
bestIndex = i;
}
}
return bestIndex;
}
@Test
public void noiseless() {
Affine2D_F64 tran = new Affine2D_F64(2, -4, 0.3, 1.1, 0.93, -3);
List<Point2D_F64> from = UtilPoint2D_F64.random(-10, 10, 30, rand);
List<Point2D_F64> to = new ArrayList<Point2D_F64>();
for (Point2D_F64 p : from) {
to.add(AffinePointOps.transform(tran, p, null));
}
MotionAffinePoint2D_F64 alg = new MotionAffinePoint2D_F64();
assertTrue(alg.process(from, to));
Affine2D_F64 tranFound = alg.getMotion();
checkTransform(from, to, tranFound, GrlConstants.DOUBLE_TEST_TOL);
}