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Running
on
Zero
File size: 3,749 Bytes
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import json
import os
import h5py
import numpy as np
import torch
from unik3d.datasets.image_dataset import ImageDataset
from unik3d.datasets.sequence_dataset import SequenceDataset
from unik3d.datasets.utils import DatasetFromList
class IBims(ImageDataset):
min_depth = 0.005
max_depth = 25.0
depth_scale = 1000.0
train_split = "ibims_val.txt"
test_split = "ibims_val.txt"
intrisics_file = "ibims_intrinsics.json"
hdf5_paths = ["ibims.hdf5"]
def __init__(
self,
image_shape,
split_file,
test_mode,
crop=None,
benchmark=False,
augmentations_db={},
normalize=True,
resize_method="hard",
mini=1.0,
**kwargs,
):
super().__init__(
image_shape=image_shape,
split_file=split_file,
test_mode=test_mode,
benchmark=benchmark,
normalize=normalize,
augmentations_db=augmentations_db,
resize_method=resize_method,
mini=mini,
**kwargs,
)
self.test_mode = test_mode
self.crop = crop
self.load_dataset()
def load_dataset(self):
h5file = h5py.File(
os.path.join(self.data_root, self.hdf5_paths[0]),
"r",
libver="latest",
swmr=True,
)
txt_file = np.array(h5file[self.split_file])
txt_string = txt_file.tostring().decode("ascii")[:-1] # correct the -1
intrinsics = np.array(h5file[self.intrisics_file]).tostring().decode("ascii")
intrinsics = json.loads(intrinsics)
h5file.close()
dataset = []
for line in txt_string.split("\n"):
image_filename, depth_filename = line.strip().split(" ")
intrinsics_val = torch.tensor(intrinsics[image_filename]).squeeze()[:, :3]
sample = [image_filename, depth_filename, intrinsics_val]
dataset.append(sample)
self.dataset = DatasetFromList(dataset)
self.log_load_dataset()
def pre_pipeline(self, results):
results = super().pre_pipeline(results)
results["dense"] = [True] * self.num_copies
results["quality"] = [1] * self.num_copies
return results
class IBims_F(SequenceDataset):
min_depth = 0.01
max_depth = 25.0
depth_scale = 1000.0
test_split = "train.txt"
train_split = "train.txt"
sequences_file = "sequences.json"
hdf5_paths = ["IBims-F.hdf5"]
def __init__(
self,
image_shape: tuple[int, int],
split_file: str,
test_mode: bool,
normalize: bool,
augmentations_db: dict[str, float],
resize_method: str,
mini: float = 1.0,
num_frames: int = 1,
benchmark: bool = False,
decode_fields: list[str] = ["image", "depth"],
inplace_fields: list[str] = ["camera_params", "cam2w"],
**kwargs,
) -> None:
super().__init__(
image_shape=image_shape,
split_file=split_file,
test_mode=test_mode,
benchmark=benchmark,
normalize=normalize,
augmentations_db=augmentations_db,
resize_method=resize_method,
mini=mini,
num_frames=num_frames,
decode_fields=(
decode_fields if not test_mode else [*decode_fields, "points"]
),
inplace_fields=inplace_fields,
**kwargs,
)
def pre_pipeline(self, results):
results = super().pre_pipeline(results)
results["dense"] = [True] * self.num_frames * self.num_copies
results["quality"] = [1] * self.num_frames * self.num_copies
return results
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