like what i want is if i move my finger fast on the iphone screen , then i want like something that it make a proper curve using quartz 2d or opengl es whatever.
i want to draw a path in curve style......
i had seen that GLPaint(OpenglES) example ,but it will not help me alot , considering if your finger movement is fast.....
something like making a smooth curve.....
any one have some kind of example please tell me
thanks
Edit: Moved from answer below:
thanks to all.......
but i had tried the bezier curve algo with two control points but problem is first how to calculate the control points whether there is no predefined points....
as i mentioned my movement of finger is fast...... so most of the time i got straight line instead of curve, due to getting less number of touch points.......
now as mark said piecewise fashion, ihad tried it like considering first four touch points and render them on screen , then remove the first point then again go for next four points ex. step 1: 1,2,3,4 step 2: 2,3,4,5 like that where as in that approach i got an overlap , which is not the issue actually , but didn't get smooth curve........
but for fast movement of finger i have to find something else?????
Depending on the number of sample points you are looking at, there are two approaches that I would recommend:
Simple Interpolation
You can simply sample the finger location at set intervals and then interpolate the sample points using something like a Catmull-Rom spline. This is easier than it sounds since you can easily convert a Catmull-Rom spline into a series of cubic Bezier curves.
Here's how. Say you have four consecutive sample points P0, P1, P2 and P3, the cubic Bezier curve that connects P1 to P2 is defined by the following control points:
B0 = P1
B1 = P1 + (P2 - P0)/6
B3 = P2 + (P1 - P3)/6
B4 = P2
This should work well as long as your sample points aren't too dense and it's super easy. The only problem might be at the beginning and end of your samples since the first and last sample point aren't interpolated in an open curve. One common work-around is to double-up your first and last sample point so that you have enough points for the curve to pass through each of the original samples.
To get an idea of how Catmull-Rom curves look, you can try out this Java applet demonstrating Catmull-Rom splines.
Fit a curve to your samples
A more advance (and more difficult) approach would be to do a Least Squares approximation to your sample points. If you want try this, the procedure looks something like the following:
Collect sample points
Define a NURBS curve (including its knot vector)
Set up a system of linear equations for the samples & curve
Solve the system in the Least Squares sense
Assuming you can pick a reasonable NURBS knot vector, this will give you a NURBS curve that closely approximates your sample points, minimizing the squared distance between the samples and your curve. The NURBS curve can even be decomposed into a series of Bezier curves if needed.
If you decide to explore this approach, then the book "Curves and Surfaces for CAGD" by Gerald Farin, or a similar reference, would be very helpful. In the 5th edition of Farin's book, section 9.2 deals specifically with this problem. Section 7.8 shows how to do this with a Bezier curve, but you'd probably need a high-degree curve to get a good fit.
Naaff gives a great overview of the NURBS technique. Unfortunately, I think generating a smooth bezier on-the-fly might be too much for the iPhone. I write drawing apps, and getting a large number of touchesMoved events per second is quite a challenge to begin with. You really need to optimize your drawing code just to get good performance while recording individual points - much less constructing a bezier path.
If you end up going with a bezier or NURBS curve representation - you'll probably have to wait until the user has finished touching the screen to compute the smoothed path. Doing the math continuously as the user moves their finger (and then redrawing the entire recomputed path using Quartz) is not going to give you a high enough data collection rate to do anything useful...
Good luck!
Do something like Shadow suggested. Get the position of the touch with some frequency and then make a Bézier curve out of it. This is how paths are drawn with a mouse (or tablet) in programs like Illustrator.
Related
I would like to follow a curve (with matlab or opencv) and to find the other end of it when it is cut by an empty space like this example, which is simplified to illustrate the problem:
Link to image of cut curve
Real images are more like this one: Link to real image to analyse
To follow the curve, I can use a skeleton and look at the neighbourhood. The problem is that I don't know how to find the other end efficiently.
