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Messages - dyoung

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General / Noisier DEM from Metashape 1.7 compared with 1.6
« on: August 21, 2021, 08:28:49 PM »
Hello -- with all the same processing parameters, I find that Metashape 1.7.0 produces noisier DEMs than Metashape 1.6.5. Is this behavior known? What parameters should I change in 1.7 to get a smoother, less noisy DEM, like the ones I am used to getting from 1.6.5? See an example forest DEM from the two versions attached. Thank you!

Great--thank you.

Hello -- in a previous thread, Alexey explained that selecting 'medium quality' during photo alignment and dense cloud generation equates to making the photos lower-resolution, by the following factors:

For matching accuracy the correspondence should be the following:
Highest = 0
High = 1
Medium = 2
Low = 4
Lowest = 8

For depth maps quality:
Ultra = 1
High = 2
Medium = 4
Low = 8
Lowest = 16

Basically, the value defines the downscaling factor by each side applied to original image. The only exception is Highest matching accuracy, where the images are upscaled by two times by each side.

So am am wondering, if I use 'medium quality' for both steps, is there any point in capturing my photos with such high resolution? Is the original-resolution photo used for any purpose in the point cloud workflow? Or could I just capture my photos at a lower resolution (say 1/2 the resolution in x and y dimensions) and then use the 'high quality' setting instead?

Thank you in advance!

Python and Java API / Re: List of Tweaks ?
« on: April 11, 2021, 08:53:46 PM »
I have the same question as Costas

Is there any correlation between values in Dense cloud max neighbors and Depth Maps max neighbors? Should they be the same, or half, or double etc? What is the best practice to set these values?

Any suggestions? Thanks!

General / Re: Using GCPs causes alignment problems
« on: March 09, 2021, 02:48:12 AM »
Ah yes that works! I got great results following those steps. Thanks for clarifying the workflow.

General / Using GCPs causes alignment problems
« on: March 07, 2021, 04:37:18 AM »
Hello -- I have noticed that in order for my GCPs to meaningfully improve my project's georeferencing, I need to uncheck all the cameras before alignment. However, when I do this, I sometimes get serious alignment problems, like in the screenshot attached. If I leave the cameras checked (default), the alignment is much better, but the georeferencing for the whole project becomes much worse. Is there something I can do differently to get good alignment and good georeferencing? My images are from a DJI Phantom 4.

Thanks in advance!

Hello Alexey,

I would still be very interested in some tips or examples for how to project a 3D point to the cameras and get the 2D coordinates in the image space. Is this done through the Metashape Python API, or other Python libraries? I understand that this can be done using geometry, but my understanding of focal length, sensor sizes, etc. is too limited to write an algorithm from scratch. Is there any documentation you could point me toward?

Thank you in advance!

Hi Alexey --  Excellent! Yes, I would love an example, if you have time. Thank you!

Hello -- if I select a specific point in my dense cloud (or any arbitrary point in 3D space), is there any way I might be able to "back-calculate" where that point would be in one of my raw photos (i.e., which pixel of the raw photo is closest to it)? I realize this is a very unusual request, but I wanted to ask just in case someone has a clever solution.

The reason I ask is that I would like to be able to mark the top and bottom of a tree in my point cloud (or simply specifying the 3D coordinates) and then identify where those two points would be (pixel coordinates) in one of my photos.

Thanks for any insight!

Python and Java API / How to get a BBox from a DenseCloud?
« on: January 18, 2020, 09:53:59 PM »
Hello -- I would like to constrain the extent of an orthomosaic that is exported by the exportRaster method. I see the method has a region parameter, which takes a BBox object. I would like to constrain the exported extent of the orthomosaic to be the same as the extent of my dense point cloud. How can I get a BBox object that contains the extent (region) of my dense cloud? I checked the documentation, but there is little information on BBox objects.

Thank you!

OK. Thanks Alexey.

Hi Alexey,

I have the 1.6 release version on Ubuntu 18.04, and the error actually occurs in the GUI and the Python API. Below is a small (2.7 GB) project that causes the problem for me.
(Many other projects also cause this problem for me...any project that contains an orthomosaic).

Here is a simple script to reproduce the problem with the project provided above:

import Metashape
doc = Metashape.Document()"20200102T2103_01c_ChipsA.psx")"temp_new_project.psx")

Ah that makes sense! That worked with a slight modification of the last parameter:

image_compression = compression


Hello -- version 1.6 dropped exportOrthomosaic() and now uses exportRaster() with source_data=Metashape.OrthomosaicData. However, exportRaster() does not have a tiff_big parameter like exportOrthomosaic() did. How do I export a BigTIFF using this function? I have tried the following, but it does not work.

Metashape.ImageCompression.tiff_big = True
doc.chunk.exportRaster(path="path/to/file.tif", source_data=Metashape.OrthomosaicData)

I need to use a BigTIFF because with normal TIFF settings, I get: libtiff error: Maximum TIFF file size exceeded

Thank in advance for any help!

EDIT: Never mind. This error resulted from a different step that I ran incorrectly.

I can't find a way to delete this message.

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