k-meech/pybdv

python tools for BigDataViewer

pyBDV

Python tools for BigDataViewer.

Installation

You can install the package from source

python setup.py install

or via conda:

conda install -c conda-forge pybdv

Usage

Python

Write out numpy array volume to bdv format:

from pybdv import make_bdv

out_path = '/path/to/out'

# the scale factors determine the levels of the multi-scale pyramid
# that will be created by pybdv.
# the downscaling factors are interpreted relative to the previous factor
# (rather than absolute) and the zeroth scale level (corresponding to [1, 1, 1])
# is implicit, i.e. DON'T specify it
scale_factors = [[2, 2, 2], [2, 2, 2], [4, 4, 4]]

# the downscale mode determines the method for downscaling:
# - interpolate: cubic interpolation
# - max: downscale by maximum
# - mean: downscale by averaging
# - min: downscale by minimum
# - nearest: nearest neighbor downscaling
mode = 'mean'

# specify a resolution of 0.5 micron per pixel (for zeroth scale level)
make_bdv(volume, out_path,
         downscale_factors=scale_factors, downscale_mode=mode,
         resolution=[0.5, 0.5, 0.5], unit='micrometer')

Convert hdf5 dataset to bdv format:

from pybdv import convert_to_bdv

in_path = '/path/to/in.h5'
in_key = 'data'
out_path = '/path/to/out'

# keyword arguments are same as for 'make_bdv'
convert_to_bdv(in_path, in_key, out_path,
               resolution=[0.5, 0.5, 0.5], unit='micrometer')

Command line

You can also call convert_to_bdv via the command line:

convert_to_bdv /path/to/in.h5 data /path/to/out --downscale_factors "[[2, 2, 2], [2, 2, 2], [4, 4, 4]]" --downscale_mode nearest --resolution 0.5 0.5 0.5 --unit micrometer

The downscale factors need to be encoded as json list.

Conversion to n5-bdv format

Bigdatviewer core also supports an n5 based data format. The data can be converted to this format by passing a path with n5 ending as output path: /path/to/out.n5. In order to support this, you need to install z5py.

Advanced IO options

If elf is available, additional file input formats are supported. For example, it is possible to convert inputs from tif slices

import os
import imageio
import numpy as np
from pybdv import convert_to_bdv


input_path = './slices'
os.makedirs(input_path, exist_ok=True)
n_slices = 25
shape = (256, 256)

for slice_id in range(n_slices):
    imageio.imsave('./slices/im%03i.tif', np.random.randint(0, 255, size=shape, dtype='uint8'))

input_key = '*.tif'
output_path = 'from_slices.h5'
convert_to_bdv(input_path, input_key, output_path)

or tif stacks:

import imageio
import numpy as np
from pybdv import convert_to_bdv


input_path = './stack.tif'
shape = (25, 256, 256)

imageio.volsave(input_path, np.random.randint(0, 255, size=shape, dtype='uint8'))

input_key = ''
output_path = 'from_stack.h5'
convert_to_bdv(input_path, input_key, output_path)

On-the-fly processing

Data can also be added on the fly, using first pybdv.initialize_dataset to create the bdv file and then BdvDataset to add (and downscale) new sub-regions of the data on the fly. See examples/on-the-fly.py for details.