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# Copyright Niantic 2021. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the ManyDepth licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def disp_to_depth(disp, min_depth=0.1, max_depth=100):
"""Convert network's sigmoid output into depth prediction
The formula for this conversion is given in the 'additional considerations'
section of the paper.
"""
min_disp = 1 / max_depth # 0.05
max_disp = 1 / min_depth # 10
scaled_disp = min_disp + (max_disp - min_disp) * disp
depth = 1 / scaled_disp
return scaled_disp, depth
def transformation_from_parameters(axisangle, translation, invert=False):
"""Convert the network's (axisangle, translation) output into a 4x4 matrix
"""
R = rot_from_axisangle(axisangle)
t = translation.clone()
if invert:
R = R.transpose(1, 2)
t *= -1
T = get_translation_matrix(t)
if invert:
M = torch.matmul(R, T)
else:
M = torch.matmul(T, R)
return M
def get_translation_matrix(translation_vector):
"""Convert a translation vector into a 4x4 transformation matrix
"""
T = torch.zeros(translation_vector.shape[0], 4, 4).to(device=translation_vector.device)
t = translation_vector.contiguous().view(-1, 3, 1)
T[:, 0, 0] = 1
T[:, 1, 1] = 1
T[:, 2, 2] = 1
T[:, 3, 3] = 1
T[:, :3, 3, None] = t
return T
def rot_from_axisangle(vec):
"""Convert an axisangle rotation into a 4x4 transformation matrix
(adapted from https://github.com/Wallacoloo/printipi)
Input 'vec' has to be Bx1x3
"""
angle = torch.norm(vec, 2, 2, True)
axis = vec / (angle + 1e-7)
ca = torch.cos(angle)
sa = torch.sin(angle)
C = 1 - ca
x = axis[..., 0].unsqueeze(1)
y = axis[..., 1].unsqueeze(1)
z = axis[..., 2].unsqueeze(1)
xs = x * sa
ys = y * sa
zs = z * sa
xC = x * C
yC = y * C
zC = z * C
xyC = x * yC
yzC = y * zC
zxC = z * xC
rot = torch.zeros((vec.shape[0], 4, 4)).to(device=vec.device)
rot[:, 0, 0] = torch.squeeze(x * xC + ca)
rot[:, 0, 1] = torch.squeeze(xyC - zs)
rot[:, 0, 2] = torch.squeeze(zxC + ys)
rot[:, 1, 0] = torch.squeeze(xyC + zs)
rot[:, 1, 1] = torch.squeeze(y * yC + ca)
rot[:, 1, 2] = torch.squeeze(yzC - xs)
rot[:, 2, 0] = torch.squeeze(zxC - ys)
rot[:, 2, 1] = torch.squeeze(yzC + xs)
rot[:, 2, 2] = torch.squeeze(z * zC + ca)
rot[:, 3, 3] = 1
return rot
def normalize(img):
return (img - img.min()) / (img.max() - img.min())
def line(img):
img = img.unsqueeze(0)
if img.shape[1] == 1:
q5, q95 = torch.quantile(img.flatten(), q=torch.tensor((0.05, 0.95), device=img.device))
img[img < q5] = q5
img[img > q95] = q95
return normalize(img)
elif img.shape[1] == 3:
for c in range(3):
img[:, c:c+1] = line(img[:, c:c+1])
return img.squeeze()
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