ImageGradient

VTKExamples/Python/VisualizationAlgorithms/ImageGradient


Description

We create an imaging pipeline to visualize gradient information.

The gradient direction is mapped into color hue value while the gradient magnitude is mapped into the color saturation.

Code

ImageGradient.py

#!/usr/bin/env python


import vtk


def main():
    fileName = get_program_parameters()
    colors = vtk.vtkNamedColors()

    # Read the CT data of the human head.
    reader = vtk.vtkMetaImageReader()
    reader.SetFileName(fileName)
    reader.Update()

    cast = vtk.vtkImageCast()
    cast.SetInputConnection(reader.GetOutputPort())
    cast.SetOutputScalarTypeToFloat()

    # Magnify the image.
    magnify = vtk.vtkImageMagnify()
    magnify.SetInputConnection(cast.GetOutputPort())
    magnify.SetMagnificationFactors(2, 2, 1)
    magnify.InterpolateOn()

    # Smooth the data.
    # Remove high frequency artifacts due to linear interpolation.
    smooth = vtk.vtkImageGaussianSmooth()
    smooth.SetInputConnection(magnify.GetOutputPort())
    smooth.SetDimensionality(2)
    smooth.SetStandardDeviations(1.5, 1.5, 0.0)
    smooth.SetRadiusFactors(2.01, 2.01, 0.0)

    # Compute the 2D gradient.
    gradient = vtk.vtkImageGradient()
    gradient.SetInputConnection(smooth.GetOutputPort())
    gradient.SetDimensionality(2)

    # Convert the data to polar coordinates.
    # The image magnitude is mapped into saturation value,
    # whilst the gradient direction is mapped into hue value.
    polar = vtk.vtkImageEuclideanToPolar()
    polar.SetInputConnection(gradient.GetOutputPort())
    polar.SetThetaMaximum(255.0)

    # Add a third component to the data.
    # This is needed since the gradient filter only generates two components,
    #  and we need three components to represent color.
    pad = vtk.vtkImageConstantPad()
    pad.SetInputConnection(polar.GetOutputPort())
    pad.SetOutputNumberOfScalarComponents(3)
    pad.SetConstant(200.0)

    # At this point we have Hue, Value, Saturation.
    # Permute components so saturation will be constant.
    # Re-arrange components into HSV order.
    permute = vtk.vtkImageExtractComponents()
    permute.SetInputConnection(pad.GetOutputPort())
    permute.SetComponents(0, 2, 1)

    # Convert back into RGB values.
    rgb = vtk.vtkImageHSVToRGB()
    rgb.SetInputConnection(permute.GetOutputPort())
    rgb.SetMaximum(255.0)

    # Set up a viewer for the image.
    # Note that vtkImageViewer and vtkImageViewer2 are convenience wrappers around
    # vtkActor2D, vtkImageMapper, vtkRenderer, and vtkRenderWindow.
    # So all that needs to be supplied is the interactor.
    viewer = vtk.vtkImageViewer()
    viewer.SetInputConnection(rgb.GetOutputPort())
    viewer.SetZSlice(22)
    viewer.SetColorWindow(255.0)
    viewer.SetColorLevel(127.0)
    viewer.GetRenderWindow().SetSize(512, 512)
    viewer.GetRenderer().SetBackground(colors.GetColor3d("Silver"))

    # Create the RenderWindowInteractor.
    iren = vtk.vtkRenderWindowInteractor()
    viewer.SetupInteractor(iren)
    viewer.Render()

    iren.Initialize()
    iren.Start()


def get_program_parameters():
    import argparse
    description = 'ImageGradient.'
    epilogue = '''
Visualization of gradient information.
   '''
    parser = argparse.ArgumentParser(description=description, epilog=epilogue,
                                     formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument('fileName',
                        help='The file FullHead.mhd. Note: file FullHead.raw.gz must also be present in the same folder.')
    args = parser.parse_args()
    return args.fileName


if __name__ == '__main__':
    main()