Is there a programmatic way to convert two images into an animation sequence (e.g., an animated GIF) like the following example?
This image sequence, taken from a http://memrise.com course, doesn't seem to have manually-edited frames, but seems automatically transformed using some kind shape morphing algorithm. Is there a common term used to describe such an animation or algorithm? Is there a feature in ImageMagick or Photoshop/Gimp that generates such animations, given a pair of images?
Ideally the technique could be scriptable so I could create animations for several pairs of start-end images.
Edit: I have just been told about Gimp's tool under Filters->Animation->Blend, which appears to do the same thing as jQuery morph: each frame i is start + (finish - start)/N*i. In other words, you're transitioning each pixel independently from the start value to the finish value, without any shape morphing. The example gives is more complicated, as it modifies the contours of both images to achieve its compelling effect.
Other examples:
http://static.memrise.com/uploads/mems/32000121024054535.gif
http://static.memrise.com/uploads/mems/225428000121109232837.gif
I have written a tool that doesn't require setting manual keypoints and is not restricted to a domain (like faces). Anyway, the images have to be similar (e.g. two faces or two cars from the same perspective).
https://github.com/kallaballa/Poppy
There is also a web-version created with emscripten.
I generated the above animation using following command line:
poppy flame.png glyph.png flame.png
Although this is an old question, since ImageMagick is mentioned, for anyone who comes here from google it may be worth looking at this imagemagick plugin called shapemorph.
GIMP can't do that directly, but over the years a series of (now poorly maintaind) plug-ins to do that where released by third parties. The keyword for searching for this is "morph" - you should find a bunch of stand alone programs to do that as well, from "gratis" to full fledged Free Software, such as xmorph
Given pairs of vector files (.wmf extension) it is possible to use linear interpolation of shapenodes in Visual Basic for Applications to create frames for GIF animations , though this would take along time to explain. For some examples see
http://www.giless.co.uk/animatorMorphGIFs.htm (it is like a slideshow)
I have made some improvements since then, as well!
Related
I'm relatively new and am still learning the basics. I've used NVIDIA DIGITS in the past, and am now looking at Tensorflow. While I've been able to fumble my way around creating some models for a few projects I'm working on, I really want to start diving deeper into what I'm doing, how I'm doing it, and ultimately a better understanding of why.
One area that I would like to start with is the Images that I'm using for training and testing. Can anyone point me to a blog, an article, a paper, or give me some insight in what I need to consider when selecting images to train a new model on. Up until recently, I've been using datasets that have already been selected and that are available for download. Lets say I'm going to start working on a project that involves object detection of ships from a variety of distances and angles.
So my thoughts would be
1) I need a large quantity of images.
2) The images need to contain ships of the different types I would like to detect. (lets just say one class, ships, don't care what type of ships)
3) I also need to have images that have a great variety of distance perspective for the different types of ships.
Ultimately, my thoughts are that the images need to reflect the distance, perspective, and types of ships I would ideally want to identify from the video. Seems simple enough.
However, there are a number of questions
Does the images need to be the same/similar resolution as the camera I'll be using, for best results?
Does the images all need to be the same resolution?
Can I use a single image and just digitally zoom out on the image to give the illusion of different distances?
I'm sure there are a number of other questions that I'm not asking, or should be asking. Are there any guide lines available for creating a solid collection of images to use when creating the collection of images for training and validation?
I recommend thinking through end to end, like would you need to classify ship models as a next step? I recommend going through well known public datasets and actually work with the structure, how to store data, labels, how to handle preprocessing etc.
More importantly, what are you trying to achieve? Talking to experts in the topic does help greatly while preparing your own dataset.
Use open source images if you can, e.g. flickr, google, imagenet.
No, you don't need them to be the same resolution.
It is not ideal to zoom in/out images to use in different categories. Preprocessing images and data augmentation already does this to create more distant representations of the same class. This is why I would recommend hands on approach with an existing dataset first.
Yes, what you need is many, different representations of classes, and a roughly balanced dataset of classes. If you define your data structure well in the beginning, it will save you a ton of time as you won't have to make changes often.
I've got retina tile maps working, 15x10 tiles, of 64x64 tiles. problem is for non-retina devices I will need to make a 15x10 tiles of 32x32 tiles. I don't want to recreate the Tile, is it just a case of changing the XML (.tmx) file? Is there an automated tool or another way around this? I've been looking online but not getting too much help.
