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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
import numpy as np
import torch
from dust3r.datasets.base.easy_dataset import EasyDataset
from dust3r.datasets.utils.transforms import ImgNorm
from dust3r.utils.geometry import depthmap_to_absolute_camera_coordinates
import dust3r.datasets.utils.cropping as cropping
class BaseStereoViewDataset (EasyDataset):
""" Define all basic options.
Usage:
class MyDataset (BaseStereoViewDataset):
def _get_views(self, idx, rng):
# overload here
views = []
views.append(dict(img=, ...))
return views
"""
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,
seed=None):
self.num_views = 2
self.split = split
self._set_resolutions(resolution)
self.transform = transform
if isinstance(transform, str):
transform = eval(transform)
self.aug_crop = aug_crop
self.seed = seed
def __len__(self):
return len(self.scenes)
def get_stats(self):
return f"{len(self)} pairs"
def __repr__(self):
resolutions_str = '['+';'.join(f'{w}x{h}' for w, h in self._resolutions)+']'
return f"""{type(self).__name__}({self.get_stats()},
{self.split=},
{self.seed=},
resolutions={resolutions_str},
{self.transform=})""".replace('self.', '').replace('\n', '').replace(' ', '')
def _get_views(self, idx, resolution, rng):
raise NotImplementedError()
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)
# check data-types
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)
# encode the image
width, height = view['img'].size
view['true_shape'] = np.int32((height, width))
view['img'] = self.transform(view['img'])
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)
# 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']
# 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 _set_resolutions(self, resolutions):
''' Set the resolution(s) of the dataset.
Params:
- resolutions: int or tuple or list of tuples
'''
assert resolutions is not None, 'undefined resolution'
if not isinstance(resolutions, list):
resolutions = [resolutions]
self._resolutions = []
for resolution in resolutions:
if isinstance(resolution, int):
width = height = resolution
else:
width, height = resolution
assert isinstance(width, int), f'Bad type for {width=} {type(width)=}, should be int'
assert isinstance(height, int), f'Bad type for {height=} {type(height)=}, should be int'
assert width >= height
self._resolutions.append((width, height))
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)
# downscale with lanczos interpolation so that image.size == resolution
# cropping centered on the principal point
W, H = image.size
cx, cy = intrinsics[:2, 2].round().astype(int)
# calculate min distance to margin
min_margin_x = min(cx, W-cx)
min_margin_y = min(cy, H-cy)
assert min_margin_x > W/5, f'Bad principal point in view={info}'
assert min_margin_y > H/5, f'Bad principal point in view={info}'
## Center crop
# Crop on the principal point, make it always centered
# the new window will be a rectangle of size (2*min_margin_x, 2*min_margin_y) centered on (cx,cy)
l, t = cx - min_margin_x, cy - min_margin_y
r, b = cx + min_margin_x, cy + min_margin_y
crop_bbox = (l, t, r, b)
image, depthmap, intrinsics = cropping.crop_image_depthmap(image, depthmap, intrinsics, crop_bbox)
# 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):
resolution = resolution[::-1]
# high-quality Lanczos down-scaling
target_resolution = np.array(resolution)
if self.aug_crop > 1:
target_resolution += rng.integers(0, self.aug_crop)
## Recale with max factor, so one of width or height might be larger than target_resolution
image, depthmap, intrinsics = cropping.rescale_image_depthmap(image, depthmap, intrinsics, target_resolution)
# actual cropping (if necessary) with bilinear interpolation
intrinsics2 = cropping.camera_matrix_of_crop(intrinsics, image.size, resolution, offset_factor=0.5)
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 is_good_type(key, v):
""" returns (is_good, err_msg)
"""
if isinstance(v, (str, int, tuple)):
return True, None
if v.dtype not in (np.float32, torch.float32, bool, np.int32, np.int64, np.uint8):
return False, f"bad {v.dtype=}"
return True, None
def view_name(view, batch_index=None):
def sel(x): return x[batch_index] if batch_index not in (None, slice(None)) else x
db = sel(view['dataset'])
label = sel(view['label'])
instance = sel(view['instance'])
return f"{db}/{label}/{instance}"
def transpose_to_landscape(view):
height, width = view['true_shape']
if 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['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]]
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