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feat(manual-correspondence): add src_mask and tgt_mask parameters #1156

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Merged
magic-vladyslav merged 1 commit into from
Jan 27, 2026
Merged

feat(manual-correspondence): add src_mask and tgt_mask parameters #1156

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33 changes: 32 additions & 1 deletion web_api/app/alignment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,10 @@ class CorrespondencesDict(BaseModel):
..., description="List of correspondence lines"
)


# TODO: Refactor endpoint:
# - receive binary blobs (json-descriptor, image-data, src-mask-data, tgt-mask-data)
# - decode blobs to numpy arrays
# - return blob files (relaxed-field-data, warped-image-data) as gzipped stream
class ApplyCorrespondencesRequest(BaseModel):
correspondences_dict: CorrespondencesDict = Field(
..., description="Dictionary with correspondence lines"
Expand All @@ -48,6 +51,16 @@ class ApplyCorrespondencesRequest(BaseModel):
optimizer_type: str = Field(
"adam", description="Optimizer to use (adam, lbfgs, sgd, adamw)"
)
src_mask: list[list[list[list[float]]]] | None = Field(
None,
description="Binary tissue mask (1=tissue, 0=non-tissue), shape (1, H, W, 1)."
"Zeros break rigidity constraints between regions.",
)
tgt_mask: list[list[list[list[float]]]] | None = Field(
None,
description="Binary tissue mask (1=tissue, 0=non-tissue), shape (1, H, W, 1)."
"Zeros break rigidity constraints between regions.",
)


class ApplyCorrespondencesResponse(BaseModel):
Expand Down Expand Up @@ -106,13 +119,31 @@ async def apply_correspondences(request: ApplyCorrespondencesRequest):
device=device
)

src_mask_tensor = None
if request.src_mask is not None:
src_mask_tensor = torch.tensor(
request.src_mask,
dtype=torch.float32,
device=device
)

tgt_mask_tensor = None
if request.tgt_mask is not None:
tgt_mask_tensor = torch.tensor(
request.tgt_mask,
dtype=torch.float32,
device=device
)

relaxed_field, warped_image = apply_correspondences_to_image(
correspondences_dict=correspondences_dict,
image=image_tensor,
num_iter=request.num_iter,
rig=request.rig,
lr=request.lr,
optimizer_type=request.optimizer_type,
src_mask=src_mask_tensor,
tgt_mask=tgt_mask_tensor,
)

# Convert to numpy arrays on CPU
Expand Down
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