4

I am reading in a raster using rasterio, and then upsampling the raster as per the example in the documentation:

def upsample_raster(raster):
    return raster.read(
        out_shape=(raster.height * 2, raster.width * 2, raster.count),
        resampling=resampling.bilinear,
    )

This seems to work fine, except that this method returns the data in a numpy array.

My current application workflow includes operations such as masking which takes as input rasterio's DatasetReader class.

Thus, I am searching for a way to resample a raster and get the result as a DatasetReader or, without dumping the data to disk and re-opening the file, convert a numpy array into a valid DatasetReader.

2

You need to recreate a DatasetReader manually and you can use a MemoryFile to avoid writing to disk.

You can re-use the metadata from the input raster in the DatasetReader, but you'll need to modify the height and width properties and the transform. From documentation:

After these resolution changing operations, the dataset’s resolution and the resolution components of its affine transform property no longer apply to the new arrays.

In the example below note that:

  1. I use a contextmanager so the DatasetReader and MemoryFile objects get cleaned up automatically. This is why I use yield not return in the function
  2. I had to change the order of indexes in raster.read as arrays are (band, row, col) order not (row, col, band) like you used in your snippet.

# Example licensed under cc by-sa 3.0 with attribution required

from contextlib import contextmanager  

import rasterio
from rasterio import Affine, MemoryFile
from rasterio.enums import Resampling

# use context manager so DatasetReader and MemoryFile get cleaned up automatically
@contextmanager
def resample_raster(raster, scale=2):
    t = raster.transform

    # rescale the metadata
    transform = Affine(t.a / scale, t.b, t.c, t.d, t.e / scale, t.f)
    height = raster.height * scale
    width = raster.width * scale

    profile = src.profile
    profile.update(transform=transform, driver='GTiff', height=height, width=width)

    data = raster.read( # Note changed order of indexes, arrays are band, row, col order not row, col, band
            out_shape=(raster.count, height, width),
            resampling=Resampling.bilinear,
        )

    with MemoryFile() as memfile:
        with memfile.open(**profile) as dataset: # Open as DatasetWriter
            dataset.write(data)
            del data

        with memfile.open() as dataset:  # Reopen as DatasetReader
            yield dataset  # Note yield not return     


with rasterio.open('path/to/raster') as src:
    with resample_raster(src) as resampled:
        print('Orig dims: {}, New dims: {}'.format(src.shape, resampled.shape))
        print(repr(resampled))

Orig dims: (4103, 4682), New dims: (8206, 9364)
<open DatasetReader name='/vsimem/95befda0-2061-4294-982b-20e46f127066.' mode='r'>
  • Wow! This is exactly the information I needed. Thank you so much! – Renier Botha Jul 22 at 7:27

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