Hi Lars,
Thank you for sharing your ideas and scripts. These will come in handy
for the future.
For this project I took a different approach of creating a master tiff
and then copying the data from each patch into that master tiff. It
turns out that this was pretty straight forward in python given that
each patch is masked array and by inverting the mask I end up with a
mask of the good data that I can conditionally copy into the master. So
now I have one image with all the real data from 100+ patches which I
was trying to get using a vrt.
Come to think of it, it might be possible to get the BBOX of the
inverted mask, and then use that to slice the 2d numpy array and just
output the sliced image. Something to play with later.
Thanks again,
-Steve
On 6/27/2019 10:30 AM, [email protected] wrote:
Hi Steve,
Sorry for the late reply!
I have some scripts that I developed this winter that contains a function
to clip an image or images to the parts that only contains data.
My aim was to create tighter tile indexes for Mapserver.
I achieved good results for my use case.
This will be one of the topics that I will present in my talk at
FOSS4G in Bucharest in August. I haven't yet prepared the details how to
explain what I did. But here we go:
1) First I retile the data to suitable size,
2) then check for empty tiles that I throw away.
The function "trim_tile" is actually what You are asking for, I think.
What I do in the function trim_tile is to use:
1) gdal_calc to produce a binary image where I have data
2) polygonize where data exists
3) compare extents
4) clip data with gdal_translate with projwin argument, if polygon extent is
smaller
All is done in a bash script, since it is my preferred language to do things
fast.
I use gnu parallel to speed things up. In my case I had 16 cpus in the server.
Some versions of parallel requires the argument "--will-cite", others don't.
I made my own bash version of retiling that keeps masks.
Further notes is that the script is working on paletted tif 8 bit image.
I have a version for rbg images also. The main difference is that the
empty image test looks like this in that case:
STDDEV=$(gdalinfo $tile -stats | \
grep STATISTICS_STDDEV | \
sed 's/STATISTICS_STDDEV=//g' | \
awk '{$1=$1;printf "%d", $1}')
if [ "$STDDEV" = "000" ]; then
/bin/rm $tile
fi
If You have tagged nodata values, those have to be removed with gdal_edit.py
before You run statistics and then reaplied afterwards with gdal_edit again.
This might hopefully give some inspiration for you how to tackle your
use case.
The full script can be found here:
https://gist.github.com/LarsSchy/9ecb31eb964dd83820c139b2f2769a7c
Have fun
Lars Schylberg
20 juni 2019 kl. 22:06, "Stephen Woodbridge" <[email protected]>
skrev:
Hi,
I'm working with VIIRS L3U images of L2P (level 2 patches). The L3U data
is gridded to the +-180 x +-90 but the patch only fills a small
percentage of that. I'm compositing a the patch for a day using a vrt.
Performance is an issue because all the patches cover the whole globe
instead of the extents of the patch.
Is there a way to automatically clip an image to the extents of the
patch, where all the rest of the image it filled with nodata? IE. remove
all rows and columns from the exterior edges that are all nodata and
stop when you hit real data.
-Steve
---
This email has been checked for viruses by Avast antivirus software.
https://www.avast.com/antivirus
_______________________________________________
gdal-dev mailing list
[email protected]
https://lists.osgeo.org/mailman/listinfo/gdal-dev
_______________________________________________
gdal-dev mailing list
[email protected]
https://lists.osgeo.org/mailman/listinfo/gdal-dev
_______________________________________________
gdal-dev mailing list
[email protected]
https://lists.osgeo.org/mailman/listinfo/gdal-dev
