Convert dataset to Parquet (#1)

- Convert dataset to Parquet (fb243e84df52059ade357368a0d5468ee990530d)
- Delete loading script auxiliary file (4ca7b0afd58ab2c6244f2b873f0ef4ac74f4fa32)
- Delete data file (26e319b7cb616f5310b458604cbc6028ed04e0c3)
- Delete data file (5f64b085b249011ede972ffa312bbd33a3973975)
- Delete data file (3f5787e7fe9c9cb7ed2c3ce2f2fb314427041184)
- Delete data file (8c592518fae8f44b6bc8d50b1406bb96e0048b1a)
- Delete data file (71547c41aa162ac9fa54fab78796053eea20372c)

README.md

@@ -1,3 +1,18 @@
 ---
 license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 4454294.0
+    num_examples: 4
+  download_size: 4456047
+  dataset_size: 4454294.0
 ---

images/frame_0a1d5ccc-e1c7-4c66-8fbc-f87080c3f408_0046.png → data/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6a0470866fd6b60d1d16f3dbaca1c81a52f21148899d3b6b2a44c034331e5822
-size 1117345
+oid sha256:641d36a565ab641a5be9eba5019f1a0dd0f5f5771bbbf4e6c66cbe1a059daf15
+size 4456047

image_dataset.py

@@ -1,95 +0,0 @@
-import os
-import json
-import random
-from datasets import (
-    BuilderConfig,
-    DatasetInfo,
-    DownloadManager,
-    GeneratorBasedBuilder,
-    SplitGenerator,
-    Split,
-    Features,
-    Image,
-    Sequence,
-    Value,
-)
-
-class ImageSubsetConfig(BuilderConfig):
-    """BuilderConfig for small vs. full dataset."""
-    def __init__(self, name, sample_size=None, **kwargs):
-        super().__init__(
-            name=name,
-            version="1.0.0",
-            description=kwargs.get("description", "")
-        )
-        self.sample_size = sample_size
-
-class MyImageDataset(GeneratorBasedBuilder):
-    """A dataset of images with actuated_angle from metadata.json."""
-    BUILDER_CONFIGS = [
-        ImageSubsetConfig(
-            name="full",
-            sample_size=None,
-            description="All images (≈100 GB). Use with streaming=True."
-        ),
-        ImageSubsetConfig(
-            name="small",
-            sample_size=2,
-            description="Random subset of 10 000 images for quick iteration."
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = "smgit all"
-
-    def _info(self):
-        return DatasetInfo(
-            description="Images with a 2-D actuated_angle from metadata.json",
-            features=Features({
-                "image": Image(),                      # the image file
-                "actuated_angle": Sequence(Value("int32")),  # [angle0, angle1]
-            }),
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager: DownloadManager):
-        data_dir = dl_manager.manual_dir or os.path.abspath(os.path.dirname(__file__))
-        images_dir = os.path.join(data_dir, "images")
-        meta_path = os.path.join(data_dir, "metadata.json")
-
-        # load your filename → {"0":…, "1":…} mapping
-        with open(meta_path, "r", encoding="utf-8") as f:
-            metadata = json.load(f)
-
-        # sample if in "small" config
-        if self.config.sample_size:
-            all_fnames = list(metadata.keys())
-            random.seed(42)
-            selected = set(random.sample(all_fnames, self.config.sample_size))
-        else:
-            selected = set(metadata.keys())
-
-        return [
-            SplitGenerator(
-                name=Split.TRAIN,
-                gen_kwargs={
-                    "images_dir": images_dir,
-                    "metadata": metadata,
-                    "selected": selected,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, images_dir, metadata, selected):
-        """Yields examples of the form { "image": path, "actuated_angle": [int, int] }"""
-        for idx, fname in enumerate(selected):
-            if fname not in metadata:
-                continue
-            image_path = os.path.join(images_dir, fname)
-            if not os.path.exists(image_path):
-                continue
-            meta = metadata[fname]
-            # extract the two angle values (keys "0" and "1")
-            angles = [ int(meta.get("0", 0)), int(meta.get("1", 0)) ]
-            yield idx, {
-                "image": image_path,
-                "actuated_angle": angles,
-            }

metadata.json

@@ -1,18 +0,0 @@
-{
-    "frame_0a0ab072-9729-4e16-88c5-a3ea9ed959d0_0036.png": {
-        "0": 90,
-        "1": 20
-    },
-    "frame_0a1d5ccc-e1c7-4c66-8fbc-f87080c3f408_0046.png": {
-        "0": 90,
-        "1": 20
-    },
-    "frame_0a1d30e1-d358-4533-a192-75c5c86a2d56_0049.png": {
-        "0": 90,
-        "1": 20
-    },
-    "frame_0a0bf5c3-9ee4-41aa-9298-ccfebdb1b5ea_0029.png": {
-        "0": 90,
-        "1": 20
-    }
-}