I will draw a curve similar to the red curve in the illustration below (can be a bezier or whatever is most convenient for my purposes I think). I would like to find points on the curve (blue dots in the illo). The points would most likely be divisions of equal parts of the length of the curve.
Can I find these points? I am not seeing a solution in the docs as of yet.
This answer covers segmentation of a Bezier curve using the de Casteljau algorithm. You already have your parameterized values along the curve for segmentation.
(If you follow the link referenced in the answer make sure you have java enabled in your browser, so you can view the example visualisations).
Related
Is there any opencv function for curve fitting?
I have a set of points (cv::points) and my aim is to fit these points to a closed/open curve.
Right now I am taking a pair of points and drawing lines with them, effectively forming a curve.
It's not quite clear from your question whether you want to smooth the curve by adding more points or to summarise it by using fewer points. If it's the latter, perhaps you should consider cv::approxPolyDP, which is documented here and copied below for reference.
I think you are talking function approximation and interpolation.
As I know, there's not a function directly about curve fitting.
If you just want to get the fitting result, you can use Matlab's curve fitting toolbox, where there is a tool named cftool. cftool is a GUI tool, you can specify the input points and the interpolation method and get the result formula.
I am looking for an efficient way to detect the small boxes around the numbers (see images)?
I already tried to use hough transformation with no success. Any ideas? I need some hints! I am using opencv...
For inspiration, you can have a look at the
Matlab video sudoku solver demo and explanation
Sudoku Grab, an Iphone App, whose author explains the computer vision part on his blog
Alternatively, if you are always hunting for the same grid you could deploy something like this:
Make a perfect artificial template of the grid and detect or save all coordinates from all corners.
In the target image, do the same thing, for example with Harris points. Be creative, you might also be able to use the distinct triangles that can be found in your images.
Using the coordinates from the template and the found harris points, determine the affine transformation x = Ax' between the template and the target image. That transformation can then be used to map the template grid onto the target image. At the very least this will give you some prior information to help guide further segmentation.
The gist of the idea and examples of the estimation of affine matrix A can be found on the site of Zissermans book Multiple View Geometry in Computer Vision and Peter Kovesi
I'd start by trying to detect the rectangular boundary of the overall sheet, then applying a perspective transform to make it truly rectangular. Crop that portion of the image out. If possible, then try to make the alternating white and grey sub-rectangles have an equal background brightness - maybe try adaptive histogram equalization.
Then the Hough transform might perform better. Alternatively, you could then take an approach that's broadly similar to this demonstration by Robert Bemis on MATLAB Central (it's analysing a DNA microarray image rather than Lotto cards, but it's essentially finding bounding boxes of items arranged in a grid). At a high level, the approach is to calculate the autocorrelation along columns and rows of pixels to detect the periodicity of the items in the grid, and use that to impose a bounding box on each item.
Sorry the above advice is mostly MATLAB-based; I'm afraid I'm not an opencv user, but hopefully it will give you some ideas at least.
Im searching for radius and the center coordinates of circle in a image. have already tried 2D Hough transform. but my circle radius is also a unknown. Im still a beginner to Computer vision so need guild lines and help for implementing three dimensional hough space.
You implement it just like 2D Hough space, but with an additional parameter. Pseudo code would look like this:
for each (x,y) in image
for each test_radius in [min_radius .. max_radius]
for each point (tx,ty) in the circle with radius test_radius around (x,y)
HoughSpace(tx,ty,test_radius) += image(x,y)
Thiton gives you the correct approach to formalize the problem. But then, you will run in other problems inherent to the hough transform:
how do you visualize the parameter space? You may implement something with a library like VTK, but 3D visualization of data is always a difficult topic. The visualization is important for debugging your detection algorithm and is one of the nice thing with 2D hough transform
the local maximum detection is non trivial. The new dimension will mean that your parameter space will be more sparse. You will have more tuning to do in this area
If you are looking for a circle detection algorithm, you may have better options than the hough transform (google "Fast Circle Detection Using Gradient Pair Vectors" looks good to me)
I'm familiar with UIBezierPath and the corresponding CG routines, but none of them appear to draw the same type of path as what I see in Photoshop, etc.
How would one go about this? I'm just talking about the UI--letting the user drag around points.
A java example I found is here: http://www.cse.unsw.edu.au/~lambert/splines/natcubic.html
I would look into CGContextAddCurveToPoint and drag around curve's control points. If you require more control points to create a complex curve, just split a resulting curve into simple segments.
Take a look at this article It explains how to calculate the control points based on knots you have on your curve.
I'm just starting to learn OpenCV programming. May I just ask about how can I identify lines and curves in OpenCV? My problem is that I have to identify if the image contains a convex or concave (horizontal or vertical curve) curve, a vertical, diagonal or a horizontal line.
In my code, I used CvSetImageROI to take a particular part of an image, and then I'm trying to identify each according to the said lines/curves.
Are there functions in OpenCV that are available? Thank you very much for the help. By the way, i'm using Linux and C++.
Hough transform http://en.wikipedia.org/wiki/Hough_transform, http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm
is the standard way to do it. In its simple form (as implemented in OpenCV) it can detect lines of arbitray position and angle and line segments.
Look here for an example
http://opencv.itseez.com/modules/imgproc/doc/feature_detection.html?highlight=hough#houghlinesp
For curves, the detection process is a bit more complicated, and you need the general Hough transform It is not yet available in OCV, but you can write it as an exercise or look for a good implementation.
http://en.wikipedia.org/wiki/Generalised_Hough_transform describes it (in short)