Scientific Visualization 1TD389, 5hp Fall 2012

Transcription

1 Department of Scientific Visualization 1TD389, 5hp Fall 2012 Visual Information Course start: Monday September 3 at 13:15 Room: P1211 Stefan Seipel, Professor Stefan.Seipel@it.uu.se 1 Frequently asked questions Department of Visual Information Do I need to have a course in computer graphics? -No. Do I need to be a professional programmer? -No. But, you should be interested in visual explanations and pretty pictures?

2 Department of Visual Information A picture is worth a thousand words refers to that complex stories can be described with a single image. This expression is valid also in scientific visualization. When large and complex data sets are resulting from experiments and computations, visualization is a way to give deeper insight and knowledge. You will learn how to select appropriate methods, possibilities and limitations with methods, and to use visualization toolkits. A focus will be on using script programming in combination with VTK (the Visualization Toolkit). 3 Teachers, Fall 2012 Department of Visual Information Stefan Seipel Anders Hast Johan Nyström Centre for Image Analysis (CBA) ITC building 2, floor 1, Office 2112

3 Course homepage: Department of Visual Information Department of The book: Visual Information Will Schroeder, Ken Martin, Bill Lorensen: The Visualization Toolkit, An Object-Oriented Approach to 3D Graphics, 4th edition, Kitware, Inc., 2006 ISBN X

4 Department of Visual Information The schedule: 9 Lectures 4 Computer exercises labs 2 Assignments 1 Project Written exam Scientific Visualization with Department of Visual Information The Visualization Toolkit (VTK): Object-oriented in its design Computer graphics in the foundation 8

5 Introduce yourselves Tell us: Your name Your hometown Your computer background Something interesting about yourself Scientific Visualization, 5hp Fall 2011 Ch 1: Introduction Ch 3: Computer Graphics Primer Ch 4: The Visualization Pipeline Stefan Seipel

6 Dictionary vi su al ize To form a mental image of; envisage: try to visualize the scene as it is described To make visible Visualization offers a way to see the unseen Visualization purposes Communication of information (emphasizing, narrating) Improve understanding (illustrating, interpreting, finding) Decision support (analyzing, extrapolation) Answering questions (diagnosing, interpreting) Support creativity (inspiration)

7 Visualizations help us getting insight When data is complex: Collected/Computed When numeric data is to bee understood When complex relations must be understood When multiple variables have to be analyzed Visualization is not a substitute to, but in addition to, statistical analysis and other quantitative methods Visualization takes advantage of human sensory abilities Pattern recognition, Trend discovery, etc. Graphs are one type of visualization Example: Which Swedish town is warmer than 20 C and has less than 10 mm of rain? Temp.[C] Rain [mm] Gävle 18,5 3,1 Göteborg 24,1 11,7 Halmstad 19,8 10,3 Karlskrona 19,9 4,8 Kiruna 13,7 5,4 Lund 22,1 13,3 Malmö 23,2 14,8 Mora 17,8 6,1 Stockholm 19,9 6,3 Sundsvall 16,5 6,7 Umeå 14,3 13,2 Uppsala 19,4 9,7 Västerås 17,8 13,1 Örebro 21,1 7,6 Östersund 13,8 15,3

8 Graphs are one type of visualization Example: Which Swedish town is warmer than 20 o C and has less than 10 mm of rain? Kiruna Östersund Umeå Sundsvall Mora Västerås Gävle Uppsala Halmstad Karlskrona Stockholm Örebro Lund Malmö Göteborg Temp.[C] Rain [mm] Some more sophisticated examples Nuclear, Quantum, and Molecular Modeling Structures, Fluids, and Fields Advanced Imaging and Data Management

