def get_grad_operator(mask):
"""Returns sparse matrix computing horizontal, vertical, and two diagonal gradients."""
horizontal_left = np.ravel_multi_index(np.nonzero(mask[:, :-1] | mask[:, 1:]), mask.shape)
horizontal_right = horizontal_left + 1
vertical_top = np.ravel_multi_index(np.nonzero(mask[:-1, :] | mask[1:, :]), mask.shape)
vertical_bottom = vertical_top + mask.shape[1]
diag_main_1 = np.ravel_multi_index(np.nonzero(mask[:-1, :-1] | mask[1:, 1:]), mask.shape)
diag_main_2 = diag_main_1 + mask.shape[1] + 1
diag_sub_1 = np.ravel_multi_index(np.nonzero(mask[:-1, 1:] | mask[1:, :-1]), mask.shape) + 1
diag_sub_2 = diag_sub_1 + mask.shape[1] - 1
indices = np.stack((
np.concatenate((horizontal_left, vertical_top, diag_main_1, diag_sub_1)),
np.concatenate((horizontal_right, vertical_bottom, diag_main_2, diag_sub_2))
), axis=-1)
return scipy.sparse.coo_matrix(
(np.tile([-1, 1], len(indices)), (np.arange(indices.size) // 2, indices.flatten())),
shape=(len(indices), mask.size))
solve_foreground_background.py 文件源码
python
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