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import io
import itertools as it
import numpy as np
import datasets as d
_DESCRIPTION = """\
The Dropjects dataset was created at the Chair of Cyber-Physical Systems in Production \
Engineering at the Technical University of Munich.
"""
SUBSETS = [
"omni",
"cps",
"linemod",
"ycbv",
"homebreweddb",
"hope",
"tless",
]
NUM_SHARDS = {
"cps": 1000,
"ycbv": 1000,
"linemod": 1000,
"tless": 1000,
"omni": 10_000,
}
BASE_PATH = "https://huggingface.co/datasets/LukasDb/dropjects/resolve/main/data/train/{subset}/{shard}.tar"
h = 1440
w = 2560
class Dropjects(d.GeneratorBasedBuilder):
BUILDER_CONFIGS = list(d.BuilderConfig(name=x) for x in SUBSETS)
def _info(self):
features = d.Features(
{
# TODO at least the resolution is different
"rgb": d.Array3D((h, w, 3), dtype="uint8"),
"rgb_R": d.Array3D((h, w, 3), dtype="uint8"),
"depth": d.Array2D((h, w), dtype="float32"),
"depth_R": d.Array2D((h, w), dtype="float32"),
"mask": d.Array2D((h, w), dtype="int32"),
"obj_ids": d.Sequence(d.Value("int32")),
"obj_classes": d.Sequence(d.Value("string")),
"obj_pos": d.Sequence(d.Sequence(d.Value("float32"))),
"obj_rot": d.Sequence(d.Sequence(d.Value("float32"))),
"obj_bbox_obj": d.Sequence(d.Sequence(d.Value("int32"))),
"obj_bbox_visib": d.Sequence(d.Sequence(d.Value("int32"))),
"cam_location": d.Sequence(d.Value("float32")),
"cam_rotation": d.Sequence(d.Value("float32")),
"cam_matrix": d.Array2D((3, 3), dtype="float32"),
"obj_px_count_all": d.Sequence(d.Value("int32")),
"obj_px_count_valid": d.Sequence(d.Value("int32")),
"obj_px_count_visib": d.Sequence(d.Value("int32")),
"obj_visib_fract": d.Sequence(d.Value("float32")),
}
)
return d.DatasetInfo(
description=_DESCRIPTION,
citation="", # TODO
homepage="", # TODO
license="cc",
features=features,
)
def _split_generators(self, dl_manager):
subset = self.config.name
archive_paths = [
BASE_PATH.format(subset=subset, shard=i) for i in range(NUM_SHARDS[subset])
]
downloaded = dl_manager.download(archive_paths)
return [
d.SplitGenerator(
name=d.Split.TRAIN,
gen_kwargs={"tars": [dl_manager.iter_archive(d) for d in downloaded]},
),
]
def _generate_examples(self, tars):
sample = {}
id = None
for tar in tars:
for file_path, file_obj in tar:
new_id = file_path.split(".")[0]
if id is None:
id = new_id
else:
if id != new_id:
yield id, sample
sample = {}
id = new_id
key = file_path.split(".")[1]
bytes = io.BytesIO(file_obj.read())
value = np.load(bytes, allow_pickle=False)
sample[key] = value
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