Generic Image Processing With Climb - Slides
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Generic Image Processing With Climb - Slides
- 1. Generic Image Processing with Climb Laurent Senta – Didier Verna April 30, 2012 LRDE EPITA Research Lab lrde.epita.fr
- 2. Outline • Introduction • Using Climb • Developping Climb • Going Further • Conclusion 2
- 4. Generic Image Processing Images: Roland Levillain. Software Architecture for Generic Image Processing Tools I H B G C A D F E segmentation I H B G C A D F E 4
- 5. Genericity Purpose Graphic: Laurent Najman. Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images algorithms segmentation S x V x A combinations values type graph bool grayscale rgb 2dmatrix 3dmatrix structures 5 type
- 6. Climb • Generic Image Processing library – Common Lisp • Inspired by Olena: C++, 10 years old – Dynamic vs Static – Usability, maintainability, performance,… • Still in beta Compilers sbcl Dependencies iterate, lisp-magick, cl-heap, lisp-unit, cl-gtk2 6
- 7. Architecture The different layers of the library GUI Chaining Image Algorithms Operator Generic types Morphers 7
- 9. Algorithms original grayscale Otsu threshold jitter median filter dilation erosion 9
- 10. Chaining Operator Chaining algorithms with the $ operator (setf img (image-load ‘images/lena128gray.jpg’)) (setf ot-img (otsu img)) (setf dil-img (dilation ot-img (4-connectivity))) (save dil-img ‘out/lena-dilated.png’) (save (erosion (otsu (image-load ‘images/lena128gray.jpg’)) (4-connectivity)) ‘out/lena-eroded.png’) image-load ‘images/lena128gray.jpg’ otsu dilation (4-connectivity) save ‘out/lena-eroded.png’ ($ (image-load ‘images/lena128gray.jpg’) (otsu) (dilation (4-connectivity)) (save ‘out/lena-eroded.png’)) 10
- 11. Chaining Operator More complex chaining erosion image-load to-gray otsu diff dilation 11
- 13. Image Definition I H B G C A D F E Image access • matrix[x, y] → pixelvalue • graph.getNode(label) → nodevalue • model[x, y, z] → voxelvalue Generalization: image(site) = value Lisp: (setf (iref image site) value) 13
- 14. Values and Sites Values: bool grayscale RGB Site: I H B x G C A D F E y 2d-point label (setf (iref image site) (value-inverse (iref image site))) 14
- 15. Browsing Images Site-set: x I H B G C A D F E y (let ((domain (image-domain image))) (loop :for s := (site-set-next domain) :while s …)) 15
- 16. Browsing Images Site-set-window: x I H B G C A D F E y (let ((neighbors (site-set-window window site))) (loop :for s := (site-set-next neighbors) :while s …)) 16
- 17. Morphers Transforming images with morphers Value morpher Structure morpher 17
- 18. GOING FURTHER 18
- 19. Properties Adapting genericity algorithms support:regular segmentation values type graph bool grayscale rgb 2dmatrix 3dmatrix structures 19 type
- 20. GUI Climb based interface Value: I H B GTK-BOX G C A Morpher D Resize F E climb graph 20
- 21. Conclusion Current status Image Processing Practitionner Algorithm implementor • Built-in algorithms • High-level domain model • Composition tools • Generic values • Chaining operator • Generic structures • Morphers • Generic implementations • GUI Hot topics Genericity Usability Performance 21
- 22. Thanks for your attention, QUESTIONS ? 22
- 23. Sources • Th. Géraud and R. Levillain. Semantics-driven genericity: A sequel to the static C++ object-oriented programming paradigm (SCOOP 2). • R. Levillain, Th. Géraud, and L. Najman. Why and how to design a generic and efficient image processing framework: The case of the Milena library. • N. Otsu. A threshold selection method from gray-level histograms. • P. Soille. Morphological Image Analysis: Principles and Applications • Roland Levillain. Software Architecture for Generic Image Processing Tools • Laurent Najman. Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images 23
Notes de l'éditeur
- Starts bydescribing Image Processing and whywe are looking for genericityThenwe’lltake a look atClimbfromdifferent point of view: First: as a IP researchersthat uses the toolsweprovide to transformits images Second: as a Developperthatwants to implements new algorithms in the libraryFinally, I’ll show you prototypes that are not fully part of the libraryyetDon’thesitate to ask question any time if I’m not clear
- Let’s talk about Image Processing
- Usingsetf and variable to store the intermediateresultsChaining all the algo call in one bigprocedureHowever, the first method: repetitive, many variable, namemistakes = errorsthatcouldbe hard to find the second methodmay push the algorithmparametersaway, hard to understand and modificateWhatwewant to do as IP researchers:chainmanyalgorithm andthe librarywouldimplicitlyunderstandthateachresultismeant to bepassed to the nextprocessingThanks to the Lisp macro system, Climbprovides the $ operatorthatwill replace the first parameter of each call by the result of the previous one.Usingthis IP-orientedsyntax, wecanwritereadblechain of algorithmwithouthaving to deal withmanyrepetition > Best of both world[Demo: erosion](loader le lena-otsu!)
