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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).
#
# --------------------------------------------------------
# base class for implementing datasets
# --------------------------------------------------------
import PIL.Image
import PIL.Image as Image
import numpy as np
import torch
import copy
from mast3r.datasets.utils.cropping import (extract_correspondences_from_pts3d,
gen_random_crops, in2d_rect, crop_to_homography)
import mast3r.utils.path_to_dust3r # noqa
from dust3r.datasets.base.base_stereo_view_dataset import BaseStereoViewDataset, view_name, is_good_type # noqa
from dust3r.datasets.utils.transforms import ImgNorm
from dust3r.utils.geometry import depthmap_to_absolute_camera_coordinates, geotrf, depthmap_to_camera_coordinates
import dust3r.datasets.utils.cropping as cropping
class MASt3RBaseStereoViewDataset(BaseStereoViewDataset):
def __init__(self, *, # only keyword arguments
split=None,
resolution=None, # square_size or (width, height) or list of [(width,height), ...]
transform=ImgNorm,
aug_crop=False,
aug_swap=False,
aug_monocular=False,
aug_portrait_or_landscape=True, # automatic choice between landscape/portrait when possible
aug_rot90=False,
n_corres=0,
nneg=0,
n_tentative_crops=4,
seed=None):
super().__init__(split=split, resolution=resolution, transform=transform, aug_crop=aug_crop, seed=seed)
self.is_metric_scale = False # by default a dataset is not metric scale, subclasses can overwrite this
self.aug_swap = aug_swap
self.aug_monocular = aug_monocular
self.aug_portrait_or_landscape = aug_portrait_or_landscape
self.aug_rot90 = aug_rot90
self.n_corres = n_corres
self.nneg = nneg
assert self.n_corres == 'all' or isinstance(self.n_corres, int) or (isinstance(self.n_corres, list) and len(
self.n_corres) == self.num_views), f"Error, n_corres should either be 'all', a single integer or a list of length {self.num_views}"
assert self.nneg == 0 or self.n_corres != 'all'
self.n_tentative_crops = n_tentative_crops
def _swap_view_aug(self, views):
if self._rng.random() < 0.5:
views.reverse()
def _crop_resize_if_necessary(self, image, depthmap, intrinsics, resolution, rng=None, info=None):
""" This function:
- first downsizes the image with LANCZOS inteprolation,
which is better than bilinear interpolation in
"""
if not isinstance(image, PIL.Image.Image):
image = PIL.Image.fromarray(image)
# transpose the resolution if necessary
W, H = image.size # new size
assert resolution[0] >= resolution[1]
if H > 1.1 * W:
# image is portrait mode
resolution = resolution[::-1]
elif 0.9 < H / W < 1.1 and resolution[0] != resolution[1]:
# image is square, so we chose (portrait, landscape) randomly
if rng.integers(2) and self.aug_portrait_or_landscape:
resolution = resolution[::-1]
# high-quality Lanczos down-scaling
target_resolution = np.array(resolution)
image, depthmap, intrinsics = cropping.rescale_image_depthmap(image, depthmap, intrinsics, target_resolution)
# actual cropping (if necessary) with bilinear interpolation
offset_factor = 0.5
intrinsics2 = cropping.camera_matrix_of_crop(intrinsics, image.size, resolution, offset_factor=offset_factor)
crop_bbox = cropping.bbox_from_intrinsics_in_out(intrinsics, intrinsics2, resolution)
image, depthmap, intrinsics2 = cropping.crop_image_depthmap(image, depthmap, intrinsics, crop_bbox)
return image, depthmap, intrinsics2
def generate_crops_from_pair(self, view1, view2, resolution, aug_crop_arg, n_crops=4, rng=np.random):
views = [view1, view2]
if aug_crop_arg is False:
# compatibility
for i in range(2):
view = views[i]
view['img'], view['depthmap'], view['camera_intrinsics'] = self._crop_resize_if_necessary(view['img'],
view['depthmap'],
view['camera_intrinsics'],
resolution,
rng=rng)
view['pts3d'], view['valid_mask'] = depthmap_to_absolute_camera_coordinates(view['depthmap'],
view['camera_intrinsics'],
view['camera_pose'])
return
# extract correspondences
corres = extract_correspondences_from_pts3d(*views, target_n_corres=None, rng=rng)
# generate 4 random crops in each view
view_crops = []
crops_resolution = []
corres_msks = []
for i in range(2):
