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import numpy as np
import cv2
import torch
from torchmin import minimize
def est_scale_iterative(slam_depth, pred_depth, iters=10, msk=None):
""" Simple depth-align by iterative median and thresholding """
s = pred_depth / slam_depth
if msk is None:
msk = np.zeros_like(pred_depth)
else:
msk = cv2.resize(msk, (pred_depth.shape[1], pred_depth.shape[0]))
robust = (msk<0.5) * (0<pred_depth) * (pred_depth<10)
s_est = s[robust]
scale = np.median(s_est)
scales_ = [scale]
for _ in range(iters):
slam_depth_0 = slam_depth * scale
robust = (msk<0.5) * (0<slam_depth_0) * (slam_depth_0<10) * (0<pred_depth) * (pred_depth<10)
s_est = s[robust]
scale = np.median(s_est)
scales_.append(scale)
return scale
def est_scale_gmof(slam_depth, pred_depth, lr=1, sigma=0.5, iters=500, msk=None):
""" Simple depth-align by robust least-square """
if msk is None:
msk = np.zeros_like(pred_depth)
else:
msk = cv2.resize(msk, (pred_depth.shape[1], pred_depth.shape[0]))
robust = (msk<0.5) * (0<pred_depth) * (pred_depth<10)
pm = torch.from_numpy(pred_depth[robust])
sm = torch.from_numpy(slam_depth[robust])
scale = torch.tensor([1.], requires_grad=True)
optim = torch.optim.Adam([scale], lr=lr)
losses = []
for i in range(iters):
loss = sm * scale - pm
loss = gmof(loss, sigma=sigma).mean()
optim.zero_grad()
loss.backward()
optim.step()
losses.append(loss.item())
scale = scale.detach().cpu().item()
return scale
def est_offset(pred_depth, hand_depth, sigma=0.5, msk=None,
far_thresh=10):
""" Depth-align by iterative + robust least-square """
if msk is None:
msk = np.zeros_like(pred_depth)
else:
msk = cv2.resize(msk, (pred_depth.shape[1], pred_depth.shape[0]))
# Stage 1: Iterative steps
s = pred_depth - hand_depth
robust = (msk<0.5) * (0<pred_depth) * (pred_depth<far_thresh)
s_est = s[robust]
offset = np.median(s_est)
return offset
def est_scale_hybrid(slam_depth, pred_depth, sigma=0.5, msk=None, near_thresh=0,
far_thresh=10):
""" Depth-align by iterative + robust least-square """
if msk is None:
msk = np.zeros_like(pred_depth)
else:
msk = cv2.resize(msk, (pred_depth.shape[1], pred_depth.shape[0]))
# Stage 1: Iterative steps
s = pred_depth / slam_depth
robust = (msk<0.5) * (near_thresh<pred_depth) * (pred_depth<far_thresh)
s_est = s[robust]
scale = np.median(s_est)
for _ in range(10):
slam_depth_0 = slam_depth * scale
robust = (msk<0.5) * (0<slam_depth_0) * (slam_depth_0<far_thresh) * (near_thresh<pred_depth) * (pred_depth<far_thresh)
s_est = s[robust]
scale = np.median(s_est)
# Stage 2: Robust optimization
robust = (msk<0.5) * (0<slam_depth_0) * (slam_depth_0<far_thresh) * (near_thresh<pred_depth) * (pred_depth<far_thresh)
pm = torch.from_numpy(pred_depth[robust])
sm = torch.from_numpy(slam_depth[robust])
def f(x):
loss = sm * x - pm
loss = gmof(loss, sigma=sigma).mean()
return loss
x0 = torch.tensor([scale])
result = minimize(f, x0, method='bfgs')
scale = result.x.detach().cpu().item()
return scale
def est_scale_wo_mask(slam_depth, pred_depth, sigma=0.5):
""" Depth-align by iterative + robust least-square """
msk=None
near_thresh=0
far_thresh=10000
if msk is None:
msk = np.zeros_like(pred_depth)
else:
msk = cv2.resize(msk, (pred_depth.shape[1], pred_depth.shape[0]))
# Stage 1: Iterative steps
s = pred_depth / slam_depth
robust = (msk<0.5) * (near_thresh<pred_depth) * (pred_depth<far_thresh)
s_est = s[robust]
scale = np.median(s_est)
for _ in range(10):
slam_depth_0 = slam_depth * scale
robust = (msk<0.5) * (0<slam_depth_0) * (slam_depth_0<far_thresh) * (near_thresh<pred_depth) * (pred_depth<far_thresh)
s_est = s[robust]
scale = np.median(s_est)
# Stage 2: Robust optimization
robust = (msk<0.5) * (0<slam_depth_0) * (slam_depth_0<far_thresh) * (near_thresh<pred_depth) * (pred_depth<far_thresh)
pm = torch.from_numpy(pred_depth[robust])
sm = torch.from_numpy(slam_depth[robust])
def f(x):
loss = sm * x - pm
loss = gmof(loss, sigma=sigma).mean()
return loss
x0 = torch.tensor([scale])
result = minimize(f, x0, method='bfgs')
scale = result.x.detach().cpu().item()
return scale
def scale_shift_align(smpl_depth, pred_depth, sigma=0.5):
""" Align pred_depth to smpl depth """
smpl = torch.from_numpy(smpl_depth)
pred = torch.from_numpy(pred_depth)
def f(x):
loss = smpl - (pred * x[0] + x[1])
loss = gmof(loss, sigma=sigma).mean()
return loss
x0 = torch.tensor([1., 0.])
result = minimize(f, x0, method='bfgs')
scale_shift = result.x.detach().cpu().numpy()
return scale_shift
def shift_align(smpl_depth, pred_depth, sigma=0.5):
""" Align pred_depth to smpl depth by only shift """
smpl = torch.from_numpy(smpl_depth)
pred = torch.from_numpy(pred_depth)
def f(x):
loss = smpl - (pred + x)
loss = gmof(loss, sigma=sigma).mean()
return loss
x0 = torch.tensor([0.])
result = minimize(f, x0, method='bfgs')
scale_shift = result.x.detach().cpu().numpy()
return scale_shift
def gmof(x, sigma=100):
"""
Geman-McClure error function
"""
x_squared = x ** 2
sigma_squared = sigma ** 2
return (sigma_squared * x_squared) / (sigma_squared + x_squared)
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