9 Some classical examples Dr. John Snow; The Cholera Epidemic in London 1854 Used spot-map to graphically depict cholera incidents. Spatial clusters led to him to the hypothesis that cholera was communicated through contaminated water. Identification and removal of contaminated pump led to reduced mortality and partly confirmed his hypothesis. Note: The visualization did not prove anything. But was influential to the development of the novel hypothesis which was later proved true. Scientific Visualization Scientific visualization is the process of exploring, transforming, and viewing data as images The data describes natural or physical phenomena or quantities Often observed (measured) or simulated data Visualization is often interactive We are not trying to create realistic images, but to visualize data in an informative way Application dependent

10 General development of visualization Rather new discipline still developing into subareas Tool users vs. tool developers Collaboration among computer scientists and computational scientists Faster computers, high-speed networks, new user-interfaces Ch 3: Computer Graphics Primer Creating images with a computer 3D Pixar Animation Studios, All Rights Reserved.

11 Computer Graphics Computer graphics aims at creating pictures by mimicking the image formation process that occurs in conventional photography. Purposes: Simulate real things (entertainment) Make visible what cannot really be seen -> CG is the foundation of Visualization Visualization is more than computer graphics! Computer Graphics - Examples Simulate and visualize real things Interior design (Linus Karlsson, CCG 2011) Interactive Games Make visible what cannot really be seen Visualization of semantic networks in SemNet. Hard-Disk utilization (WinDirStat)

12 Computer Graphics - Ingredients What is needed to mimic photography i.e. to render images with a computer? Virtual objects: 3D models, geometry, material properties Virtual light sources: position, color, attenuation, etc. Virtual camera: position, direction, lens projection Illumination model: Rendering algorithms that model the propagation of light and its interaction with objects in the scene. Computer Graphics & Visualization Graphical rendering is one pillar of Scientific/Information Visualization Graphical Model Lights Camera(s) 3D Geometry Computer Graphics Rendering Algorithms Colorful Pictures

13 Computer Graphics & Visualization Graphical rendering is one pillar of Scientific/Information Visualization Scientific/Information Visualization Graphical Model Lights Camera(s) 3D Geometry Computer Graphics Rendering Algorithms Colorful Pictures We gain Insight Ah Ha!!! Data Transfor- Mation & Mapping Conceptual Model Visualization is more than computer graphics! 3.1 Rendering Categories of algorithms 3D Object representation Explicit Implicit Image formation Object Order Image Order e.g. Polygon Rasterizer e.g. Polygon Raytracing or Raycasting Volume Splatting Volume Raytracing or Raycasting

14 Rendering techniques Image order rendering techniques: Image is rendered for each image element (pixels) Ray-casting and ray-tracing most common techniques Simulate the interaction of light with objects by following the path of each light ray Objects can be polygonal surfaces, parametric surfaces or discrete (3D) images Ray-casting or ray-tracing (here: explicit geometric models) Ray-casting Ray-tracing image sampling in screen space establish ray from eye through pixel into the scene local lighting model assumed at surface (shading and specular highlights) same as ray casting plus: iterative back-tracing of reflected and transmitted rays global lighting effects (shadows, reflections, transparency, atmospheric effects)

15 Ray-casting or ray-tracing (here: implicit models sampled 3D data) image sampling in screen space 3D data set is sampled along the trajectory of the viewing ray profiles of sampled data are recorded for every pixel/ray pixel color is determined by compositing function there exist many different compositing techniques depending on the desired visual effect more details, see Chapter 7 (Stefan s lecture no. 4) Rendering techniques Object order rendering techniques: Rendering is processed in the order of graphical objects for every object at a time (points, polygons, or voxels) Pixels in the final images are written to many times Visibility problem (which object is closest) Non-visible objects are processed Rasterization and splatting are two typical examples

