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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# dataset utilities
# --------------------------------------------------------
import numpy as np
import quaternion
import torchvision.transforms as tvf
from dust3r.utils.geometry import geotrf
def cam_to_world_from_kapture(kdata, timestamp, camera_id):
camera_to_world = kdata.trajectories[timestamp, camera_id].inverse()
camera_pose = np.eye(4, dtype=np.float32)
camera_pose[:3, :3] = quaternion.as_rotation_matrix(camera_to_world.r)
camera_pose[:3, 3] = camera_to_world.t_raw
return camera_pose
ratios_resolutions = {
224: {1.0: [224, 224]},
512: {
4 / 3: [512, 384],
32 / 21: [512, 336],
16 / 9: [512, 288],
2 / 1: [512, 256],
16 / 5: [512, 160],
},
}
def get_HW_resolution(H, W, maxdim, patchsize=16):
assert (
maxdim in ratios_resolutions
), "Error, maxdim can only be 224 or 512 for now. Other maxdims not implemented yet."
ratios_resolutions_maxdim = ratios_resolutions[maxdim]
mindims = set([min(res) for res in ratios_resolutions_maxdim.values()])
ratio = W / H
ref_ratios = np.array([*(ratios_resolutions_maxdim.keys())])
islandscape = W >= H
if islandscape:
diff = np.abs(ratio - ref_ratios)
else:
diff = np.abs(ratio - (1 / ref_ratios))
selkey = ref_ratios[np.argmin(diff)]
res = ratios_resolutions_maxdim[selkey]
# check patchsize and make sure output resolution is a multiple of patchsize
if isinstance(patchsize, tuple):
assert (
len(patchsize) == 2
and isinstance(patchsize[0], int)
and isinstance(patchsize[1], int)
), "What is your patchsize format? Expected a single int or a tuple of two ints."
assert patchsize[0] == patchsize[1], "Error, non square patches not managed"
patchsize = patchsize[0]
assert max(res) == maxdim
assert min(res) in mindims
return res[::-1] if islandscape else res # return HW
def get_resize_function(maxdim, patch_size, H, W, is_mask=False):
if [max(H, W), min(H, W)] in ratios_resolutions[maxdim].values():
return lambda x: x, np.eye(3), np.eye(3)
else:
target_HW = get_HW_resolution(H, W, maxdim=maxdim, patchsize=patch_size)
ratio = W / H
target_ratio = target_HW[1] / target_HW[0]
to_orig_crop = np.eye(3)
to_rescaled_crop = np.eye(3)
if abs(ratio - target_ratio) < np.finfo(np.float32).eps:
crop_W = W
crop_H = H
elif ratio - target_ratio < 0:
crop_W = W
crop_H = int(W / target_ratio)
to_orig_crop[1, 2] = (H - crop_H) / 2.0
to_rescaled_crop[1, 2] = -(H - crop_H) / 2.0
else:
crop_W = int(H * target_ratio)
crop_H = H
to_orig_crop[0, 2] = (W - crop_W) / 2.0
to_rescaled_crop[0, 2] = -(W - crop_W) / 2.0
crop_op = tvf.CenterCrop([crop_H, crop_W])
if is_mask:
resize_op = tvf.Resize(
size=target_HW, interpolation=tvf.InterpolationMode.NEAREST_EXACT
)
else:
resize_op = tvf.Resize(size=target_HW)
to_orig_resize = np.array(
[[crop_W / target_HW[1], 0, 0], [0, crop_H / target_HW[0], 0], [0, 0, 1]]
)
to_rescaled_resize = np.array(
[[target_HW[1] / crop_W, 0, 0], [0, target_HW[0] / crop_H, 0], [0, 0, 1]]
)
op = tvf.Compose([crop_op, resize_op])
return op, to_rescaled_resize @ to_rescaled_crop, to_orig_crop @ to_orig_resize
def rescale_points3d(pts2d, pts3d, to_resize, HR, WR):
# rescale pts2d as floats
# to colmap, so that the image is in [0, D] -> [0, NewD]
pts2d = pts2d.copy()
pts2d[:, 0] += 0.5
pts2d[:, 1] += 0.5
pts2d_rescaled = geotrf(to_resize, pts2d, norm=True)
pts2d_rescaled_int = pts2d_rescaled.copy()
# convert back to cv2 before round [-0.5, 0.5] -> pixel 0
pts2d_rescaled_int[:, 0] -= 0.5
pts2d_rescaled_int[:, 1] -= 0.5
pts2d_rescaled_int = pts2d_rescaled_int.round().astype(np.int64)
# update valid (remove cropped regions)
valid_rescaled = (
(pts2d_rescaled_int[:, 0] >= 0)
& (pts2d_rescaled_int[:, 0] < WR)
& (pts2d_rescaled_int[:, 1] >= 0)
& (pts2d_rescaled_int[:, 1] < HR)
)
pts2d_rescaled_int = pts2d_rescaled_int[valid_rescaled]
# rebuild pts3d from rescaled ps2d poses
pts3d_rescaled = np.full(
(HR, WR, 3), np.nan, dtype=np.float32
) # pts3d in 512 x something
pts3d_rescaled[pts2d_rescaled_int[:, 1], pts2d_rescaled_int[:, 0]] = pts3d[
valid_rescaled
]
return (
pts2d_rescaled,
pts2d_rescaled_int,
pts3d_rescaled,
np.isfinite(pts3d_rescaled.sum(axis=-1)),
)
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