G'MIC 1.7.1
When the flowers are blooming, image filters abound!
David Tschumperlé — May 18th, 2016
When the flowers are blooming, image filters abound!
David Tschumperlé — May 18th, 2016
A new version 1.7.1 “Spring 2016” of G’MIC (GREYC’s Magic for Image Computing), the open-source framework for image processing, has been released recently (26 April 2016). This is a great opportunity to summarize some of the latest advances and features over the last 5 months.
G’MIC is an open-source project started in August 2008. It has been developed in the IMAGE team of the GREYC laboratory from the CNRS (one of the major French public research institutes). This team is made up of researchers and teachers specializing in the algorithms and mathematics of image processing. G’MIC is released under the free software licence CeCILL (GPL-compatible) for various platforms (Linux, Mac and Windows). It provides a set of various user interfaces for the manipulation of generic image data, that is images or image sequences of multispectral data being _2D_ or _3D_, and with high-bit precision (up to 32bits floats per channel). Of course, it manages “classical” color images as well.
Note that the project just got a redesign of its mascot Gmicky, drawn by David Revoy, a French illustrator well-known to free graphics lovers for being responsible for the great libre webcomics Pepper&Carott.
G’MIC is probably best known for it’s GIMP plug-in, first released in 2009. Today, this popular GIMP extension proposes more than 460 customizable filters and effects to apply on your images.
But G’MIC is not a plug-in for GIMP only. It also offers a command-line interface, that can be used in addition with the CLI tools from ImageMagick or GraphicsMagick (this is undoubtly the most powerful and flexible interface of the framework). G’MIC also has a web service G’MIC Online to apply effects on your images directly from a web browser. Other G’MIC-based interfaces also exist (ZArt, a plug-in for Krita, filters for Photoflow…). All these interfaces are based on the generic C++ libraries CImg and libgmic which are portable, thread-safe and multi-threaded (through the use of OpenMP). Today, G’MIC has more than 900 functions to process images, all being fully configurable, for a library of only approximately 150 kloc of source code. It’s features cover a wide spectrum of the image processing field, with algorithms for geometric and color manipulations, image filtering (denoising/sharpening with spectral, variational or patch-based approaches…), motion estimation and registration, drawing of graphic primitives (up to 3d vector objects), edge detection, object segmentation, artistic rendering, etc. This is a versatile tool, useful to visualize and explore complex image data, as well as elaborate custom image processing pipelines (see these slides to get more information about the motivations and goals of the G’MIC project).
Here we look at the descriptions of some of the most significant filters recently added. We illustrate their usage
from the G’MIC plug-in for GIMP. All of these filters are of course available from other interfaces as well
(in particular within the CLI tool gmic).
The filter Artistic / Brushify tries to transform an image into a painting. Here, the idea is to simulate the process of painting with brushes on a white canvas. One provides a template image and the algorithm first analyzes the image geometry (local contrasts and orientations of the contours), then attempt to reproduce the image with a single brush that will be locally rotated and scaled accordingly to the contour geometry. By simulating enough of brushstrokes, one gets a “painted” version of the template image, which is more or less close to the original one, depending on the brush shape, its size, the number of allowed orientations, etc. All these settings being customizable by the user as parameters of the algorithm: This filter allows thus to render a wide variety of painting effects.
The animation below illustrates the diversity of results one can get with this filter, applied on the same input picture of a lion. Various brush shapes and geometries have been supplied to the algorithm. Brushify is computationally expensive so its implementation is parallelized (each core gives several brushstrokes simultaneously).
Note that it’s particularly fun to invoke this filter from the command line interface (using the option -brushify
available in gmic) to process a sequence of video frames
(see this example of “ brushified “ video):
G’MIC gets a new algorithm to reconstruct missing data in images. This is a classical problem in image processing, often named “Image Inpainting“, and G’MIC already had a lot of useful filters to solve this problem. Here, the newly added interpolation method assumes only a sparse set of image data is known, for instance a few scattered pixels over the image (instead of continuous chuncks of image data). The analysis and the reconstruction of the global image geometry is then particularly tough.
