How to use

apply

method

in

boofcv.abst.distort.FDistort

/**
 * Applies distortion removal to the specified region in the input image.  The undistorted image is returned.
 * @param input Input image
 * @param corner0 Top left corner
 * @param corner1 Top right corner
 * @param corner2 Bottom right corner
 * @param corner3 Bottom left corner
 * @return true if successful or false if it failed
 */
public boolean apply( T input ,
        Point2D_F64 corner0 , Point2D_F64 corner1 ,
        Point2D_F64 corner2 , Point2D_F64 corner3 )
{
  if( createTransform(corner0, corner1, corner2, corner3)) {
    distort.input(input).apply();
    return true;
  } else {
    return false;
  }
}

  private synchronized void setLevel( int level ) {
//        System.out.println("level "+level);
    if( level > 0 && ss != null ) {

      ImageGray small = ss.getLayer(level-1);
      ImageGray enlarge = GeneralizedImageOps.createSingleBand(small.getClass(), ss.getInputWidth(), ss.getInputHeight());
      new FDistort(small,enlarge).interpNN().apply();

      // if the size isn't the same null it so a new image will be declared
      if( levelImage != null &&
          (levelImage.getWidth() != enlarge.width || levelImage.getHeight() != enlarge.height )) {
        levelImage = null;
      }
      levelImage = ConvertBufferedImage.convertTo(enlarge,levelImage,true);

      double scale = ss.getScale(level-1);
      levelPoints.clear();
      for( ScalePoint p : points ) {
        if( p.scale == scale ) {
          levelPoints.add(p);
        }
      }
    } else {
      levelPoints.clear();
      levelPoints.addAll(points);
    }

    this.activeLevel = level;
  }

  private synchronized void setLevel( int level ) {
//        System.out.println("level "+level);
    if( level > 0 && ss != null ) {

      ImageGray small = (ImageGray)ss.getLayer(level-1);
      ImageGray enlarge = GeneralizedImageOps.createSingleBand(small.getClass(), ss.getInputWidth(), ss.getInputHeight());
      new FDistort(small,enlarge).interpNN().apply();

      // if the size isn't the same null it so a new image will be declared
      if( levelImage != null &&
          (levelImage.getWidth() != enlarge.width || levelImage.getHeight() != enlarge.height )) {
        levelImage = null;
      }
      levelImage = ConvertBufferedImage.convertTo(enlarge,levelImage,true);

      double scale = ss.getScale(level-1);
      levelPoints.clear();
      for( ScalePoint p : points ) {
        if( p.scale == scale ) {
          levelPoints.add(p);
        }
      }
    } else {
      levelPoints.clear();
      levelPoints.addAll(points);
    }

    this.activeLevel = level;
  }

private synchronized void render( Rectangle visibleRect ) {
  
  if( visibleRect.width == 0 || visibleRect.height == 0 )
    return;
  if( transformed.width != visibleRect.width || transformed.height != visibleRect.height ||
      workImage == null ) {
    transformed.reshape(visibleRect.width,visibleRect.height);
    workImage = new BufferedImage(visibleRect.width,visibleRect.height,BufferedImage.TYPE_INT_RGB);
  }
  double x = -visibleRect.x;
  double y = -visibleRect.y;
  new FDistort(input,transformed).interpNN().affine(scale,0,0,scale,x,y).apply();
  ConvertBufferedImage.convertTo(transformed,workImage,true);
}

distort.input(input).apply();

  public static void main(String[] args) {
    BufferedImage original = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));

    Planar<GrayF32> input = new Planar<>(GrayF32.class,
        original.getWidth(),original.getHeight(),3);

    ConvertBufferedImage.convertFromPlanar(original,input,true,GrayF32.class);

    Planar<GrayF32> output = new Planar<>(GrayF32.class,
        original.getWidth()/3,original.getHeight()/3,3);
    Planar<GrayF32> output2 = new Planar<>(GrayF32.class,
        original.getWidth()/3,original.getHeight()/3,3);

    AverageDownSampleOps.down(input, output);
    new FDistort(input,output2).scaleExt().apply();

    BufferedImage outputFull = ConvertBufferedImage.convertTo_F32(output, null, true);
    BufferedImage outputFull2 = ConvertBufferedImage.convertTo_F32(output2, null, true);

