After some simulations with fenics, I saved the results in vtk format, so that I can load it in ParaView. The results are 1D-data, that are time-dependent. In the end, it should look like these example figures, I found in some papers:
This means one axis for space coordinates, one for the time and one for the actual data. ParaView shows me my data and with "warp by scalar" I get the desired result, however only for exactly one time step. Animating works too, but I do not want to create a video for 1D-data, when it looks much nicer in one 3D plot.
How can I add the time as additional axis in ParaView?
This, I am afraid, is not supported currently. It's certainly a nice feature, however. I'd suggest making a feature request on the ParaView Bug Tracker
Related
I'm trying to figure out a good way to programmatically generate contours describing a 2D surface, from a 3D STEP model. Application is generating NC code for a laser-cutting program from a 3D model.
Note: it's easy enough to do this in a wide variety of CAD systems. I am writing software that needs to do it automatically.
For example, this (a STEP model):
Needs to become this (a vector file, like an SVG or a DXF):
Perhaps the most obvious way of tackling the problem is to parse the STEP model and run some kind of algorithm to detect planes and select the largest as the cut surface, then generate the contour. Not a simple task!
I've also considered using a pre-existing SDK to render the model using an orthographic camera, capture a high-res image, and then operating on it to generate the appropriate contours. This method would work, but it will be CPU-heavy, and its accuracy will be limited to the pixel resolution of the rendered image - not ideal.
This is perhaps a long shot, but does anyone have thoughts about this? Cheers!
I would use a CAD library to load the STEP file (not a CAD API), look for the planar face with the higher number of edge curves in the face loop and transpose them on the XY plane. Afterward, finding 2D geometry min/max for centering etc. would be pretty easy.
Depending on the programming language you are using I would search for "CAD control" or "CAD component" on Google combining it with "STEP import".
Given are two monochromatic images of same size. Both are prealigned/anchored to one common point. Some points of the original image did move to a new position in the new image, but not in a linear fashion.
Below you see a picture of an overlay of the original (red) and transformed image (green). What I am looking for now is a measure of "how much did the "individual" points shift".
At first I thought of a simple average correlation of the whole matrix or some kind of phase correlation, but I was wondering whether there is a better way of doing so.
I already found that link, but it didn't help that much. Currently I implement this in Matlab, but this shouldn't be the point I guess.
Update For clarity: I have hundreds of these image pairs and I want to compare each pair how similar they are. It doesn't have to be the most fancy algorithm, rather easy to implement and yielding in a good estimate on similarity.
An unorthodox approach uses RASL to align an image pair. A python implementation is here: https://github.com/welch/rasl and it also
provides a link to the RASL authors' original MATLAB implementation.
You can give RASL a pair of related images, and it will solve for the
transformation (scaling, rotation, translation, you choose) that best
overlays the pixels in the images. A transformation parameter vector
is found for each image, and the difference in parameters tells how "far apart" they are (in terms of transform parameters)
This is not the intended use of
RASL, which is designed to align large collections of related images while being indifferent to changes in alignment and illumination. But I just tried it out on a pair of jittered images and it worked quickly and well.
I may add a shell command that explicitly does this (I'm the author of the python implementation) if I receive encouragement :) (today, you'd need to write a few lines of python to load your images and return the resulting alignment difference).
You can try using Optical Flow. http://www.mathworks.com/discovery/optical-flow.html .
It is usually used to measure the movement of objects from frame T to frame T+1, but you can also use it in your case. You would get a map that tells you the "offset" each point in Image1 moved to Image2.
Then, if you want a metric that gives you a "distance" between the images, you can perhaps average the pixel values or something similar.
Being used to Matlab and its great capabilities of drawing vector graphics, I am looking for something similar in OpenCV. OpenCV drawing functions seem to raster the lines or points at pixel level. Currently, I am dumping the data into text, copy-paste to Matlab and doing all the plots. I also thought about using Matlab engine to pass it the parameters and running plots, but it seems to be too much mess for simple debug operation.
I want to be able to do the following:
Zoom in, out of the image
Draw a line/point which is re-rastered each time I do zoom, like in Matlab.
Currently, I found image watch plugin to take care of zooming, but it does not help with the second part.
Any idea?
OpenCV has a lot of capabilities to process an image but only minimal ones for displaying the result. It has nothing that can display vector graphics like Matlab. When I need to see polygons on image (or just polygons) I am dumping them to file and using third party viewer (usually Giv viewer).
I'm trying to write a code The helps me in my biology work.
Concept of code is to analyze a video file of contracting cells in a tissue
Example 1
Example 2: youtube.com/watch?v=uG_WOdGw6Rk
And plot out the following:
Count of beats per min.
Strenght of Beat
Regularity of beating
And so i wrote a Matlab code that would loop through a video and compare each frame vs the one that follow it, and see if there was any changes in frames and plot these changes on a curve.
