[gdal-dev] Transformation of image data between pixel/line and projected coordinates - using geolocation arrays, in both directions, without disk access

[Even Rouault]

Daniel,
>
> I am trying to convert image data from cartesian/image coordinates to 
> projected coordinates AND vice versa using geolocation arrays in GDAL. 
> I have two questions:
>
>  1. Since this transformation is part of a processing chain
>     implemented in Python, I try to transform the data directly
>     in-memory, i.e, without any disk access. This saves IO time and
>     avoids permission errors when trying to write temporary data on
>     Windows. How can this be done? I got correct results with the code
>     below, however, only when I temporarily write the data to disk. I
>     tried to write the data to /vsimem/ using the MEM, GTiff and NUMPY
>     drivers. However, gdal.Warp can´t find the data there
>     (FileNotFoundError). I think, also the gdal.Transformer class
>     might be useful and I found an interesting thread on that here
>     <https://lists.osgeo.org/pipermail/gdal-dev/2012-January/031502.html>
>     and a related test in the GDAL autotest suite (here
>     <https://github.com/OSGeo/gdal/blob/master/autotest/alg/transformgeoloc.py>).
>     However, I can´t get it to work for my specific case.
>
There's no reason it won't work with a in-memory dataset.

If you use a MEM dataset, then you need to provide the dataset object 
itself as the input dataset of gdal.Warp() (the name of a MEM dataset is 
completely ignored. a MEM dataset can't be opened, just created).

Similarly with a NUMPY dataset.   With GTiff and /vsimem/, they behave 
as a regular file. If you pass it by name as input of gdal.Warp(), you 
need to make sure to close (ds = None typically) the dataset before, so 
it is properly flushed and can be opened. But you can also pass it as an 
object without that constraint.

You can also create purely in-memory VRT files by assigning them an 
empty name. Then of course you need to provide them as objects to 
gdal.Warp()


>  1. My second question is how I can invert the transformation, i.e.,
>     how can I transform an image with projected coordinates back to
>     cartesian/image coordinates, given that a geolocation array tells
>     GDAL where to put which pixel in the output? Background is a
>     processing pipeline for satellite data where some processing steps
>     are running in sensor geometry (image data as acquired by the
>     sensor, without any geocoding and projection) and I need to
>     provide corresponding AUX data which originally come with
>     projected coordinates.
>
The logic of GDALCreateGenImgProjTransformer2() around 
https://github.com/OSGeo/gdal/blob/master/alg/gdaltransformer.cpp#L1825 
which is for the source dataset should be ported a few lines after for 
the target dataset.  Probably with a new transformer option to be able 
to point to an auxiliary dataset, such as a VRT one of your example, to 
extract the geolocation metadata items from it, that would be different 
from the target dataset itself, because, except perhaps for the netCDF 
case, most GDAL datasets that expose a GEOLOCATION metadata domain must 
be read-only.
>
> 1.
>
> Here is the code I already have to convert a sample image from 
> cartesian to projected coordinates:
>
>     import os
>     from tempfile import TemporaryDirectory
>     from osgeo import gdal, osr
>     import numpy as np
>     from matplotlib import pyplot as plt
>
>
>     # get some test data
>     swath_data = np.random.randint(1, 100, (500, 400))
>     lons, lats = np.meshgrid(np.linspace(3, 5, 500),
>                              np.linspace(40, 42, 400))
>
>     with TemporaryDirectory() as td:
>         p_lons_tmp = os.path.join(td, 'lons.tif')
>         p_lats_tmp = os.path.join(td, 'lats.tif')
>         p_data_tmp = os.path.join(td, 'data.tif')
>         p_data_vrt = os.path.join(td, 'data.vrt')
>         p_data_mapgeo_vrt = os.path.join(td, 'data_mapgeo.vrt')
>
>         # save numpy arrays to temporary tif files
>         for arr, path in zip((swath_data, lons, lats), (p_data_tmp,
>     p_lons_tmp, p_lats_tmp)):
>             rows, cols = arr.shape
>             drv = gdal.GetDriverByName('GTiff')
>             ds = drv.Create(path, cols, rows, 1, gdal.GDT_Float64)
>             ds.GetRasterBand(1).WriteArray(arr)
>             del ds
>
>         # add geolocation information to input data
>         wgs84_wkt = osr.GetUserInputAsWKT('WGS84')
>         utm_wkt = osr.GetUserInputAsWKT('EPSG:32632')
>         ds = gdal.Translate(p_data_vrt, p_data_tmp, format='VRT')
>         ds.SetMetadata(
>
>             dict(
>                 SRS=wgs84_wkt,
>                 X_DATASET=p_lons_tmp,
>                 Y_DATASET=p_lats_tmp,
>                 X_BAND='1',
>                 Y_BAND='1',
>                 PIXEL_OFFSET='0',
>                 LINE_OFFSET='0',
>                 PIXEL_STEP='1',
>                 LINE_STEP='1'
>             ),
>             'GEOLOCATION'
>         )del ds
>
>         # warp from geolocation arrays and read the result
>         gdal.Warp(p_data_mapgeo_vrt, p_data_vrt, format='VRT',
>     geoloc=True,
>                   srcSRS=wgs84_wkt, dstSRS=utm_wkt)
>         data_mapgeo = gdal.Open(p_data_mapgeo_vrt).ReadAsArray()
>
>     # visualize input and output data
>     fig, axes = plt.subplots(1, 4)
>     for i, (arr, title) in enumerate(zip((swath_data, lons, lats,
>     data_mapgeo),
>                                       ('swath data', 'lons', 'lats',
>     'projected data'))):
>         axes[i].imshow(arr, cmap='gray')
>         axes[i].set_title(title)
>     plt.tight_layout()
>     plt.show()
>
>
> Any help would be highly appreciated!
>
> Best,
>
> Daniel Scheffler
>
>
> -- 
>
> M.Sc. Geogr. Daniel Scheffler
>
> Helmholtz Centre Potsdam
> GFZ German Research Centre For Geosciences
> Department 1 - Geodesy and Remote Sensing
> Section 1.4  - Remote Sensing
> Telegrafenberg, 14473 Potsdam, Germany
>
> Phone:  +49 (0)331/288-1198
> e-mail:daniel.scheffler at gfz-potsdam.de
>
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> gdal-dev at lists.osgeo.org
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