Hi Michael,
I may be missing your question, but why aren't you just using ReadAsArray? It has an option to return a smaller array from the input array. Now, I'm not sure how it does the resampling (you could look to see), but you can make a call like data = banddata.ReadAsArray(0,0,filehandle.RasterXSize,filehandle.RasterYSize,xsize,ysize) where xsize and ysize are smaller than the true RasterXSize or RasterYSize. I haven't looked at this in a while, but I'm pretty sure this will work. Did I miss the point of what you were asking? Thanks, Scott On Wed, Apr 11, 2012 at 6:31 AM, K.-Michael Aye <[email protected]>wrote: > Dear all, > > is there a Python API for downsampling a huge dataset? > What I would like to do: > > * get my dataset > * read out RasterXSize and RasterYSize > * calculate how many lines and rows I need to skip to get a quick overview > image, e.g. 10 lines to skip. > * Have a ReadAsArray interface where I can say something like this: > ** data = ds.ReadAsArray(xoffset, yoffset, 10000, 10000, skipping=10) > > which in numpy terms would give me every 10nth line like this: > array[:,:,10] > > I really don't need quality at all, just speed, for a rough overview for > further zooming in with lassos, as the images I deal with sometimes have > more than 200 MPixels. > > Is this possible in Python? > I was thinking now, maybe one could use numpy's memmap somehow for this, > don't know much about it, though… > > Thanks for any hints! > > Best regards, > Michael > > > ______________________________**_________________ > gdal-dev mailing list > [email protected] > http://lists.osgeo.org/**mailman/listinfo/gdal-dev<http://lists.osgeo.org/mailman/listinfo/gdal-dev> >
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