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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 numpy as np
import torch
def align_depth_medium_mask(
gt: torch.Tensor,
valid_mask: torch.Tensor,
max_resolution=None,
):
ori_shape = gt.shape[-2:] # input shape
batch_size = gt.shape[0]
# print(gt.shape)
# Downsample
if max_resolution is not None:
scale_factor = np.min(max_resolution / np.array(ori_shape[-2:]))
if scale_factor < 1:
downscaler = torch.nn.Upsample(scale_factor=scale_factor, mode="nearest")
gt = downscaler(gt)
valid_mask = downscaler(valid_mask).bool()
scale_ls = []
shift_ls = []
for i in range(batch_size):
# print('yes')
gt_masked = gt[i][valid_mask[i]]
shift = torch.median(gt_masked).unsqueeze(0)
scale = torch.mean(torch.abs(gt_masked - shift)).unsqueeze(0)
# print(scale)
scale_ls.append(scale)
shift_ls.append(shift)
# print(len(scale_ls))
scale = torch.concat(scale_ls, dim=0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
shift = torch.concat(shift_ls, dim=0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
return scale, shift
def align_depth_least_square(
gt_arr: np.ndarray,
pred_arr: np.ndarray,
valid_mask_arr: np.ndarray,
return_scale_shift=True,
max_resolution=None,
):
ori_shape = pred_arr.shape # input shape
gt = gt_arr.squeeze() # [H, W]
pred = pred_arr.squeeze()
valid_mask = valid_mask_arr.squeeze()
# Downsample
if max_resolution is not None:
scale_factor = np.min(max_resolution / np.array(ori_shape[-2:]))
if scale_factor < 1:
downscaler = torch.nn.Upsample(scale_factor=scale_factor, mode="nearest")
gt = downscaler(torch.as_tensor(gt).unsqueeze(0)).numpy()
pred = downscaler(torch.as_tensor(pred).unsqueeze(0)).numpy()
valid_mask = (
downscaler(torch.as_tensor(valid_mask).unsqueeze(0).float())
.bool()
.numpy()
)
assert (
gt.shape == pred.shape == valid_mask.shape
), f"{gt.shape}, {pred.shape}, {valid_mask.shape}"
gt_masked = gt[valid_mask].reshape((-1, 1))
pred_masked = pred[valid_mask].reshape((-1, 1))
# numpy solver
_ones = np.ones_like(pred_masked)
A = np.concatenate([pred_masked, _ones], axis=-1)
X = np.linalg.lstsq(A, gt_masked, rcond=None)[0]
scale, shift = X
aligned_pred = pred_arr * scale + shift
# restore dimensions
aligned_pred = aligned_pred.reshape(ori_shape)
if return_scale_shift:
return aligned_pred, scale, shift
else:
return aligned_pred
# ******************** disparity space ********************
def depth2disparity(depth, return_mask=False):
if isinstance(depth, torch.Tensor):
disparity = torch.zeros_like(depth)
elif isinstance(depth, np.ndarray):
disparity = np.zeros_like(depth)
non_negtive_mask = depth > 0
disparity[non_negtive_mask] = 1.0 / depth[non_negtive_mask]
if return_mask:
return disparity, non_negtive_mask
else:
return disparity
def disparity2depth(disparity, **kwargs):
return depth2disparity(disparity, **kwargs)
def align_depth_least_square_torch_mask(
gt: torch.Tensor,
pred: torch.Tensor,
valid_mask: torch.Tensor,
max_resolution=None,
):
ori_shape = pred.shape[-2:] # input shape
batch_size = gt.shape[0]
# gt = gt_arr.squeeze() # [B, H, W]
# pred = pred_arr.squeeze()
# valid_mask = valid_mask_arr.squeeze()
# Downsample
if max_resolution is not None:
scale_factor = np.min(max_resolution / np.array(ori_shape[-2:]))
if scale_factor < 1:
downscaler = torch.nn.Upsample(scale_factor=scale_factor, mode="nearest")
gt = downscaler(gt)
pred = downscaler(pred)
valid_mask = downscaler(valid_mask).bool()
assert (
gt.shape == pred.shape
), f"{gt.shape}, {pred.shape}"
scale_ls = []
shift_ls = []
for i in range(batch_size):
gt_masked = gt[i][valid_mask[i]].view(-1, 1)
pred_masked = pred[i][valid_mask[i]].view(-1, 1)
# torch solver
ones = torch.ones_like(pred_masked)
A = torch.cat([pred_masked, ones], dim=-1)
X, *_ = torch.linalg.lstsq(A, gt_masked)
scale, shift = X[0, :].detach(), X[1, :].detach()
scale_ls.append(scale)
shift_ls.append(shift)
scale = torch.concat(scale_ls, dim=0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
shift = torch.concat(shift_ls, dim=0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
return scale, shift
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