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dropjects.py

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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