squidpy.im.calculate_image_features

squidpy.im.calculate_image_features(adata, img, layer=None, library_id=None, features='summary', features_kwargs=mappingproxy({}), key_added='img_features', copy=False, n_jobs=None, backend='loky', show_progress_bar=True, **kwargs)[source]

Calculate image features for all observations in adata.

Parameters

adata (AnnData) – Annotated data object.
img (ImageContainer) – High-resolution image.
layer (Optional[str]) – Image layer in img that should be processed. If None and only 1 layer is present, it will be selected.
library_id (Union[str, Sequence[str], None]) –
- If None, there should only exist one entry in anndata.AnnData.uns ['{spatial_key}'].
- If a str, first search anndata.AnnData.obs ['{library_id}'] which contains the mapping from observations to library ids, then search anndata.AnnData.uns ['{spatial_key}'].
features (Union[str, Sequence[str]]) –
Features to be calculated. Valid options are:
- ’texture’ - summary stats based on repeating patterns squidpy.im.ImageContainer.features_texture().
- ’summary’ - summary stats of each image channel squidpy.im.ImageContainer.features_summary().
- ’histogram’ - counts in bins of image channel’s histogram squidpy.im.ImageContainer.features_histogram().
- ’segmentation’ - stats of a cell segmentation mask squidpy.im.ImageContainer.features_segmentation().
- ’custom’ - extract features using a custom function squidpy.im.ImageContainer.features_custom().
features_kwargs (Mapping[str, Mapping[str, Any]]) – Keyword arguments for the different features that should be generated, such as { 'texture': { ... }, ... }.
key_added (str) – Key in anndata.AnnData.obsm where to store the calculated features.
copy (bool) – If True, return the result, otherwise save it to the adata object.
n_jobs (Optional[int]) – Number of parallel jobs.
backend (str) – Parallelization backend to use. See joblib.Parallel for available options.
show_progress_bar (bool) – Whether to show the progress bar or not.
kwargs (Any) – Keyword arguments for squidpy.im.ImageContainer.generate_spot_crops().

Return type