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
   

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2. Outline
   • Introduction
   • Using Climb
   • Developping Climb
   • Going Further
   • Conclusion
   2
   

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3. INTRODUCTION
   3
   

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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
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   H B
   D
   C
   E
   F
   G
   segmentation
   

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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
   

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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
   

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7. Architecture
   7
   The different layers of the library
   Generic types Morphers
   Image Algorithms
   Chaining
   Operator
   GUI
   

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8. USING CLIMB
   8
   

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9. Algorithms
   9
   original grayscale
   median filter
   Otsu threshold
   dilation erosion
   jitter
   

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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’))
    

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11. dilation
    diff
    erosion
    otsu
    to-gray
    image-load
    Chaining Operator
    11
    More complex chaining
    

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12. DEVELOPPING CLIMB
    12
    

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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:
    

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14. (value-inverse )
    (iref image site)
    (setf (iref image site)
    )
    Values and Sites
    14
    Values:
    Site:
    bool grayscale RGB
    x
    y
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    H B
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    F
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    2d-point label
    

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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
    

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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:
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    H B
    D
    C
    E
    F
    G
    

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17. Morphers
    17
    Transforming images with morphers
    Value morpher Structure morpher
    

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18. GOING FURTHER
    18
    

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19. Properties
    19
    Adapting genericity
    algorithms
    values
    type
    structures
    type
    segmentation
    graph
    2dmatrix
    3dmatrix
    bool grayscale rgb
    support:regular
    

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20. Value:
    GUI
    20
    Climb based interface
    I
    A
    H B
    D
    C
    E
    F
    G
    GTK-BOX
    Morpher
    Resize
    climb graph
    

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21. Conclusion
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    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
    

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22. QUESTIONS ?
    Thanks for your attention,
    22
    

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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
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