16 Polygon rasterization (for explicit geometric models) Vertices of polygons are projected from 3D object space onto 2D screen space Pixels in the interior area of a polygon on screen is then filled Compare 3.7 and slide further down Screen Space Transform & Projection Object Space vp Voxel splatting (for eg. volumetric data sets) A discrete voxel space is traversed in voxel order Voxel is projected onto image plane (position [x,y]) A 2D footprint (splat) of the voxel is determined (size and shape) The color of all pixels in the image covered by the splat are updated using compositing functions Requires strictly ordered traversal (back-to-front / front-to-back) to maintain correct visual results

17 3.2 Elements of color Visible spectrum

18 Colour The eye s and the brain s impression of electromagnetic radiation in the visual spectra How is colour perceived? light source s( ) reflecting object r( ) detector rods & cones red-sensitive green-sensitive blue-sensitive r() g() b() RGB color space RGB - for additive colour mixing, e.g., on a computer screen

19 HLS colour space Hue Lightness Saturation Hue: dominant wavelength, tone Lightness: intensity, brightness Saturation: purity, dilution by white Important aspects: Intensity decoupled from colour Related to how humans perceive colour Color angles for the hue Red = 0 Yellow = 60 Green = 120 Cyan = 180 Blue = 240 Magenta = 300

20 3.3 Lights o We simplify by assuming an infinitely distant point light source o Light is emitted in all directions from a single point in space o Far distance implies parallel rays o Intensity is constant compared to 1/distance 2 relationship 3.4 Surface properties The Phong reflection model = Ambient reflection + Diffuse reflection + Specular reflection l n v r

21 Putting it all together ambient + diffuse + specular => ingela@cb.uu.se composed color 3.5 Cameras

22 Camera movements 3.6 Coordinate systems 4 coordinate systems Model: where the object is defined World: 3D space where actors are positioned View: what is visible to the camera Display: (x, y) pixel locations See Figure 3-14

23 3.7 Coordinate transformations o 3D to 3D and 3D to 2D o (Perspective) projection o Homogeneous coordinates o 4x4 transformation matrices o Rotation, translation, scaling 3.9 Rasterization Line drawing DDA digital differential analyzer e.g. Bresenham algorithm Polygon filling Flood filling Scan conversion

24 Filling polygons Draw one scan-line at a time, i.e., scan conversion Polygonal shading Flat Gouraud Phong

25 Hidden Surface Removal (HSR) hidden surface z-buffer algorithm fill z-buffer with infinite distance for all polygons for each pixel calculate z-value if z(x,y) is closer than z-buffer(x,y) draw pixel z-buffer(x,y)=z(x,y) end end end

26 Ch 4: The Visualization Pipeline Visualization addresses the issues transformation and representation Transformation: converting data from its original form into graphics primitives and into computer images Representation: the internal data structures and the graphics primitives Visualization transforms a computational form into a graphical form Visualization pipeline, cont d The pipeline consists of objects to represent data objects to operate on data indicated direction of data flow (arrow connections between objects)

27 Data objects Represent information Provide methods to create, access, and delete data Modifying data is not really allowed; reserved for process objects Process objects Operate on input data to generate output data New data or new form Source objects initiate (read, generate) visualization data flow Filter objects maintain visualization data flow Mapper objects terminate (write, graph) visualization data flow

28 4.2 A visualization pipeline Data Object Computational methods, Measured data Process Object Display Source Filter Mapper Procedural, Reader Transforms the data Creates geometric primitives 4.3 Pipeline topology How to connect data objects and process objects Pipeline connections type concerns the form of data that process objects take as input or generate as output multiplicity deals with # of input and # of output allowed Feedback loops view intermediate results

29 4.4 Executing the pipeline Causing each process object to operate Most frequent executions due to user interaction change parameters of process object change input to process object For efficiency reasons, see to that only execute the process objects whose input has changed Synchronization between process objects required prior to execution 4.5 Memory and computation trade-off Visualization is resource demanding in computer memory due to input data size computational times due to algorithm complexity Static memory model intermediate data saved to reduce overall computation Dynamic memory model intermediate data discarded, but may have to be re-computed, re-loaded Combination of static and dynamic models