http://gmic.eu
Présentation Equipe IMAGE / GREYC, Avril 2017

Context

Research in the field of image processing at the GREYC lab of ENSICAEN /CNRS / University of Normandy (Caen).
⇒ Design of innovative algorithms to solve generic image processing problems(denoising, enhancement, segmentation, feature detection,...).

Context

Frequent collaborations with companies / laboratories having specific images toprocess.

...

⇒ Various image data coming from very diverse sensors.

Context
Image data are diverse: 2D, 2D+t, 3D, 3D+t, vector or matrix-valued pixels, float
values, ...
⇒ We stray far from usual 2D color pictures!

(a) I1 ∶ W × H → [0, 255]3(b) I2 ∶ W × H × D → [0, 65535]32(d) I4 ∶ W × H × T → [0, 4095](c) I3 ∶ W × H × T → [0, 4095]

Motivations

Needs for tools to visualize / explore data, convert image formats, apply classicalIP operators (filtering, geometric transformations, frequential analysis, ...) for very
generic image data, sometimes on thousands of images at the same time.
Typical “technical” question we can ask for:“How to convolve 500 volumetric images having 32 channels each by 3danisotropic gaussian kernels ?”Motivations⇒ Very few open-source tools exist for these tasks. They tend to be either:▸

Easy to use, but not generic enough for our image data (GIMP, ImageMagick,
GraphicsMagick, ...).

▸

Or very flexible, but reserved for experienced programmers (require the writing of code,using specialized external libraries).

We did like others: The team has developed generic libraries for image
processing: CImg and Pandore (in C++):

http://cimg.eu
https://clouard.users.greyc.fr/Pandore/

Motivations

In practice, the libraries are used only by a few hundred “experimented”
programmers.
⇒ Cause: High diversity of people in the image processing field !

Mathematicians

Physicists

Programmers

Biologists ...

⇒ We clearly need more simpler interfaces (than C++ libraries) if we want toenlarge our audience.

Goals of the G’MIC project

http://gmic.eu
For the users : Define different user interfaces to do image processing. (provided
interfaces are more or less friendly - and powerful - depending on the user’s skill.
For the developers : Ease algorithm prototyping and maintenance.
→ Technical mean : Definition of a full-featured, concise script language for theprocessing of generic image data (G’MIC language). Interpreter used as a baselayer for all user interfaces.

Goals of the G’MIC project

Definition of a comprehensive and concise script language for the processingof generic image data (G’MIC language).
▸

Full-featured: More than 950 commands available for the visualization, filtering,geometric and colorimetric transformations, feature extractions, 3d rendering, matrix
calculation, primitive drawing, ...

Ð→ Documentation (.pdf) has more than 450 pages.
Ð→ Reference documentation: http://gmic.eu/referenceGoals of the G’MIC project

Distribution of a open-source implementation of the G’MIC language interpreter(as a C++ library).
▸

Integration: Possible integration of G’MIC features in third-party software or plug-ins(photo retouching, digital painting, video editing software, ...).

▸

Free software license: Distributed under the CeCILL license (GPL-compatible).

Ð→ Some existing integrations of libgmic to date:☀

Krita, digital painting software.
Photoflow, non-destructive photo retouching software.
☀ EKD, video post-production software.
☀

Goals of the G’MIC project

Provide user interfaces for everyone, embedding the G’MIC language
interpreter (multi-platform).
▸

gmic: Command-line tool to manipulate generic images.
Complementary to the CLI tools from ImageMagick / GraphicsMagick.

Example: using the CLI tool “gmic”$ gmic lena.bmp -blur 3 -sharpen 1000 -noise 30 -+ "’cos(x/3)*30’"

Example: using the CLI tool “gmic”$ gmic reference.inr -flood 23,53,30,50,1,1,1000 -flood[-2] 0,0,0,30,1,1,1000-blur 1 -isosurface3d 900 -opacity3d[-2] 0.2 -color3d[-1] 255,128,0 -+3d

Example: using the CLI tool “gmic”$ gmic -isosurface3d "’sin(x*y*z)’",0,-10,-10,-10,10,10,10,128,128,64

