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Fix the input name ofcompute_optimal_rotation_intrinsics_batch #180

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Fix the input name ofcompute_optimal_rotation_intrinsics_batch #180

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24 changes: 12 additions & 12 deletions src/depth_anything_3/utils/ray_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,15 +18,15 @@


def compute_optimal_rotation_intrinsics_batch(
rays_origin, rays_target, z_threshold=1e-4, reproj_threshold=0.2, weights=None,
rays_direction, rays_target, z_threshold=1e-4, reproj_threshold=0.2, weights=None,
n_sample = None,
n_iter=100,
num_sample_for_ransac=8,
rand_sample_iters_idx=None,
):
"""
Args:
rays_origin (torch.Tensor): (B, N, 3)
rays_direction (torch.Tensor): (B, N, 3)
rays_target (torch.Tensor): (B, N, 3)
z_threshold (float): Threshold for z value to be considered valid.

Expand All @@ -35,28 +35,28 @@ def compute_optimal_rotation_intrinsics_batch(
focal_length (torch.tensor): (2,)
principal_point (torch.tensor): (2,)
"""
device = rays_origin.device
B, N, _ = rays_origin.shape
device = rays_direction.device
B, N, _ = rays_direction.shape
z_mask = torch.logical_and(
torch.abs(rays_target[:, :, 2]) > z_threshold, torch.abs(rays_origin[:, :, 2]) > z_threshold
torch.abs(rays_target[:, :, 2]) > z_threshold, torch.abs(rays_direction[:, :, 2]) > z_threshold
) # (B, N, 1)
rays_origin = rays_origin.clone()
rays_direction = rays_direction.clone()
rays_target = rays_target.clone()
rays_origin[:, :, 0][z_mask] /= rays_origin[:, :, 2][z_mask]
rays_origin[:, :, 1][z_mask] /= rays_origin[:, :, 2][z_mask]
rays_direction[:, :, 0][z_mask] /= rays_direction[:, :, 2][z_mask]
rays_direction[:, :, 1][z_mask] /= rays_direction[:, :, 2][z_mask]
rays_target[:, :, 0][z_mask] /= rays_target[:, :, 2][z_mask]
rays_target[:, :, 1][z_mask] /= rays_target[:, :, 2][z_mask]

rays_origin = rays_origin[:, :, :2]
rays_direction = rays_direction[:, :, :2]
rays_target = rays_target[:, :, :2]
assert weights is not None, "weights must be provided"
weights[~z_mask] = 0

A_list = []
max_chunk_size = 2
for i in range(0, rays_origin.shape[0], max_chunk_size):
for i in range(0, rays_direction.shape[0], max_chunk_size):
A = ransac_find_homography_weighted_fast_batch(
rays_origin[i:i+max_chunk_size],
rays_direction[i:i+max_chunk_size],
rays_target[i:i+max_chunk_size],
weights[i:i+max_chunk_size],
n_iter=n_iter,
Expand Down Expand Up @@ -520,4 +520,4 @@ def get_extrinsic_from_camray(camray, conf, patch_size_y, patch_size_x, training
],
dim=-2,
) # B, S, 4, 4
return pred_extrinsic, pred_focal_lengths, pred_principal_points
return pred_extrinsic, pred_focal_lengths, pred_principal_points