pyjacket.filetools.image package

Submodules

pyjacket.filetools.image.image module

class pyjacket.filetools.image.image.FileType[source]

Bases: object

read(file_path, **kwargs) → ndarray[source]

read_lazy(file_path, **kwargs) → ImageHandle[source]

read_meta() → Metadata[source]

seq_read(file_path, **kwargs) → ndarray[source]

seq_read_lazy(file_path, **kwargs) → ImageHandle[source]

write()[source]

write_lazy()[source]

class pyjacket.filetools.image.image.ImageHandle(file_path, unzip=1)[source]

Bases: object

Access image data lazily with numpy-like slicing

close()[source]

copy()[source]

get(i: int) → ndarray[source]

get_max_shape()[source]

property ndim

open()[source]

operator: object

property shape: The shape of a cropped variant of this data

slices: list[slice]

class pyjacket.filetools.image.image.Metadata(file_path)[source]

Bases: object

property description

property dtype: Data format, e.g. 8bit or 12bit

exif: dict[int, ExifTag]

exif_value(i: int, default=None)[source]

fractional(tup=None)[source]

get_description()[source]

get_resolution()[source]: Pixels per um

read()[source]

property resolution: Pixels per um

property shape: Size of each dimension (pixels)

property shape_um

pyjacket.filetools.image.image.iter_dir(dirpath: str, ext: str = None, nat=True, exclude: set = None)[source]

Obtain files/folders in the <root>/<rel_path>/<folder>

ext: types of files to return

None: yield all file types
‘/’: yield directories only
‘.png’: yield only png.

pyjacket.filetools.image.image.read_img(file_path: str, lazy=False, **kwargs)[source]

Read image data from file.

Parameters:

filepath (str) – Location of the image file
lazy (bool, optional) – Read lazy to save memory. Defaults to False.

Raises:

ValueError – _description_
NotImplementedError – _description_

Returns:

pixel data

Return type:

np.ndarray (default) or ImageHandle (if lazy)

pyjacket.filetools.image.image.read_img_meta(file_path: str, **kwargs) → Metadata[source]

pyjacket.filetools.image.image.write_img(file_path: str, data: ndarray | ImageHandle, meta: Metadata = None, **kwargs)[source]: Write image data. Supports tif, nd2

pyjacket.filetools.image.models module

class pyjacket.filetools.image.models.ExifTag[source]

Bases: object

name: None

value: None

class pyjacket.filetools.image.models.ImageHandle(file_path, unzip=1)[source]

Bases: object

Access image data lazily with numpy-like slicing

close()[source]

copy()[source]

get(i: int) → ndarray[source]

get_max_shape()[source]

property ndim

open()[source]

operator: object

property shape: The shape of a cropped variant of this data

slices: list[slice]

class pyjacket.filetools.image.models.ImageReader[source]

Bases: object

read(file_path: str, **kwargs) → ndarray[source]

read_lazy(file_path: str, **kwargs) → ImageHandle[source]

read_meta(file_path: str, **kwargs) → Metadata[source]

seq_read(file_path: str, **kwargs) → ndarray[source]

seq_read_lazy(file_path: str, **kwargs) → ImageHandle[source]

write(file_path: str, data: ndarray, meta: Metadata = None, **kwargs)[source]

write_lazy(file_path: str, data: ImageHandle, meta: Metadata = None, **kwargs)[source]

class pyjacket.filetools.image.models.Metadata(file_path)[source]

Bases: object

property description

property dtype: Data format, e.g. 8bit or 12bit

exif: dict[int, ExifTag]

exif_value(i: int, default=None)[source]

fractional(tup=None)[source]

get_description()[source]

get_resolution()[source]: Pixels per um

read()[source]

property resolution: Pixels per um

property shape: Size of each dimension (pixels)

property shape_um

pyjacket.filetools.image.models.slice_length(s: slice, n: int)[source]: Compute how many elements belong to a slice of an iterable of size n

Module contents

class pyjacket.filetools.image.ImageHandle(file_path, unzip=1)[source]

Bases: object

Access image data lazily with numpy-like slicing

close()[source]

copy()[source]

get(i: int) → ndarray[source]

get_max_shape()[source]

property ndim

open()[source]

operator: object

property shape: The shape of a cropped variant of this data

slices: list[slice]

class pyjacket.filetools.image.Metadata(file_path)[source]

Bases: object

property description

property dtype: Data format, e.g. 8bit or 12bit

exif: dict[int, ExifTag]

exif_value(i: int, default=None)[source]

fractional(tup=None)[source]

get_description()[source]

get_resolution()[source]: Pixels per um

read()[source]

property resolution: Pixels per um

property shape: Size of each dimension (pixels)

property shape_um

pyjacket.filetools.image.read_img(file_path: str, lazy=False, **kwargs)[source]

Read image data from file.

Parameters:

filepath (str) – Location of the image file
lazy (bool, optional) – Read lazy to save memory. Defaults to False.

Raises:

ValueError – _description_
NotImplementedError – _description_

Returns:

pixel data

Return type:

np.ndarray (default) or ImageHandle (if lazy)

pyjacket.filetools.image.read_img_meta(file_path: str, **kwargs) → Metadata[source]

pyjacket.filetools.image.write_img(file_path: str, data: ndarray | ImageHandle, meta: Metadata = None, **kwargs)[source]: Write image data. Supports tif, nd2