diff --git a/ngff_zarr/methods/_itk.py b/ngff_zarr/methods/_itk.py index 3faa9115..09e059d8 100644 --- a/ngff_zarr/methods/_itk.py +++ b/ngff_zarr/methods/_itk.py @@ -149,6 +149,11 @@ def _downsample_itk_bin_shrink( # For consistency for now, do not utilize direction until there is standardized support for # direction cosines / orientation in OME-NGFF # block_0.attrs.pop("direction", None) + if "c" in previous_image.dims: + raise ValueError( + "Downsampling with ITK BinShrinkImageFilter does not support channel dimension 'c'. " + "Use ITK Gaussian downsampling instead." + ) block_input = itk.image_from_array(np.ones_like(block_0)) spacing = [previous_image.scale[d] for d in spatial_dims] block_input.SetSpacing(spacing) diff --git a/ngff_zarr/methods/_itkwasm.py b/ngff_zarr/methods/_itkwasm.py index 49106747..c006d749 100644 --- a/ngff_zarr/methods/_itkwasm.py +++ b/ngff_zarr/methods/_itkwasm.py @@ -146,7 +146,7 @@ def _downsample_itkwasm( ] assert all( block_output.size[dim] == computed_size[dim] - for dim in range(block_output.data.ndim) + for dim in range(len(block_output.size)) ) output_chunks = list(previous_image.data.chunks) output_chunks_start = 0 @@ -172,7 +172,7 @@ def _downsample_itkwasm( ] assert all( block_output.size[dim] == computed_size[dim] - for dim in range(block_output.data.ndim) + for dim in range(len(block_output.size)) ) for i in range(len(output_chunks)): output_chunks[i][-1] = block_output.data.shape[i] @@ -181,7 +181,7 @@ def _downsample_itkwasm( non_spatial_dims = [d for d in dims if d not in _spatial_dims] if "c" in non_spatial_dims and previous_image.dims[-1] == "c": - non_spatial_dims.pop("c") + non_spatial_dims.remove("c") if output_chunks_start > 0: # We'll iterate over each index for the non-spatial dimensions, run the desired diff --git a/ngff_zarr/methods/_support.py b/ngff_zarr/methods/_support.py index c7f062cd..00252deb 100644 --- a/ngff_zarr/methods/_support.py +++ b/ngff_zarr/methods/_support.py @@ -111,15 +111,25 @@ def _dim_scale_factors(dims, scale_factor, previous_dim_factors): result_scale_factors = { d: int(scale_factor[d] / previous_dim_factors[d]) for d in scale_factor } + # if a dim is not in the scale_factors, add it with a scale factor of 1 + for d in dims: + if d not in result_scale_factors: + result_scale_factors[d] = 1 + return result_scale_factors + def _update_previous_dim_factors(scale_factor, spatial_dims, previous_dim_factors): + previous_dim_factors = copy.copy(previous_dim_factors) if isinstance(scale_factor, int): - previous_dim_factors = { d : scale_factor for d in spatial_dims } + for d in spatial_dims: + previous_dim_factors[d] = scale_factor else: - previous_dim_factors = scale_factor + for d in scale_factor: + previous_dim_factors[d] = scale_factor[d] return previous_dim_factors + def _align_chunks(previous_image, default_chunks, dim_factors): block_0_shape = [c[0] for c in previous_image.data.chunks] diff --git a/pixi.lock b/pixi.lock index 72e9afa0..b78e7d61 100644 --- a/pixi.lock +++ b/pixi.lock @@ -7,25 +7,25 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py313h46c70d0_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py313h46c70d0_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.4.26-pyhd8ed1ab_0.conda + - 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version: 1.0.5 - sha256: 0b289572c33a0deae62daa57e44a25b99b783e5f7aed81b314232b3d3c81a11d + version: 1.1.0 + sha256: 