The new option -solidify in G’MIC allows the reconstruction of dense image data from such a sparse sampling,
based on a multi-scale diffusion PDE’s-based technique.
The figure below illustrates the ability of the algorithm with an example of image reconstruction. We start from
an input image of a waterdrop,
and we keep only 2.7% of the image data (a very little amount of data!). The algorithm is able to reconstruct
a whole image that looks like the input, even if all the small details have not been
fully reconstructed (of course!). The more samples we have, the finer details we can recover.
As this reconstruction technique is quite generic, several new G’MIC filters takes advantage of it:
From an artistic point of view, there are many possibilities offered by this filters. For instance, it becomes really easy to generate color gradients with complex shapes, as shown with the two examples below (also in this video that details the whole process).
From an algorithmic point of view, such a color mapping is extremely simple to implement : for each of the 300+ presets, G’MIC actually has an HaldCLUT, that is a function defining for each possible color (R,G,B) (of the original image), a new color (R’,G’,B’) color to set instead. As this function is not necessarily analytic, a HaldCLUT is stored in a discrete manner as a lookup table that gives the result of the mapping for all possible colors of the RGB cube (that is 2^24 = 16777216 values if we work with a 8bits precision per color component). This HaldCLUT-based color mapping is illustrated below for all values of the RGB color cube.
This is a large amount of data: even by subsampling the RGB space (e.g. with 6 bits per component) and compressing the corresponding HaldCLUT file, you ends up with approximately 200 and 300 kB for each mapping file. Multiply this number by 300+ (the number of available mappings in G’MIC), and you get a total of 85MB of data, to store all these color transformations. Definitely not convenient to spread and package!
The idea was then to develop a new lossy compression technique focused on HaldCLUT files, that is volumetric discretised vector-valued functions which are piecewise smooth by nature. And that what has been done in G’MIC, thanks to the new sparse reconstruction algorithm. Indeed, the reconstruction technique also works with _3D_ image data (such as a HaldCLUT!), so one simply has to extract a sufficient number of significant keypoints in the RGB cube and interpolate them afterwards to allow the reconstruction of a whole HaldCLUT (taking care to have a reconstruction error small enough to be sure that the color mapping we get with the compressed HaldCLUT is indistinguishable from the non-compressed one).
Thus, G’MIC doesn’t need to store all the color data from a HaldCLUT, but only a sparse sampling of it (i.e. a sequence of { rgb_keypoint, new_rgb_color }).
Depending on the geometric complexity of the HaldCLUTs to encode, more or less keypoints are necessary (roughly from _30_ to 2000).
As a result, the storage of the 300+ HaldCLUTs in G’MIC requires now only 850 KiB of data (instead of 85 MiB), that is a compression gain of 99% !
That makes the whole HaldCLUT data storable in a single file that is easy to ship with the G’MIC package. Now, a user can then apply all the G’MIC color transformations
while being offline (previously, each HaldCLUT had to be downloaded separately from the G’MIC server when requested).
It looks like this new reconstruction algorithm from sparse samples is really great, and no doubts it will be used in other filters in the future.
Filter Arrays & tiles / Make seamless [patch-based] tries to transform an input texture to make it tileable, so that it can be duplicated as tiles along the horizontal and vertical axes without visible seams on the borders of adjacent tiles. Note that this is something that can be extremely hard to achieve, if the input texture has few auto-similarity or glaring luminosity changes spatially. That is the case for instance with the “Salmon” texture shown below as four adjacent tiles (configuration 2x2) with a lighting that goes from dark (on the left) to bright (on the right). Here, the algorithm modifies the texture so that the tiling shows no seams, but where the aspect of the original texture is preserved as much as possible (only the texture borders are modified).
We can imagine some great uses of this filter, for instance in video games, where texture tiling is common to render large virtual worlds.
