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import os
import h5py
import numpy as np
import torch
from unik3d.datasets.image_dataset import ImageDataset
from unik3d.datasets.pipelines import AnnotationMask, Compose, KittiCrop
from unik3d.datasets.sequence_dataset import SequenceDataset
from unik3d.datasets.utils import DatasetFromList
from unik3d.utils import identity
class KITTI(ImageDataset):
CAM_INTRINSIC = {
"2011_09_26": torch.tensor(
[
[7.215377e02, 0.000000e00, 6.095593e02, 4.485728e01],
[0.000000e00, 7.215377e02, 1.728540e02, 2.163791e-01],
[0.000000e00, 0.000000e00, 1.000000e00, 2.745884e-03],
]
),
"2011_09_28": torch.tensor(
[
[7.070493e02, 0.000000e00, 6.040814e02, 4.575831e01],
[0.000000e00, 7.070493e02, 1.805066e02, -3.454157e-01],
[0.000000e00, 0.000000e00, 1.000000e00, 4.981016e-03],
]
),
"2011_09_29": torch.tensor(
[
[7.183351e02, 0.000000e00, 6.003891e02, 4.450382e01],
[0.000000e00, 7.183351e02, 1.815122e02, -5.951107e-01],
[0.000000e00, 0.000000e00, 1.000000e00, 2.616315e-03],
]
),
"2011_09_30": torch.tensor(
[
[7.070912e02, 0.000000e00, 6.018873e02, 4.688783e01],
[0.000000e00, 7.070912e02, 1.831104e02, 1.178601e-01],
[0.000000e00, 0.000000e00, 1.000000e00, 6.203223e-03],
]
),
"2011_10_03": torch.tensor(
[
[7.188560e02, 0.000000e00, 6.071928e02, 4.538225e01],
[0.000000e00, 7.188560e02, 1.852157e02, -1.130887e-01],
[0.000000e00, 0.000000e00, 1.000000e00, 3.779761e-03],
]
),
}
min_depth = 0.05
max_depth = 80.0
depth_scale = 256.0
log_mean = 2.5462
log_std = 0.5871
test_split = "kitti_eigen_test.txt"
train_split = "kitti_eigen_train.txt"
test_split_benchmark = "kitti_test.txt"
hdf5_paths = ["kitti.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.masker = AnnotationMask(
min_value=0.0,
max_value=self.max_depth if test_mode else None,
custom_fn=self.eval_mask if test_mode else lambda x, *args, **kwargs: x,
)
self.test_mode = test_mode
self.crop = crop
self.cropper_base = KittiCrop(crop_size=(352, 1216))
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 = line.strip().split(" ")[0]
depth_filename = line.strip().split(" ")[1]
if depth_filename == "None":
continue
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, idx, image_name):
return self.CAM_INTRINSIC[image_name.split("/")[0]][:, :3].clone()
def preprocess(self, results):
results = self.replicate(results)
for i, seq in enumerate(results["sequence_fields"]):
self.resizer.ctx = None
results[seq] = self.cropper_base(results[seq])
results[seq] = self.resizer(results[seq])
num_pts = torch.count_nonzero(results[seq]["depth"] > 0)
if num_pts < 50:
raise IndexError(f"Too few points in depth map ({num_pts})")
for key in results[seq].get("image_fields", ["image"]):
results[seq][key] = results[seq][key].to(torch.float32) / 255
# update fields common in sequence
for key in ["image_fields", "gt_fields", "mask_fields", "camera_fields"]:
if key in results[(0, 0)]:
results[key] = results[(0, 0)][key]
results = self.pack_batch(results)
return results
def eval_mask(self, valid_mask, info={}):
"""Do grag_crop or eigen_crop for testing"""
mask_height, mask_width = valid_mask.shape[-2:]
eval_mask = torch.zeros_like(valid_mask)
if "garg" in self.crop:
eval_mask[
...,
int(0.40810811 * mask_height) : int(0.99189189 * mask_height),
int(0.03594771 * mask_width) : int(0.96405229 * mask_width),
] = 1
elif "eigen" in self.crop:
eval_mask[
...,
int(0.3324324 * mask_height) : int(0.91351351 * mask_height),
int(0.03594771 * mask_width) : int(0.96405229 * mask_width),
] = 1
return torch.logical_and(valid_mask, eval_mask)
def get_mapper(self):
return {
"image_filename": 0,
"depth_filename": 1,
}
def pre_pipeline(self, results):
results = super().pre_pipeline(results)
results["dense"] = [False] * self.num_copies
results["quality"] = [1] * self.num_copies
return results
import json
class KITTIBenchmark(ImageDataset):
min_depth = 0.05
max_depth = 80.0
depth_scale = 256.0
test_split = "test_split.txt"
train_split = "val_split.txt"
intrinsics_file = "intrinsics.json"
hdf5_paths = ["kitti_benchmark.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=True,
normalize=normalize,
augmentations_db=augmentations_db,
resize_method=resize_method,
mini=mini,
**kwargs,
)
self.test_mode = test_mode
self.crop = crop
self.masker = AnnotationMask(
min_value=self.min_depth,
max_value=self.max_depth if test_mode else None,
custom_fn=lambda x, *args, **kwargs: x,
)
self.collecter = Collect(keys=["image_fields", "mask_fields", "gt_fields"])
self.load_dataset()
def load_dataset(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] # correct the -1
intrinsics = np.array(h5file[self.intrinsics_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 = torch.tensor(
intrinsics[os.path.join(*image_filename.split("/")[:2])]
).squeeze()[:, :3]
sample = {
"image_filename": image_filename,
"depth_filename": depth_filename,
"K": intrinsics,
}
dataset.append(sample)
self.dataset = DatasetFromList(dataset)
self.log_load_dataset()
class KITTIRMVD(SequenceDataset):
min_depth = 0.05
max_depth = 80.0
depth_scale = 256.0
default_fps = 10
test_split = "test.txt"
train_split = "test.txt"
sequences_file = "sequences.json"
hdf5_paths = ["kitti_rmvd.hdf5"]
def __init__(
self,
image_shape,
split_file,
test_mode,
crop=None,
augmentations_db={},
normalize=True,
resize_method="hard",
mini: float = 1.0,
num_frames: int = 1,
benchmark: bool = False,
decode_fields: list[str] = ["image", "depth"],
inplace_fields: list[str] = ["K", "cam2w"],
**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,
num_frames=num_frames,
decode_fields=decode_fields,
inplace_fields=inplace_fields,
**kwargs,
)
self.crop = crop
self.resizer = Compose([KittiCrop(crop_size=(352, 1216)), self.resizer])
def eval_mask(self, valid_mask, info={}):
"""Do grag_crop or eigen_crop for testing"""
mask_height, mask_width = valid_mask.shape[-2:]
eval_mask = torch.zeros_like(valid_mask)
if "garg" in self.crop:
eval_mask[
...,
int(0.40810811 * mask_height) : int(0.99189189 * mask_height),
int(0.03594771 * mask_width) : int(0.96405229 * mask_width),
] = 1
elif "eigen" in self.crop:
eval_mask[
...,
int(0.3324324 * mask_height) : int(0.91351351 * mask_height),
int(0.03594771 * mask_width) : int(0.96405229 * mask_width),
] = 1
else:
return valid_mask
return torch.logical_and(valid_mask, eval_mask)
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