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Moved registration across channels #164

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Merged
joshua-gould merged 1 commit into from
Jun 23, 2026
+56 −37
Merged

Moved registration across channels #164

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93 changes: 56 additions & 37 deletions scallops/stitch/fuse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -393,6 +393,50 @@ def _fuse(
)


def _register_across_channels(
img: np.ndarray,
channel_reference: int | None = None,
channel_cross_correlation_upsample: int = 1,
channel_window: int = 1,
channel_filter_percentiles: tuple[float, float] | None = None,
):
ref_values = img[channel_reference]
img_dtype = ref_values.dtype
if channel_window > 1:
ref_values = _apply_window(ref_values, channel_window)
if channel_filter_percentiles is not None:
ref_values = _filter_percentiles(
ref_values,
q1=channel_filter_percentiles[0],
q2=channel_filter_percentiles[1],
)

for c in range(img.shape[0]):
if c != channel_reference:
moving = img[c]
if channel_window > 1:
moving = _apply_window(moving, channel_window)
if channel_filter_percentiles is not None:
moving = _filter_percentiles(
moving,
q1=channel_filter_percentiles[0],
q2=channel_filter_percentiles[1],
)

offset, _ = calc_best_shift(
moving,
ref_values,
upsample_factor=channel_cross_correlation_upsample,
overlap_min=0,
)
# offset, _, _ = phase_cross_correlation(moving, ref_values)

if not np.all(offset == 0.0):
# skimage SimilarityTransform has (x,y,[z]) convention
st = SimilarityTransform(translation=offset[::-1])
img[c] = warp(img[c], st, preserve_range=True).astype(img_dtype)


def _fuse_image(
image_paths: list[str] | zarr.Group,
image_attrs: dict[str, str | list[str]],
Expand Down Expand Up @@ -431,46 +475,11 @@ def _fuse_image(
if "z" in img.dims:
img = img.max(dim="z") if not isinstance(z_index, int) else img.isel(z=z_index)
img = img.values
if channel_reference is not None:
ref_values = img[channel_reference]
img_dtype = ref_values.dtype
if channel_window > 1:
ref_values = _apply_window(ref_values, channel_window)
if channel_filter_percentiles is not None:
ref_values = _filter_percentiles(
ref_values,
q1=channel_filter_percentiles[0],
q2=channel_filter_percentiles[1],
)

for c in range(img.shape[0]):
if c != channel_reference:
moving = img[c]
if channel_window > 1:
moving = _apply_window(moving, channel_window)
if channel_filter_percentiles is not None:
moving = _filter_percentiles(
moving,
q1=channel_filter_percentiles[0],
q2=channel_filter_percentiles[1],
)

offset, _ = calc_best_shift(
moving,
ref_values,
upsample_factor=channel_cross_correlation_upsample,
overlap_min=0,
)
# offset, _, _ = phase_cross_correlation(moving, ref_values)

if not np.all(offset == 0.0):
# skimage SimilarityTransform has (x,y,[z]) convention
st = SimilarityTransform(translation=offset[::-1])
img[c] = warp(img[c], st, preserve_range=True).astype(img_dtype)
if output_channels is not None:
if output_channels is not None and channel_reference is None:
img = img[output_channels]

# order: radial, illumination, crop
# order: radial, illumination, channel registration, crop
if radial_correction_k is not None:
img = radial_correct(img, radial_correction_k)
img = dtype_convert(img, target_dtype)
Expand All @@ -481,6 +490,16 @@ def _fuse_image(
if ffp is not None:
img /= ffp
img.clip(0, 1, out=img)
if channel_reference is not None:
_register_across_channels(
img=img,
channel_reference=channel_reference,
channel_cross_correlation_upsample=channel_cross_correlation_upsample,
channel_window=channel_window,
channel_filter_percentiles=channel_filter_percentiles,
)
if output_channels is not None:
img = img[output_channels]

img = _crop_image(img, crop_width)

Expand Down