Example: using the CLI tool “gmic”$ gmic milla.bmp -f ’255*(i/255)ˆ1.7’ -histogram 128,0,255 -a c -plotis the G’MIC equivalent to this C++ code (using CImg):#include "CImg.h"using namespace cimg_library;
int main(int argc,char **argv) {const CImg<>
img("milla.bmp"),hist = img.get_histogram(128,0,255),
img2 = img.get_fill("255*((i/255)ˆ1.7)",true),
hist2 = img2.get_histogram(128,0,255);(hist,hist2).get_append(’c’).display_graph("Histograms");
return 0;}

First release: August 2008

First release done in August 2008 Ô⇒ very few downloads (approx. 300/month).
But... Writing image processing pipelines in G’MIC also allows to design anddevelop artistic filters and effects easily...
$ gmic lena.jpg -pencilbw 0.3 -o gmic_lena1.jpg$ gmic lena.jpg -flower 10 -o gmic_lena4.jpg

Ð→ Why not writing a G’MIC plug-in for GIMP ?

Goals of the G’MIC project
Provide user interfaces for everyone, embedding the G’MIC language
interpreter (multi-platform).
▸

gmic_gimp: Plug-in for GIMP that provides hundred of G’MIC -based image filters for
2D RGB or RGBA images.

Before / after the GIMP plug-in
First release of the G’MIC plug-in GIMP in January 2009.
Ð→ Significant increase of the downloads and page views.

Goals of the G’MIC project
Provide user interfaces for everyone, embedding the G’MIC language
interpreter (multi-platform).
▸

G’MIC Online: Web service for applying image filters and effects online (requires only
a web browser).
https://gmicol.greyc.fr

Goals of the G’MIC project
Provide user interfaces for everyone, embedding the G’MIC language
interpreter (multi-platform).
▸

ZArt: Qt-based interface for the manipulation of video sequences (webcam or videofile). Used as a demonstration platform.

Global view of the G’MIC frameworkgmicol(web service)gmic_gimp(plug-in GIMP)

libgmic(C++)

ZArt(webcam GUI)

G'MIC interpreter
(C++)

CImg
(C++ library)gmic(console)

Custom commands(G'MIC script)

The G’MIC programming language
Script language (interpreted).
Define a set of native “low-level” commands :e.g, -convolve, -display, -if, -then, -else, ...
→ C++, compiled, often multi-threaded (OpenMP).
Define a set of custom commands :e.g, -polygonize, -apply_gamma, -x_pacman, ...
→ Grouped in the G’MIC standard library.
Users can defined their own custom libraries of commands :e.g, $ gmic user.gmic -my_command ...
→ Versatile and evolutive framework (standard library updatable from network).
Most G’MIC commands are actually custom commands.
200 native, +750 custom.
Commands to manage also display windows and user events.

The G’MIC programming language
Portion of the -x_pacman command:

The G’MIC programming language
Embedded math expression evaluator:$ gmic user.gmic -julia(0.631 seconds to run)

The G’MIC programming language

Embedded math expression evaluator:→ JIT compiler : expression is compiled by G’MIC into specific bytecode for fasterevaluation.
→ OpenMP : expression is evaluated with multiple threads (when possible).
→ Manage usual calculations with scalars, complexes, matrices(SVD, solve, ...).
→ Expression may contain variables, loops, conditions, etc...
(looks like C code).

The G’MIC programming language
NL-means code :Run with $ gmic user.gmic leno.png -nlmeans_expr 35,3,1

The G’MIC programming language$ gmic user.gmic leno.png -nlmeans_expr 35,3,1

Takes 3.156 seconds for a 512x512 RGB image, with 24 cores used.
→ Very convenient for quick algorithm prototyping.

Current project state
Today, G’MIC is a project with:
Approx. 150.000 lines of code (without the code of interfaces)(in C++ and G’MIC language mainly).
▸ 700+ downloads / day (more than. 3.000.000 since August 2008).
▸ 400+ unique visitors / day on the project web pages.
⇒ Unexpected results considering the first targeted audience(researchers in image processing!).
▸The open-source effectMore users: G’MIC becomes referenced on forums, blogs, news articles (about
computer graphics or free software): framasoft, linuxfr, webupd8,
libregraphicsworld, pcastuces, gimpfr, linuxgraphics,
gimpusers, ...).
More contributors: Help from beta-testers, packagers (Debian, Ubuntu, Arch,
Mageia, Gentoo, Windows, MacOSX,..), bug reports, language translations,design of mascots, new filters, ...

