imageio backend and example data

Author: keewis

advanced/backends/imageio_.py

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+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+import numpy as np
+import xarray as xr
+
+if TYPE_CHECKING:
+    import os
+    from typing import Any, Protocol
+
+    import numpy.typing as npt
+
+    class FileLike(Protocol): ...
+
+    class LockType(Protocol):
+        def __enter__(self) -> Any: ...
+
+        def acquire(self, blocking: bool = True, timeout: int = -1) -> bool: ...
+        def release(self) -> None: ...
+
+    IndexerType = int | slice | npt.NDArray[np.integer]
+    FilenameOrObjectType = str | os.PathLike | bytes | FileLike
+
+
+@dataclass
+class ImageIOBackendArray(xr.backends.BackendArray):
+    filename_or_obj: FilenameOrObjectType
+
+    shape: tuple[int]
+    dtype: npt.DtypeLike
+
+    lock: LockType
+
+    def __getitem__(self, key: tuple[IndexerType]):
+        return xr.core.indexing.explicit_indexing_adapter(
+            key,
+            self.shape,
+            xr.core.indexing.IndexingSupport.BASIC,
+            self.basic_indexing,
+        )
+
+    def basic_indexing(self, key: tuple[IndexerType]) -> npt.NDArray:
+        import imageio.v3 as iio
+
+        with self.lock, iio.imopen(self.filename_or_obj, io_mode='r') as f:
+            if key == (slice(None),) * len(self.shape):
+                return f.read()
+
+            first_indexer = key[0]
+            if isinstance(first_indexer, int):
+                data = f.read(index=first_indexer, mode='P', writable=True)
+
+                remaining_indexers = key[1:]
+            else:
+                if isinstance(first_indexer, slice):
+                    indices = range(*first_indexer.indices(self.shape[0]))
+                else:
+                    indices = first_indexer
+
+                data = np.concatenate([f.read(index=index, mode='P') for index in indices], axis=0)
+
+                remaining_indexers = (..., *key[1:])
+
+            return data[remaining_indexers]
+
+
+class ImageIOBackend(xr.backends.BackendEntrypoint):
+    def open_dataset(
+        self, filename_or_obj: FilenameOrObjectType, *, drop_variables: bool | None = None
+    ) -> xr.Dataset:
+        import imageio.v3 as iio
+
+        with iio.imopen(filename_or_obj, io_mode='r') as f:
+            properties = f.properties()
+            metadata = f.metadata()
+
+            dims = ['n_images', 'height', 'width', 'color']
+
+            background = metadata['background']
+            duration = metadata['duration']
+
+            shape = properties.shape
+            dtype = properties.dtype
+
+        backend_array = ImageIOBackendArray(
+            filename_or_obj=filename_or_obj,
+            shape=shape,
+            dtype=dtype,
+            lock=xr.backends.locks.SerializableLock(),
+        )
+        data = xr.core.indexing.LazilyIndexedArray(backend_array)
+
+        var = xr.Variable(
+            dims=dims,
+            data=data,
+            attrs={'duration': duration, 'background': background},
+            encoding={'preferred_chunks': dict(zip(dims, (1, *shape[1:])))},
+        )
+
+        return xr.Dataset({'data': var}, coords={'color': ['red', 'green', 'blue']})