[gdal-dev] best way to compare in a numpy table the values of two images with different projection

[Matthieu Rigal]

Hi folks,

I am currently implementing a tool to allow band-to-band computations on
array values out of 2 images with different projection and different 
resolution in python.

The operation is quite complex, so I will summarize it here and below give 
the solution I am currently testing to see if it is the best one and ask 
for some enhancements and performance hints. Unfortunately, the high-level 
warping class is, at my knowledge, only available in C++.

Workflow (could also be improved by all-in-one functions like for C++)
- Open image A, 5000*5000 pixels, 5m resolution, UTM
- Open image B, approx 100m resolution, LatLon, much larger than the 
previous one
- Cut out the buffered extents of image A from image B
- Reproject this cutted image to the projection and resolution of image A
- Read full image A as array
- Read extents of image A from image B as array

It sounds easy, but when I started implementing it, I figured out that it 
was quite complicated and that it would be quite time consuming, so if you 
have any suggestions, this is my pseudo-code (I thought about writing a 
real code, but it may be too long for the list, if I want to also include 
the generation of image A and B at the beginning):
- gdal.Open(imgA, imgB)
- GetGeoTransform(imgA, imgB)
- CreateGeometry(boundaries of imgA).Buffer
- Transform(projection imgB)
- GetEnvelope(transformed geometry)
- transform to in-image of imgB x, y, x_off, y_off
- Create new image in memory
- Calculate its geotransform
- Read the bands into memory (GetRasterBand().Read and .Write() or 
ReadIO() ?)
- Reproject the image
- Get GeoTransform
- Calculate in-image coordinates to finally read exactly the same window as 
array

Best Regards,
Matthieu

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