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#
# Copyright 2025 Ziyang Song, USTC. All rights reserved.
#
# This file has been modified from the original version.
# Original copyright (c) 2023 Bingxin Ke, ETH Zurich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/indu1ge/DepthMaster#-citation
# More information about the method can be found at https://indu1ge.github.io/DepthMaster_page
# --------------------------------------------------------------------------
import torch
import logging
def get_depth_normalizer(cfg_normalizer):
if cfg_normalizer is None:
def identical(x):
return x
depth_transform = identical
elif "scale_shift_depth" == cfg_normalizer.type:
depth_transform = ScaleShiftDepthNormalizer(
norm_min=cfg_normalizer.norm_min,
norm_max=cfg_normalizer.norm_max,
min_max_quantile=cfg_normalizer.min_max_quantile,
clip=cfg_normalizer.clip,
)
else:
raise NotImplementedError
return depth_transform
class DepthNormalizerBase:
is_absolute = None
far_plane_at_max = None
def __init__(
self,
norm_min=-1.0,
norm_max=1.0,
) -> None:
self.norm_min = norm_min
self.norm_max = norm_max
raise NotImplementedError
def __call__(self, depth, valid_mask=None, clip=None):
raise NotImplementedError
def denormalize(self, depth_norm, **kwargs):
# For metric depth: convert prediction back to metric depth
# For relative depth: convert prediction to [0, 1]
raise NotImplementedError
class ScaleShiftDepthNormalizer(DepthNormalizerBase):
"""
Use near and far plane to linearly normalize depth,
i.e. d' = d * s + t,
where near plane is mapped to `norm_min`, and far plane is mapped to `norm_max`
Near and far planes are determined by taking quantile values.
"""
is_absolute = False
far_plane_at_max = True
def __init__(
self, norm_min=-1.0, norm_max=1.0, min_max_quantile=0.02, clip=True
) -> None:
self.norm_min = norm_min
self.norm_max = norm_max
self.norm_range = self.norm_max - self.norm_min
self.min_quantile = min_max_quantile
self.max_quantile = 1.0 - self.min_quantile
self.clip = clip
def __call__(self, depth_linear, valid_mask=None, clip=None):
clip = clip if clip is not None else self.clip
if valid_mask is None:
valid_mask = torch.ones_like(depth_linear).bool()
valid_mask = valid_mask & (depth_linear > 0)
# Take quantiles as min and max
_min, _max = torch.quantile(
depth_linear[valid_mask],
torch.tensor([self.min_quantile, self.max_quantile]),
)
# scale and shift
depth_norm_linear = (depth_linear - _min) / (
_max - _min
) * self.norm_range + self.norm_min
if clip:
depth_norm_linear = torch.clip(
depth_norm_linear, self.norm_min, self.norm_max
)
return depth_norm_linear
def scale_back(self, depth_norm):
# scale to [0, 1]
depth_linear = (depth_norm - self.norm_min) / self.norm_range
return depth_linear
def denormalize(self, depth_norm, **kwargs):
logging.warning(f"{self.__class__} is not revertible without GT")
return self.scale_back(depth_norm=depth_norm)
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