Comments (1)
As currently implemented, the program needs to load enough slices at once to fill the whole thickness of a chunk, which may consume a significant amount of memory if each slice is very large.
You may try to decrease the chunk size in the direction of slice thickness, so less slices need to be loaded simultaneously. You could do so by adding a first scale to the info
file with reduced chunk thickness, and discarding that first scale after the rest of the pyramid is computed.
We could think of a method that automates this process to fill only portions of a chunk at a time, and automatically discards the intermediate data. I have no plans to implement that feature myself though, but I would be happy to review a PR if some good soul wants to work on it 🙂
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Related Issues (16)
- conversion of nii file to segmentations, NG does not properly display converted segment if original encoding is float instead of uint HOT 1
- UnboundLocalError: local variable 'triangles' referenced before assignment HOT 1
- Off-by-one when converting Nifti with scaling to integer data_type HOT 1
- flat files not created with '--flat' option using slices-to-precomputed command HOT 2
- uint64 TIFF files not loading (scikit-image loader only supports strings as input not Path objects)
- tqdm causes slices-to-precomputed to fail HOT 2
- pypi version has bug HOT 1
- get_accessor_for_url doesn't recognize Windows filesystem paths HOT 1
- [feature request] support RGB24 in nifti and multichanneled tiff (RGB) HOT 1
- Apache config HOT 17
- RGB nifti does not work with `volume-to-precomputed-pyramid` out of the box
- [bug] RGB nifti encoding standard unclear
- Support for chunking dataset in memory HOT 2
- [feature request ?] dropping support for py3.5 (ideally also py3.6 and py3.7) HOT 2
- [feature request] neuroglancer http accessor profiler HOT 2
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