I'm writing my first program using Qwt. I've created a QwtCurve object and I used QwtCurve::setData to add my points. Then I attached the plot to my curve and replotted the graph to see the curve.
I have to add a point every 500ms, is there a way to avoid replotting the whole thing every time I add a point?
You may want to consider using QCustomPlot. It's well documented and easy to implement.
If you go this route, the addData() function will do what you are asking.
Related
I'm aware my question is maybe somewhat lazy. But I hope someone could maybe give me head start with my idea, or can provide me with an existing code example that points me in the right direction.
I want to create an organic shape/blob that more or less fills up existing space, but wraps around typographical elements. Whenever these elements move around, the shape should adjust itself accordingly. I was looking at Paper.js where examples like http://paperjs.org/examples/candy-crash/ and http://paperjs.org/examples/voronoi/ make it seem like this should be possible.
You can use the path.subtract() boolean operation, along with the path.smooth() function to smooth your shape with the type of smoothing of your choice.
Here is a demo sketch. You can also try to smooth the rectangles ; and maybe randomly add points on your curves or randomly displace all segment handles.
I have an idea for an app that takes a printed page with four squares in each corner and allows you to measure objects on the paper given at least two squares are visible. I want to be able to have a user take a picture from less than perfect angles and still have the objects be measured accurately.
I'm unable to figure out exactly how to find information on this subject due to my lack of knowledge in the area. I've been able to find examples of opencv code that does some interesting transforms and the like but I've yet to figure out what I'm asking in simpler terms.
Does anyone know of papers or mathematical concepts I can lookup to get further into this project?
I'm not quite sure how or who to ask other than people on this forum, sorry for the somewhat vague question.
What you describe is very reminiscent of augmented reality marker tracking. Maybe you can start by searching these words on a search engine of your choice.
A single marker, if done correctly, can be used to identify it without confusing it with other markers AND to determine how the surface is placed in 3D space in front of the camera.
But that's all very difficult and advanced stuff, I'd greatly advise to NOT try and implement something like this, it would take years of research... The only way you have is to use a ready-made open source library that outputs the data you need for your app.
It may even not exist. In that case you'll have to buy one. Given the niché of your problem that would be perfectly plausible.
Here I give you only the programming aspect and if you want you can find out about the mathematical aspect from those examples. Most of the functions you need can be done using OpenCV. Here are some examples in python:
To detect the printed paper, you can use cv2.findContours function. The most outer contour is possibly the paper, but you need to test on actual images. https://docs.opencv.org/3.1.0/d4/d73/tutorial_py_contours_begin.html
In case of sloping (not in perfect angle), you can find the angle by cv2.minAreaRect which return the angle of the contour you found above. https://docs.opencv.org/3.1.0/dd/d49/tutorial_py_contour_features.html (part 7b).
If you want to rotate the paper, use cv2.warpAffine. https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.html
To detect the object in the paper, there are some methods. The easiest way is using the contours above. If the objects are in certain colors, you can detect it by using color filter. https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.html
first of all, I have to say I'm new to the field of computervision and I'm currently facing a problem, I tried to solve with opencv (Java Wrapper) without success.
Basicly I have a picture of a part from a Model taken by a camera (different angles, resoultions, rotations...) and I need to find the position of that part in the model.
Example Picture:
Model Picture:
So one question is: Where should I start/which algorithm should I use?
My first try was to use KeyPoint Matching with SURF as Detector, Descriptor and BF as Matcher.
It worked for about 2 pcitures out of 10. I used the default parameters and tried other detectors, without any improvements. (Maybe it's a question of the right parameters. But how to find out the right parameteres combined with the right algorithm?...)
Two examples:
My second try was to use the color to differentiate the certain elements in the model and to compare the structure with the model itself (In addition to the picture of the model I also have and xml representation of the model..).
Right now I extraxted the color red out of the image, adjusted h,s,v values manually to get the best detection for about 4 pictures, which fails for other pictures.
