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KeypointAwareCrop: uniform sampling when no keypoints are present #2801

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Merged
MMathisLab merged 2 commits into from
Dec 3, 2024
Merged

KeypointAwareCrop: uniform sampling when no keypoints are present #2801

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c7f6655
bug fix KeypointAwareCrop: uniform sampling when no keypoints present
n-poulsen Dec 3, 2024
1eda615
symmetric translation px
n-poulsen Dec 3, 2024
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2 changes: 1 addition & 1 deletion deeplabcut/pose_estimation_pytorch/data/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,7 +250,7 @@ def _prepare_final_annotation_dict(

# we use ..., :3 to pass the visibility flag along
return {
"keypoints": pad_to_length(keypoints[..., :3], num_animals, -1).astype(
"keypoints": pad_to_length(keypoints[..., :3], num_animals, 0).astype(
np.single
),
"keypoints_unique": keypoints_unique[..., :3].astype(np.single),
Expand Down
6 changes: 4 additions & 2 deletions deeplabcut/pose_estimation_pytorch/data/transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,7 @@ def build_transforms(augmentations: dict) -> A.BaseCompose:
if rotation is not None:
rotation = (-rotation, rotation)
if translation is not None:
translation = (0, translation)
translation = (-translation, translation)

transforms.append(
A.Affine(
Expand Down Expand Up @@ -281,6 +281,8 @@ def get_params_dependent_on_targets(self, params: dict[str, Any]) -> dict[str, A
sampling = self.crop_sampling
if self.crop_sampling == "hybrid":
sampling = np.random.choice(["uniform", "density"])
if len(kpts) == 0:
sampling = "uniform"
if sampling == "uniform":
center = np.random.random(2)
else:
Expand Down Expand Up @@ -310,7 +312,7 @@ def apply_to_keypoints(
self,
keypoints,
**params,
) -> list[float]:
) -> list[tuple[float]]:
keypoints = super().apply_to_keypoints(keypoints, **params)
new_keypoints = []
for kp in keypoints:
Expand Down