Volume Visualization. Volume Data. Volume Data. Tutorials Applied Visualizaton Summer Term 2009 Part VII - 3D Scalar Fields

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1 Tutorials Applied Visualizaton Summer Term 2009 Part VII - 3D Scalar Fields 3D Scalar Fields Essential information in the interior Can not be described by geometric representation Fire, clouds, gaseous phenomena Even if the data could be described geometrically, there are, in general, too many primitives to be represented Computer Graphics Group Friedrich-Alexander Universität Erlangen - Nürnberg Volume Data Grid Structures Structured Uniform Rectilinear Curvilinear Volume Data Here Unstructured Tetrahedral Mixed elements Hexahedron, Tetrahedron, Prism, Pyramid Scattered data Regular, structured grid Defined by Dimensions X Y Z Spacing δx δy δz Voxel Size vx vy vz Represented in VTK as vtkimagedata

2 Volume Data Here RAW data Scalar data stored linearly in X-Y-Z order in a file/ memory y Dataset x z idx = x + y X + z X Y Volume Data Here DAT file provides information about RAW file ObjectFileName: CutDataDestination.raw TaggedFileName: --- Resolution: SliceThickness: Format: UCHAR NbrTags: 0 ObjectType: TEXTURE_VOLUME_OBJECT ObjectModel: RGBA GridType: EQUIDISTANT Parse DAT file to load RAW file Resolution equates Dimensions SliceThickness is equal to Voxel Size ObjectFileName is the RAW data filename Format indicates number of bytes per scalar value Volume Data Example how to read RAW file DAT file parsing will be left as an exercise vfile = open("test.raw", "rb") lines = vfile.read() vfile.close() How to visualize the data with VTK? Three principal possibilities Slicing imgdata = vtk.vtkimagedata() carray = vtk.vtkunsignedchararray() imgdata.setdimensions(128,128,128) imgdata.setspacing(1,1,1) carray.setnumberofcomponents(1) carray.setnumberoftuples(128*128*128) for x in range(128): for y in range(128): for z in range(128): idx = x + (y * 128) + (z * 128 *128) carray.inserttuple1(idx, ord(lines[idx])) imgdata.getpointdata().setscalars(carray) Isosurfaces Direct Volume Rendering

3 Slicing Easiest way VTK provides the class vtkimageviewer Encapsulates everything necessary to view an image (renderer, actor, window, mapper) All that is needed is an interactor Isosurfaces Similar to contouring for 2D scalar fields Simple approach: Opaque Cubes Better: Marching Cubes/Marching Tetrahedra imgview = vtk.vtkimageviewer() imgview.setinput(imgdata) imgview.setupinteractor(iren) imgview.setzslice(50) imgview.render() iren.start() In VTK vtkmarchingcubes class One of the main problems: Finding a good iso value mcubes = vtk.vtkmarchingcubes() mcubes.setinput(imgdata) mcubes.computenormalson() mcubes.generatevalues(1, 125, 200) mcubesmapper = vtk.vtkpolydatamapper() mcubesmapper.setinputconnection(mcubes.getoutputport())

4 Direct Volume Rendering Most complicated technique Best visual results Allows a wide variety of visualizations at a very high quality Provides possibilities not achievable with other methods Basics Voxels viewed as particles emitting and absorbing light Cast ray through volume and integrate over image Object (vis.-data) contributions viewer Pixel I(s) = I(s0 )e τ (s0,s) +! s! E(s" )e τ (s,s) ds" s0 Can calculate that in software Pretty slow (vtkvolumeraycastmapper) GPU acceleration usual today Simplest method: blending textured slices Equivalent of evaluating the integral In VTK vtkvolumetexturemapper2d vtkvolumeproperty Instead of vtkproperty vtkvolume Instead of vtkactor Use renderer.addvolume(volume) You can still use normal actors additionally (i.e. to draw geometry alongside with the volume rendering)

5 Example volprop = vtk.vtkvolumeproperty() voltexmap = vtk.vtkvolumetexturemapper2d() volume = vtk.vtkvolume() opacityfunction = vtk.vtkpiecewisefunction() volprop.setindependentcomponents(1) opacityfunction.addsegment(start[0], start[1], end[0], end[1]) volprop.setscalaropacity(opacityfunction) Color as Emission/Absorption Need a transfer function Maps scalar intensity value to RGB and Alpha Scalar s T(s) RGB! RGB! voltexmap.setinput(imgdata) volume.setmapper(voltexmap) volume.setproperty(volprop) renderer.addvolume(volume) T(s) Emission RGB Absorption A s R ) E(s) E(s) = G T (s) =( B A(s) A(s) =(α) In VTK Use vtkcolortransferfunction for RGB Use vtkpiecewisefunction to generate Alpha volprop = vtk.vtkvolumeproperty() voltexmap = vtk.vtkvolumetexturemapper2d() volume = vtk.vtkvolume() opacityfunction = vtk.vtkpiecewisefunction() colortf = vtk.vtkcolortransferfunction() Guess & Experiment volprop.setindependentcomponents(1) opacityfunction.addsegment(start[0], start[1], end[0], end[1]) volprop.setscalaropacity(opacityfunction) colortf.addrgbpoint(x_pos, r, g, b) volprop.setcolor(colortf) volprop.setinterpolationtypetolinear() voltexmap.setinput(imgdata) volume.setmapper(voltexmap) volume.setproperty(volprop) renderer.addvolume(volume)

6 Better: User interaction - realtime response Better: User interaction - realtime response Better: User interaction - realtime response Better: User interaction - realtime response

7 Better: User interaction - realtime response Histogram as basis for orientation More general: a histogram is a mapping that counts the number of observations that fall into various disjoint categoies I.e. the histogram meets the following condition m i n = k j=1 m i

8 Implementation (very simple) def BuildHistogram(d): histo = [] for i in range((max(d)-min(d)+1)): histo.append(0) for value in d: index = math.floor(value) histo[int(index)] += 1 return histo Drawing (also very simple) tfcanvas = Canvas(root, width=cvw, height=cvh, relief=raised, selectbackground='grey') def DrawHisto(cv, w, h, histo): max_v_h = max(histo) min_v_h = min(histo) max_i_h = len(histo) - 1 print max_v_h, max_i_h for v in histo: i = histo.index(v) fv = float(v) fi = float(i) fw = float(w-1) fh = float(h) xpos = fi/float(max_i_h)*fw ypos = LinearInterpolate(fv, min_v_h, max_v_h, fh, 0) cv.create_line(xpos, h, xpos, ypos, fill="black") Drawing (also very simple) That is approximately all you need to know to build something like this

9 What you should know by now There are easy to use tools for arbitrary tasks Python Tkinter VTK for quick visualization Know where to start if you need to visualize something on your own Know where to look if you don t know how to do something Final hint: Good looking graphics can give a boost to almost any engineering thesis :-)