Two examples:
I also tried to use edge detection (canny, gray, with histogramm Equalization) to detect geometric structures. For some results I could imagine, that it will work, but using the same canny parameters for other pictures "fails". Two examples:
As I said I'm not familiar with computervision and just tried out some algorithms. I'm facing the problem, that I don't know which combination of algorithms and techniques is the best and in addition to that which parameters should I use. Testing it manually seems to be impossible.
Thanks in advance
gemorra
Your initial idea of using SURF features was actually very good, just try to understand how the parameters for this algorithm work and you should be able to register your images. A good starting point for your parameters would be varying only the Hessian treshold, and being fearles while doing so: your features are quite well defined, so try to use tresholds around 2000 and above (increasing in steps of 500-1000 till you get good results is totally ok).
Alternatively you can try to detect your ellipses and calculate an affine warp that normalizes them and run a cross-correlation to register them. This alternative does imply much more work, but is quite fascinating. Some ideas on that normalization using the covariance matrix and its choletsky decomposition here.
My need is to draw a basic x-axis, y-axis plot of several lines, with the lines becoming known in sequence as the user enters data. jqPlot appears to have the ability (unlike flot, at least as I understand it) to add to an existing plot. My experimentation thus far is:
$.jqplot('dpCum',[ld.fCumPairFwd[0]],{axes:{xaxis:{min:0,max:2500},yaxis:{min:0,max:200000}}});
$.jqplot('dpCum',[ld.fCumPairAft[0]],{axes:{xaxis:{min:0,max:2500},yaxis:{min:0,max:200000}}});
which produces two lines as I want them, except the background of the 2nd obscures the the 1st line. In practice, the data for the 2nd line won't be known until the user responds to the 1st line, and then they're going to want to see both at once.
I've made a couple of passes at the jqplot documentation (it's capabilities are obviously impressive) but how to keep existing lines visible as new lines are added escapes me. I'm thinking there may be some kind of z-axis opacity, but haven't been able to understand it yet.
The answer to your problem, I believe, is to use the replot() method and paint a new plot with the modified data set.
This approach is presented in the following sample. Please notice I made only the series with index 0 responsive to clicks. On click on the series' data points another is painted.
EDIT: The reason I went for replot() was that I couldn't figure out how to draw just a single series. I tried the approach presented by #Mark here with no success. He might know better though. I am rather fresh to jqPlot myself. Also taking into account that when we add a new series some points might reach outside the current scale, therefore, since redraw() doesn't rescale as mentioned here by the jqPlot author - though in my case it will work since we reinitialize the graph. Thus, I think if you also will not manage to apply single series draw you might try using the redraw() method instead, taking from the doc I think it is less expensive to call.
Maybe actually in this case you will not use replot() or redraw(), as in the sample I am making a new plot each time. Therefore, it seems to me to be more appropriate to call destroy() on the previous graph before we paint the new one. This is what currently is in the code sample.
I'm messing around with image manipulation, mostly using Python. I'm not too worried about performance right now, as I'm just doing this for fun. Thus far, I can load bitmaps, merge them (according to some function), and do some REALLY crude analysis (find the brightest/darkest points, that kind of thing).
I'd like to be able to take an image, generate a set of control points (which I can more or less do now), and then smudge the image, starting at a control point and moving in a particular direction. What I'm not sure of is the process of smudging itself. What's a good algorithm for this?
This question is pretty old but I've recently gotten interested in this very subject so maybe this might be helpful to someone. I implemented a 'smudge' brush using Imagick for PHP which is roughly based on the smudging technique described in this paper. If you want to inspect the code feel free to have a look at the project: Magickpaint
Try PythonMagick (ImageMagick library bindings for Python). If you can't find it on your distribution's repositories, get it here: http://www.imagemagick.org/download/python/
It has more effect functions than you can shake a stick at.
One method would be to apply a Gaussian blur (or some other type of blur) to each point in the region defined by your control points.
One method would be to create a grid that your control points moves and then use texture mapping techniques to map the image back onto the distorted grid.
I can vouch for a Gaussian Blur mentioned above, it is quite simple to implement and provides a fairly decent blur result.
James