Open JoOkuma opened 3 years ago
Yes, ELEPHANT expects img.zarr
to have a specific channel order and shape (T, Z, Y, X)
, while its dtype and chunksize can be flexible.
https://github.com/elephant-track/elephant-server/blob/v0.2.0/elephant-core/elephant/tool/dataset.py
By default, ELPHANT creates imgs.zarr
from .h5
with a dtype of uint8
or uint16
depending on the original data, and a shape of (T, Z, Y, X)
.
If you manually prepare imgs.zarr
, please additionally prepare other .zarr
files (see below) for ELEPHANT with the format specified in the table. The existances of these files and its dtype and shape are checked before each command.
dataset
├── flow_hashes.zarr
├── flow_labels.zarr
├── flow_outputs.zarr
├── imgs.zarr
├── seg_labels_vis.zarr
├── seg_labels.zarr
└── seg_outputs.zarr
file | dtype | shape |
---|---|---|
flow_hashes.zarr | S16 | (T - 1,) |
flow_labels.zarr | f4 | (T - 1, 4, Z, Y, X) |
flow_outputs.zarr | f2 | (T - 1, 3, Z, Y, X) |
seg_labels_vis.zarr | u1 | (T, Z, Y, X, 3) |
seg_labels.zarr | u1 | (T, Z, Y, X) |
seg_outputs.zarr | f2 | (T, Z, Y, X, 3) |
The BigDataViewer .h5
files store image data using uint16
. If the maximum value in the image data is smaller than 256
, we use uint8
to save the storage, otherwise we use uint16
.
At runtime, image data stored in img.zarr
is converted to float32
and normalized in the range [0, 1]
.
Hi, Is there any restriction/standard for the
dataset
imgs.zarr` format (dtype, channel order, chunksize, etc.)?I'm currently converting my data from zarr to the BigDataViewer format and I would like to skip the step of converting back to zarr for the machine learning and use my original data insted.
Does it work with any zarr array with axis T, Z, Y, X, and integer or floating values?