Building ImageJ Hyperstacks from Python

Building ImageJ Hyperstacks from Python

ImageJ calls a 4D image a hyperstack (it actually be 5D or even higher). You can save these and open them and it’ll show a nice GUI for them.

Unfortunately, it’s not very well documented (if at all) how it recognises some images as hyperstacks. This is what I found: A hyperstack is a multi-page TIFF with the image description tag containing information on the hyperstack.

I can generate one by outputting the individual slices to files (in the right order) and then calling tiffcp on the command line to concatenate them together. If the metadata is there, ImageJ will recognise it as a hyperstack.


Here is how to do it in Python and mahotas-imread [1].

Define the metadata (as a template):

_imagej_metadata = """ImageJ=1.47a

Now, we write a function which takes a z-stack and a filename to write to

def output_hyperstack(zs, oname):
    Write out a hyperstack to ``oname``

    zs : 4D ndarray
        dimensions should be (c,z,x,y)
    oname : str
        filename to write to

Some basic imports:

import tempfile
import shutil
from os import system
    # We create a directory to save the results
    tmp_dir = tempfile.mkdtemp(prefix='hyperstack')

    # Channels are in first dimension
    nr_channels = zs.shape[0]
    nr_slices = zs.shape[1]
    nr_images = nr_channels*nr_slices
    metadata = _imagej_metadata.format(

We built up the final metadata string by replacing the right variables into the template. Now, we output all the images as separate TIFF files:

frames = []
next = 0
for s1 in xrange(zs.shape[1]):
    for s0 in xrange(zs.shape[0]):
        fname = '{}/s{:03}.tiff'.format(tmp_dir,next)
        # Do not forget to output the metadata!
        mh.imsave(fname, zs[s0,s1], metadata=metadata)
        next += 1

We call tiffcp to concatenate all the inputs

cmd = "tiffcp {inputs} {tmp_dir}/stacked.tiff".format(inputs=" ".join(frames), tmp_dir=tmp_dir)
r = system(cmd)
if r != 0:
    raise IOError('tiffcp call failed')
shutil.copy('{tmp_dir}/stacked.tiff'.format(tmp_dir=tmp_dir), oname)

Finally, we remove the temporary directory:


And, voilà! This function will output a file with the right format for ImageJ to think it is a hyperstack.

[1] Naturally, you can use other packages, but you need one which lets you write the image description TIFF tag.


Mahotas-imread Now Accepts Options When Writing

This week, I committed to mahotas-imread, some code to allow for setting options when saving:

from imread import imsave
image = ...
imsave('file.jpeg', image, opts={ 'jpeg:quality': 95 })

This saves the image array to file file.jpeg with quality 95 (out of 100).


This is only available in the version from github (at the moment), but I will probably put up a new release soon.

(If you like and use mahotas, please cite the software paper.)

Using imread to save disk space

Imread recently gained the ability to read&write metadata.

We deal with images around here and they can get very large in terms of disk space. To make things worse, the microscope does not save them in compressed form.

Imread, however, saves in compressed TIFF. So, we needed to (1) open the file and (2) resave it. We also do not want to lose the metadata that comes with the file. in the meanwhile.

This is what I ended up with:

def resave_file(f):

    Resave a file using imread preserving metadata    Parameters
    f : str
    imdata, meta = imread.imread(f, return_metadata=True)
    tf = tempfile.NamedTemporaryFile('w',
    imread.imsave(, imdata, metadata=meta)
    os.rename(, f)

On a test directory, disk usage went from 55GB down to 12GB. We use a two-step

Note: This only works with the github version of imread.

Making Your Mistakes in Public

I recently released a new version of mahotas, my computer vision package. It was version 1.0.1, which was quickly followed by 1.0.2.

1.0 had introduced a mistake, which was caught and fixed by Tony S Yu on github (issue 33). Along with a few other improvements, this warranted a new release.

Then, 1.0.1 had a little mistake of its own, caught by Jean-Patrick Pommier [jip’s homepage] on the pythonvision mailing list.

Thus, 1.0.2 was necessary. I released 1.0.2 about an hour after I was alerted to the errors.


This is why I develop my scientific code as open source. (1) Other people catch your mistakes and fix them for you  (and the whole community wins)! (2) because the code was public, I felt a great urge to fix the mistakes very fast. I even added tests to make sure they would not happen again. I am harnessing public pressure for good.

Public code becomes better as its openness pushes me to have it as pristine as possible.