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283 | @validate_call
def calculate_registration_image_based(
*,
# Fractal arguments
zarr_url: str,
init_args: InitArgsRegistration,
# Core parameters
wavelength_id: str,
method: RegistrationMethod = RegistrationMethod.PHASE_CROSS_CORRELATION,
roi_table: str = "FOV_ROI_table",
level: int = 2,
) -> None:
"""
Calculate registration based on images
This task consists of 3 parts:
1. Loading the images of a given ROI (=> loop over ROIs)
2. Calculating the transformation for that ROI
3. Storing the calculated transformation in the ROI table
Args:
zarr_url: Path or url to the individual OME-Zarr image to be processed.
(standard argument for Fractal tasks, managed by Fractal server).
init_args: Intialization arguments provided by
`image_based_registration_hcs_init`. They contain the
reference_zarr_url that is used for registration.
(standard argument for Fractal tasks, managed by Fractal server).
wavelength_id: Wavelength that will be used for image-based
registration; e.g. `A01_C01` for Yokogawa, `C01` for MD.
method: Method to use for image registration. The available methods
are `phase_cross_correlation` (scikit-image package, works for 2D
& 3D) and "chi2_shift" (image_registration package, only works for
2D images).
roi_table: Name of the ROI table over which the task loops to
calculate the registration. Examples: `FOV_ROI_table` => loop over
the field of views, `well_ROI_table` => process the whole well as
one image.
level: Pyramid level of the image to be used for registration.
Choose `0` to process at full resolution.
"""
logger.info(
f"Running for {zarr_url=}.\n"
f"Calculating translation registration per {roi_table=} for "
f"{wavelength_id=}."
)
init_args.reference_zarr_url = init_args.reference_zarr_url
# Read some parameters from Zarr metadata
ngff_image_meta = load_NgffImageMeta(str(init_args.reference_zarr_url))
coarsening_xy = ngff_image_meta.coarsening_xy
# Get channel_index via wavelength_id.
# Intially only allow registration of the same wavelength
channel_ref: OmeroChannel = get_channel_from_image_zarr(
image_zarr_path=init_args.reference_zarr_url,
wavelength_id=wavelength_id,
)
channel_index_ref = channel_ref.index
channel_align: OmeroChannel = get_channel_from_image_zarr(
image_zarr_path=zarr_url,
wavelength_id=wavelength_id,
)
channel_index_align = channel_align.index
# Lazily load zarr array
data_reference_zyx = da.from_zarr(
f"{init_args.reference_zarr_url}/{level}"
)[channel_index_ref]
data_alignment_zyx = da.from_zarr(f"{zarr_url}/{level}")[
channel_index_align
]
# Check if data is 3D (as not all registration methods work in 3D)
# TODO: Abstract this check into a higher-level Zarr loading class
if is_3D(data_reference_zyx):
if method == RegistrationMethod(RegistrationMethod.CHI2_SHIFT):
raise ValueError(
f"The `{RegistrationMethod.CHI2_SHIFT}` registration method "
"has not been implemented for 3D images and the input image "
f"had a shape of {data_reference_zyx.shape}."
)
# Read ROIs
ROI_table_ref = ad.read_zarr(
f"{init_args.reference_zarr_url}/tables/{roi_table}"
)
ROI_table_x = ad.read_zarr(f"{zarr_url}/tables/{roi_table}")
logger.info(
f"Found {len(ROI_table_x)} ROIs in {roi_table=} to be processed."
)
# Check that table type of ROI_table_ref is valid. Note that
# "ngff:region_table" and None are accepted for backwards compatibility
valid_table_types = [
"roi_table",
"masking_roi_table",
"ngff:region_table",
None,
]
ROI_table_ref_group = zarr.open_group(
f"{init_args.reference_zarr_url}/tables/{roi_table}",
mode="r",
)
ref_table_attrs = ROI_table_ref_group.attrs.asdict()
ref_table_type = ref_table_attrs.get("type")
if ref_table_type not in valid_table_types:
raise ValueError(
(
f"Table '{roi_table}' (with type '{ref_table_type}') is "
"not a valid ROI table."
)
)
# For each acquisition, get the relevant info
# TODO: Add additional checks on ROIs?
if (ROI_table_ref.obs.index != ROI_table_x.obs.index).all():
raise ValueError(
"Registration is only implemented for ROIs that match between the "
"acquisitions (e.g. well, FOV ROIs). Here, the ROIs in the "
f"reference acquisitions were {ROI_table_ref.obs.index}, but the "
f"ROIs in the alignment acquisition were {ROI_table_x.obs.index}"
)
# TODO: Make this less restrictive? i.e. could we also run it if different
# acquisitions have different FOVs? But then how do we know which FOVs to
# match?
# If we relax this, downstream assumptions on matching based on order
# in the list will break.
# Read pixel sizes from zarr attributes
ngff_image_meta_acq_x = load_NgffImageMeta(zarr_url)
pxl_sizes_zyx = ngff_image_meta.get_pixel_sizes_zyx(level=0)
pxl_sizes_zyx_acq_x = ngff_image_meta_acq_x.get_pixel_sizes_zyx(level=0)
if pxl_sizes_zyx != pxl_sizes_zyx_acq_x:
raise ValueError(
"Pixel sizes need to be equal between acquisitions for "
"registration."
)
# Create list of indices for 3D ROIs spanning the entire Z direction
list_indices_ref = convert_ROI_table_to_indices(
ROI_table_ref,
level=level,
coarsening_xy=coarsening_xy,
full_res_pxl_sizes_zyx=pxl_sizes_zyx,
)
check_valid_ROI_indices(list_indices_ref, roi_table)
list_indices_acq_x = convert_ROI_table_to_indices(
ROI_table_x,
level=level,
coarsening_xy=coarsening_xy,
full_res_pxl_sizes_zyx=pxl_sizes_zyx,
)
check_valid_ROI_indices(list_indices_acq_x, roi_table)
num_ROIs = len(list_indices_ref)
compute = True
new_shifts = {}
for i_ROI in range(num_ROIs):
logger.info(
f"Now processing ROI {i_ROI+1}/{num_ROIs} "
f"for channel {channel_align}."
)
img_ref = load_region(
data_zyx=data_reference_zyx,
region=convert_indices_to_regions(list_indices_ref[i_ROI]),
compute=compute,
)
img_acq_x = load_region(
data_zyx=data_alignment_zyx,
region=convert_indices_to_regions(list_indices_acq_x[i_ROI]),
compute=compute,
)
##############
# Calculate the transformation
##############
if img_ref.shape != img_acq_x.shape:
raise NotImplementedError(
"This registration is not implemented for ROIs with "
"different shapes between acquisitions."
)
shifts = method.register(np.squeeze(img_ref), np.squeeze(img_acq_x))[0]
##############
# Store the calculated transformation ###
##############
# Adapt ROIs for the given ROI table:
ROI_name = ROI_table_ref.obs.index[i_ROI]
new_shifts[ROI_name] = calculate_physical_shifts(
shifts,
level=level,
coarsening_xy=coarsening_xy,
full_res_pxl_sizes_zyx=pxl_sizes_zyx,
)
# Write physical shifts to disk (as part of the ROI table)
logger.info(f"Updating the {roi_table=} with translation columns")
image_group = zarr.group(zarr_url)
new_ROI_table = get_ROI_table_with_translation(ROI_table_x, new_shifts)
write_table(
image_group,
roi_table,
new_ROI_table,
overwrite=True,
table_attrs=ref_table_attrs,
)
|