LandmarkMatcher

class lib.training.data.collate.LandmarkMatcher(folders: list[str], size: int, centering: CenteringType, coverage: float, y_offset: float, num_choices: int = 10)

Bases: object

Prepares landmarks when Warp-to-Landmarks is enabled.

2 sides (A/B) only.

For each side, stores the aligned landmarks for each side and collates the 10 nearest matches on the other side for random warping

Parameters:

  • folders (list[str]) – Two training folders for sides A and B

  • size (int) – The aligned face size to transform the landmarks to

  • centering (CenteringType) – The aligned centering to transform the landmarks to

  • coverage (float) – Additional coverage ratio to be applied

  • y_offset (float) – Additional vertical offset to be applied

  • num_choices (int) – Number of choices from the opposite side to cache for each landmark. Default: 10

Methods Summary

get_close_landmarks(indices)

For the given image indices, obtain a randomly selected close match landmarks from the other side

Methods Documentation

get_close_landmarks(indices: npt.NDArray[np.int64]) → npt.NDArray[np.float32]

For the given image indices, obtain a randomly selected close match landmarks from the other side

Parameters:

indices (npt.NDArray[np.int64]) – The (num_inputs, landmark_indices) image file indices to obtain the matches for

Returns:

  • 2 sets of landmarks in shape (num_sides * batch_size, num_sides, 68, 2) stacked to a batch

  • of landmark points for augmentation

Return type:

npt.NDArray[np.float32]

get_close_landmarks(indices: npt.NDArray[np.int64]) → npt.NDArray[np.float32]

For the given image indices, obtain a randomly selected close match landmarks from the other side

Parameters:

indices (npt.NDArray[np.int64]) – The (num_inputs, landmark_indices) image file indices to obtain the matches for

Returns:

  • 2 sets of landmarks in shape (num_sides * batch_size, num_sides, 68, 2) stacked to a batch

  • of landmark points for augmentation

Return type:

npt.NDArray[np.float32]