Thanks
You have to update the TMX file and scale certain attributes. Unless your TMX map is very simple this will be a tedious and error-prone task that's best left to a tool.
There are a variety of TMX rescaling tools out there, but some didn't work for me or simply were incomplete at the time (ie one didn't scale object layers). All the tools I know are generally are written in rather unusual languages (for an iOS developer at least) like Python, Ruby or Bash scripts. Others are only available as binary without the source code.
Check out this cocos2d forum post. Specifically this tool or HDx on the App Store. iTilemaps might also work for you.
Because I wasn't happy with either of the choices, I wrote my own command line tool tmx2scale in Objective-C to rescale TMX maps intelligently in all directions. The tmx2scale tool is not currently available but it will be distributed complete with source code with the KoboldScript Game Kit project.
This is by far not a showstopper problem just something I've been curious about for some time.
There is this well-known -[UIImage resizableImageWithCapInsets:] API for creating resizable images, which comes really handy when texturing variable size buttons and frames, especially on the retina iPad and especially if you have lots of those and you want to avoid bloating the app bundle with image resources.
The cap insets are typically constant for a given image, no matter what size we want to stretch it to. We can also put that this way: the cap insets are characteristic for a given image. So here is the thing: if they logically belong to the image, why don't we store them together with the image (as some kind of metadata), instead of having to specify them everywhere where we got to create a new instance?
In the daily practice, this could have serious benefits, mainly by means of eliminating the possibility of human error in the process. If the designer who creates the images could embed the appropriate cap values upon exporting in the image file itself then the developers would no longer have to write magic numbers in the code and maintain them updated each time the image changes. The resizableImage API could read and apply the caps automatically. Heck, even a category on UIImage would make do.
Thus my question is: is there any reliable way of embedding metadata in images?
I'd like to emphasize these two words:
reliable: I have already seen some entries on the optional PNG chunks but I'm afraid those are wiped out of existence once the iOS PNG optimizer kicks in. Or is there a way to prevent that? (along with letting the optimizer do its job)
embedding: I have thought of including the metadata in the filename similarly to what Apple does, i.e. "#2x", "~ipad" etc. but having kilometer-long names like "image-20.0-20.0-40.0-20.0#2x.png" just doesn't seem to be the right way.
Can anyone come up with smart solution to this?
Android has a filetype called nine-patch that is basically the pieces of the image and metadata to construct it. Perhaps a class could be made to replicate it. http://developer.android.com/reference/android/graphics/NinePatch.html
We currently have a dynamically updated network graph with around 1,500 nodes and 2,000 edges. It's ever-growing. Our current layout engine uses Prefuse - the force directed layout in particular - and it takes about 10 minutes with a hefty server to get a nice, stable layout.
I've looked a little GraphViz's sfpd algorithm, but haven't tested it yet...
Are there faster alternatives I should look at?
I don't care about the visual appearance of the nodes and edges - we process that separately - just putting x, y on the nodes.
We do need to be able to tinker with the layout properties for specific parts of the graph, for instance, applying special tighter or looser springs for certain nodes.
Thanks in advance, and please comment if you need more specific information to answer!
EDIT: I'm particularly looking for speed comparisons between the layout engine options. Benchmarks, specific examples, or just personal experience would suffice!
I wrote a JavaScript-based graph drawing library VivaGraph.js.
It calculates layout and renders graph with 2K+ vertices, 8.5K edges in ~10-15 seconds. If you don't need rendering part it should be even faster.
Here is a video demonstrating it in action: WebGL Graph Rendering With VivaGraphJS.
Online demo is available here. WebGL is required to view the demo but is not needed to calculate graphs layouts. The library also works under node.js, thus could be used as a service.
Example of API usage (layout only):
var graph = Viva.Graph.graph(),
layout = Viva.Graph.Layout.forceDirected(graph);
graph.addLink(1, 2);
layout.run(50); // runs 50 iterations of graph layout
// print results:
graph.forEachNode(function(node) { console.log(node.position); })
Hope this helps :)
I would have a look at OGDF, specifically http://www.ogdf.net/doku.php/tech:howto:frcl
I have not used OGDF, but I do know that Fast Multipole Multilevel is a good performant algorithm and when you're dealing with the types of runtimes involved with force directed layout with the number of nodes you want, that matters a lot.
