glasscut.dataset package¶

Submodules¶

glasscut.dataset.generator module¶

Dataset generation orchestration for multi-slide tiling workflows.

class glasscut.dataset.generator.DatasetGenerator(dataset_id, output_dir, *, tiler, n_workers=4, batch_size=128, save_thumbnails=True, save_masks=True, save_processed_json=True, show_progress=True, verbose=True)[source]¶

Bases: object

Generate a tile dataset from one or more slide files.

Parameters:

dataset_id (str)
output_dir (str | Path)
tiler (Tiler)
n_workers (int)
batch_size (int)
save_thumbnails (bool)
save_masks (bool)
save_processed_json (bool)
show_progress (bool)
verbose (bool)

__init__(dataset_id, output_dir, *, tiler, n_workers=4, batch_size=128, save_thumbnails=True, save_masks=True, save_processed_json=True, show_progress=True, verbose=True)[source]¶

Initialize generator from direct parameters.

Parameters:

dataset_id (str) – Dataset identifier.
output_dir (str | Path) – Output root directory.
tiler (Tiler) – Preconfigured tiler instance used for extraction.
n_workers (int, optional) – Number of workers for batched tile extraction. Default is 4.
batch_size (int, optional) – Number of tiles per extraction batch. Default is 128.
save_thumbnails (bool, optional) – Whether to save slide thumbnail artifacts.
save_masks (bool, optional) – Whether to save tissue mask artifacts.
save_processed_json (bool, optional) – Whether to save processed.json at dataset root.
show_progress (bool, optional) – Whether to display progress bars for slides and tiles.
verbose (bool, optional) – Whether to enable info-level logs.

Return type:

None

process_dataset(slide_paths)[source]¶

Process all provided slides and persist tiles, artifacts, and metadata.

Parameters:: slide_paths (Sequence[str | Path])
Return type:: DatasetMetadata

glasscut.dataset.live module¶

Live in-memory slide-level dataset utilities.

This module provides a dataset-like interface where each item corresponds to one slide and contains all extracted tiles for that slide, without writing artifacts to disk.

class glasscut.dataset.live.LiveSlideSample(slide_id, slide_name, slide_path, dimensions, mpp, magnifications, tiles)[source]¶

Bases: object

Container for one in-memory slide sample.

Parameters:

slide_id (str)
slide_name (str)
slide_path (str)
dimensions (tuple[int, int])
mpp (float)
magnifications (list[float])
tiles (list[Tile])

slide_id¶

Slide identifier in slide_XXX format.

Type:: str

slide_name¶

Slide basename without extension.

Type:: str

slide_path¶

Absolute slide path.

Type:: str

dimensions¶

Level-0 dimensions as (width, height).

Type:: tuple[int, int]

mpp¶

Microns-per-pixel at level 0.

Type:: float

magnifications¶

Available magnification values.

Type:: list[float]

tiles¶

All extracted tiles for this slide, in extraction order.

Type:: list[Tile]

slide_id: str¶

slide_name: str¶

slide_path: str¶

dimensions: tuple[int, int]¶

mpp: float¶

magnifications: list[float]¶

tiles: list[Tile]¶

__init__(slide_id, slide_name, slide_path, dimensions, mpp, magnifications, tiles)¶

Parameters:

slide_id (str)
slide_name (str)
slide_path (str)
dimensions (tuple[int, int])
mpp (float)
magnifications (list[float])
tiles (list[Tile])

Return type:

None

class glasscut.dataset.live.LiveSlideDataset(slide_paths, *, tiler, n_workers=4, batch_size=128, use_cucim=True)[source]¶

Bases: object

Slide-level in-memory dataset.

Each __getitem__ call opens one slide, extracts all tiles in memory using the configured tiler, and returns a LiveSlideSample.

Parameters:

slide_paths (Sequence[str | Path])
tiler (Tiler)
n_workers (int)
batch_size (int)
use_cucim (bool)

__init__(slide_paths, *, tiler, n_workers=4, batch_size=128, use_cucim=True)[source]¶

Parameters:

slide_paths (Sequence[str | Path])
tiler (Tiler)
n_workers (int)
batch_size (int)
use_cucim (bool)

Return type:

None

__len__()[source]¶

Return number of slides in the live dataset.

