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Zero
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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 DiodeIndoor(ImageDataset):
CAM_INTRINSIC = {
"ALL": torch.tensor([[886.81, 0, 512], [0, 927.06, 384], [0, 0, 1]])
}
min_depth = 0.01
max_depth = 25.0
depth_scale = 256.0
test_split = "val.txt"
train_split = "train.txt"
hdf5_paths = ["DiodeIndoor.hdf5"]
def __init__(
self,
image_shape,
split_file,
test_mode,
crop=None,
benchmark=False,
augmentations_db={},
normalize=True,
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,
mini=mini,
**kwargs,
)
self.test_mode = test_mode
# load annotations
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
h5file.close()
dataset = []
for line in txt_string.split("\n"):
image_filename, depth_filename = line.strip().split(" ")
sample = [
image_filename,
depth_filename,
]
dataset.append(sample)
if not self.test_mode:
dataset = self.chunk(dataset, chunk_dim=1, pct=self.mini)
self.dataset = DatasetFromList(dataset)
self.log_load_dataset()
def get_intrinsics(self, *args, **kwargs):
return self.CAM_INTRINSIC["ALL"].clone()
def get_mapper(self):
return {
"image_filename": 0,
"depth_filename": 1,
}
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 DiodeIndoor_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 = ["DiodeIndoor-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
class DiodeOutdoor(ImageDataset):
CAM_INTRINSIC = {
"ALL": torch.tensor([[886.81, 0, 512], [0, 927.06, 384], [0, 0, 1]])
}
min_depth = 0.1
max_depth = 80.0
log_mean = 0
log_std = 1
test_split = "diode_outdoor_val.txt"
train_split = "diode_outdoor_train.txt"
hdf5_paths = ["diode.hdf5"]
def __init__(
self,
image_shape,
split_file,
test_mode,
depth_scale=256,
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.depth_scale = depth_scale
self.masker = AnnotationMask(
min_value=self.min_depth,
max_value=self.max_depth if test_mode else None,
custom_fn=self.eval_mask if test_mode else lambda x, *args, **kwargs: x,
)
# load annotations
self.load_dataset()
def load_dataset(self):
self.h5file = h5py.File(
os.path.join(self.data_root, self.hdf5_path),
"r",
libver="latest",
swmr=True,
)
txt_file = np.array(self.h5file[self.split_file])
txt_string = txt_file.tostring().decode("ascii")[:-1]
dataset = {"depth_filename": [], "image_filename": []}
for line in txt_string.split("\n"):
depth_filename = line.strip().split(" ")[1]
img_name = line.strip().split(" ")[0]
image_filename = img_name
dataset["depth_filename"].append(depth_filename)
dataset["image_filename"].append(image_filename)
self.dataset = pl.from_dict(dataset)
if not self.test_mode and self.mini:
self.dataset = self.dataset[::2]
class Diode(ImageDataset):
CAM_INTRINSIC = {
"ALL": torch.tensor([[886.81, 0, 512], [0, 927.06, 384], [0, 0, 1]])
}
log_mean = 0
log_std = 1
min_depth = 0.6
max_depth = 80.0
test_split = "diode_val.txt"
train_split = "diode_train.txt"
hdf5_paths = ["diode.hdf5"]
def __init__(
self,
image_shape,
split_file,
test_mode,
depth_scale=256,
crop=None,
benchmark=False,
augmentations_db={},
normalize=True,
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,
mini=mini,
**kwargs,
)
self.test_mode = test_mode
self.depth_scale = depth_scale
self.masker = AnnotationMask(
min_value=self.min_depth,
max_value=self.max_depth if test_mode else None,
custom_fn=self.eval_mask if test_mode else lambda x, *args, **kwargs: x,
)
# load annotations
self.load_dataset()
def load_dataset(self):
self.h5file = h5py.File(
os.path.join(self.data_root, self.hdf5_path),
"r",
libver="latest",
swmr=True,
)
txt_file = np.array(self.h5file[self.split_file])
txt_string = txt_file.tostring().decode("ascii")[:-1]
dataset = {"depth_filename": [], "image_filename": []}
for line in txt_string.split("\n"):
depth_filename = line.strip().split(" ")[1]
image_filename = line.strip().split(" ")[0]
dataset["depth_filename"].append(depth_filename)
dataset["image_filename"].append(image_filename)
self.dataset = pl.from_dict(dataset)
if not self.test_mode and self.mini:
self.dataset = self.dataset[::2]
def get_intrinsics(self, *args, **kwargs):
return self.CAM_INTRINSIC["ALL"].clone()
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