Why, among other reasons, that algorithm is awesome: Fast Multipole method. The fast multipole method is a matrix multiplication approximation which reduces the O() runtime of matrix multiplication for approximation to a small degree. Ideally, you'd have code from something like this: http://mgarland.org/files/papers/layoutgpu.pdf but I can't find it anywhere; maybe a CUDA solution isn't up your alley anyways.
Good luck.
The Gephi Toolkit might be what you need: some layouts are very fast yet with a good quality: http://gephi.org/toolkit/
30 secondes to 2 minutes are enough to layout such a graph, depending on your machine.
You can use the ForAtlas layout, or the Yifan Hu Multilevel layout.
For very large graphs (+50K nodes and 500K links), the OpenOrd layout wil
In a commercial scenario, you might also want to look at the family of yFiles graph layout and visualization libraries.
Even the JavaScript version of it can perform layouts for thousands of nodes and edges using different arrangement styles. The "organic" layout style is an implementation of a force directed layout algorithm similar in nature to the one used in Neo4j's browser application. But there are a lot more layout algorithms available that can give better visualizations for certain types of graph structures and diagrams. Depending on the settings and structure of the problem, some of the algorithms take only seconds, while more complex implementations can also bring your JavaScript engine to its knees. The Java and .net based variants still perform quite a bit better, as of today, but the JavaScript engines are catching up.
You can play with these algorithms and settings in this online demo.
Disclaimer: I work for yWorks, which is the maker of these libraries, but I do not represent my employer on SO.
I would take a look at http://neo4j.org/ its open source which is beneficial in your case so you can customize it to your needs. The github account can be found here.
In case somebody doesn't know: A cartogram is a type of map where some country/region-dependent numeric property scales the respective regions so that that property's density is (close to) constant. An example is
from worldmapper.org. In this example, countries are scaled according to their population, resulting in near-constant population density.
Needless to say, this is really cool. Does anyone know of a Matplotlib-based library for drawing such maps? The method used at worldmapper.org is described in (1), so it would surprise me if no one has implemented this yet...
I'm also interested in hearing about other cartogram libraries, even if they're not made for Matplotlib.
(1) Michael T. Gastner and M. E. J. Newman,
Diffusion-based method for producing density-equalizing maps,
Proc. Nat. Acad. Sci. USA, 101, 7499-7504 (2004). Available at arXiv.
There's this, though it's based and a different algorithm (and though it's on the ESRI site, it doesn't require ArcGIS). Of course, once you have the cartogram you can plot it in matplotlib.
Here is a Javascript plugin to make cartograms using D3. It is a good, simple solution if you are not too concerned about the regions being sized accurately. If accuracy is important, there are other options available that give you more freedom to play with the algorithm's parameters to get to a more accurate result.
Here are two great standalone programs I know of:
Scapetoad
Carto3F
Scapetoad is very easy to use. Just give it a shapefile, tell it which attribute to use for the scaling, and set a few accuracy parameters. If there is any doubt, this post describes the process.
Carto3F is more complex and allows for greater accuracy, though it is a bit trickier to figure out - lots of parameter settings without much documentation explaining them.
There is also a QGIS cartogram plugin, written in Python. Though I have not been able to get it to work, so cannot comment on that one.
In short, no. But Newman has an excellent little implementation of his and Gastner's method on his website. Installing it is easy and it works from the command line. Here's an example of a workflow using this software that worked for me.
Compute a grid of density estimates over some region, e.g. in Python. Store it as a matrix of numbers.
Run the cart program with your density matrix as input from the command line or from as subprocess in Python.
The program returns a list of new coordinates for each grid point.
Pipe your shapefile points through the interp program and into a new shapefile to get the transformed map.
There are nice instructions on the main page.
The geoplot.cartogram function in
Geoplot: geospatial data visualization — geoplot 0.2.0
says it is a high-level Python geospatial plotting library, and an extension to cartopy and matplotlib.
Try this library if you are using geopandas, it is quick and doesnt require much customization. https://github.com/mthh/cartogram_geopandas