Package net.imglib2

Examples of net.imglib2.RealPoint


  }

  @Override
  public FlatIterationOrder iterationOrder()
  {
    return new FlatIterationOrder( this );
  }
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   * {@inheritDoc}
   */
  @Override
  public Object subIntervalIterationOrder( final Interval interval )
  {
    return new FlatIterationOrder( interval );
  }
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  public Object subIntervalIterationOrder( final Interval interval )
  {
    if ( this.sourceInterval instanceof SubIntervalIterable )
      return ( ( SubIntervalIterable< T > ) this.sourceInterval ).subIntervalIterationOrder( interval );
    else
      return new FlatIterationOrder( interval );
  }
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        }

        if ( optimizable )
        {
//          System.out.println( "interval = " + Util.printInterval( interval ) );
          final Interval sliceInterval = t.transform( new BoundingBox( interval ) ).getInterval();
//          System.out.println( "transformed interval = " + Util.printInterval( sliceInterval ) );
          if ( iterableSource.supportsOptimizedCursor( sliceInterval ) )
          {
            // check for FlatIterationOrder
            boolean flat = FlatIterationOrder.class.isInstance( iterableSource.subIntervalIterationOrder( sliceInterval ) );
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    {
      final double[] position = new double[ n ];
      for ( int d = 0; d < n; ++d )
        position[ d ] = rnd.nextDouble();

      final RealPoint realPoint = new RealPoint( position );
      final DoubleType sample = new DoubleType( rnd.nextDouble() );

      realPointList.add( realPoint );
      sampleList.add( sample );
      realPointSampleList.add( realPoint, sample );
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  @Before
  public void init()
  {
    for ( int i = 0; i < samples.length; ++i )
      realPointSampleList.add( new RealPoint( coordinates[ i ] ), new DoubleType( samples[ i ] ) );
  }
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  public void testKNearestNeighborSearch()
  {
    final RealCursor< DoubleType > cursor = realPointSampleList.cursor();
    final KNearestNeighborSearchOnIterableRealInterval< DoubleType > search1 = new KNearestNeighborSearchOnIterableRealInterval< DoubleType >( realPointSampleList, 1 );

    search1.search( new RealPoint( new double[] { 0.1, 0.2 } ) );
    assertTrue( "Position mismatch ", positionEquals( search1.getPosition( 0 ), new RealPoint( coordinates[ 0 ] ) ) );
    assertTrue( "Sample mismatch ", search1.getSampler( 0 ).get() == cursor.next() );

    search1.search( new RealPoint( new double[] { -1, 20 } ) );
    assertTrue( "Position mismatch ", positionEquals( search1.getPosition( 0 ), new RealPoint( coordinates[ 1 ] ) ) );
    assertTrue( "Sample mismatch ", search1.getSampler( 0 ).get() == cursor.next() );
  }
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    for ( int i = 0; i < numPoints; ++i )
    {
      for ( int d = 0; d < numDimensions; ++d )
        p[ d ] = rnd.nextFloat() * size + min;

      final RealPoint t = new RealPoint( p );
      points.add( t );
    }

    long start = System.currentTimeMillis();
    final KDTree< RealPoint > kdTree = new KDTree< RealPoint >( points, points );
    final NearestNeighborSearchOnKDTree< RealPoint > kd = new NearestNeighborSearchOnKDTree< RealPoint >( kdTree );
    final long kdSetupTime = System.currentTimeMillis() - start;
    System.out.println( "kdtree setup took: " + ( kdSetupTime ) + " ms." );

    start = System.currentTimeMillis();
    final ArrayList< RealPoint > testpoints = new ArrayList< RealPoint >();
    for ( int i = 0; i < numTests; ++i )
    {
      for ( int d = 0; d < numDimensions; ++d )
        p[ d ] = rnd.nextFloat() * 2 * size + min - size / 2;

      final RealPoint t = new RealPoint( p );
      testpoints.add( t );
    }

    for ( final RealPoint t : testpoints )
    {
      kd.search( t );
      final RealPoint nnKdtree = kd.getSampler().get();
      final RealPoint nnExhaustive = findNearestNeighborExhaustive( points, t );

      boolean equal = true;
      for ( int d = 0; d < numDimensions; ++d )
        if ( nnKdtree.getFloatPosition( d ) != nnExhaustive.getFloatPosition( d ) )
          equal = false;
      if ( !equal )
      {
        System.out.println( "Nearest neighbor to: " + t );
        System.out.println( "KD-Tree says: " + nnKdtree );
        System.out.println( "Exhaustive says: " + nnExhaustive );
        return false;
      }
    }
    final long compareTime = System.currentTimeMillis() - start;
    System.out.println( "comparison (kdtree <-> exhaustive) search took: " + ( compareTime ) + " ms." );

    start = System.currentTimeMillis();
    for ( final RealPoint t : testpoints )
    {
      kd.search( t );
      final RealPoint nnKdtree = kd.getSampler().get();
      nnKdtree.getClass();
    }
    final long kdTime = System.currentTimeMillis() - start;
    System.out.println( "kdtree search took: " + ( kdTime ) + " ms." );
    System.out.println( "kdtree all together took: " + ( kdSetupTime + kdTime ) + " ms." );

    start = System.currentTimeMillis();
    for ( final RealPoint t : testpoints )
    {
      final RealPoint nnExhaustive = findNearestNeighborExhaustive( points, t );
      nnExhaustive.getClass();
    }
    final long exhaustiveTime = System.currentTimeMillis() - start;
    System.out.println( "exhaustive search took: " + ( exhaustiveTime ) + " ms." );

    return true;
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  }

  private static RealPoint findNearestNeighborExhaustive( final ArrayList< RealPoint > points, final RealPoint t )
  {
    float minDistance = Float.MAX_VALUE;
    RealPoint nearest = null;

    final int n = t.numDimensions();
    final float[] tpos = new float[ n ];
    final float[] ppos = new float[ n ];
    t.localize( tpos );
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    for ( int i = 0; i < numPoints; ++i )
    {
      for ( int d = 0; d < numDimensions; ++d )
        p[ d ] = rnd.nextFloat() * size + min;

      final RealPoint t = new RealPoint( p );
      points.add( t );
    }

    long start = System.currentTimeMillis();
    final KDTree< RealPoint > kdTree = new KDTree< RealPoint >( points, points );
    final KNearestNeighborSearchOnKDTree< RealPoint > kd = new KNearestNeighborSearchOnKDTree< RealPoint >( kdTree, neighbors );
    final long kdSetupTime = System.currentTimeMillis() - start;
    System.out.println( "kdtree setup took: " + ( kdSetupTime ) + " ms." );

    start = System.currentTimeMillis();
    final ArrayList< RealPoint > testpoints = new ArrayList< RealPoint >();
    for ( int i = 0; i < numTests; ++i )
    {
      for ( int d = 0; d < numDimensions; ++d )
        p[ d ] = rnd.nextFloat() * 2 * size + min - size / 2;

      final RealPoint t = new RealPoint( p );
      testpoints.add( t );
    }

    final RealPoint[] nnKdtree = new RealPoint[ neighbors ];
    for ( final RealPoint t : testpoints )
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