I don't think that closing or opening operations could help because as shown on the previous image, there are other curves and the two parts of the curve are quite far from each other so it could lead to boundaries between the different curves instead of the two parts.
I was thinking about polynomial evaluation which could be a solution for simple curves but I am not sure about the precision I could get. If I use a skeleton, I have to find exactly the right pixel or to search in a reasonable neighbourhood which would take some time and once again, as there are other curves in the images, I have to be sure that I will find the good one.
That's why I am searching for an existing function which could estimate precisely the trajectory of the curve and give an usefull output to go further and find the second part of the curve.
If that kind of function doesn't exist, I'm open to any other way of analysing the problem if it can help.
I will start to explain with the first image you provided, you can implement common OpenCV function useful for detecting contour(black region in your case as you have binary image) known as cv2.findContours(), which returns the coordinates of the edges of the surface detected then you can plot each detected contour separately in a blank image to get the edge of your desired line.
Now coming to your 2nd image you have to be slightly careful while performing above analysis as there are many tiny lines. get back to me for further help
hi i want to detect fingertips point and valleypoint of hand by using hough transform.Simply the Question is what is the [H,theta,rho]=hough(BW) is good for extract these point.
the image is here:
https://www.dropbox.com/sh/n1lz7b5eedzbui7/AADwy5O1l7sWf5aOx7KWAmhOa?dl=0
tnx
The standard hough transformation is just for detecting straight lines. Not more and not less. The Matlab function hough (please see here) returns the so-called hough space H, a parametric space which is used to find these lines and the parametric representation of each line: rho = x*cos(theta) + y*sin(theta).
You will have to do more than this to detect your desired points. Since your fingers usually won't consist of straight lines, I think you should think of something else anyway, e.g. if you can assume such a perfect curve as the one in your image maybe this is interesting for you.
Another simple technique you might consider is to compare the straight line distance between two points on your hand line to the distance between those two points along the perimeter (geodesic distance). For this you would need an ordered list of points along the perimeter.
Along regions of high curvature, the straight line distance between two points will be smaller than the number of pixels between those two points along the perimeter.
For example, you could check perimeter pixels separate by 10 pixels. That is, you would search through the list and compare the point at index N and the point index N+10. (You'll need to loop back around to the beginning of the list as you approach the end.) If the straight line distance between these two points is nearly 10 pixels as well, then you know those points lie on a straight section of the perimeter. If the straight line distance is much smaller than 10, then you know the perimeter curves in some fashion between those points. Whether you check pixels that are 5, 10, 20, or 30 items apart in the list will depend on the resolution of your image and the curves you're looking for.
This technique is useful because it's simple and quick to implement. Maybe it would work well enough for your needs.
Yet another way: simplify the outline to small line segments, and then you can calculate the line-line angle between adjacent segments. To simplify the curves, implement the Ramer-Douglas-Puecker algorithm. A little experimentation will reveal what parameter settings will work for your application.
https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
Finally, you could look into piecewise curve fitting: a curve would be fitted to small segments of the outline. This can get very complicated, and researchers continue to find ways to decompose complex figures into a limited number of more basic shapes or curves. I recommend trying the simplest technique and then only adding complexity if you need it.
I have a multiple plants in a single binary image. How would I identify each leaf in the image assuming that each leaf is approximately elliptical?
example input: http://i.imgur.com/BwhLVmd.png
I was thinking a good place to start would be finding the tip of each leaf and then getting the center of each plant. Then I could fit the curves starting from the tip and then going to the center. I've been looking online and saw something involving a watershed method, but I do not know where to begin with that idea.
You should be aware that these things are tricky to get working robustly - there will always be a failure case.
This said, I think your idea is not bad.
You could start as follows:
Identify the boundary curve of each plant (i.e. pixels with both foreground and background in their neighbourhood).
Compute the centroid of each plant.
