Basically this is the question - how to plot a transparent cube with given coordinates of two opposite corners. Surely something simple exists, but I cannot find it. I have tried using tvtk.visual.box, but it doesn't support transparency as far as I could tell.
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I have been looking for a Python library that would allow me, from a set of points in 3D and RGB colors associated to each point, to get a transparent surface (i. e., with some degree of transparency). The idea would be to be able to display (and manipulate/rotate) things similar to the image below:
The atoms and bonds are inside a 3D surface that is smooth and constructed from a series of points each with a RGB color.
I could get some rough Poisson reconstruction running with Mayavi but the colors appeared very pixelized and I couldn't find a way to make the surface transparent. I could obtain a lot of features I wanted for this work with Open3D (I actually place these objects inside crystal structures so I need to represent bonds, atoms, crystal edges, axes and so on), but here again I couldn't find a Poisson reconstruction algorithm to recreate the smooth surface from points nor any functionality to make a surface transparent. Any suggestion would be appreciated.
I'm beginning to work on a project with OpenCV (in python), and I'm trying to figure out the best way to tackle the problem I'm facing. I'm trying to get the area of an item in an image...but the surface area I'm looking for is on a 3D object.
So I found this while searching for calculating the area of a contour, but as you can see, this is only for a 2D object. For example, how could I find the area of the red question marks on the sphere in this image? Or the size of the rash on this baby's face (which is most certainly not 2-dimensional)? Is there a way to find the depth of the surface in the image, then use the high color gradient difference to find contours and calculate the difference based on the depths?
I found this deep learning paper (and associated PyTorch library), but was wondering if there was another way that I was missing...
Thanks to all for any ideas / replies.
picture example
I have recently started learning Python with Spyder IDE and I'm a bit lost so I ask for advice.
The thing is that I need to program an algorithm that, given a random image representing a board with black spots in it (in the picture I upload It is a 4x5 board) so It recognizes the edges properly and draw a AxB grid on it. I also need to save each cell separately so as to work with them.
I know that open CV treat images and I have even tried auto_canny but I don't really know how to solve this problem. Can anybody give me some indications please?
as I understand from your question you need to have as an output the grid of the matrix in your picture (eg. 4x3) and each cell as separate image.
This is the way I would approach this problem:
Use canny + corner detection to get the intersection of the lines
With the coordinates of the corners you can form your regions of interest, crop each individually and save it as a new image
For the grid you can check the X's and the Y's of the coordinates, for example you will have something like: ((50, 30), (50,35),(50,40)) and from this you can tell that there are 3 points on the horizontal axis. I would encourage you to set a error margin as the points might not be all on the same coordinate, but may not differ a lot.
Good luck!
I was wondering if there is a way to get the shape of the contour of a region determined by several points. For instance, in the image below, I show as a background (in gray) a collection of points, but it does not look very nice, so I would like to determine automatically what are the edges or the contour that the points are delimiting, to just plot the shape of the background instead of plotting thousands of points to show it.
Edit: As kindly pointed out by #heltonbiker, actually the \chi-shape in http://www.geosensor.net/papers/duckham08.PR.pdf would perfectly do the job, however I still have no clue on how to implement it. Any help would be highly appreciated!
I need to detect the different shapes on the image such as ellipses and circles with a dashed-border.
Using OpenCV causes some troubles, for e.g. openCV detects not a contour but an amount of small separated lines(contours).
In case of ellipse with a "dashed-border" I need it to be as single contour, not a separated objects.
What would you suggest to solve this problem?
I also need to recognize the ellipses(or circles) with solid border and ellipses(circles) with a dashed-border
Ellipse with solid border
Ellipse with dashed border:
The Hough transform should not have any troubles with either dashed or solid borders, or a mix thereof. I have used it to detect dashed lines in one of my project and ellipses should not be much harder.
OpenCV by default only implements the Hough transform for circles and straight lines but there is a blog entry with code on how to adapt it for elliptical shapes.
The better idea there is to use an specific transform to detect circle and ellipses rather than a general "find contour" call.
I am not enough acquainted with OpenCV to know if there this sort of detection is built in there - it should be.
An internet search for the more generic "image ellipse detection" shows it is not a settled matter - unlike square and rectangle detection, so whatever OpenCV already contains will have to be tunned up.
(The basic idea is that the underlying algorithm creates a new n-dimensional space where every possible paramterized ellipse for the target image could be represented (with, for example, an axis for each x and y center coordinates, w and h radii, and one for rotation), and fill in hits in this matrix according to the pixels contrast in the target image).- Sebastian's answer put the correct name for this approach I could not recall: "Hough transform", and provides the needed links on how it works, and how to extend it on OpenCV.