if aug_crop_arg == 'auto':
S = min(views[i]['img'].size)
R = min(resolution)
aug_crop = S * (S - R) // R
aug_crop = max(.1 * S, aug_crop) # for cropping: augment scale of at least 10%, and more if possible
else:
aug_crop = aug_crop_arg
# tranpose the target resolution if necessary
assert resolution[0] >= resolution[1]
W, H = imsize = views[i]['img'].size
crop_resolution = resolution
if H > 1.1 * W:
# image is portrait mode
crop_resolution = resolution[::-1]
elif 0.9 < H / W < 1.1 and resolution[0] != resolution[1]:
# image is square, so we chose (portrait, landscape) randomly
if rng.integers(2):
crop_resolution = resolution[::-1]
crops = gen_random_crops(imsize, n_crops, crop_resolution, aug_crop=aug_crop, rng=rng)
view_crops.append(crops)
crops_resolution.append(crop_resolution)
# compute correspondences
corres_msks.append(in2d_rect(corres[i], crops))
# compute IoU for each
intersection = np.float32(corres_msks[0]).T @ np.float32(corres_msks[1])
# select best pair of crops
best = np.unravel_index(intersection.argmax(), (n_crops, n_crops))
crops = [view_crops[i][c] for i, c in enumerate(best)]
# crop with the homography
for i in range(2):
view = views[i]
imsize, K_new, R, H = crop_to_homography(view['camera_intrinsics'], crops[i], crops_resolution[i])
# imsize, K_new, H = upscale_homography(imsize, resolution, K_new, H)
# update camera params
K_old = view['camera_intrinsics']
view['camera_intrinsics'] = K_new
view['camera_pose'] = view['camera_pose'].copy()
view['camera_pose'][:3, :3] = view['camera_pose'][:3, :3] @ R
# apply homography to image and depthmap
homo8 = (H / H[2, 2]).ravel().tolist()[:8]
view['img'] = view['img'].transform(imsize, Image.Transform.PERSPECTIVE,
homo8,
resample=Image.Resampling.BICUBIC)
depthmap2 = depthmap_to_camera_coordinates(view['depthmap'], K_old)[0] @ R[:, 2]
view['depthmap'] = np.array(Image.fromarray(depthmap2).transform(
imsize, Image.Transform.PERSPECTIVE, homo8))
if 'track_labels' in view:
# convert from uint64 --> uint32, because PIL.Image cannot handle uint64
mapping, track_labels = np.unique(view['track_labels'], return_inverse=True)
track_labels = track_labels.astype(np.uint32).reshape(view['track_labels'].shape)
# homography transformation
res = np.array(Image.fromarray(track_labels).transform(imsize, Image.Transform.PERSPECTIVE, homo8))
view['track_labels'] = mapping[res] # mapping back to uint64
# recompute 3d points from scratch
view['pts3d'], view['valid_mask'] = depthmap_to_absolute_camera_coordinates(view['depthmap'],
view['camera_intrinsics'],
view['camera_pose'])
def __getitem__(self, idx):
if isinstance(idx, tuple):
# the idx is specifying the aspect-ratio
idx, ar_idx = idx
else:
assert len(self._resolutions) == 1
ar_idx = 0
# set-up the rng
if self.seed: # reseed for each __getitem__
self._rng = np.random.default_rng(seed=self.seed + idx)
elif not hasattr(self, '_rng'):
seed = torch.initial_seed() # this is different for each dataloader process
self._rng = np.random.default_rng(seed=seed)
# over-loaded code
resolution = self._resolutions[ar_idx] # DO NOT CHANGE THIS (compatible with BatchedRandomSampler)
views = self._get_views(idx, resolution, self._rng)
assert len(views) == self.num_views
for v, view in enumerate(views):
assert 'pts3d' not in view, f"pts3d should not be there, they will be computed afterwards based on intrinsics+depthmap for view {view_name(view)}"
view['idx'] = (idx, ar_idx, v)
view['is_metric_scale'] = self.is_metric_scale
assert 'camera_intrinsics' in view
if 'camera_pose' not in view:
view['camera_pose'] = np.full((4, 4), np.nan, dtype=np.float32)
else:
assert np.isfinite(view['camera_pose']).all(), f'NaN in camera pose for view {view_name(view)}'
assert 'pts3d' not in view
assert 'valid_mask' not in view
assert np.isfinite(view['depthmap']).all(), f'NaN in depthmap for view {view_name(view)}'
pts3d, valid_mask = depthmap_to_absolute_camera_coordinates(**view)
view['pts3d'] = pts3d
view['valid_mask'] = valid_mask & np.isfinite(pts3d).all(axis=-1)
self.generate_crops_from_pair(views[0], views[1], resolution=resolution,
aug_crop_arg=self.aug_crop,
n_crops=self.n_tentative_crops,
