🎉 init(image_folder): Forked and updated nateraw/image-folder

image-folder.py

@@ -0,0 +1,106 @@
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import datasets
+import pyarrow as pa
+from datasets.features import ClassLabel, Image
+from datasets.tasks import ImageClassification
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+@dataclass
+class ImageFolderConfig(datasets.BuilderConfig):
+    """BuilderConfig for ImageFolder."""
+
+    features: Optional[datasets.Features] = None
+
+    @property
+    def schema(self):
+        return (
+            pa.schema(self.features.type)
+            if self.features is not None
+            else None
+        )
+
+
+class ImageFolder(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIG_CLASS = ImageFolderConfig
+
+    def _info(self):
+        filepaths = None
+        if isinstance(self.config.data_files, str):
+            filepaths = self.config.data_files
+        elif isinstance(self.config.data_files, dict):
+            filepaths = self.config.data_files.get("train", None)
+        if filepaths is None:
+            raise RuntimeError("data_files must be specified")
+
+        classes = sorted(
+            [Path(file_path).parent.name.lower() for file_path in filepaths]
+        )
+
+        # Remove duplicates
+        classes = list(set(classes))
+
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image_filepath": Image(),
+                    "labels": ClassLabel(names=classes),
+                }
+            ),
+            task_templates=[
+                ImageClassification(
+                    image_column="image_filepath",
+                    label_column="labels",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        if not self.config.data_files:
+            raise ValueError(
+                f"At least one data file must be specified, but got data_files={self.config.data_files}"
+            )
+
+        data_files = self.config.data_files
+        if isinstance(data_files, str):
+            file_paths = data_files
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"file_paths": file_paths},
+                )
+            ]
+        splits = []
+        for split_name, file_paths in data_files.items():
+            splits.append(
+                datasets.SplitGenerator(
+                    name=split_name, gen_kwargs={"file_paths": file_paths}
+                )
+            )
+        return splits
+
+    def _generate_examples(self, file_paths):
+        logger.info("generating examples from = %s", file_paths)
+        extensions = {
+            ".jpg",
+            ".jpeg",
+            ".png",
+            ".ppm",
+            ".bmp",
+            ".pgm",
+            ".tif",
+            ".tiff",
+            ".webp",
+        }
+        for i, path in enumerate(file_paths):
+            path = Path(path)
+            if path.suffix in extensions:
+                yield i, {
+                    "image_filepath": path.as_posix(),
+                    "labels": path.parent.name.lower(),
+                }