The open-source effectG’MIC has a great community of users: Flickr (+800 followers), Pixls.us,
GimpChat, Twitter, Google+ (+2200 followers), ...

The open-source effect
Recent development of some G’MIC -based interfaces by external developers:
Plug-ins for Krita, After Effects, Natron, PhotoFlow.

Why G’MIC does raise interest ?

Demonstration of some G’MIC features(for artistic purposes)

Filter Showcase:Rodilius

Artistic: Rodilius

Goal: Exaggerate the structure and length of the image contours to make themmore visible.
Principle: Several image convolution with oriented gaussian kernels arecomputed along different orientations of the plane. The resulting images are
simply combined with layer blending modes Lighten only or Darken only. Finally,
we smooth the blended image anisotropically, then sharpen its contours.
Similar to: Filter banks for the geometric analysis of images (contourlets).

Artistic: Rodilius

Open input image.

Artistic: Rodilius

Invoke G’MIC plug-in and select Artistic / Rodilius.

Artistic: Rodilius

Wait a little bit, then enjoy ! (recently parallelized for speeding up FFTs).

Artistic: Rodilius

Two other examples, works quite well on fur.

Artistic: Rodilius

Another example: with Darken only blending mode used.

Artistic: Rodilius

3. Reproduces the ’Fractalius’ effect (49$ plug-in for Photoshop) but for 0$ and 10
lines of G’MIC code !):Redfield Fractalius

G’MIC Rodilius

Rodilius code in G’MIC: 10 lines

Filter Showcase:
Color transfer

Colors: Color transfer

Goal: Give a color ambiance to an image, from a reference image.
Principle: We register two colorimetric functions in the RGB cube to determine acolor correspondence map to apply to the input image.
Similar to: Optical flow, image registration.

Colors: Color transfer

Open input image.

Colors: Color transfer

Open reference image (as a new layer).

Colors: Color transfer

Invoke G’MIC plug-in and select Colors / Transfer color [advanced].

Colors: Color transfer

Original image.

Colors: Color transfer

Color-transferred result.

Colors: Color transfer

Reference image (reminder).

Colors: Color transfer

Other examples.

Filter Showcase:
Extract foreground [interactive]

Contours: Extract foreground [interactive]

Goal: Extract foreground objects from background in an image, and get the result
as two distinct (complementary) layers.
Principle: Same as before, but done only with key points having labels“foreground” or “background” instead of colors.

Contours: Extract foreground [interactive]

Open input image (single-layer color photograph).

Contours: Extract foreground [interactive]

Invoke G’MIC plug-in and select Contours / Extract foreground [interactive].

Contours: Extract foreground [interactive]

Place some “foreground” and “background” key points.

Contours: Extract foreground [interactive]

Result of the filter: 2 layers (foreground shown here).

Contours: Extract foreground [interactive]

Result of the filter: Image after modification of the color hue on foreground layer only.

Contours: Extract foreground [interactive]

Another example of result, processing background and foreground independently.

Filter Showcase:
Split details

Details: Split details

Goal: Decompose an image into several levels of details, in order to work
separately on the different image scales before recomposing the image.
Principle: The image is decomposed/recomposed using a pyramidal
representation obtained by the iterative convolution by gaussians kernels +residues.
Similar to: Scale space analysis.

Details: Split details

Open input image.

Details: Split details

Invoke G’MIC plug-in and select Details / Split details.

Details: Split details

You get your input (top-left) + the decomposition into scales (here 3 scales).

Details: Split details

Do what you want on the scales (here, we simply erase the skin defects on the middle scale).

Details: Split details

Invoke G’MIC plug-in again, to recompose the final image.

Details: Split details

Result of the recomposition, with cleaner skin (5mn work !).

Details: Split details

Comparison with initial image.