A “new” filter Details / Split details [wavelets] has been added to decompose an image into several levels of details. It is based on the so-called “à trous” wavelet decomposition. For those who already know the popular Wavelet Decompose plug-in for GIMP, there won’t be so much novelty here, as it is mainly the same kind of decomposition technique that has been implemented. Having it directly in G’MIC is still a great news: it offers now a preview of the different scales that will be computed, and the implementation is parallelized to take advantage of multiple cores.
The filter outputs several layers, so that each layer contains the details of the image at a given scale. All those layers blended together gives the original image back.
Thus, one can work on those output layers separately and modify the image details only for a given scale. There are a lot of applications for this kind of image decomposition, one of the most spectacular being the ability to retouch the skin in portraits : the flaws of the skin are indeed often present in layers with middle-sized scales, while the natural skin texture (the pores) are present in the fine details. By selectively removing the flaws while keeping the pores, the skin aspect stays natural after the retouch (see this wonderful link for a detailed tutorial about skin retouching techniques, with GIMP).
G’MIC is also well known to offer a wide range of algorithms for image denoising and smoothing (currently more than a dozen). And he got one more ! Filter Repair / Smooth [patch-pca] proposed a new image denoising algorithm that is both efficient and computationally intensive (despite its multi-threaded implementation, you probably should avoid it on a machine with less than 8 cores…). In return, it sometimes does magic to suppress noise while preserving small details.
The Droste effect (also known as “mise en abyme“ in art) is the effect of a picture appearing within itself recursively. To achieve this, a new filter Deformations / Continuous droste has been added into G’MIC. It’s actually a complete rewrite of the popular Mathmap’s Droste filter that has existed for years. Mathmap was a very popular plug-in for GIMP, but it seems to be not maintained anymore. The Droste effect was one of its most iconic and complex filter. Martin “Souphead”, one former user of Mathmap then took the bull by the horns and converted the complex code of this filter specifically into a G’MIC script, resulting in a parallelized implementation of the filter.
This filter allows all artistic delusions. For instance, it becomes trivial to create the result below in a few steps: create a selection around the clock, move it on a transparent background, run the Droste filter, et voilà!.
The filter Deformations / Equirectangular to nadir-zenith is another filter converted from Mathmap to G’MIC. It is specifically used for the processing of panoramas: it reconstructs both the Zenith and the Nadir regions of a panorama so that they can be easily modified (for instance to reconstruct missing parts), before being reprojected back into the input panorama.
Morgan Hardwood has wrote a quite detailled tutorial, here on pixls.us, about the reconstruction of missing parts in the Zenith/Nadir of an equirectangular panorama. Check it out!
Finally, here are other highlights about the G’MIC project:
$ gmic 512,512,1,1,"c = 2.4*[x/w,y/h] - [1.8,1.2]; z = [0,0]; for (iter = 0, cabs(z)<=2 && ++iter<256, z = z**z + c); 6*iter" -map 7,2
This clearly enlarge the math evaluator ability, as you are not limited to scalar variables anymore. You can now create complex filters which are able to solve linear systems or compute eigenvalues/eigenvectors, and this, for each pixel of an input image. It’s a bit like having a micro-(micro!)-Octave inside G’MIC. Note that the Brushify filter described earlier uses these new features extensively. It’s also interesting to know that the G’MIC math expression evaluator has its own JIT compiler to achieve a fast evaluation of expressions when applied on thousands of image values simultaneously.
As you see, the G’MIC project is doing well, with an active development and cool new features added months after months. You can find and use interfaces to G’MIC in more and more opensource software, as GIMP, Krita, Blender, Photoflow, Flowblade, Veejay, EKD and Natron in a near future (at least we hope so!).
At the same time, we can see more and more external resources available for G’MIC : tutorials, blog articles (here, here, here,…), or demonstration videos (here, here, here, here,…). This shows the project becoming more useful to users of opensource software for graphics and photography.
The development of version 1.7.2 already hit the ground running, so stay tuned and visit the official G’MIC forum on pixls.us to get more info about the project developement and get answers to your questions. Meanwhile, feel the power of free software for image processing!
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