20ecc8abbd957046f1fe9562757903f5eaf57c3bce70929fda6c7711bb58074a requires_dist: - anyio>=3.0.0 requires_python: '>=3.9' diff --git a/pyproject.toml b/pyproject.toml index a13478a0..a7ac68fc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ dependencies = [ "dask[array]", "importlib_resources", "itkwasm >= 1.0b183", - "itkwasm-downsample >= 1.2.0", + "itkwasm-downsample >= 1.7.1", "numpy", "platformdirs", "psutil; sys_platform != \"emscripten\"", diff --git a/test/_data.py b/test/_data.py index e1b83a09..c7d5803d 100644 --- a/test/_data.py +++ b/test/_data.py @@ -31,7 +31,8 @@ def input_images(): pooch.retrieve( fname="data.tar.gz", path=test_dir, - url=f"https://itk.mypinata.cloud/ipfs/{test_data_ipfs_cid}/data.tar.gz", + url="https://github.com/thewtex/ngff-zarr/releases/download/v0.13.2/ngff-zarr-0.13.2-test-data.tar.gz", + # url=f"https://itk.mypinata.cloud/ipfs/{test_data_ipfs_cid}/data.tar.gz", # url=f"https://{test_data_ipfs_cid}.ipfs.w3s.link/ipfs/{test_data_ipfs_cid}/data.tar.gz", known_hash=f"sha256:{test_data_sha256}", processor=untar, diff --git a/test/test_to_ngff_zarr_itk.py b/test/test_to_ngff_zarr_itk.py index 4f5fa101..ce71c9e0 100644 --- a/test/test_to_ngff_zarr_itk.py +++ b/test/test_to_ngff_zarr_itk.py @@ -1,6 +1,6 @@ import pytest -from ngff_zarr import Methods, to_multiscales +from ngff_zarr import Methods, to_multiscales, to_ngff_image from ._data import verify_against_baseline import platform @@ -20,6 +20,41 @@ def test_bin_shrink_isotropic_scale_factors(input_images): verify_against_baseline(dataset_name, baseline_name, multiscales) +def test_bin_shrink_tzyxc(): + import dask.array as da + + test_array = da.ones((96, 64, 64, 64, 2), chunks=(1, 64, 64, 64, 1), dtype="uint8") + img = to_ngff_image( + test_array, + dims=list("tzyxc"), + scale={ + "t": 100_000.0, + "z": 0.98, + "y": 0.98, + "x": 0.98, + "c": 1.0, + }, + axes_units={ + "t": "millisecond", + "z": "micrometer", + "y": "micrometer", + "x": "micrometer", + }, + name="000x_000y_000z", + ) + + # expect a ValueError + try: + to_multiscales( + img, + scale_factors=[{"z": 2, "y": 2, "x": 2}, {"z": 4, "y": 4, "x": 4}], + method=Methods.ITK_BIN_SHRINK, + ) + assert False, "Expected ValueError for non-spatial dimensions" + except ValueError: + pass + + @pytest.mark.skipif( platform.system() == "Linux" and platform.machine() == "aarch64", reason="Skipping on Linux ARM systems", diff --git a/test/test_to_ngff_zarr_itkwasm.py b/test/test_to_ngff_zarr_itkwasm.py index d5f4a113..569829dc 100644 --- a/test/test_to_ngff_zarr_itkwasm.py +++ b/test/test_to_ngff_zarr_itkwasm.py @@ -107,6 +107,39 @@ def test_bin_shrink_tczyx(): assert multiscales.images[1].data.shape[2] == 16 +def test_bin_shrink_tzyxc(): + import dask.array as da + + test_array = da.ones( + (96, 256, 256, 256, 2), chunks=(1, 128, 128, 128, 1), dtype="uint16" + ) + img = to_ngff_image( + test_array, + dims=list("tzyxc"), + scale={ + "t": 100_000.0, + "z": 0.98, + "y": 0.98, + "x": 0.98, + "c": 1.0, + }, + axes_units={ + "t": "millisecond", + "z": "micrometer", + "y": "micrometer", + "x": "micrometer", + }, + name="000x_000y_000z", + ) + + multiscales = to_multiscales( + img, + scale_factors=[{"z": 2, "y": 2, "x": 2}, {"z": 4, "y": 4, "x": 4}], + method=Methods.ITKWASM_BIN_SHRINK, + ) + assert len(multiscales.images) == 3 + + def test_bin_shrink_isotropic_scale_factors(input_images): dataset_name = "cthead1" image = input_images[dataset_name]