Example of My code Results
Core of Current code i wrote:
for i=2:totalframes
compared=read(vidObj,i);
ref=rgb2gray(compared);%% convert to gray
level=graythresh(ref);%% calculate threshold
compared=im2bw(compared,level);%% convert to binary
differ=sum(sum(imabsdiff(vid,compared))); %% get sum of difference between 2 frames
if (differ ~=0) && (any(amp==differ)==0) %%0 is = no change happened so i dont wana record that !
amp(end+1)=differ; % save difference to array amp wi
time(end+1)=i/framerate; %save to time array with sec's, used another array so i can filter both later.
vid=compared; %% save current frame as refrence to compare the next frame against.
end
end
figure,plot(amp,time);
=====================
So thats my code, but is there a way i can improve it so i can get better results ?
because i get fealing that imabsdiff is not exactly what i should use because my video contain alot of noise and that affect my results alot, and i think all my amp data is actually faked !
Also i actually can only extract beating rate out of this, by counting peaks, but how can i improve my code to be able to get all required data out of it ??
thanks also really appreciate your help, this is a small portion of code, if u need more info please let me know.
thanks
You say you are trying to write a "simple code", but this is not really a simple problem. If you want to measure the motion accuratly, you should use an optical flow algorithm or look at the deformation field from a registration algorithm.
EDIT: As Matt is saying, and as we see from your curve, your method is suitable for extracting the number of beats and the regularity. To accuratly find the strength of the beats however, you need to calculate the movement of the cells (more movement = stronger beat). Unfortuantly, this is not straight forwards, and that is why I gave you links to two algorithms that can calculate the movement for you.
A few fairly simple things to try that might help:
I would look in detail at what your thresholding is doing, and whether that's really what you want to do. I don't know what graythresh does exactly, but it's possible it's lumping different features that you would want to distinguish into the same pixel values. Have you tried plotting the differences between images without thresholding? Or you could threshold into multiple classes, rather than just black and white.
If noise is the main problem, you could try smoothing the images before taking the difference, so that differences in noise would be evened out but differences in large features, caused by motion, would still be there.
You could try edge-detecting your images before taking the difference.
As a previous answerer mentioned, you could also look into motion-tracking and registration algorithms, which would estimate the actual motion between each image, rather than just telling you whether the images are different or not. I think this is a decent summary on Wikipedia: http://en.wikipedia.org/wiki/Video_tracking. But they can be rather complicated.
I think if all you need is to find the time and period of contractions, though, then you wouldn't necessarily need to do a detailed motion tracking or deformable registration between images. All you need to know is when they change significantly. (The "strength" of a contraction is another matter, to define that rigorously you probably would need to know the actual motion going on.)
What are the structures we see in the video? For example what is the big dark object in the lower part of the image? This object would be relativly easy to track, but would data from this object be relevant to get data about cell contraction?
Is this image from a light microscop? At what magnification? What is the scale?
From the video it looks like there are several motions and regions of motion. So should you focus on a smaller or larger area to get your measurments? Per cell contraction or region contraction? From experience I know that changing what you do at the microscope might be much better then complex image processing ;)
I had sucsess with Gunn and Nixons Dual Snake for a similar problem:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.6831
I placed the first aproximation in the first frame by hand and used the segmentation result as starting curv for the next frame and so on. My implementation for this is from 2000 and I only have it on paper, but if you find Gunn and Nixons paper interesting I can probably find my code and scan it.
#Matt suggested smoothing and edge detection to improve your results. This is good advise. You can combine smoothing, thresholding and edge detection in one function call, the Canny edge detector.Then you can dialate the edges to get greater overlap between frames. Little overlap will probably mean a big movement between frames. You can use this the same way as before to find the beat. You can now make a second pass and add all the dialated edge images related to one beat. This should give you an idea about the area traced out by the cells as they move trough a contraction. Maybe this can be used as a useful measure for contraction of a large cluster of cells.
I don't have access to Matlab and the Image Processing Toolbox now, so I can't give you tested code. Here are some hints: http://www.mathworks.se/help/toolbox/images/ref/edge.html , http://www.mathworks.se/help/toolbox/images/ref/imdilate.html and http://www.mathworks.se/help/toolbox/images/ref/imadd.html.
I have two images of yeast plates:
Permissive:
Xgal:
The to images should be in the same spot and roughly the same size. I am trying to use one of the images to generate a grid and then apply that grid to the other image. The grid is made by looking at the colonies on permissive plate, the plate should have 1536 colonies on it. The problem is that the camera that was used to take the images moves a bit up and down and the images can also be shifted slightly due to the other plate not being in exactly the same place.
This then means that when I use the permissive plate to generate the grid on the xgal plate the grid shifts. Does anyone know a way in which I can compensate for this? I am using perl with the gd module. Any advice would be greatly appreciated. Thank you
I've done this in other languages in relation to motion analysis. You can mathematically determine the shift in position between two images using cross correlation.
Fortunately, you may not need to actually do the maths :) You could use something like ImageMagick, which provides a lot of image processing functions for you, and is perl scriptable. Independently scripts already exists for tasks very much like yours -- see.
If you have only a few pairs of images and, as in the examples, they are very different in appearance then an alternative method to Tim Barrass' would be
Open the first image in gimp, find the co-ordinates of a landmark feature
Open the second image in gimp, find the co-ordinates of the same landmark
Calculate the offset
Shift the second image using ImageMagick's convert command with the affine option. Set the parameters sx=sy=1.0, rx=ry=0.0, tx= negative horizontal offset, ty= negative vertical offset