Filter Showcase:
Extract objects

Arrays & Tiles: Extract objects

Goal: Extract independent objects localized on a single image.
Principle: Background detection is performed, then residual pixels are grouped asseveral connexe regions.
Similar to: Object detection and segmentation on static background.

Arrays & Tiles: Extract objects

Open input image (single-layer).

Arrays & Tiles: Extract objects

Invoke G’MIC plug-in and select Arrays & tiles / Extract object.

Arrays & Tiles: Extract objects

Output looks similar as input, but is divided into several layers.

Arrays & Tiles: Extract objects

Managing each object independently is now possible (here, position change).

Filter Showcase:
Pack sprites

Patterns: Pack sprites

Goal: Generate a synthetic image where multiple image thumbnails have been
packed together without overlapping (resized and rotated).
Principle: Valid pseudo-random positions are iteratively checked for the insertionof new objects, with decreasing dimensions.
Similar to: Bin-packing problem (NP-hard).

Patterns: Pack sprites

Select your objects to pack (multi-layer image).

Patterns: Pack sprites

Invoke G’MIC plug-in and select Patterns / Pack sprites.

Patterns: Pack sprites

Get your image with randomly packed sprites (after a while).

Patterns: Pack sprites

Now, you can add a bottom layer to restrict packing on transparent regions.

Patterns: Pack sprites

Invoke G’MIC again, and select Mask: Mask as bottom layer.

Patterns: Pack sprites

Go for a coffee, and you get this.

Patterns: Pack sprites

Detail of the result.

Patterns: Pack sprites

Example of rendering, by Chris Fiedler, on GimpChat.

Filter Showcase:
Shapeism

Artistic: Shapeism

Goal: Use the previous Sprite packing filter to create image abstraction (such as
the Circlism from artist Ben Heine).
Principle: Monochrome shapes are packed together at different scales, withconstraints to put only small shapes on image contours. Shape colorization is
performed afterwards by averaging the color pixels covered by each shape.

Artistic: Shapeism

Open input image.

Artistic: Shapeism

Invoke G’MIC plug-in and select Artistic / Shapeism.

Artistic: Shapeism

Go drink a (big) coffee, and enjoy the result ! (can be slow to compute).

Artistic: Shapeism

Result with another shape selected (a star).

Filter Showcase:
Inpainting [patch-based]Repair: Inpaint [patch-based]

Goal: Allow the reconstruction of “holes” in images (groups of pixels considered
as missing or invalid).
Principle: Implementation of an extension to the inpaiting algorithm of
Criminisi-Perez-etal + patch blending technique.
Similar to: Inpainting, “classical” (and hard-to-solve!) reconstruction problem inimage processing.

Repair: Inpainting

Open input image.

Repair: Inpainting

Draw an inpainting mask directly on it (with a constant known color).

Repair: Inpainting

Invoke G’MIC plug-in and select Repair / Inpaint [patch-based].

Repair: Inpainting

Input image.

Repair: Inpainting

If you choose carefully the parameters, this is what you get.

Repair: Inpainting
G’MIC is one of the few software to offer several “inpainting” algorithms:

Repair: Inpainting
G’MIC is one of the few software to offer several “inpainting” algorithms:

Repair: Inpainting
G’MIC is one of the few software to offer several “inpainting” algorithms:

Repair: Inpainting

G’MIC is one of the few software to offer several “inpainting” algorithms:

Repair: Inpainting

Example from Patrick David: Input image.

Repair: Inpainting

Example from Patrick David: Inpainted image.

Repair: Inpainting

Example from Patrick David: Input image.

Repair: Inpainting

Example from Patrick David: Inpainted image.

Repair: Inpainting(Extreme case!):
Input image (boat to be removed).

Repair: Inpainting

Result by the G’MIC inpainting algorithm.

Filter Showcase:Denoising filters

Repair: Denoising filters

Goal: Algorithms to smooth an image while preserving the image details andtextures.
Principle: Reccuring issue in image processing, with a lot of algorithms existing(PDE’s, Wavelets, Patch-based smoothing, etc...).

Repair: Denoising filters

Invoke G’MIC plug-in, and select one of the denoising filters(more than 20 methods available).

