dask_image.imread.imread regression

It appears that the more recent upgrades to dask-image’s imread have broken the use case of reading multiple 3D tiffs. (Probably the problem is more general than that, but this is my test case.) To reproduce:

import numpy as np
from dask_image.imread import imread
import tifffile
import tempfile
from skimage import data


blobs = data.binary_blobs(64, n_dim=3)
with tempfile.TemporaryDirectory() as tmpdir:
    for i in range(5):
        tifffile.imsave(os.path.join(tmpdir, f'{i}.tiff'), blobs)
    image = imread(os.path.join(tmpdir, '*.tiff'))
    print(image)
    timepoint = np.asarray(image[0])

Produces the following print output:

dask.array<_map_read_frame, shape=(5, 64, 64, 64), dtype=bool, chunksize=(1, 64, 64, 64), chunktype=numpy.ndarray>

And this traceback:

IndexError: too many indices for array: array is 3-dimensional, but 4 were indexed

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-7-7f255eff488b> in <module>
      4     image = imread(os.path.join(tmpdir, '*.tiff'))
      5     print(image)
----> 6     timepoint = np.asarray(image[0])
      7 

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order, like)
    100         return _asarray_with_like(a, dtype=dtype, order=order, like=like)
    101 
--> 102     return array(a, dtype, copy=False, order=order)
    103 
    104 

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/array/core.py in __array__(self, dtype, **kwargs)
   1474 
   1475     def __array__(self, dtype=None, **kwargs):
-> 1476         x = self.compute()
   1477         if dtype and x.dtype != dtype:
   1478             x = x.astype(dtype)

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/base.py in compute(self, **kwargs)
    283         dask.base.compute
    284         """
--> 285         (result,) = compute(self, traverse=False, **kwargs)
    286         return result
    287 

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/base.py in compute(*args, **kwargs)
    565         postcomputes.append(x.__dask_postcompute__())
    566 
--> 567     results = schedule(dsk, keys, **kwargs)
    568     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    569 

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
     77             pool = MultiprocessingPoolExecutor(pool)
     78 
---> 79     results = get_async(
     80         pool.submit,
     81         pool._max_workers,

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/local.py in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
    512                             _execute_task(task, data)  # Re-execute locally
    513                         else:
--> 514                             raise_exception(exc, tb)
    515                     res, worker_id = loads(res_info)
    516                     state["cache"][key] = res

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/local.py in reraise(exc, tb)
    323     if exc.__traceback__ is not tb:
    324         raise exc.with_traceback(tb)
--> 325     raise exc
    326 
    327 

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    221     try:
    222         task, data = loads(task_info)
--> 223         result = _execute_task(task, data)
    224         id = get_id()
    225         result = dumps((result, id))

~/miniconda3/envs/new-dask-image/lib/python3.9/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
    119         # temporaries by their reference count and can execute certain
    120         # operations in-place.
--> 121         return func(*(_execute_task(a, cache) for a in args))
    122     elif not ishashable(arg):
    123         return arg

IndexError: too many indices for array: array is 3-dimensional, but 4 were indexed

This works fine with dask-image 0.5.0.

Environment:

• Dask version: 2021.04.1
• Python version: 3.9.4
• Operating System: Linux
• Install method (conda, pip, source): pip

CC @DragaDoncila, who first discovered the bug.

[jni]

@VolkerH to get over the performance issue with da.stack, here’s my example for loading many tiffs using map_blocks:

https://github.com/jni/napari-demos/blob/6860b1fe86a51b30874c34150ae216f4c39b2dd6/rootomics.py#L20-L54

[GenevieveBuckley]

Ii believe the current workarounds were:

1. Revert to an earlier version of dask-image, or
2. Try from dask.array.image import imread

It’s on my to-do list to benchmark all these variations and fix the regression. (No timeframe on that, though)