I read this question about how to set initial centers of k-mean on StackOverflow. I read the answer, but I have no idea how to implement it in C++.
Could anyone give me any suggestions? I'd like to calculate first labels using centers that I already have and pass them to kmeans function with flag KMEANS_USE_INITIAL_LABELS for calculating new centers on new (but similar) data. Data in this case are features of images.
Related
I don't really know how to explain it in a better way, so please look at the following images :
This is what I create for the moment
This is what I whish to create instead
I am currently using C++ with Qt 4.8.
Do you know a way that would allow me to reach my goal ? Using a library or a transformation matrix ? Or something else ?
I am a total newbie to image manipulation, so every advice is precious for me.
Thanks
EDIT :
I draw each colored pixel from Lat/Long measures, if it can help.
Use what is called a morphological operator. In this case, you would require the 'open' operator. OpenCV provides a pretty good implementation (and documentation of these) which can be found here.
Draw circles instead of points is all I can think of. Creating a triangle mesh is tricky with the concave elements of the distribution.
EDIT: Just looked at the full size version of the image and wondered if the data set is stored radially? You could scan adjacent radial lines and try to match up the changes in value along each line to form a set of quads. There will be a large number of edge conditions to consider though.
EDIT2: Alternatively, form a uniformly distributed set of quads and interpolate the vertex colours.
you can start by increasing the size of the points,
you could create a triangle mesh by using a sweepline algorithm:
sort the points by lat
keep a subset sorted by long
when you add a point compare to the 4 adjacent points and add triangles to the "to draw" set (remove points too far away from the current lat as needed)
with opengl you can use an index buffer to hold which point should be drawn
Hi frens I am using geopy to calculate the latitude and longitude. Now I want to get the list of areas given distance from a zipcode.How to get that?
Well, as I can see, geopy doesn't have any built-in capability to get a list of areas around some coordinates.
But you can use a workaround. Take your geocode and calculate coordinates (latitue and longitude). Then imagine a grid on the map with a cell size equal to area of the smallest one you need to find around your location.
Use geopy to get an area name belonging to the each cell corner of your grid. Is that ok for you? It will get you some kind of approximation because a grid is not a circle and you may miss some small areas. But I think in most cases the solution will work fine.
It is much easier to locate zipcodes inside a rectangle than in a circle so I would recommend that you approximate your problem by looking for zipcodes inside a given rectangle.
Here are answers to the question of how to get list of zipcodes in given polygone: Find zipcodes inside polygon shape using google maps api
Summary
You need geometry for each zipcode. Once you have that you need to be able to query it using database that supports geoquery. One such database is Google's Fusion Table and there is already a geometry data table for zipcodes available here: https://www.google.com/fusiontables/DataSource?docid=1AxB511DayCdtmyBuYOIPHIe_WM9iG87Q3jKh6EQ#rows:id=1
Here's the sample query for Fusion Table data.
Another approach is server side code using PHP and CSV data. Here's live demo: http://daim.snm.ku.dk/demo/zip/. The page also has download for code.
If you use any of above technique please make sure to upvote answers of original authors :).
I've been using matplotlib.pyplot.contour to plot the contour of images, and I wonder how to implement the contour plotting, but I found that the code of pyplot.contour hard for me to understand. I have this idea that, for a grayscale image, each pixel has an intensity value, to plot its contour, I might choose a set of intensity values, like partition the range [min-intensity-value, max-intensity-value] to 10 segments [min-intensity-value, val0, val1,..., val8, max-intensity-value], then for each segment's boundary intensity value (like val0, val1,...,val8), find out all those pixels which has the same intensity value, and I think those pixels will form a contour line.
Is my idea a right way to go? Hope anyone can give me a basic idea about how to implement it.
Thanks.
Your idea of connecting all values with the same colour won't work, as there is no guarantee that these are connected. Also, consider e.g. the case of two equal-coloured pixels touching at the corner: There is no way to say whether these are connected or e.g. the other diagonal.
I believe the question is basically what you want to achieve. If you just want to vectorize the image, there are existing tools for that, like e.g. POtrace. If you need something special or have special input data, then you will get better results when you tell people about this. In that case, I would also take an hour or two to look up the very good description for the POtrace algorithm from their website, maybe you can borrow a few good ideas from it?
I have an image which was shown to groups of people with different domain knowledge of its content. I than recorded gaze fixation data of them watching the image.
I now kind of want to compare the results of the two groups - so what I need to know is, if there is a correlation of the positions of the sampling data between the two groups or not.
I have the original image as well as the fixation coords. Do you have any good idea how to start analyzing the data?
It's more about the idea or the plan so you don't have to be too technical on that one.
Thanks
Simple idea: render all the coordinates on the original image in a 'heat map' like way, one image for each group. You can then visually compare the images for correlation, and you have some nice graphics for in your paper.
There is something like the two-dimensional correlation coefficient. With software like R or Matlab you can do the number crunching for the correlation.
Matlab has a function for this:
Two Dimensional Correlation Function: corr2
Computes two dimensional correlation coefficient between two matrices
and the matrices must be of the same size. r = corr2 (A,B)
In gaze tracking, the most interesting data lies in two areas.
In where all people look, for that you can use the heat map Daan suggests. Make a heat map for all people, and heat maps for separate groups of people.
In when people look there. For that I would recommend you start by making heat maps as above, but for short time intervals starting from the time the picture was first shown. Again, for all people, and for the separate groups you have.
The resulting set of heat-maps, perhaps animated for the ones from the second point, should give you some pointers for further analysis.
I am currently working on a data visualization project.My aim is to produce contour lines ,in other words iso-lines, from gridded data.Data can be temperature, weather data or any kind of other environmental parameters but only condition is it must be regularly spaced.
I searched in internet , however i could not find a good algorithm, pseudo-code or source code for producing contour lines from grids.
Does anybody knows a library, source code or an algorithm for producing contour lines from gridded data?
it will be good if your suggestion has a good run time performance, i don't want to wait my users so much :)
Edit: thanks for response but isolines have some constrains like they should not intersects
so just generating bezier curves does not accomplish my goal.
See this question: How to approximate a vector contour from an elevation raster?
It's a near duplicate, but uses quite different terminology. You'll find that cartography and computer graphics solve many of the same problems, but use different terminology for them.
there's some reasonably good contouring available in GNUplot - if you're able to use GPL code that may help.
If your data is placed at regular intervals, this can be done fairly easily (assuming I understand your problem correctly). First you need to determine at what interval you want your contours. Next create the grid you are going to use to store the contour information (i'm assuming just a simple on/off or elevation at this contour level type of data), which should be one interval smaller than the source data.
Now the trick here is to offset the 2 grids by 1/2 an interval (won't actually show up in code like this, but its the concept I'm dealing with here) and compare the 4 coordinates surrounding the current point in the contour data grid you are calculating. If any of the 4 points are in a different interval range, then that 'pixel' in the contour grid should be set to true (or the value of the contour range being crossed).
With this method, there will be a problem when the interval is too fine which will cause several contours to overlap onto each other.
As the link from Paul Tomblin suggests, Bezier curves (which are a subset of B-splines) are a ripe solution for your problem. If runtime performance is an issue, Bezier curves have the added benefit of being constructable via the very fast de Casteljau algorithm, instead of drawing them according to the parametric equations. On the off chance you're working with DirectX, it has a library function for the de Casteljau, but it should not be challenging to brew one yourself using the 1001 web pages that describe it.