[gdal-dev] gdalwarp performance with many GCPs

[Jan Hartmann]

No, this is unaaceptable, but I cannot reproduce it. The only problem I 
had was with control points that were too close together. Are yours 
evenly distributed? Can you do tests with subsets of control points?

On 12/12/2012 01:27 PM, Knut-Frode Dagestad wrote:
> Hi Jan,
>
> That sounds interesting and promising.
>
> For the mentioned file it takes about 2 minutes with "-et 5" (low 
> accuracy), and 12 minutes without this switch. Without -tps it takes 
> less than 0.5 seconds.
> The machine is quite decent with 16 GB memory and SSD disk.
>
> Best regards from Knut-Frode
>
>
>
> On 12. des. 2012 13:24, Jan Hartmann wrote:
>> Hi Knut,
>>
>> What do you mean by "very slow"? I regularly use gdalwarp -tps on much
>> larger rasters with a few thousand gcp-s, and never noticed unacceptable
>> delays. Do you have very little physical memory, or many parallel
>> processes running?
>>
>> Jan
>>
>> On 12/12/2012 01:12 PM, Knut-Frode Dagestad wrote:
>>> Hi list,
>>>
>>> When warping images with many GCPs, the -tps switch (Thin Plate
>>> Spline) is found to be necessary to get decent accuracy. This makes
>>> however warping very slow. The only method I found to increase speed
>>> is the -et switch, but at cost of spatial accuracy.
>>>
>>>
>>> Below some comments about the other tries which did not help:
>>>
>>> - compiling GDAL with armadillo support had no effect on speed.
>>> Btw, to compile with armadillo I had to manually insert -llapack in
>>> the following line in configure:
>>> if test -z "`${CXX} testarmadillo.cpp -o testarmadillo -larmadillo
>>> 2>&1`" ; then .....
>>> Perhaps ${LIBS} should be added permanently to this line in trunk?
>>>
>>> - compiling GDAL with OpenCL had also no effect. At first surprising,
>>> but looking at the opencl warp kernel it seems that it only makes a
>>> difference for other resampling algorithms than nearest neighbour?
>>>
>>> - Increasing  memory with -wm had no effect
>>>
>>> - Using several threads (-multi -wo NUM_THREADS=ALL_CPUS) actually
>>> increased computing time significantly (my proj version is 4.7.1).
>>>
>>> From the debug output, a lot of the time is apparently spent on:
>>> "WARP: Copying metadata from first source to destination dataset"
>>> Is this an indication that much time is simply spent on reading and
>>> writing the GCPs to/from file? If so, we could perhaps expect improved
>>> performance if Geolocation Arrays could be used instead (not possible
>>> due to http://trac.osgeo.org/gdal/ticket/4907).
>>>
>>>
>>> The tests are made with the following file and command on Ubuntu:
>>> http://dl.dropbox.com/u/15885758/testgcp.tif  (3212 GCPs and 2048x2511
>>> pixels covering Southern Europe)
>>>
>>> time gdalwarp --debug on -et 5 -tps -t_srs '+proj=merc' testgcp.tif
>>> out.tif  (+ other swithces mentioned above)
>>>
>>>
>>> Best regards from Knut-Frode
>>>
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>>> gdal-dev mailing list
>>> gdal-dev at lists.osgeo.org
>>> http://lists.osgeo.org/mailman/listinfo/gdal-dev
>>
>>
>>
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