    ShowImages.showWindow(original,"Original");
    ShowImages.showWindow(outputFull,"3x small average");
    ShowImages.showWindow(outputFull2,"3x small bilinear");
  }
}

new FDistort(unscaled,input0).scaleExt().apply();
ConvertBufferedImage.convertFrom(image1, unscaled, false);
new FDistort(unscaled,input1).scaleExt().apply();

  private void scaleUpLayers() {
    T l = pyramid.getLayer(0);
    if( upscale == null ) {
      interp = (InterpolatePixelS<T>) FactoryInterpolation.nearestNeighborPixelS(l.getClass());
      upscale = (T)l.createNew(l.width,l.height);
    } else {
      upscale.reshape(l.width,l.height);
    }

    int N = pyramid.getNumLayers();

    for( int i = 0; i < N; i++ ) {
      new FDistort(pyramid.getLayer(i),upscale).interpNN().scaleExt().apply();
      BufferedImage b = ConvertBufferedImage.convertTo(upscale,null,true);
      if( showScales )
        addImage(b,String.format("%5.2f",pyramid.getScale(i)));
      else
        addImage(b,String.format("%5.2f",pyramid.getSigma(i)));
    }
  }
}

  private void scaleUpLayers() {
    T l = pyramid.getLayer(0);
    if( upscale == null ) {
      interp = (InterpolatePixelS<T>) FactoryInterpolation.nearestNeighborPixelS(l.getClass());
      upscale = (T)l.createNew(l.width,l.height);
    } else {
      upscale.reshape(l.width,l.height);
    }

    int N = pyramid.getNumLayers();

    for( int i = 0; i < N; i++ ) {
      new FDistort(pyramid.getLayer(i),upscale).interpNN().scaleExt().apply();
      BufferedImage b = ConvertBufferedImage.convertTo(upscale,null,true);
      if( showScales )
        addImage(b,String.format("%5.2f",pyramid.getScale(i)));
      else
        addImage(b,String.format("%5.2f",pyramid.getSigma(i)));
    }
  }
}

@Override
public void setActiveAlgorithm(String name, Object cookie) {
  DerivType type = (DerivType)cookie;
  panel.reset();
  for( int radius = 1; radius <= 40; radius += 2 ) {
    int maxOrder = Math.max(type.orderX,type.orderY);
    double sigma = FactoryKernelGaussian.sigmaForRadius(radius,maxOrder);
    Class typeKer1 = FactoryKernel.getKernelType(imageType,1);
    Kernel1D kerX =  FactoryKernelGaussian.derivativeK(typeKer1,type.orderX,sigma,radius);
    Kernel1D kerY = FactoryKernelGaussian.derivativeK(typeKer1,type.orderY,sigma,radius);
    Kernel2D kernel = GKernelMath.convolve(kerY, kerX);
    T smallImg = GKernelMath.convertToImage(kernel);
    new FDistort(smallImg,largeImg).interpNN().scaleExt().apply();
    double maxValue = GImageStatistics.maxAbs(largeImg);
    BufferedImage out = VisualizeImageData.colorizeSign(largeImg,null,maxValue);
    panel.addImage(out,String.format("%5d",radius));
  }
}

private void applyScaling() {
  scaledImage.reshape(panel.getWidth(), panel.getHeight());
  if( scaledImage.width <= 0 || scaledImage.height <= 0 ) {
    return;
  }
  if( latestImage.width != 0 && latestImage.height != 0 ) {
    new FDistort(latestImage, scaledImage).interp(interpType).border(BorderType.EXTENDED).scale().apply();
    BufferedImage out = ConvertBufferedImage.convertTo(scaledImage, null, true);
    panel.setImageUI(out);
    panel.repaint();
  }
}

  AverageDownSampleOps.down(inputGray,scaled);
} else {
  new FDistort(inputGray,scaled).scaleExt().apply();

new FDistort(image,imageRotated).rotate(angle).apply();

@Override
public void setActiveAlgorithm(String name, Object cookie) {
  DisplayGaussianKernelApp.DerivType dt = (DisplayGaussianKernelApp.DerivType)cookie;
  // add basis
  SteerableKernel<K> steerable = createKernel(dt.orderX,dt.orderY);
  basisPanel.reset();
  for( int i = 0; i < steerable.getBasisSize(); i++ ) {
    T smallImg = GKernelMath.convertToImage(steerable.getBasis(i));
    new FDistort(smallImg,largeImg).scaleExt().interpNN().apply();
    double maxValue = GImageStatistics.maxAbs(largeImg);
    BufferedImage out = VisualizeImageData.colorizeSign(largeImg,null,maxValue);
    basisPanel.addImage(out,"Basis "+i);
  }
  // add steered kernels
  steerPanel.reset();
  for( int i = 0; i <= 20; i++  ) {
    double angle = Math.PI*i/20.0;
    K kernel = steerable.compute(angle);
    T smallImg = GKernelMath.convertToImage(kernel);
    new FDistort(smallImg,largeImg).scaleExt().interpNN().apply();
    double maxValue = GImageStatistics.maxAbs(largeImg);
    BufferedImage out = VisualizeImageData.colorizeSign(largeImg,null,maxValue);
    steerPanel.addImage(out,String.format("%5d",(int)(180.0*angle/Math.PI)));
  }
  repaint();
}

new FDistort(featureImg,scaledIntensity).interpNN().scaleExt().apply();