rng=self._rng)
for v, view in enumerate(views):
# encode the image
width, height = view['img'].size
view['true_shape'] = np.int32((height, width))
view['img'] = self.transform(view['img'])
# Pixels for which depth is fundamentally undefined
view['sky_mask'] = (view['depthmap'] < 0)
if self.aug_swap:
self._swap_view_aug(views)
if self.aug_monocular:
if self._rng.random() < self.aug_monocular:
views = [copy.deepcopy(views[0]) for _ in range(len(views))]
# automatic extraction of correspondences from pts3d + pose
if self.n_corres > 0 and ('corres' not in view):
corres1, corres2, valid = extract_correspondences_from_pts3d(*views, self.n_corres,
self._rng, nneg=self.nneg)
views[0]['corres'] = corres1
views[1]['corres'] = corres2
views[0]['valid_corres'] = valid
views[1]['valid_corres'] = valid
if self.aug_rot90 is False:
pass
elif self.aug_rot90 == 'same':
rotate_90(views, k=self._rng.choice(4))
elif self.aug_rot90 == 'diff':
rotate_90(views[:1], k=self._rng.choice(4))
rotate_90(views[1:], k=self._rng.choice(4))
else:
raise ValueError(f'Bad value for {self.aug_rot90=}')
# check data-types metric_scale
for v, view in enumerate(views):
if 'corres' not in view:
view['corres'] = np.full((self.n_corres, 2), np.nan, dtype=np.float32)
# check all datatypes
for key, val in view.items():
res, err_msg = is_good_type(key, val)
assert res, f"{err_msg} with {key}={val} for view {view_name(view)}"
K = view['camera_intrinsics']
# check shapes
assert view['depthmap'].shape == view['img'].shape[1:]
assert view['depthmap'].shape == view['pts3d'].shape[:2]
assert view['depthmap'].shape == view['valid_mask'].shape
# last thing done!
for view in views:
# transpose to make sure all views are the same size
transpose_to_landscape(view)
# this allows to check whether the RNG is is the same state each time
view['rng'] = int.from_bytes(self._rng.bytes(4), 'big')
return views
def transpose_to_landscape(view, revert=False):
height, width = view['true_shape']
if width < height:
if revert:
height, width = width, height
# rectify portrait to landscape
assert view['img'].shape == (3, height, width)
view['img'] = view['img'].swapaxes(1, 2)
assert view['valid_mask'].shape == (height, width)
view['valid_mask'] = view['valid_mask'].swapaxes(0, 1)
assert view['sky_mask'].shape == (height, width)
view['sky_mask'] = view['sky_mask'].swapaxes(0, 1)
assert view['depthmap'].shape == (height, width)
view['depthmap'] = view['depthmap'].swapaxes(0, 1)
assert view['pts3d'].shape == (height, width, 3)
view['pts3d'] = view['pts3d'].swapaxes(0, 1)
# transpose x and y pixels
view['camera_intrinsics'] = view['camera_intrinsics'][[1, 0, 2]]
# transpose correspondences x and y
view['corres'] = view['corres'][:, [1, 0]]
def rotate_90(views, k=1):
from scipy.spatial.transform import Rotation
# print('rotation =', k)
RT = np.eye(4, dtype=np.float32)
RT[:3, :3] = Rotation.from_euler('z', 90 * k, degrees=True).as_matrix()
for view in views:
view['img'] = torch.rot90(view['img'], k=k, dims=(-2, -1)) # WARNING!! dims=(-1,-2) != dims=(-2,-1)
view['depthmap'] = np.rot90(view['depthmap'], k=k).copy()
view['camera_pose'] = view['camera_pose'] @ RT
RT2 = np.eye(3, dtype=np.float32)
RT2[:2, :2] = RT[:2, :2] * ((1, -1), (-1, 1))
H, W = view['depthmap'].shape
if k % 4 == 0:
pass
elif k % 4 == 1:
# top-left (0,0) pixel becomes (0,H-1)
RT2[:2, 2] = (0, H - 1)
elif k % 4 == 2:
# top-left (0,0) pixel becomes (W-1,H-1)
RT2[:2, 2] = (W - 1, H - 1)
elif k % 4 == 3:
# top-left (0,0) pixel becomes (W-1,0)
RT2[:2, 2] = (W - 1, 0)
else:
raise ValueError(f'Bad value for {k=}')
view['camera_intrinsics'][:2, 2] = geotrf(RT2, view['camera_intrinsics'][:2, 2])
if k % 2 == 1:
K = view['camera_intrinsics']
np.fill_diagonal(K, K.diagonal()[[1, 0, 2]])
pts3d, valid_mask = depthmap_to_absolute_camera_coordinates(**view)
view['pts3d'] = pts3d
view['valid_mask'] = np.rot90(view['valid_mask'], k=k).copy()
view['sky_mask'] = np.rot90(view['sky_mask'], k=k).copy()
view['corres'] = geotrf(RT2, view['corres']).round().astype(view['corres'].dtype)
view['true_shape'] = np.int32((H, W))
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