Return type:: int

__getitem__(index)[source]¶

Return all extracted tiles for one slide.

Parameters:: index (int) – Slide index in the dataset.
Return type:: LiveSlideSample

Module contents¶

Dataset generation module.

class glasscut.dataset.DatasetGenerator(dataset_id, output_dir, *, tiler, n_workers=4, batch_size=128, save_thumbnails=True, save_masks=True, save_processed_json=True, show_progress=True, verbose=True)[source]¶

Bases: object

Generate a tile dataset from one or more slide files.

Parameters:

dataset_id (str)
output_dir (str | Path)
tiler (Tiler)
n_workers (int)
batch_size (int)
save_thumbnails (bool)
save_masks (bool)
save_processed_json (bool)
show_progress (bool)
verbose (bool)

Initialize generator from direct parameters.

Parameters:

dataset_id (str) – Dataset identifier.
output_dir (str | Path) – Output root directory.
tiler (Tiler) – Preconfigured tiler instance used for extraction.
n_workers (int, optional) – Number of workers for batched tile extraction. Default is 4.
batch_size (int, optional) – Number of tiles per extraction batch. Default is 128.
save_thumbnails (bool, optional) – Whether to save slide thumbnail artifacts.
save_masks (bool, optional) – Whether to save tissue mask artifacts.
save_processed_json (bool, optional) – Whether to save processed.json at dataset root.
show_progress (bool, optional) – Whether to display progress bars for slides and tiles.
verbose (bool, optional) – Whether to enable info-level logs.

Return type:

None

process_dataset(slide_paths)[source]¶

Process all provided slides and persist tiles, artifacts, and metadata.

Parameters:: slide_paths (Sequence[str | Path])
Return type:: DatasetMetadata

class glasscut.dataset.LiveSlideDataset(slide_paths, *, tiler, n_workers=4, batch_size=128, use_cucim=True)[source]¶

Bases: object

Slide-level in-memory dataset.

Each __getitem__ call opens one slide, extracts all tiles in memory using the configured tiler, and returns a LiveSlideSample.

Parameters:

slide_paths (Sequence[str | Path])
tiler (Tiler)
n_workers (int)
batch_size (int)
use_cucim (bool)

__init__(slide_paths, *, tiler, n_workers=4, batch_size=128, use_cucim=True)[source]¶

Parameters:

slide_paths (Sequence[str | Path])
tiler (Tiler)
n_workers (int)
batch_size (int)
use_cucim (bool)

Return type:

None

__len__()[source]¶

Return number of slides in the live dataset.

Return type:: int

__getitem__(index)[source]¶

Return all extracted tiles for one slide.

Parameters:: index (int) – Slide index in the dataset.
Return type:: LiveSlideSample

class glasscut.dataset.LiveSlideSample(slide_id, slide_name, slide_path, dimensions, mpp, magnifications, tiles)[source]¶

Bases: object

Container for one in-memory slide sample.

Parameters:

slide_id (str)
slide_name (str)
slide_path (str)
dimensions (tuple[int, int])
mpp (float)
magnifications (list[float])
tiles (list[Tile])

slide_id¶

Slide identifier in slide_XXX format.

Type:: str

slide_name¶

Slide basename without extension.

Type:: str

slide_path¶

Absolute slide path.

Type:: str

dimensions¶

Level-0 dimensions as (width, height).

Type:: tuple[int, int]

mpp¶

Microns-per-pixel at level 0.

Type:: float

magnifications¶

Available magnification values.

Type:: list[float]

tiles¶

All extracted tiles for this slide, in extraction order.

Type:: list[Tile]

slide_id: str¶

slide_name: str¶

slide_path: str¶

dimensions: tuple[int, int]¶

mpp: float¶

magnifications: list[float]¶

tiles: list[Tile]¶

__init__(slide_id, slide_name, slide_path, dimensions, mpp, magnifications, tiles)¶

Parameters:

slide_id (str)
slide_name (str)
slide_path (str)
dimensions (tuple[int, int])
mpp (float)
magnifications (list[float])
tiles (list[Tile])

Return type:

None