Convert each plant boundary to a polar coordinate system, with the centroid as the origin. This amounts to setting up a coordinate system with the distance of each boundary curve point on the Y axis and the angle on the X axis.
In this representation of the boundary curve, try to identify maxima; these are the tips of the leaves. You will probably need to do some smoothing. Use the parts of the curve before and after the maxima the start fitting your ellipses or some other shape.
Generally, a polar coordinate system is always useful for analysing stuff thats roughly circular.
To fit you ellipses, once you have a rough initial position, I would probably try an EM-style approach.
I would do something like this (I is your binary image)
I=bwmorph(bwmorph(I, 'bridge'), 'clean');
SK=bwmorph(I, 'skel', Inf);
endpts = bwmorph(SK,'endpoints');
props=regionprops(I, 'All');
And then connect every segment from the centroids listed in props.centroid to the elements of endpts that should give you your leaves (petals?).
A bit of filtering is probably necessary, bwmorph is your friend. Have fun!
I am trying to detect corners (x/y coordinates) in 2D scatter vectors of data.
The data is from a laser rangefinder and our current platform uses Matlab (though standalone programs/libs are an option, but the Nav/Control code is on Matlab so it must have an interface).
Corner detection is part of a SLAM algorithm and the corners will serve as the landmarks.
I am also looking to achieve something close to 100Hz in terms of speed if possible (I know its Matlab, but my data set is pretty small.)
Sample Data:
[Blue is the raw data, red is what I need to detect. (This view is effectively top down.)]
[Actual vector data from above shots]
Thus far I've tried many different approaches, some more successful than others.
I've never formally studied machine vision of any kind.
My first approach was a homebrew least squares line fitter, that would split lines in half resurivly until they met some r^2 value and then try to merge ones with similar slope/intercepts. It would then calculate the intersections of these lines. It wasn't very good, but did work around 70% of the time with decent accuracy, though it had some bad issues with missing certain features completely.
My current approach uses the clusterdata function to segment my data based on mahalanobis distance, and then does basically the same thing (least squares line fitting / merging). It works ok, but I'm assuming there are better methods.
[Source Code to Current Method] [cnrs, dat, ~, ~] = CornerDetect(data, 4, 1) using the above data will produce the locations I am getting.
I do not need to write this from scratch, it just seemed like most of the higher-class methods are meant for 2D images or 3D point clouds, not 2D scatter data. I've read a lot about Hough transforms and all sorts of data clustering methods (k-Means etc). I also tried a few canned line detectors without much success. I tried to play around with Line Segment Detector but it needs a greyscale image as an input and I figured it would be prohibitivly slow to convert my vector into a full 2D image to feed it into something like LSD.
Any help is greatly appreciated!
I'd approach it as a problem of finding extrema of curvature that are stable at multiple scales - and the split-and-merge method you have tried with lines hints at that.
You could use harris corner detector for detecting corners.
I have a list of 200 points I garnered from a graph digitization software I would like to transform into a smooth curve and then into Solidworks.
My points form an ellipse (airfoil shape to be more precise), so the commands I've tried in Matlab didn't have a circular curve.
My issues are:
* Obtaining a smooth curve that doesn't necessarily pass through all points, smooth being motus operandi.
* Being able to have a elliptical curve
* Somehow being able to export this curve into Solidwords
If anyone knows the right software, command line or anything that could get me started, I would be extremely thankful.
imacube
I've used Solid Works before. It's a very powerful tool. There should be some way to draw a curved spline through these points, such as a cubic spline.
If you are using a standard(ish) airfoil, then you can use a variety of tools to plot the points without having to use a graph digitization software.
Javafoil, for instance, is one of those. Even if you know the characteristics of your airfoil, you can use this to give you a smooth set of points.
Again, if your airfoil is a naca 4-series, then these are governed by a set of equations.
But I take it that the airfoil you want a more complicated one. Let me know if I can help anymore.