- ray.data.read_images(paths: str | List[str], *, filesystem: pyarrow.fs.FileSystem | None = None, parallelism: int = -1, meta_provider: BaseFileMetadataProvider | None = None, ray_remote_args: Dict[str, Any] = None, arrow_open_file_args: Dict[str, Any] | None = None, partition_filter: PathPartitionFilter | None = None, partitioning: Partitioning = None, size: Tuple[int, int] | None = None, mode: str | None = None, include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Literal['files'] | None = None, file_extensions: List[str] | None = ['png', 'jpg', 'jpeg', 'tif', 'tiff', 'bmp', 'gif']) Dataset #
Datasetfrom image files.
>>> import ray >>> path = "s3://anonymous@ray-example-data/batoidea/JPEGImages/" >>> ds = ray.data.read_images(path) >>> ds.schema() Column Type ------ ---- image numpy.ndarray(shape=(32, 32, 3), dtype=uint8)
If you need image file paths, set
>>> ds = ray.data.read_images(path, include_paths=True) >>> ds.schema() Column Type ------ ---- image numpy.ndarray(shape=(32, 32, 3), dtype=uint8) path string >>> ds.take(1)["path"] 'ray-example-data/batoidea/JPEGImages/1.jpeg'
If your images are arranged like:
root/dog/xxx.png root/dog/xxy.png root/cat/123.png root/cat/nsdf3.png
Then you can include the labels by specifying a
>>> import ray >>> from ray.data.datasource.partitioning import Partitioning >>> root = "s3://anonymous@ray-example-data/image-datasets/dir-partitioned" >>> partitioning = Partitioning("dir", field_names=["class"], base_dir=root) >>> ds = ray.data.read_images(root, size=(224, 224), partitioning=partitioning) >>> ds.schema() Column Type ------ ---- image numpy.ndarray(shape=(224, 224, 3), dtype=uint8) class string
paths – A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories.
filesystem – The pyarrow filesystem implementation to read from. These filesystems are specified in the pyarrow docs. Specify this parameter if you need to provide specific configurations to the filesystem. By default, the filesystem is automatically selected based on the scheme of the paths. For example, if the path begins with
parallelism – The amount of parallelism to use for the dataset. Defaults to -1, which automatically determines the optimal parallelism for your configuration. You should not need to manually set this value in most cases. For details on how the parallelism is automatically determined and guidance on how to tune it, see Tuning read parallelism. Parallelism is upper bounded by the total number of records in all the CSV files.
meta_provider – A file metadata provider. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If
None, this function uses a system-chosen implementation.
ray_remote_args – kwargs passed to
remote()in the read tasks.
arrow_open_file_args – kwargs passed to pyarrow.fs.FileSystem.open_input_file. when opening input files to read.
partition_filter – A
PathPartitionFilter. Use with a custom callback to read only selected partitions of a dataset. By default, this filters out any file paths whose file extension does not match
partitioning – A
Partitioningobject that describes how paths are organized. Defaults to
size – The desired height and width of loaded images. If unspecified, images retain their original shape.
include_paths – If
True, include the path to each image. File paths are stored in the
ignore_missing_paths – If True, ignores any file/directory paths in
pathsthat are not found. Defaults to False.
shuffle – If setting to “files”, randomly shuffle input files order before read. Defaults to not shuffle with
file_extensions – A list of file extensions to filter files by.
PublicAPI (beta): This API is in beta and may change before becoming stable.