Lecture 4

VTK

(includes text and images from the 3rd edition of the VTK User's Guide)

Four views of the Earth

So now lets get into some specifics ...

VTK - what is it, what does it do, and why are we talking about it.

vtk can be obtained from http://www.vtk.org/

There is an installer for windows on the main vtk page and some notes from the previous class on the 526 vtk page. Here is a nice page with hints for installing on OS-X - http://www.macresearch.org/installing_vtk_on_mac_os_x though I would also set BUILD_EXAMPLES:BOOL=ON and VTK_WRAP_TCL:BOOL=ON

You can use vtk with C++, Tcl, Python, and Java.

VTK comes with an example directory and one of the simplest ways to test your vtk install is to try running the Tcl examples. For example you can cd to VTK/Examples/Medical/tcl and then vtk Medical1.tcl

To compile the cxx examples you should be able to go into the cxx directory and type "cmake ." and then "make". On my macbook pro I needed to edit the CMakeCache.txt file to set VTK_DIR:PATH=/usr/local/VTKBuild/

Then you can run medical example 1 in VTK/Examples/Medical/Cxx with ./Medical1 /usr/local/vtkData/DATA/headsq/quarter

To make a new C project (adapted from https://visualization.hpc.mil/wiki/VTK_in_CPP)

PROJECT (ProjectName)

INCLUDE (${CMAKE_ROOT}/Modules/FindVTK.cmake)
IF (USE_VTK_FILE)
  INCLUDE(${USE_VTK_FILE})
ENDIF (USE_VTK_FILE)

ADD_EXECUTABLE(Binary SourceFile.cxx)
TARGET_LINK_LIBRARIES(Binary vtkRendering vtkIO)

2. type "cmake ." to create the Makefile. It may complain about VTK_DIR so then just edit CMakeCache.txt

3.  Create a sample cxx file, or copy one form the examples directory line Cone.cxx

3. make

4. run

5. make changes

Visualization Pipeline/Network (Chap 4 vtk)

Pretty much all of these packages have a pipeline architecture.

Data Objects - information plus methods to create, access, delete information

Process Objects - Operate on input data to generate output data

• Source Objects
  • Reader Objects that interface with external data sources
  • Procedural Objects that generate data from local parameters
• Filter Objects
  • One or more inputs generating one or more outputs
• Mapper Objects (sinks)
  • One or more inputs that usually generate graphical primitives
  • Writer Objects write out to files

VTK has a demand driven pipeline archictecture

These various software packages work in different ways:

Explicit Execution

• centralized executive coordinating network execution
• used by AVS, IRIS/Data Explorer
• better for parallel/distributed processing situations
• demand-driven - changes are accumulated but network not executed until user asks
• event-driven - when an object is changed the network is executed

Implicit Execution

• Process object executes only if its input or local parameters change
• demand driven - execution only when output data is requested
• no global knowledge required

Memory Models

• Static - intermediate data stored to reduce overall (repeated) computation good for small data or when network not executing that often
• Dynamic - intermediate data discarded to save memory space good for large data or when network is constantly re-executing

So, lets get a little more specific with an example of how we can use the vtk pipeline to go from data to a visualization.

Here is a brief demonstration of ParaView (which is built on top of vtk) with the Visible Woman dataset

From the virual human web page (http://www.nlm.nih.gov/research/visible/) "The Visible Human Male data set consists of MRI, CT and anatomical images. Axial MRI images of the head and neck and longitudinal sections of the rest of the body were obtained at 4 mm intervals. The MRI images are 256 pixel by 256 pixel resolution. Each pixel having 12 bits of grey tone resolution. The CT data consists of axial CT scans of the entire body taken at 1 mm intervals at a resolution of 512 pixels by 512 pixels with each pixel made up of 12 bits of grey tone. The approximately 7.5 megabyte axial anatomical images are 2048 pixels by 1216 pixels, with each pixel defined by 24 bits of color. The anatomical cross-sections are at 1 mm intervals to coincide with the CT axial images. There are 1871 cross-sections for both CT and anatomy. The complete male data set, 15 gigabytes in size, was made publicly available in November, 1994.