- Now, sequence of algo: too simpleOftenyouwan to branch the processing in order to construct more complexoperatorsHereis an example of a basic border detectionfilterthatworks by computing the diffbetween the eroded and dilated version of the same image.$-operatordedicated to IP, soanothersyntaxisadded to support thisparadigmwith the 2 slash operator[Demo]I’mgoing to applythisfilter on a a new image,Copy and paste the code fromels.rtWhathappen ?Add the //But I’mmaywant to call functionthat are not part of the image processlikeloggingAdd the ‘(timer-start) ‘(timer-print ‘’Time:’)CallDetailresultsShow the printed messageThis toolis important to us because shows capabilitiesof Lisp to transformitselfconstructDSL not simplydedicated to complex programmer trick but canbeusedto adapt the language to the userswe’reaiming for.
- Well,nowlet’s a look at the Genericity layer and how wecanadd new algorithm in the library
- Site-set, set of sites used to browse an areaThey are iterator that returns a site each time you call site-set-next[Slide1-2-3]retrieve all the site for a given image[Slides]Explain line image-domain, call code on s (could be the previous code) no-info underlying typeDemo: InvertNow that we can browse a while image, we also want to browse a specific area,That we call the neighborhood of a site.
- Anothercommon patternBrowsing a neighborhood allows us to compute local information,And for example, in the segmentation algorithm, find the sitesThat are close, in the value space but also in the structure spaceBrowse a neighborhood: site-set centered on a site and retrieve all it's neighborhoodsUsed for example to compute the mean value in the mean filter or in the erosion/dilationTo spread the values to their neighborsExplain lineAgain, no infoLet’swrite a bluralgorithm[DEMO]
- In order to finish with the climbdeveloppement, let’stake a look atmorphers.Morpher are objectsbuiltaround an image thatwilltransformsome of it’spropertiesdynamically.Values: transforme the valuesFor example: gray morpher, convert values, image saw by the outside world as grayscaleStructure: Transform the structuresFor example: restrictmorpher, the image saw by the outside world as a smaller oneAvoidWaste of memorySimplifygenericitysinceyoucaneasilyconvert an image that do not fit the prerequisite for an algorithmA thresholdalgorithmthatneeds a grayscale imageAn algorithmthatneeds an image with a size being a power of 2Slower ? Not necessarily: morpherblurwithmorpherresize[Demomorpherblur]
- NowI’mgoing to talk about prototype thingswe’reworking on
- these algo are available in a GUI Provide the algorithm without knownledge in programming interesting: based on the generic types defined in ClimbClimb based GUI: climb graph for the algo call and the gtk displaymorpher used for image resizing from the computation to the display (no duplication)Interesting: library generic enough to allows unusual uses of the types[Demo] run the previous $-chain using the GUIAdvantage over $: Cycle for density computation,…
- Conclusion:Even if Climbis not yet a stable generic IP library, we have been able to developtoolsthat shows how Lisp canbe an interestinglanguage in a domainwere C and C++ are king.Westill have somedevbeforebeing able to release it, howeverheresome point of enhancementwe’dlike to be able to work ongenericity and usability: automorphing performances: bench the wholelibraryusability: goingfurtherwith the gui by discoveringautomatically the algorithms and parameters
- Label, applicable ?, rIsize