Too bad Dimple.JS doesn’t have this feature built-in. They only have a boring old legend, where pie charts scream for labels that circle the actual pie (or donut).
Anyway, here’s how to get it done using some d3.js love.
Category: code
I was looking for a way to get a fluid container live side-by-side with a custom width sidebar.
A custom width sidebar can’t be achieved with a Bootstrap column, and is a total mess to get right with floats if you then need a fluid container to get a grid system for the main section.
So, here’s one solution:
JSFiddle: https://jsfiddle.net/6sfog80k/
Sharing a small libjpeg snippet.
Some SO questions about it have only partial snippets:
- http://stackoverflow.com/questions/16390783/how-to-save-yuyv-raw-data-to-jpeg-using-libjpeg
- http://stackoverflow.com/questions/17029136/weird-image-while-trying-to-compress-yuv-image-to-jpeg-using-libjpeg
- http://stackoverflow.com/questions/19282402/how-to-compress-a-yuyv-image-into-a-jpeg
Enjoy!
Roy
Using Poppler, of course!
Poppler is a very useful tool for handling PDF, so I’ve discovered lately. Having tried both muPDF and ImageMagick’s Magick++ and failed, Poppler stepped up to the challenge and paid off.
So here’s a small example of how work the API (with OpenCV, naturally):
#include <iostream> #include <fstream> #include <sstream> #include <opencv2/opencv.hpp> #include <poppler-document.h> #include <poppler-page.h> #include <poppler-page-renderer.h> #include <poppler-image.h> using namespace cv; using namespace std; using namespace poppler; Mat readPDFtoCV(const string& filename,int DPI) { document* mypdf = document::load_from_file(filename); if(mypdf == NULL) { cerr << "couldn't read pdf\n"; return Mat(); } cout << "pdf has " << mypdf->pages() << " pages\n"; page* mypage = mypdf->create_page(0); page_renderer renderer; renderer.set_render_hint(page_renderer::text_antialiasing); image myimage = renderer.render_page(mypage,DPI,DPI); cout << "created image of " << myimage.width() << "x"<< myimage.height() << "\n"; Mat cvimg; if(myimage.format() == image::format_rgb24) { Mat(myimage.height(),myimage.width(),CV_8UC3,myimage.data()).copyTo(cvimg); } else if(myimage.format() == image::format_argb32) { Mat(myimage.height(),myimage.width(),CV_8UC4,myimage.data()).copyTo(cvimg); } else { cerr << "PDF format no good\n"; return Mat(); } return cvimg; }
All you have to do is give it the DPI (say you want to render in 100 DPI) and a filename.
Keep in mind it only renders the first page, but getting the other pages is just as easy.
That’s it, enjoy!
Roy.
Years ago I wanted to implement PTAM. I was young and naïve 🙂
Well I got a few moments to spare on a recent sleepless night, and I set out to implement the basic bootstrapping step of initializing a map with a planar object – no known markers needed, and then tracking it for augmented reality purposes.
Just sharing a code snippet about how to implement a jQuery+Bootstrap progress bar for a background operation in Tapestry 5. There’s not a lot to it, but it took me a while and serious digging through the internet to find how to make it work. Essentially it’s based on a couple of examples and references I found:
- http://permalink.gmane.org/gmane.comp.java.tapestry.user/85776
- https://github.com/uklance/tapestry-stitch
But I simplified things because I don’t like the over-design Java can easily make you do…
You already know I love libQGLViewer. So here a snippet on how to do AR in a QGLViewer widget. It only requires a couple of tweaks/overloads to the plain vanilla widget setup (using the matrices properly, disable the mouse binding) and it works.
The major problems I recognize with getting a working AR from OpenCV’s intrinsic and extrinsic camera parameters are their translation to OpenGL. I saw a whole lot of solutions online, and I contributed from my own experience a while back, so I want to reiterate here again in the context of libQGLViewer, with a couple extra tips.
Just sharing a simple recipe for a video stabilizer in OpenCV based on goodFeaturesToTrack() and calcOpticalFlowPyrLK().
Well… it’s a bit more than 20 lines, but it is short. And it doesn’t work for every kind of video (although the results are funny anyway! :).
So lately I’m into Optical Music Recognition (OMR), and a central part of that is doing staff line removal. That is when you get rid of the staff lines that obscure the musical symbols to make recognition much easier. There are a lot of ways to do it, but I’m going to share with you how I did it (fairly easily) with Hidden Markov Models (HMMs), which will also teach us a good lesson on this wonderfully useful approach.
OMR has been around for ages, and if you’re interested in learning about it [Fornes 2014] and [Rebelo 2012] are good summary articles.
The matter of Staff Line Removal has occupied dozens of researchers for as long as OMR exists; [Dalitz 2008] give a good overview. Basically the goal is to remove the staff lines that obscure the musical symbols, so they would be easier to recognize.
But, the staff lines are connected to the symbols, so simply removing them will cut up the symbols and make them hardly recognizable.
So let’s see how we could do this with HMMs.