The Visible Human Female data set, released in November, 1995, has the same characteristics as the The Visible Human Male with one exception, the axial anatomical images were obtained at 0.33 mm intervals. This resulted in 5,189 anatomical images, and a data set of about 40 gigabytes. Spacing in the "Z" direction was reduced to 0.33mm in order to match the 0.33mm pixel sizing in the "X-Y" plane, thus enabling developers interested in three-dimensional reconstructions to work with cubic voxels."

(and let us remember that in 1995 personal computers were running at 200Mhz, with 16MB of RAM and 1 GB hard drives. Now the visible woman dataset fits on an iPod)

Here are some small jpeg images of a very few of the slices of the visible woman.

and here is ParaView viewing the 75MB version of the Visible Woman dataset. This version of the dataset is made up of 577 slices, 1 slide every 3mm, where each slice is 256 x 256 16-bit values. Once the data is loaded in we can set up two contours - one for the skin and one for the bones.

In paraview 3.4.0 you can read in the visible woman data as raw data then set:

• Data Scalar Type:      sunsigned short
• Data Byte Order:       BigEndian
• File Dimensionality:   3
• Data Origin:           0 0 0
• Data Spacing:          1 1 1
• Data Extent:           0 255, 0 255, 0 576
• Num Scalar Components: 1

and then create 2 contours - one transparent at 500 for skin, and one opaque at 1200 for bone, and you will see something similar to this.

Discrete nature of data (Chap 5 vtk)

Interpolation

• (nearest neighbour)
• linear
• quadratic
• cubic
• spline

 http://www.sws.uiuc.edu/warm/icnstationmap.asp

Here is another nice example of interpolating winds from the San Francisco Bay area

http://sfports.wr.usgs.gov/wind

In particular, click on the 'Observed Wind over S.F. Bay' link to see the observed data, then go back to the modelled data. Also click on the streakline version to see the modelled data in motion.

Structured or Unstructured Data

• Regular (Structured)
  • stored more compactly, faster computation
  • single mathematical relationship within composing points (geometry) and cells (topology)
• Irregular (Unstructured)
  • more adaptive

Dimension (number of independent variables)

Abstract Dataset

• Structure
  • Topology - set of properties invariant under rotation, translation, non-uniform scaling
  • Geometry - instantiation of topology, the specification of positions in 3-space
  • Consists of one or more Cells (topology) and Points (geometry)
• Data Attributes - supplemental info associated with topology or geometry eg scalars, vectors, normals, texture coords, tensors

Cells specify topology while points specify geometry

Types of Datasets (each of these has their own class in vtk)

• image data - points and cells on a regular rectangular lattice
  • if points and cells are arranged on a plane -> pixmap / bitmap / image
  • if arranged as stacked planes -> volume
  • regular (structured) geometry and topology
• rectilinear grid - points and cells on a regular lattice
  • rows, columns, planes are parallel to x-y-z coordinate system
  • regular topology, geometry only partially regular (spacing between points may vary)
• structured grid
  • regular topology, irregular geometry
  • warped into any configuration w/o overlap or intersection
  • topology stored implicitely
  • geometry explicitely represented by array of point coordinates
  • cells are quadrilaterals (2D) or hexahedron (3D)
  • useful for finite difference computions - fluid flow, heat transfer, combustion
• unstructured points - points irregularly located in space
  • no topology, completely unstructured geometry
  • typically converted to a more structured form for visualization
• polygonal data
  • unstructured topology and geometry
  • collection of graphics primitives (vertices, lines, polygons, triangle strips, etc)
• unstructured grid
  • most general
  • completely unstructured topology and geometry
  • requires most memory and processing
  • used in structural design, vibration, dynamics, heat transfer

Cell

• type plus connectivity list (ordered list of points)

Linear Cells - linear or constant interpolation functions

• vertex - 0D cell
  • single point
• polyvertex - composite 0-dimensional cell
  • arbitrary ordered list of points