Repair: Denoising filters

Comparison between original / denoised image (equalized images for clarity).

Repair: Denoising filters
G’MIC is one of the few software to offer efficient image denoising algorithms:

Repair: Denoising filters
G’MIC is one of the few software to offer efficient image denoising algorithms:

Filter Showcase:Dream smoothingArtistic: Dream smoothing

Goal: Apply one of the previous image smoothing technique, deliberatly
exaggerated and make the colors more contrasted to create a painting effect.
Principle: We apply multiple iterations of anisotropic smoothing with an“aggressive” color mix in the Lab color space.

Artistic: Dream smoothing

Open input image.

Artistic: Dream smoothing

Invoke G’MIC plug-in and select Artistic / Dream Smoothing.

Artistic: Dream smoothing

Enjoy your result ! (takes some time to render, recently parallelized).

Artistic: Dream smoothing

How artists use it for real: Processing done by Zarir Madon.

Artistic: Dream smoothing

How artists use it for real: Processing done by Arto Huotari.

Filter Showcase:
Poisson editing

Poisson editing

Goal: Copy/paste a piece of image into another one, without visible seams in the
result.
Principle: Solving the Poisson equation to reconstruct the final image from thegradient map where the paste has been done.

Poisson editing

Example of face swapping, using Poisson editing.

Poisson editing

Example of face swapping, using Poisson editing.

Poisson editing

Example of face swapping, using Poisson editing.

Poisson editing

Example of face swapping, using Poisson editing.

Poisson editing

Example of object insertion, using Poisson editing.

Poisson editing

Example of face swapping, using Poisson editing.

Poisson editing

Example of face swapping, using Poisson editing (on the same input picture).

Poisson editing

Example of face swapping, using Poisson editing (on the same input picture).

Filter Showcase:
SketchBlack & White: Sketch

Goal: Algorithms to transform a picture into a sketch.
Principle: Pencil strokes are iteratively simulated on a white canvas, by analyzing
the contour geometry of the original picture.
Similar to: Contour detection and extraction, texture analysis.

Black & White: Sketch(Courtesy of Tom Keil)Black & White: Sketch(Courtesy of Tom Keil)Black & White: Sketch(Courtesy of Tom Keil)Black & White: Sketch

Black & White: Sketch(Courtesy of Tom Keil)Black & White: Engrave(Example of another similar filter in G’MIC : Engrave)⇒ +440 filters like this available in the G’MIC plug-in for GIMP!

Filter Showcase:
Film emulation

Repair: Film emulation

Goal: Provide free film emulation filters, similar to what proprietary DXO FilmPack
proposes.
Made by: Patrick requested David to make his color profiles easily available foreveryone.
How is this done? Color transformations are encoded as RGB CLUT files, stored
on the G’MIC server. Each color profile is downloaded on demand.
Ô⇒ 476 lines of G’MIC code (mostly for GUI).
(all included: GUI description + algorithm).

Film emulation

Open input image.

Film emulation

Invoke G’MIC plug-in, and choose one filter in folder Film emulation/.

Film emulation

Comparison: Before (left) / After (right).

Film emulation

Two other examples: TMAX-3200 (left) and Kodak Kodachrome 64 (right).

Film emulation

Patrick David has indeed done a lot of presets (here, a sample of them).

Film emulation

Technically speaking:
Each preset defines a mapping function from
RGB to RGB (CLUT).
The values of these functions are explicitely
stored for all RGB colors.
To avoid huge datasets, we consider
64x64x64 downsampled versions of the
CLUTs and interpolate intermediate colors.
→ 77Mb of data for 271 film emulation presets.
As the original color mappings are smoothfunctions, interpolation has almost no
incidence on the quality.

Conclusions

G’MIC [ http://gmic.eu ]A full-featured open-source framework for image processing: Several userinterfaces available, more to come.

, Positive for the GREYC: Evolving software.
“Image Processing” showcase for the general public.

, Positive for the IMAGE team: Useful software on a daily basis, for the analysis andexploration of image data, and the fast prototyping of new algorithms + derived
publications.

/ Time consuming: developing/maintenance, community animation, web pages,
answering questions... (approx. 10-15h of work / week).