Upload open_cortex_fx_v3.py with huggingface_hub

open_cortex_fx_v3.py

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+"""Open Cortex FX v3 Dataset."""
+
+import csv
+import io
+import zipfile
+from pathlib import Path
+from typing import Iterator
+
+import datasets
+
+_DESCRIPTION = """\
+Open Cortex FX v3 is a video dataset focusing on human manual labor and physical work activities. 
+Each video has been carefully annotated to identify work-related content and categorized into specific labor types.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/Standout/open-cortex-fx-v3"
+
+_LICENSE = "Apache 2.0"
+
+_CITATION = """\
+@dataset{open_cortex_fx_v3,
+  title={Open Cortex FX v3: A Classified Dataset of Human Manual Labor},
+  author={Standout},
+  year={2024},
+  url={https://huggingface.co/datasets/Standout/open-cortex-fx-v3}
+}
+"""
+
+
+class OpenCortexFXv3(datasets.GeneratorBasedBuilder):
+    """Open Cortex FX v3 Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="default",
+            version=VERSION,
+            description="Open Cortex FX v3 dataset",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "name": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                    "split": datasets.Value("string"),
+                    "video": datasets.Value("binary"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract("https://huggingface.co/datasets/Standout/open-cortex-fx-v3/resolve/main/")
+
+        # Find all zip files
+        zip_files = sorted(Path(data_dir).glob("final_*.zip"))
+        
+        splits = []
+        for zip_file in zip_files:
+            split_name = zip_file.stem  # e.g., "final_0"
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split(split_name),
+                    gen_kwargs={"zip_path": zip_file},
+                )
+            )
+        
+        return splits
+
+    def _generate_examples(self, zip_path: Path) -> Iterator[tuple[int, dict]]:
+        """Yields examples."""
+        with zipfile.ZipFile(zip_path, "r") as z:
+            # Read metadata
+            with z.open("metadata.csv") as f:
+                content = f.read().decode("utf-8")
+                reader = csv.DictReader(io.StringIO(content))
+                metadata = list(reader)
+            
+            # Get split name from zip filename
+            split_name = zip_path.stem
+            
+            for idx, row in enumerate(metadata):
+                video_name = row["name"]
+                category = row["category"]
+                
+                # Find video in zip
+                video_path_in_zip = f"{category}/{video_name}"
+                
+                try:
+                    video_data = z.read(video_path_in_zip)
+                    yield idx, {
+                        "name": video_name,
+                        "category": category,
+                        "split": split_name,
+                        "video": video_data,
+                    }
+                except KeyError:
+                    # Try alternative path format
+                    video_path_in_zip = video_path_in_zip.replace(" ", "_")
+                    try:
+                        video_data = z.read(video_path_in_zip)
+                        yield idx, {
+                            "name": video_name,
+                            "category": category,
+                            "split": split_name,
+                            "video": video_data,
+                        }
+                    except KeyError:
+                        # Skip if video not found
+                        continue
+