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Messages - M.Eli

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Hello MEli,

the point_cloud point coordinates are in the Internal CS. to get the coordinates in the project CRS, you must apply the chunk transform matrix to get geocentric CS coordinates and then chunk CRS projection to get coordinates in chunk CRS projection  (ex. UTM)....

Ah ok! This makes absolute sense. Thanks a lot, Paulo :)

Hey Alexey,

thanks a lot for the fast fix!
Yep, the error estimation works perfectly fine (results are the same compared to those from the GUI) and the point coordinates makes sense.

One minor question (might be off topic). The 3D point coords looks like being aligned in a local or the camera CRS (very small numbers) and not in the global project CRS (UTM). The processing included reference information (RTK-measured GCPs). Thus, I would expect that the 3D points from  pointCloud.get().getPoints() are located in the georeferenced project CRS, too.
Are those in the camera CRS and still need to be transfered into the project CRS?

Hey Alexey,

thank you very much for your fast response and intended fix in the next version!
Much appreciated :)

Best regards!

Hey everybody,

I updated the Metashape JAR in my java project to version 1.8.2. and run into a problem. I generated a georeferenced point cloud with ground control points. When I open the project everything is fine.
However, if I like to continue working with the java code and print the trackID + coordinates of my (sparse) point cloud, e.g. using this command:

for (int p = 0; p < pointCloud.get().getPoints().get().getSize(); p++) {
               PointCloud.Point pp = pointCloud.get().getPoints().get().get(p);
               System.out.println(pp.getTrackId() + ","+ pp.getCoord().getX() + "," + pp.getCoord().getY() +","+pp.getCoord().getZ());   
I get an weird result like this:
1617,-1.001014896494884E-14, 0.007812505590406374, 3.4312671939913E-311
1618,-1.4160203828280833E-14, 0.007812505591220983, 3.4333891897823E-311
1619,-3.233755625624215E-14, 0.007812505590504133, 3.4355111855733E-311
1620,-2.344639034314182E-14, 0.007812505590581154, 3.437633181364E-311

Almost all coordinates are 0,0,0 (rounded). When I open the project in the GUI, I can see the correctly determined sparse cloud (coordinates are as expected...).
Furthermore, I got an error when I try to calculate the reprojection error by camera.getError (pt (3D), proj (2)) -> "Exception in thread "main" java.lang.IllegalArgumentException: invalid array length" using the Vector from points.get(pointIdx).getCoord(). The 2D projections look good and plausible.

The entire procedure worked before the update!
What I´m doing wrong now?

Thank you very much!

General / Re: Add further floating license
« on: January 25, 2022, 03:04:57 PM »
Dear Alexey,
thank you very much for the quick feedback! That worked perfectly!
Thanks again and all the best,

General / Add further floating license
« on: January 25, 2022, 11:35:43 AM »

Some time ago we bought 5 floating licenses to work with Metashape Professional. The licenses are managed using the RLM License Administration tool.
Now we bought another license and I was wondering how to add this new license in RLM to our running system? (So that we can use 6 licenses at the same time)?
When I try to add this using this instruction I get an error "Alternate hostID Activation failed: HostID already exists" at stage 5.

Could you please provide an instruction on how to add more licenses?

Thanks and all the best,

Python and Java API / Re: Java API: Filter dense cloud by confidence
« on: June 07, 2021, 12:34:51 PM »

Unfortunately not... the only solution was to switch to the GUI version and do things there. Not really satisfying...  :-\

I think the filtering could also be coded natively, if we could somehow access the single points of the Dense Cloud. But even that doesn't work for me...

Python and Java API / Java API: Filter dense cloud by confidence
« on: April 28, 2021, 09:46:41 AM »
Hey @all,

I integrate Metashape into my workflows using the Java API. Unfortunately, the documentation is a bit " sparse" but with a lot of trial and error I have always reached my goal - until now. I have to filter my dense cloud by confidence. I found a nice workflow in this forum using the Python API ( but I am not able to reproduce using the Java API.

My question is: How to filter dense clouds by confidence using the Java API? The calculation of point confidence has been set 'true' in 'BuildDenseCloud'.

Thanks a lot and stay well,

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