Addition of chi2_shift as a Method to calculate Shifts to the "Calculate Registration (image-based)" Task

fractal_tasks_core/__FRACTAL_MANIFEST__.json

@@ -1030,6 +1030,16 @@
             "type": "string",
             "description": "Wavelength that will be used for image-based registration; e.g. `A01_C01` for Yokogawa, `C01` for MD."
           },
+          "method": {
+            "default": "phase_cross_correlation",
+            "allOf": [
+              {
+                "$ref": "#/definitions/RegistrationMethod"
+              }
+            ],
+            "title": "Method",
+            "description": "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": {
             "title": "Roi Table",
             "default": "FOV_ROI_table",
@@ -1063,6 +1073,14 @@
             "required": [
               "reference_zarr_url"
             ]
+          },
+          "RegistrationMethod": {
+            "title": "RegistrationMethod",
+            "description": "An enumeration.",
+            "enum": [
+              "phase_cross_correlation",
+              "chi2_shift"
+            ]
           }
         }
       },

fractal_tasks_core/tasks/_registration_utils.py

@@ -8,8 +8,10 @@
 import copy
 
 import anndata as ad
+import dask.array as da
 import numpy as np
 import pandas as pd
+from image_registration import chi2_shift
 
 from fractal_tasks_core.ngff.zarr_utils import load_NgffWellMeta
 from fractal_tasks_core.tasks._zarr_utils import _split_well_path_image_path
@@ -235,3 +237,51 @@ def apply_registration_to_single_ROI_table(
             + float(min_df.loc[roi, "translation_x"])
         )
     return roi_table
+
+
+def chi2_shift_out(img_ref, img_cycle_x) -> list[np.ndarray]:
+    """
+    Helper function to get the output of chi2_shift into the same format as
+    phase_cross_correlation. Calculates the shift between two images using
+    the chi2_shift method.
+
+    Args:
+        img_ref (np.ndarray): First image.
+        img_cycle_x (np.ndarray): Second image.
+
+    Returns:
+        List containing numpy array of shift in y and x direction.
+    """
+    x, y, a, b = chi2_shift(np.squeeze(img_ref), np.squeeze(img_cycle_x))
+
+    """
+    Running into issues when using direct float output for fractal.
+    When rounding to integer and using integer dtype, it typically works
+    but for some reasons fails when run over a whole 384 well plate (but
+    the well where it fails works fine when run alone). For now, rounding
+    to integer, but still using float64 dtype (like the scikit-image
+    phase cross correlation function) seems to be the safest option.
+    """
+    shifts = np.array([-np.round(y), -np.round(x)], dtype="float64")
+    # return as a list to adhere to the phase_cross_correlation output format
+    return [shifts]
+
+
+def is_3D(dask_array: da.array) -> bool:
+    """
+    Check if a dask array is 3D.
+
+    Treats singelton Z dimensions as 2D images.
+    (1, 2000, 2000) => False
+    (10, 2000, 2000) => True
+
+    Args:
+        dask_array: Input array to be checked
+
+    Returns:
+        bool on whether the array is 3D
+    """
+    if len(dask_array.shape) == 3 and dask_array.shape[0] > 1:
+        return True
+    else:
+        return False

fractal_tasks_core/tasks/calculate_registration_image_based.py

@@ -13,6 +13,7 @@
 Calculates translation for image-based registration
 """
 import logging
+from enum import Enum
 
 import anndata as ad
 import dask.array as da
@@ -36,14 +37,27 @@
 from fractal_tasks_core.tasks._registration_utils import (
     calculate_physical_shifts,
 )
+from fractal_tasks_core.tasks._registration_utils import chi2_shift_out
 from fractal_tasks_core.tasks._registration_utils import (
     get_ROI_table_with_translation,
 )
+from fractal_tasks_core.tasks._registration_utils import is_3D
 from fractal_tasks_core.tasks.io_models import InitArgsRegistration
 
 logger = logging.getLogger(__name__)
 
