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import io
import itertools as it
import numpy as np
import datasets as d
_DESCRIPTION = """\
The Dropjects Real dataset was created at the Chair of Cyber-Physical Systems in Production \
Engineering at the Technical University of Munich.
"""
CLASSES = [
"battery_holder",
"buckle_socket",
"buckle_plug",
"chew_toy_big",
"cpsduck",
"cpsglue_big",
"group1",
"group2",
"nema_holder",
"stapler",
]
SCENES = [
"box",
"array", # but only for group1 and group2, and only uncluttered
]
CLUTTER = [
"cluttered",
"uncluttered",
]
LIGHTING = [
"diffuseRight",
"diffuseLeft",
"spotRight",
"spotLeft",
"dark",
"normal",
]
NUM_SHARDS = 3
WILDCARD = "{}"
def is_valid_config(spec):
cls, scene, clutter, lighting = spec
if scene == "array" and cls not in ["group1", "group2", WILDCARD]:
return False
if scene == "array" and clutter not in ["uncluttered", WILDCARD]:
return False
return True
ALL_CONFIGS = list(
it.product(
[WILDCARD] + CLASSES, [WILDCARD] + SCENES, [WILDCARD] + CLUTTER, [WILDCARD] + LIGHTING
)
)
ALL_CONFIGS = [x for x in ALL_CONFIGS if is_valid_config(x)]
BASE_PATH = "https://huggingface.co/datasets/LukasDb/dropjects_real/resolve/main/data/test/{cls}/{scene}/{clutter}/{lighting}/{shard}.tar"
class DropjectsRealConfig(d.BuilderConfig):
def __init__(self, cls: str, scene: str, clutter: str, lighting: str, **kwargs):
name = f"{cls}-{scene}-{clutter}-{lighting}"
super().__init__(version=d.Version("1.0.0"), **kwargs, name=name)
# transform wildcards into concrete lists
# this does not respect the validity of the config
cls = CLASSES if cls == WILDCARD else [cls]
scene = SCENES if scene == WILDCARD else [scene]
clutter = CLUTTER if clutter == WILDCARD else [clutter]
lighting = LIGHTING if lighting == WILDCARD else [lighting]
self.cls = cls
self.scene = scene
self.clutter = clutter
self.lighting = lighting
class DropjectsReal(d.GeneratorBasedBuilder):
BUILDER_CONFIGS = list(
DropjectsRealConfig(cls=cls, scene=scene, clutter=clutter, lighting=lighting)
for cls, scene, clutter, lighting in ALL_CONFIGS
)
DEFAULT_CONFIG_NAME = f"{WILDCARD}-{WILDCARD}-{WILDCARD}-{WILDCARD}"
def _info(self):
features = d.Features(
{
"rgb": d.Array3D((1242, 2208, 3), dtype="uint8"),
"rgb_R": d.Array3D((1242, 2208, 3), dtype="uint8"),
"depth": d.Array2D((1242, 2208), dtype="float32"),
"depth_gt": d.Array2D((1242, 2208), dtype="float32"),
"mask": d.Array2D((1242, 2208), 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):
clss = self.config.cls
scenes = self.config.scene
clutters = self.config.clutter
lightings = self.config.lighting
# wildcards to concrete list can generate invalid configs
configs = [c for c in it.product(clss, scenes, clutters, lightings) if is_valid_config(c)]
archive_paths = [
BASE_PATH.format(cls=c, scene=s, clutter=cl, lighting=l, shard=i)
for c, s, cl, l in configs
for i in range(NUM_SHARDS)
]
downloaded = dl_manager.download(archive_paths)
return [
d.SplitGenerator(
name=d.Split.TEST,
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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