I am a bigginer in game programming and now I write 3D game with DirectX 10 which can be free downloaded from website. I need some animation 3D models for game personals. What is the best format for models?
Thanks.
As you have asked this question, I'd go with obj file format.
Very simple, and recommended for your first model format.
See this for more information.
Related
Hello I'm using the CMU Face Images Data Set https://archive.ics.uci.edu/ml/datasets/CMU+Face+Images to build a model that allows me to recognize subjects who wear sunglasses, these images are in pgm format, I'm very new in machine learning and I do not know how to read the images in R.
Does anyone know in what way I can do it? and if you have any recommendation for the construction of the model, I would appreciate it very much.
You can try using this to read a pgm image.
x=read.pnm(file = "Aaron_Guiel_0001.pgm")
I have a dataset, downloaded from link.
I know about coremltool (created by Apple).
The question is:
is it possible to convert Stanford dataset to CoreML ?
If yes - can somebody give me instructions ?
Thanks in advance !
This question is asked so often, that finally I've decided to draw a diagram.
Explanation:
Dataset is a "fuel" that you put into your model to make it work.
Model is a machine learning algorithm: neural network, decision tree etc.
Supported ML frameworks and models are listed here together with the instructions for conversion.
You can make your own .mlmodel file using your own data set with a python script and a python library called coremltools. You can train your model using sklearn, keras, etc. and can customize what is uses to train like SVM, kNN, regression, and so on. Then you save it as a .mlmodelfile and drop that into your project. This video is helpful:
https://youtu.be/T4t73CXB7CU
I'm looking for ways to visualize data in vector-based images through R. Here are links to some examples of something I'm having designed:
https://dribbble.com/shots/2195053-The-Truth-About-B-zier-Graphs/attachments/405731
https://dribbble.com/shots/1728095-Line-Chart/attachments/278191
I understand there are some R libraries for SVG and other file types that permit export to higher quality designs, but haven't seen any tutorials or tools. Any direction is appreciated. I'm not even sure what steps would be necessary to dynamically generate charts like that through R.
Thank you.
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Before any of you run at the closing vote let me say that I understand that this question may be subjective, and the expected answer may begin by "it depends". Nevertheless, it is an actually relevant problem I run into, as I am creating more and more graphs, and I don't necessarily know the exact way I am going to use them, or don't have the time to test for the final use case immediately.
So I am leveraging the experience of SO R users to get good reasons to choose one over the other, between jpg(), bmp(), png(), tiff(), pdf() and possibly with which options. I don't have the experience in R and the knowledge in the different formats to choose wisely.
Potential use cases:
quick look after or during run time of algorithms
presentations (.ppt mainly)
reports (word or latex)
publication (internet)
storage (without too much loss and to transform it later for a specific use)
anything relevant I forgot
Thanks! I'm happy to make the question clearer.
To expand a little on my comment, there is no real easy answer, but my suggestions:
My first totally flexible choice would be to simply store the final raw data used in the plot(s) and a bit of R code for generating the plot(s). That way you could easily enough send the output to whatever device that suits your particular purpose. It would not be that arduous a task to set yourself up a couple of basic templates based on png()/pdf() that you could call upon.
Use the svg() device. As noted by #gung, storing the output using pdf() , svg() , cairo_ps() or cairo_pdf() are your only real options for retaining scalable, vector images. I would tend to lean towards svg() rather than pdf() due to the greater editing options available using programs like Inkscape. It is also becoming a quite widely supported format for internet publication (see - http://caniuse.com/svg )
If on the other hand you're a latex user, most headaches seem to be solved by going straight to pdf() - you can usually import and convert pdf files using Inkscape or command line utilities like Imagemagick if you have to format shift.
For Word/Powerpoint interaction, if you are running R on Windows, you can also export directly using win.metafile() which will give you scalable/component emf images which you can import into Word or Powerpoint directly. I have heard of people running R through Wine or using intermediary steps on Linux to get emf files out for later use. For Mac, there are roundabout pathways as well.
So, to summarise, in order of preference.
Don't store images at all, store code to generate images
Use svg/pdf and convert formats as required.
Use a backup win.metafile export directly for those cases where you can't escape using Word/Powerpoint and are primarily going to be based on Windows systems.
So far the answers for this question have all recommended outputting plots in vector based formats. This will give you the best output, allowing you to resize your image as you need for whatever medium your image will end up in (whether that be a webpage, document, or presentation), but this comes at a computational cost.
For my own work, I often find it is much more convenient to save my plots in a raster format of sufficient resolution. You probably want to do this whenever your data takes a non-trivial amount of time to plot.
Some examples of where I find a raster format is more convenient:
Manhattan plots: A plot showing p-value significance for hundreds of thousands-millions of DNA markers across a genome.
Large Heatmaps: Clustering the top 5000 differentially expressed genes between two groups of people, one with a disease, and one healthy.
Network Rendering: When drawing a large number of nodes connected to each other by edges, redrawing the edges (as vectors) can slow down your computer.
Ultimately it comes down to a trade-off in your own sanity. What annoys you more? your computer grinding to a halt trying to redraw an image? or figuring out the exact dimensions to render an image in raster format so it doesn't look awful for your final publishing medium?
The most basic distinction to bear in mind here is raster graphics versus vector graphics. In general, vector graphics will preserve options for you later. Of the options you listed, jpeg, bmp, tiff, and png are raster formats; only pdf will give you vector graphics. Thus, that is probably the best default of your listed options.
I am doing a project that involves processing large, sparse graphs. Does anyone know of any publicly available data sets that can be processed into large graphs for testing? I'm looking for something like a Facebook friend network, or something a little smaller with the same flavor.
I found the Stanford Large Network Dataset Collection pretty useful.
If you asked nicely, you might be able to get Brian O'Meara's data set for treetapper. It's a pretty nice example of real-world data in that genre. Particularly, you'd probably be interested in the coauthorship data.
http://www.treetapper.org/
http://www.brianomeara.info/
Github's API is nice for building out graphs. I've messed around using the python lib networkx to generate graphs of that network. Here's some sample code if you're interested.
Apologies for the double post, evidently I can only post two links at a time since I have <10 reputation...
DIMACS also has some data sets from their cluser challenge and there's always the Graph500. The Boost Graph Library has a number of graph generators as well.
Depending on what you consider "large", there's the University of Florida Sparse Matrix Collection as well as some DIMACS Road Networks (mostly planar of course).
A few other ones:
Newman's page
Barabasi's page
Pajek software
Arena's page
Network Science