I have two geometries, one is made from another by offsetting its vertices,
so both have same structure and hierarchy.
Need to connect these two geometries with caps (yellow geometry).
Pretty sure that the problem could be solved by finding edge points (yellow lines) on both sides for each element. As soon as these geometries have same # of vertices and herarchy caps could be easilly calculated.
But, for now, I don't have any idea how to determine these edge points.
My solution to this problem. Not ideal.
http://vkuchinov.github.io/BuildingCaps/
Related
I have a markered robot with circular markers and two images from different perspective as shown: (Circular white rings are the markers)
I want to match the markers in the two images, by matching I mean the bottommost marker of 1st image should be treated as correspondence point of bottom most marker of 2nd image and so on.
The finger-like robot given in the image can bend in any direction given in space (can also bend in a U-like manner).
If it helps, the camera geometry is fixed and known beforehand.
I am lost, as simple correspondence algorithm would not work, since the perspectives are very different. How should I go about matching the two images?
You can start like this:
You know the position of the mounting point on the base panel for each perspective.
You know the positions of the white rings for each perspective as discussed here.
You can derive the direction of the arm at each ring by its tilt.
So you can easily determine the sequence of the positions starting with the mounting point stepping from ring to ring even if the arm is bent. With this you can match the rings from both images. If you have any situation where this fails, please add an according example to your question!
Unfortunately, you don't have matching points but matching curves. You might try to fit ellipses on the rings and take the ellipse centers for points to be matched.
This is an approximation, as the center of a circle does not exactly project as the center of the ellipse, but I don't think that this will be the major source of error: as you only see half circles, the fitting will not be that accurate.
If all nine circles remain visible and are ordered vertically, the matching of the centers is trivial. If they are not ordered but don't form a loop, you can probably start from the lowest and follow the chain of nearest neighbors.
So, I'm using a boolean operator to get the intersection of a bunch of pieces and a wall. Most pieces work fine but occasionally the intersection isn't perfect and you get these vertices that aren't connected to the rest of the mesh and this results in the mesh collider being incorrect, as seen in this picture.
My question is whether there is a way to detect these 'island' or 'lone' vertices.
I can provide additional images, code, or such if needed.
Thanks for any help! Ps. first question here so please be patient with me :)
In the end, I kind of solved it by finding all connected vertices starting from a single vertex.
I started by picking the first triangle and adding it's vertices to a list of connected vertices. Then I go through the list of triangles comparing the position of their vertices to the list of connected vertices. If the triangle has a vertex with a position corresponding to a position in the list of connected vertices I add the entire triangle to the list. That's one iteration and I repeat this until all connected triangles are already in the list. If the connected triangle list is more than half of all triangles then I remove all other triangles, otherwise, I remove the current list of connected triangles. After that, I clear the vertices that aren't in a triangle like Leo Bartkus suggested.
It's extremely slow and it assumes there are only 2 separate islands or that you started on the biggest island, but it worked most of the time and was more for learning purposes anyways.
Thank's for the help!
I have two data layers, one with points and one with polygons. Both layers have ID's and I would like to check whether the points with ID x lay inside or outside the polygon with ID x.
Does someone know how to do this?
Thanks,
Marie
One potential solution which gives you a comma separated list in the python console is to run a small script from the python console:
mapcanvas = iface.mapCanvas()
layers = mapcanvas.layers()
for a in layers[0].getFeatures():
for b in layers[1].getFeatures():
if a.geometry().intersects(b.geometry()):
print a.id(),",",b.id()
This should produce a result of cases where one feature intersects the other. The order of the layers did not matter in my testing, however, both layers had to use the same coordinate reference system, so you might need to re-project your data if both layers have different reference systems. This worked for points in polygons and polygons intersecting polygons (I'm sure it would work with lines as well).
Answers such as this: https://gis.stackexchange.com/questions/168266/pyqgis-a-geometry-intersectsb-geometry-wouldnt-find-any-intersections may help with further refinement of such a script, and was a primary source on this answer.
I have a table that contains a bunch of Earth coordinates (latitude/longitude) and associated radii. I also have a table containing a bunch of points that I want to match with those circles, and vice versa. Both are dynamic; that is, a new circle or a new point can be added or deleted at any time. When either is added, I want to be able to match the new circle or point with all applicable points or circles, respectively.
I currently have a PostgreSQL module containing a C function to find the distance between two points on earth given their coordinates, and it seems to work. The problem is scalability. In order for it to do its thing, the function currently has to scan the whole table and do some trigonometric calculations against each row. Both tables are indexed by latitude and longitude, but the function can't use them. It has to do its thing before we know whether the two things match. New information may be posted as often as several times a second, and checking every point every time is starting to become quite unwieldy.
I've looked at PostgreSQL's geometric types, but they seem more suited to rectangular coordinates than to points on a sphere.
How can I arrange/optimize/filter/precalculate this data to make the matching faster and lighten the load?
You haven't mentioned PostGIS - why have you ruled that out as a possibility?
http://postgis.refractions.net/documentation/manual-2.0/PostGIS_Special_Functions_Index.html#PostGIS_GeographyFunctions
Thinking out loud a bit here... you have a point (lat/long) and a radius, and you want to find all extisting point-radii combinations that may overlap? (or some thing like that...)
Seems you might be able to store a few more bits of information Along with those numbers that could help you rule out others that are nowhere close during your query... This might avoid a lot of trig operations.
Example, with point x,y and radius r, you could easily calculate a range a feasible lat/long (squarish area) that could be used to help rule it out if needless calculations against another point.
You could then store the max and min lat and long along with that point in the database. Then, before running your trig on every row, you could Filter your results to eliminate points obviously out of bounds.
If I undestand you correctly then my first idea would be to cache some data and eliminate most of the checking.
Like imagine your circle is actually a box and it has 4 sides
you could store the base coordinates of those lines much like you have lines (a mesh) on a real map. So you store east, west, north, south edge of each circle
If you get your coordinate and its outside of that box you can be sure it won't be inside the circle either since the box is bigger than the circle.
If it isn't then you have to check like you do now. But I guess you can eliminate most of the steps already.
I have some data points which I have devided into them into some clusters with some clustering algorithms as the picture below:(it might takes some time for the image to appear)
alt text http://www.freeimagehosting.net/uploads/05a807bc42.png
Each color represents different cluster. I have to draw polygons around each cluster. I use convhull for this reason. But as you can see the polygon for the red cluster is very big and covers a lot of areas, which is not the one I am looking for. I need to draw lines(ploygons) exactly around my data sets. For example in the picture above I want a polygon that is drawn exactly the same(and around) as the red cluster with the 3 branches. In other words, in this case I need a polygon with 3 branches to cover my red clusters not that big polygon that covers the whole area. Can anyone help me with this?
Please Note that the solution should be general, because the clusters will change in each run of the algorithm, so it needs to be in a way that is general.
I am not sure this is a fully specified question. I see this variants on this question come up quite often.
Why this can not really be answered here: Imagine six points, three in an equilateral triangle with another three in an equilateral triangle inside it in the same orientation.
What is the correct hull around this? Is it just the convex hull? Is it the inner triangle with three line spurs coming out from it? Does it matter what the relative sizes of the triangles are? Should you have to specify that parameter then?
If your clusters are very compact, you could try the following:
Create a grid, say with a spacing of 0.1.
Set every pixel in the grid to 1 if there's at least one data point covering it, set the pixel to 0 if there is no data point covering the pixel.
You may need to run imclose on your mask in order to fill little holes inside that have not been colored due to sheer bad luck.
Extract the border pixels using, e.g. bwperim. This is the outline of the polygon you're looking for.