@Override public void add( final T c ) { t.add( c.t ); valid &= c.valid; }
@Override public void add( final T c ) { t.add( c.t ); valid &= c.valid; }
public final O eval() { this.scrap.set( this.a.eval() ); this.scrap.add( this.b.eval() ); return this.scrap; }
public final O eval( final Localizable loc ) { this.scrap.set( this.a.eval( loc ) ); this.scrap.add( this.b.eval( loc ) ); return this.scrap; } }
public static final <T extends RealType<T>> void addGaussianNoiseToImage(Img<T> img, double sigma_noise) { Cursor<T> lc = img.localizingCursor(); double val; T var = img.firstElement().createVariable(); while (lc.hasNext()) { lc.fwd(); val = Math.max(0, sigma_noise * ran.nextGaussian()); var.setReal(val); lc.get().add(var); } }
public static final < T extends RealType< T >> void addGaussianNoiseToImage( RandomAccessibleInterval< T > img, double sigma_noise ) { IterableInterval< T > iterImg = Views.iterable( img ); Cursor< T > lc = iterImg.localizingCursor(); double val; T var = iterImg.firstElement().createVariable(); while ( lc.hasNext() ) { lc.fwd(); val = Math.max( 0, sigma_noise * ran.nextGaussian() ); var.setReal( val ); lc.get().add( var ); } }
@Override public void compute(final Histogram<T> histogram, final T output) { sum.setZero(); for (int i=0; i<histogram.bins.length; ++i) { op.setReal(histogram.bins[i] * histogram.binValue(i).getRealDouble()); sum.add(op); } output.setReal(sum.getRealDouble() / histogram.nPixels); } }
/** @see Histogram */ public LinearHistogram( final int nBins, final int numDimensions, final T min, final T max) { super(nBins, numDimensions, min, max); // Compute values of each bin this.K = nBins -1; int i = -1; for (final T bin : binValues) { bin.set(range); bin.mul(++i / this.K); bin.add(min); } dmin = min.getRealDouble(); dmax = max.getRealDouble(); drange = range.getRealDouble(); }
type.add( ra.get() );
@Override public void compute(final Histogram<T> histogram, final T median, final T output) { sum.setZero(); for (int i = histogram.nBins() -1; i> -1; --i) { e.setReal(histogram.binValue(i).getRealDouble() - median.getRealDouble()); ecopy.set(e); e.mul(ecopy); // pow(x, 2) e.mul(histogram.bins[i]); sum.add(e); } output.setReal(Math.sqrt(sum.getRealDouble() / histogram.nPixels)); } }
public static final <T extends RealType<T>> void addGaussianSpotToImage(Img<T> img, double[] params) { Cursor<T> lc = img.localizingCursor(); double[] position = new double[img.numDimensions()]; double val; T var = img.firstElement().createVariable(); while (lc.hasNext()) { lc.fwd(); position[0] = lc.getDoublePosition(0); position[1] = lc.getDoublePosition(1); val = g.val(position, params); var.setReal(val); lc.get().add(var); } }
final private static < T extends RealType< T >> void computeSum( final long startX, final long startY, final long vX, final long vY, final RandomAccess< T > c2, final T sum ) { c2.setPosition( startX, 0 ); c2.setPosition( startY, 1 ); sum.set( c2.get() ); c2.move( vX, 0 ); sum.sub( c2.get() ); c2.move( vY, 1 ); sum.add( c2.get() ); c2.move( -vX, 0 ); sum.sub( c2.get() ); }
final private static <T extends RealType<T>> void computeSum( final long startX, final long startY, final long vX, final long vY, final RandomAccess< T > c2, final T sum ) { c2.setPosition( startX, 0 ); c2.setPosition( startY, 1 ); sum.set( c2.get() ); c2.move( vX, 0 ); sum.sub( c2.get() ); c2.move( vY, 1 ); sum.add( c2.get() ); c2.move( -vX, 0 ); sum.sub( c2.get() ); }
public static final < T extends RealType< T >> void addGaussianSpotToImage( RandomAccessibleInterval< T > img, double[] params ) { IterableInterval< T > iterImg = Views.iterable( img ); Cursor< T > lc = iterImg.localizingCursor(); int nDims = img.numDimensions(); double[] position = new double[ nDims ]; double val; T var = iterImg.firstElement().createVariable(); while ( lc.hasNext() ) { lc.fwd(); lc.localize( position ); val = gaussian.val( position, params ); var.setReal( val ); lc.get().add( var ); } }
public static final < T extends RealType< T >> void addEllipticGaussianSpotToImage( RandomAccessibleInterval< T > img, double[] params ) { IterableInterval< T > iterImg = Views.iterable( img ); Cursor< T > lc = iterImg.localizingCursor(); double[] position = new double[ img.numDimensions() ]; double val; T var = iterImg.firstElement().createVariable(); while ( lc.hasNext() ) { lc.fwd(); position[ 0 ] = lc.getDoublePosition( 0 ); position[ 1 ] = lc.getDoublePosition( 1 ); val = ellipticGaussian.val( position, params ); var.setReal( val ); lc.get().add( var ); } }
avgCursor.get().add( psfCursor.get() );
rh.setPosition(1L, 0); for (long pos0 = 1; pos0 < integralHistogram.dimension(0); ++pos0) { // for every element in row sum.add(rh.get()); rh.get().set(sum); rh.fwd(0); rh.setPosition(1L, 1); for (long pos1 = 1; pos1 < integralHistogram.dimension(1); ++pos1) { // for every element in column sum.add(rh.get()); rh.get().set(sum); rh.fwd(1);
rh.setPosition(1L, rowDimension); for (long i = 1; i < integralHistogram.dimension(rowDimension); ++i) { sum.add(rh.get()); rh.get().set(sum); rh.fwd(rowDimension);
sum.add(rh.get()); rh.get().set(sum);