Generic Image Processing With Climb – 5th ELS

Transcript

1. Generic Image Processing with Climb Laurent Senta – Didier Verna LRDE EPITA Research Lab April 30, 2012 lrde.epita.fr
2. Outline • Introduction • Using Climb • Developping Climb • Going Further • Conclusion 2
3. INTRODUCTION 3
4. Generic Image Processing 4 Images: Roland Levillain. Software Architecture for Generic Image Processing Tools I A H B D C E F G I A H B D C E F G segmentation
5. Genericity Purpose 5 Graphic: Laurent Najman. Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images algorithms values type structures type segmentation graph 2dmatrix 3dmatrix bool grayscale rgb S x V x A combinations
6. Climb • Generic Image Processing library – Common Lisp • Inspired by Olena: C++, 10 years old – Dynamic vs Static – Usability, maintainability, performance,… • Still in beta 6 Compilers sbcl Dependencies iterate, lisp-magick, cl-heap, lisp-unit, cl-gtk2
7. Architecture 7 The different layers of the library Generic types Morphers Image Algorithms Chaining Operator GUI
8. USING CLIMB 8
9. Algorithms 9 original grayscale median filter Otsu threshold dilation erosion jitter
10. (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’) Chaining Operator Chaining algorithms with the $ operator 10 (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’))
11. dilation diff erosion otsu to-gray image-load Chaining Operator 11 More complex chaining
12. DEVELOPPING CLIMB 12
13. • matrix[x, y] → pixelvalue • graph.getNode(label) → nodevalue • model[x, y, z] → voxelvalue • matrix[x, y] → pixelvalue • graph.getNode(label) → nodevalue • model[x, y, z] → voxelvalue Image Definition 13 I A H B D C E F G Image access image(site) = value (setf (iref image site) value) Generalization: Lisp:
14. (value-inverse ) (iref image site) (setf (iref image site) ) Values and Sites 14 Values: Site: bool grayscale RGB x y I A H B D C E F G 2d-point label
15. Browsing Images 15 x y Site-set: (let ( ) (loop :for s := :while s )) (domain (image-domain image)) (site-set-next domain) … I A H B D C E F G
16. (let ( ) (loop :for s := :while s )) … (site-set-next neighbors) (neighbors (site-set-window window site)) Browsing Images 16 x y Site-set-window: I A H B D C E F G
17. Morphers 17 Transforming images with morphers Value morpher Structure morpher
18. GOING FURTHER 18
19. Properties 19 Adapting genericity algorithms values type structures type segmentation graph 2dmatrix 3dmatrix bool grayscale rgb support:regular
20. Value: GUI 20 Climb based interface I A H B D C E F G GTK-BOX Morpher Resize climb graph
21. Conclusion 21 Current status Image Processing Practitionner Algorithm implementor • Built-in algorithms • Composition tools • Chaining operator • Morphers • GUI • High-level domain model • Generic values • Generic structures • Generic implementations Hot topics Genericity Usability Performance
22. QUESTIONS ? Thanks for your attention, 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