[gdal-dev] Question to warping images using python-bindings
Jacob Mendt
jacobmendt at googlemail.com
Wed Feb 18 06:09:14 PST 2015
Dear Gdal-Team and List,
At first I want to thank you for the great work you guys are doing with
GDAL. I really enjoy using this tools. I am especially doing rectification
of unreferenced raster images with the gdal tools. At the beginning I did
the rectification with the gdal_translate and gdalwarp tools. This works
pretty well and allowed me to use different order polynomials and a plate
spline transformer for my image transformation. Also it was possible for me
to use different resampling algorithm for my images.
The problem was that I always had to clip the input image (using pixel
coordinates) first, save the result in a temporally dataset - first I/O
process - and doing than the transformation using gdal_translate (add gcps)
and gdalwarp (transformation and resampling) - second I/O process. In my use
case the most time the processes spent was for the I/O operations. So I
decided to use the python-bindings for processing, in hoping to do some
operations in memory.
This works great for doing a affine transformation (dataset.SetGeotransform)
without resampling and gave a big performance improvement. The improvement
came through reading the file once in memory, doing there the clipping and
adding of the transformation matrix and then writing this to disk. So I got
only once to read the image from the disk and once to write the clipped
image, together with the transformation matrix, back to disk. But I couldn't
figure out how to do for example a plate spline transformation or an order 2
polynomial transformation. After further investigation it seems like this is
not possible with the python bindings, are I am right with this? Also it
seems that the python bindings doesn't allow to do a resampling of an image
without using the gdal.ReprojectImage method, what from my understand would
make sense in my case or?
Hope I make my questions clear and somebody could give me some feedback.
Kind regards,
JM
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