Function unnormalize_depth

diff2flow/dataset/depth_preprocessing.py:190–224

Unnormalize depth map from [-1, 1] to actual depth values. Args: depth: Torch tensor depth map in range [-1, 1]. q_min: Min quantile used for normalization (planar depths). q_max: Max quantile used for normalization (planar depths). normalization_fn: Function

(depth, q_min, q_max, normalization_fn: str = 'log')

def unnormalize_depth(depth, q_min, q_max, normalization_fn: str = 'log'):
    """
    Unnormalize depth map from [-1, 1] to actual depth values.
    Args:
        depth: Torch tensor depth map in range [-1, 1].
        q_min: Min quantile used for normalization (planar depths).
        q_max: Max quantile used for normalization (planar depths).
        normalization_fn: Function used for normalization (log,
            inverse, identity).
    Returns:
        Unnormalized depth map in range [0, inf].
    """
    # First of all, unnormalize from [-1,1] to [0,1]
    depth = depth / 2 + 0.5

    # Then, unnormalize from [0,1] using the corresponding quantile
    if normalization_fn == "log":
        q_min = torch.log(torch.tensor(q_min))
        q_max = torch.log(torch.tensor(q_max))
    elif normalization_fn == "inverse":
        warnings.warn("Inverse normalization is not working well yet!")
        q_min = 1.0 / q_min
        q_max = 1.0 / q_max
    depth = depth * (q_max - q_min) + q_min

    # Finally, unnormalize using the function
    if normalization_fn == "identity":
        return depth
    elif normalization_fn == "log":
        return torch.exp(depth)
    elif normalization_fn == "inverse":
        depth = depth.clamp(1e-6, 1e6)
        return 1.0 / depth
    else:
        raise ValueError(f"Unknown normalization function: {normalization_fn}")