 
+class RegistrationMethod(Enum):
+    PHASE_CROSS_CORRELATION = "phase_cross_correlation"
+    CHI2_SHIFT = "chi2_shift"
+
+    def register(self, img_ref, img_acq_x):
+        if self == RegistrationMethod.PHASE_CROSS_CORRELATION:
+            return phase_cross_correlation(img_ref, img_acq_x)
+        elif self == RegistrationMethod.CHI2_SHIFT:
+            return chi2_shift_out(img_ref, img_acq_x)
+
+
 @validate_arguments
 def calculate_registration_image_based(
     *,
@@ -52,6 +66,7 @@
     init_args: InitArgsRegistration,
     # Core parameters
     wavelength_id: str,
+    method: RegistrationMethod = "phase_cross_correlation",
     roi_table: str = "FOV_ROI_table",
     level: int = 2,
 ) -> None:
@@ -73,6 +88,10 @@
             (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
@@ -115,6 +134,16 @@
         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 == "chi2_shift":
+            raise ValueError(
+                "The `chi2_shift` registration method has not been "
+                "implemented for 3D images and the input image had a shape of "
+                f"{data_reference_zyx.shape}."
+            )
+
     # Read ROIs
     ROI_table_ref = ad.read_zarr(
         f"{init_args.reference_zarr_url}/tables/{roi_table}"
@@ -211,30 +240,17 @@
         ##############
         #  Calculate the transformation
         ##############
-        # Basic version (no padding, no internal binning)
         if img_ref.shape != img_acq_x.shape:
             raise NotImplementedError(
                 "This registration is not implemented for ROIs with "
                 "different shapes between acquisitions."
             )
-        shifts = phase_cross_correlation(
-            np.squeeze(img_ref), np.squeeze(img_acq_x)
-        )[0]
-
-        # Registration based on scmultiplex, image-based
-        # shifts, _, _ = calculate_shift(np.squeeze(img_ref),
-        #           np.squeeze(img_acq_x), bin=binning, binarize=False)
 
-        # TODO: Make this work on label images
-        # (=> different loading) etc.
+        shifts = method.register(np.squeeze(img_ref), np.squeeze(img_acq_x))[0]
 
         ##############
-        # Storing the calculated transformation ###
+        # Store the calculated transformation ###
         ##############
-        # Store the shift in ROI table
-        # TODO: Store in OME-NGFF transformations: Check SpatialData approach,
-        # per ROI storage?
-
         # Adapt ROIs for the given ROI table:
         ROI_name = ROI_table_ref.obs.index[i_ROI]
         new_shifts[ROI_name] = calculate_physical_shifts(

fractal_tasks_core/tasks/find_registration_consensus.py

@@ -150,6 +150,7 @@ def find_registration_consensus(
             new_roi_table,
             shifted_rois[acq_zarr_url],
             table_attrs=roi_tables_attrs[acq_zarr_url],
+            overwrite=True,
         )
 
     # TODO: Optionally apply registration to other tables as well?

pyproject.toml

@@ -46,9 +46,10 @@ napari-skimage-regionprops = { version = "^0.8.1", optional = true }
 napari-tools-menu = { version = "^0.1.19", optional = true }
 cellpose = { version = "~2.2", optional = true }
 torch = { version = "<=2.0.0", optional = true }
+image_registration = { version = ">=0.2.9", optional = true }
 
 [tool.poetry.extras]
-fractal-tasks = ["Pillow", "imageio-ffmpeg", "scikit-image", "llvmlite", "napari-segment-blobs-and-things-with-membranes", "napari-workflows", "napari-skimage-regionprops", "napari-tools-menu", "cellpose", "torch"]
+fractal-tasks = ["Pillow", "imageio-ffmpeg", "scikit-image", "llvmlite", "napari-segment-blobs-and-things-with-membranes", "napari-workflows", "napari-skimage-regionprops", "napari-tools-menu", "cellpose", "torch", "image_registration"]
 
 [tool.poetry.group.dev]
 optional = true