datumaro.util.image#

Module Attributes

ImageMeta

filename -> height, width

Functions

`copyto_image`(src, dst, src_crypter, dst_crypter)
`decode_image`(image_bytes[, dtype])
`decode_image_context`(image_backend, ...)	Change Datumaro image color channel while decoding.
`encode_image`(image, ext[, dtype])
`find_images`(dirpath[, exts, recursive, ...])
`is_image`(path)
`load_image`(path[, dtype, crypter])	Reads an image in the HWC Grayscale/BGR(A) [0; 255] format (default dtype is uint8).
`load_image_meta_file`(image_meta_path)	Loads image metadata from a file with the following format:
`save_image`(dst, image[, ext, create_dir, ...])
`save_image_meta_file`(image_meta, image_meta_path)	Saves image_meta to the path specified by image_meta_path in the format defined in load_image_meta_file's documentation.

Classes

`ImageBackend`(value)	An enumeration.
`ImageColorChannel`(value)	Image color channel
`lazy_image`(path, loader, ...)	Cache:

class datumaro.util.image.ImageBackend(value)[source]#

Bases: Enum

An enumeration.

cv2 = 1#

PIL = 2#

class datumaro.util.image.ImageColorChannel(value)[source]#

Bases: Enum

Image color channel

UNCHANGED: Use the original image’s channel (default)
COLOR_BGR: Use BGR 3 channels (it can ignore the alpha channel or convert the gray scale image)
COLOR_RGB: Use RGB 3 channels (it can ignore the alpha channel or convert the gray scale image)

UNCHANGED = 0#

COLOR_BGR = 1#

COLOR_RGB = 2#

decode_by_cv2(image_bytes: bytes) → ndarray[source]#: Convert image color channel for OpenCV image (np.ndarray).

decode_by_pil(image_bytes: bytes) → PILImage[source]#: Convert image color channel for PIL Image.

datumaro.util.image.decode_image_context(image_backend: ImageBackend, image_color_channel: ImageColorChannel)[source]#

Change Datumaro image color channel while decoding.

For model training, it is recommended to use this context manager to load images in the BGR 3-channel format. For example,

import datumaro as dm
with decode_image_context(image_backend=ImageBackend.cv2, image_color_channel=ImageColorScale.COLOR):
    item: dm.DatasetItem
    img_data = item.media_as(dm.Image).data
    assert img_data.shape[-1] == 3  # It should be a 3-channel image

datumaro.util.image.load_image(path: str, dtype: ~numpy.dtype[~typing.Any] | None | type[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | str | tuple[~typing.Any, int] | tuple[~typing.Any, ~typing.SupportsIndex | ~collections.abc.Sequence[~typing.SupportsIndex]] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | tuple[~typing.Any, ~typing.Any] = <class 'numpy.uint8'>, crypter: ~datumaro.components.crypter.Crypter = <datumaro.components.crypter.NullCrypter object>)[source]#: Reads an image in the HWC Grayscale/BGR(A) [0; 255] format (default dtype is uint8).

datumaro.util.image.copyto_image(src: str | IOBase, dst: str | IOBase, src_crypter: Crypter, dst_crypter: Crypter) → None[source]#

datumaro.util.image.save_image(dst: str | ~io.IOBase, image: ~numpy.ndarray, ext: str | None = None, create_dir: bool = False, dtype: ~numpy.dtype[~typing.Any] | None | type[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | str | tuple[~typing.Any, int] | tuple[~typing.Any, ~typing.SupportsIndex | ~collections.abc.Sequence[~typing.SupportsIndex]] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | tuple[~typing.Any, ~typing.Any] = <class 'numpy.uint8'>, crypter: ~datumaro.components.crypter.Crypter = <datumaro.components.crypter.NullCrypter object>, **kwargs) → None[source]#

datumaro.util.image.encode_image(image: ~numpy.ndarray, ext: str, dtype: ~numpy.dtype[~typing.Any] | None | type[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | str | tuple[~typing.Any, int] | tuple[~typing.Any, ~typing.SupportsIndex | ~collections.abc.Sequence[~typing.SupportsIndex]] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | tuple[~typing.Any, ~typing.Any] = <class 'numpy.uint8'>, **kwargs) → bytes[source]#

datumaro.util.image.decode_image(image_bytes: bytes, dtype: ~numpy.dtype[~typing.Any] | None | type[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | str | tuple[~typing.Any, int] | tuple[~typing.Any, ~typing.SupportsIndex | ~collections.abc.Sequence[~typing.SupportsIndex]] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | tuple[~typing.Any, ~typing.Any] = <class 'numpy.uint8'>) → ndarray[source]#

datumaro.util.image.find_images(dirpath: str, exts: str | Iterable[str] | None = None, recursive: bool = False, max_depth: int | None = None, min_depth: int | None = None) → Iterator[str][source]#

datumaro.util.image.is_image(path: str) → bool[source]#

class datumaro.util.image.lazy_image(path: str, loader: ~typing.Callable[[str], ~numpy.ndarray] | None = None, cache: bool | ~datumaro.util.image_cache.ImageCache = True, crypter: ~datumaro.components.crypter.Crypter = <datumaro.components.crypter.NullCrypter object>)[source]#

Bases: object

Cache:

False: do not cache
True: use the global cache
ImageCache instance: an object to be used as cache

datumaro.util.image.ImageMeta#

filename -> height, width

alias of Dict[str, Tuple[int, int]]

datumaro.util.image.load_image_meta_file(image_meta_path: str) → Dict[str, Tuple[int, int]][source]#

Loads image metadata from a file with the following format:

<image name 1> <height 1> <width 1>

<image name 2> <height 2> <width 2>

…

Shell-like comments and quoted fields are allowed.

This can be useful to support datasets in which image dimensions are required to interpret annotations.

datumaro.util.image.save_image_meta_file(image_meta: Dict[str, Tuple[int, int]], image_meta_path: str) → None[source]#: Saves image_meta to the path specified by image_meta_path in the format defined in load_image_meta_file’s documentation.