Delete loading script

OpenHumnoidDataset.py

@@ -1,117 +0,0 @@
-# TestNew.py
-import json
-import random
-from datasets import (
-    BuilderConfig,
-    DatasetInfo,
-    DownloadManager,
-    GeneratorBasedBuilder,
-    SplitGenerator,
-    Split,
-    Features,
-    Image,
-    Sequence,
-    Value,
-)
-from huggingface_hub import hf_hub_url
-
-
-_REPO_ID = "iamirulofficial/OpenHumnoidDataset"          # change if you ever fork the repo
-
-
-class ImageSubsetConfig(BuilderConfig):
-    """BuilderConfig for full dataset vs. small random sample."""
-    def __init__(self, name, sample_size=None, **kwargs):
-        super().__init__(
-            name=name,
-            version="1.0.1",
-            description=kwargs.get("description", "")
-        )
-        self.sample_size = sample_size
-
-
-class MyImageDataset(GeneratorBasedBuilder):
-    """Images + 2‑D actuated_angle labels stored in metadata.json."""
-    BUILDER_CONFIGS = [
-        ImageSubsetConfig(
-            name="full",
-            sample_size=None,                     # all images
-            description="Entire dataset (≈100 GB)"
-        ),
-        ImageSubsetConfig(
-            name="small",
-            sample_size=2,                        # tiny sample for quick tests
-            description="Two random images"
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = "small"
-
-    def _info(self) -> DatasetInfo:
-        return DatasetInfo(
-            description="Images with a 2‑D actuated_angle from metadata.json",
-            features=Features(
-                {
-                    "image": Image(),                    # PIL.Image will be returned
-                    "actuated_angle": {
-                    "0": Value("int32"),
-                    "1": Value("int32"),
-                },  # [angle0, angle1]
-                }
-            ),
-            supervised_keys=None,
-        )
-
-    # --------------------------------------------------------------------- #
-    # Download phase                                                         #
-    # --------------------------------------------------------------------- #
-    def _split_generators(self, dl_manager: DownloadManager):
-        # 1️⃣  Download metadata.json (tiny text file)
-        meta_path = dl_manager.download(
-            hf_hub_url(_REPO_ID, "metadata.json", repo_type="dataset")
-        )
-
-        # 2️⃣  Decide which filenames we need
-        with open(meta_path, encoding="utf-8") as f:
-            metadata = json.load(f)                      # {"frame_000.png": {"0":…, …}, …}
-
-        all_fnames = list(metadata)
-        if self.config.sample_size:                      # small‑config branch
-            random.seed(42)
-            selected = sorted(random.sample(all_fnames, self.config.sample_size))
-        else:
-            selected = sorted(all_fnames)                # full dataset
-
-        # 3️⃣  Build URLs → dl_manager.download() → local paths
-        url_dict = {
-            fname: hf_hub_url(
-                _REPO_ID, f"images/{fname}", repo_type="dataset"
-            )
-            for fname in selected
-        }
-        img_paths = dl_manager.download(url_dict)        # same keys, but local files
-
-        return [
-            SplitGenerator(
-                name=Split.TRAIN,
-                gen_kwargs={
-                    "img_paths": img_paths,
-                    "metadata": metadata,
-                },
-            )
-        ]
-
-    # --------------------------------------------------------------------- #
-    # Generate examples                                                     #
-    # --------------------------------------------------------------------- #
-    def _generate_examples(self, img_paths: dict, metadata: dict):
-        """
-        Yields (key, example) where example =
-        { "image": <local‑file‑path>, "actuated_angle": [int, int] }
-        """
-        for idx, (fname, local_path) in enumerate(img_paths.items()):
-            meta = metadata.get(fname, {})
-            angles = [int(meta.get("0", 0)), int(meta.get("1", 0))]
-            yield idx, {"image": local_path, "actuated_angle": {               # 👈  dict, not list
-                "0": int(meta.get("0", 0)),
-                "1": int(meta.get("1", 0)),
-            }}