• line- 1D cell
  • 2 points - direction is from first to second
• polyline - composite 1-dimensional cell
  • one or more connected lines, from point i to i+1

• triangle - 2D cell
  • counter-clockwise ordering of 3 points
• triangle strip - composite 2-dimensional cell
  • one or more triangles, (i, i+1, i+2) defines a triangle
  • points need not lie in a plane
• quadrilateral - 2D cell
  • ordered list of 4 points in a plane,
  • counter-clockwise ordering, convex, edges do not intersect
• pixel* - 2D cell
  • special case of quadrilateral
  • ordered list of 4 points in a plane
  • each edge is parallel to one of the 3 axis
  • each edge perpendicular to its adjacent ones
  • points ordered in direction of increasing axis coordinate
  • 4 'traditional' pixels form the corners of this kind of pixel
• polygon - 2D cell
  • ordered list of 3+ points lying in a plane
  • counter-clockwise ordering
  • may be concave, but not intersecting

• tetrahedron - 3D cell
  • 6 edges and 4 triangular faces defined by 4 non-planar points
• hexahedron - 3D cell
  • 12 edges and 6 quadrilateral faces defined by 8 vertices, convex
• voxel* - 3D cell
  • special case of hexahedron
  • 12 edges and 6 quadrilateral faces defined by 8 vertices, convex
  • each face is perpendicular to one of the axis
  • point list ordered in direction of increasing coordinate value
  • 8 'traditional' voxels form the corners of this kind of voxel
• wedge - 3D cell
  • 3 quadrilateral faces and 2 triangular and 9 edges defined by 6 vertices
  • must be convex, faces and edges must not intersect
  • ordered list of 6 vertices
• pyramid - 3D cell
  • 1 quadrilateral face and 4 triangular faces and 8 edges defined by 5 vertices
  • 4 points defining base must be convex, apex not coplanar w/ base

Nonlinear Cells

more accurate rate interpolation functions
model curved geometry better

linear easily converted to linear graphics primitives. nonlinear (except maybe for NURBS) must first be converted (tesselated) into linear form

• quadratic edge - 3 points - 2 points at endpoints, 1 in middle of edge
• quadratic triangle - 6 points - 3 at endpoints, 3 in middle of edges
• quadratic quadrilateral - 8 points - 4 at vertices, 4 in middle of edges
• quadratic tetrahedron - 10 points - 4 at vertices, 6 in middle of edges
• quadratic hexahedron - 20 points - 8 at vertices, 12 in middle of edges

Attribute Data - each Cell has 0 or more pieces of attribute data associated with it.

• scalar - single value at each location in dataset
• vector - magnitude and direction
• normal - direction vector (magnitude = 1)
• texture coordinate (2D, 3D)
• tensor (generalization of vector and matrix)
  • rank 0 - scalar
  • rank 1 - vector
  • rank 2 - matrix
  • rank 3 - 3D rectangular array

Some More examples with ParaView

canyon_elev.raw (which can be found at ftp.evl.uic.edu in pub/INcoming/andy) contains a height map of the grand canyon as a 1024x512 greyscale image.

ParaView can load this image by setting:
data type to 'unsigned char'
File Dimensionality to 2,
extents as 0-1023, 0-511

This will bring up a colour image. You can use the information tab to bring up information on the dataset. In this case that the values in the image range from 68 to 242.

 then you can use the Contour filter to define a series of contours. As the scalar values in the file range from 68 to 242 you could set up a et of separate contours at 125, 150, and 175 for example. These contours would initially all be in the same plane, but you can also translate them in Z.

A dataset that comes with the vtk cdrom in VTKData/Data is ironProt.vtk. Since it is a .vtk file the header of this file contains the information that vtk needs to properly load the data in. Like the visible woman dataset we can use the contour tool to generate contours. In the image below there is a contour at 50 shown as wireframe, another contour at 200 shown as a surface, and a slice showing the data values in a plane.

Coming Next Time

Fundamental Algorithms (1 of 2)