I have positional data of 24 joints which I want to visualize. I am able to read in the data into a pandas data frame. But I am not sure how to do the visualization. I am able to plot the points in a scatter plot with matplotlib, but I am still looking for an elegant way to draw the lines, because not all joints are connected. I know which ones are connected, but implementing it seems very tedious. One way of doing this I thought was to have a 24x24 matrix with entries 0 if the two joints are not connected and 1 if the two joints are connected. Such a matrix I can easily type out myself. But I am not sure how to use this then to plot the lines.
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I am using the new Kinect v2 and I am getting the depth map of the Kinect.
After I get the depth map I convert the depth data from Depth Space to Camera Space.
As far as I understand this is done, by converting all the X,Y coordinate of each pixel to Camera Space + adding the depth value as Z coordinate (also Kinect gives the depth value in millimetres so it is also converted to hold meters).
Because of this, the point cloud is actually on 2D grid extended with the depth value. The visualization also confirms this, since it is easy to notice that the points are ordered in a grid due to the above conversation.
For visualization I am using OpenGL the old fashion way (glBegin(...) and glEnd()).
I want to create a mesh out of the points. I kind of managed to do it with GL_TRIANGLES, but then I have lot of duplicated vertices and edges. So I thought I should create a better triangulation with GL_TRIANGLE_STRIP, but I am stuck here because I can't come up with a good algorithm which can go through my 2D grid in a way that I can feed it to the GL_TRIANGLE_STRIP so it creates a nice surface.
The problems:
For each triangle's vertices I am checking the Z coordinate. If it exceeds a certain threshold I disregard the triangle => this might create holes in my 2D grid.
Some depth values are NaN, because the Kinect can't "see" there nothing (for example an object is too far or too close) => this also creates holes in the 2D grid.
Anybody has any suggestion what would be the best method to solve this issue?
If you're able to use the point cloud library, you could use the
class pcl::OrganizedFastMesh< PointInT >.
http://docs.pointclouds.org/trunk/classpcl_1_1_organized_fast_mesh.html
I use it to triangulate complete depth frames.
You can try also a delanauy triangulation in 3d and look for the tetrahedons on the exterior. An easy algorithm is the bowyer-watson with tetrahedons and circumspheres. Cgal is a good example.
I have a set of points which form some curve, see picture. This curve consists of straight and curved sections and I do not know exactly where they start or end. In the presented picture I know at least five pieces: two straight sections, one section with a constant radius, two other types of curves. I want to be able to recognise those sections, to separate them from each other and to work with them as with separate curves.
Update.
In my opinion splines would not work in the way I want, and not surely Besier. I was thinking about Non-linear Least Squares but was not sure if it suits this case. If I am able to separate the parts, then I can use Linear Least Squares for straights and some non-linear for other parts. Otherwise, I need some universal method which will work for all types of curves: 1, 2 and 3 power.
Please share your thoughts.
Thank you.
For a bent tube I would suggest multidimensional spline fitting. Your tube does not look like a composition of of straight and curved sections, but curves smoothly all the way.
http://en.wikipedia.org/wiki/Spline_interpolation
This is the starting point if you want to read further.
Taking a guess I suppose you are not experienced with splines, so if you could put out the dataset in table form me or some other reader could interpolate the given data with a 3D spline curve.
I have a seemingly simple problem, but an easy solution is alluding me. I have a very large series (tens or hundreds of thousands of points), and I just need to visualize it at different zoom levels, but generally zoomed well out. Basically, I want to plot it in a tool like Matlab or Pyplot, but knowing that each pixel can't represent the potentially many hundreds of points that map to it, I'd like to see both the min and the max of all the array entries that map to a pixel, so that I can generally understand what's going on. Is there a simple way of doing this?
Try hexbin. By setting the reduce_C_function I think you can get what you want. Ex:
import matplotlib.pyplot as plt
import numpy as np
plt.hexbin(x,y,C=C, reduce_C_function=np.max) # C = f(x,y)
would give you a hexagonal heatmap where the color in the pixel is the maximum value in the bin.
If you only want to bin in one direction, see this this method.
First option you may want to try is Gephi- https://gephi.org/
Here is another option, though I'm not quite sure it will work. It's hard to say without seeing the data.
Try going to this link- http://bl.ocks.org/3887118. Do you see toward the bottom of the page data.tsv with all of the values? IF you can save your data to resemble this then the HTML code above should be able to build your data in the scatter plot example shown in that link.
Otherwise, try visiting this link to fashion your data to a more appropriate web page.
There are a set of research tools called TimeSearcher 1--3 that provide some examples of how to deal with large time-series datasets. Below are some example images from TimeSearcher 2 and 3.
I realized that simple plot() in MATLAB actually gives me more or less what I want. When zoomed out, it renders all of the datapoints that map to a pixel column as vertical line segments from the minimum to the maximum within the set, so as not to obscure the function's actual behavior. I used area() to increase the contrast.
I have a question regarding matplotlib and I already know that what I am doing is not statistically / mathematically correct in a way but I want visualize anyways using stacked line / area graphs.
The measurements I have do not use the same x axis as a basis. I mean the different lines does not have the same number of data points. I want to use time as x axis and the measurements taken are not related to exact same timestamps (think distributed systems).
I guess my question is: "can I do that in matplotlib without doing the interpolation myself?"
here some indeepth elaboration about what a stacked graph is:
http://www.leebyron.com/else/streamgraph/download.php?file=stackedgraphs_byron_wattenberg.pdf
Cheers,
Mark
I am probably not quiet understanding exactly what you want to do, but what about switching the axis so that your x-axis becomes the y-axis and then you can use something like whats suggested here Multiple overlapping plots with independent scaling in Matplotlib for multiple y-axis?
I have a few 1000s triangles connected in a 2D mesh grid. It represents water flow. This grid is a delaunay triangulation. I need to merge the triangles back into a minimal amount of simple polygons such that each polygon is constraint not to have interior holes. The output polygons should be the same shape.
Is there a known algorithm for accomplishing this?
answering my own question :)
I found the best way to do this is to use polygon union methods similar to disjoint subset merging. Here's a blog post on a fast implementation by taking advantage of spatial indices
http://lin-ear-th-inking.blogspot.com/2007/11/fast-polygon-merging-in-jts-using.html