Spaces:
Running on Zero
Running on Zero
File size: 4,224 Bytes
4b35c4e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | # Author: Bingxin Ke
# Last modified: 2024-01-11
import numpy as np
import torch
def align_depth_least_square_video(
gt_arr: np.ndarray,
pred_arr: np.ndarray,
valid_mask_arr: np.ndarray,
return_scale_shift=True,
max_resolution=None,
):
"""
gt_arr, pred_arr, valid_mask_arr: shape can be (T, H, W) or (T, 1, H, W)
"""
ori_shape = pred_arr.shape
squeeze = lambda x: x.squeeze() # handle (T,1,H,W) -> (T,H,W)
gt = squeeze(gt_arr)
pred = squeeze(pred_arr)
valid_mask = squeeze(valid_mask_arr)
# -----------------------------
# Optional downsampling (applied per-frame identically)
# -----------------------------
if max_resolution is not None:
H, W = gt.shape[-2:]
scale_factor = np.min(max_resolution / np.array([H, W]))
if scale_factor < 1:
downscaler = torch.nn.Upsample(scale_factor=float(scale_factor), mode="nearest")
gt = downscaler(torch.as_tensor(gt).unsqueeze(1)).squeeze(1).numpy()
pred = downscaler(torch.as_tensor(pred).unsqueeze(1)).squeeze(1).numpy()
valid_mask = (
downscaler(torch.as_tensor(valid_mask).unsqueeze(1).float())
.squeeze(1).bool().numpy()
)
assert gt.shape == pred.shape == valid_mask.shape, f"{gt.shape}, {pred.shape}, {valid_mask.shape}"
# -----------------------------
# Flatten ALL frames
# -----------------------------
gt_masked = gt[valid_mask].reshape(-1, 1) # (N, 1)
pred_masked = pred[valid_mask].reshape(-1, 1) # (N, 1)
# -----------------------------
# Solve least squares over ALL pixels (T*H*W)
# -----------------------------
_ones = np.ones_like(pred_masked)
A = np.concatenate([pred_masked, _ones], axis=-1) # (N, 2)
X = np.linalg.lstsq(A, gt_masked, rcond=None)[0]
scale, shift = X
# Apply to original resolution (not the downsampled)
aligned_pred = pred_arr * scale + shift
aligned_pred = aligned_pred.reshape(ori_shape)
if return_scale_shift:
return aligned_pred, scale, shift
else:
return aligned_pred
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)
|