"""Define common code required for cli."""
from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any
import click
if TYPE_CHECKING: # pragma: no cover
from tiatoolbox.models.models_abc import IOConfigABC
[docs]
def add_default_to_usage_help(
usage_help: str,
default: str or int or float or bool,
) -> str:
"""Adds default value to usage help string.
Args:
usage_help (str):
usage help for click option.
default (str or int or float):
default value as string for click option.
Returns:
str:
New usage_help value.
"""
if default is not None:
return f"{usage_help} default={default}"
return usage_help
[docs]
def cli_name(
usage_help: str = "User defined name to be used as an identifier.",
multiple: bool | None = None,
) -> callable:
"""Enable --name option for cli."""
if multiple is None:
multiple = False
if multiple:
usage_help = usage_help + " Multiple instances may be provided."
return click.option("--name", help=usage_help, type=str, multiple=multiple)
[docs]
def cli_output_path(
usage_help: str = "Path to output directory to save the output.",
default: str | None = None,
) -> callable:
"""Enables --output-path option for cli."""
return click.option(
"--output-path",
help=add_default_to_usage_help(usage_help, default),
type=str,
default=default,
)
[docs]
def cli_file_type(
usage_help: str = "File types to capture from directory.",
default: str = "*.ndpi, *.svs, *.mrxs, *.jp2",
) -> callable:
"""Enables --file-types option for cli."""
return click.option(
"--file-types",
help=add_default_to_usage_help(usage_help, default),
default=default,
type=str,
)
[docs]
def cli_mode(
usage_help: str = "Selected mode to show or save the required information.",
default: str = "save",
input_type: click.Choice | None = None,
) -> callable:
"""Enables --mode option for cli."""
if input_type is None:
input_type = click.Choice(["show", "save"], case_sensitive=False)
return click.option(
"--mode",
help=add_default_to_usage_help(usage_help, default),
default=default,
type=input_type,
)
[docs]
def cli_region(
usage_help: str = "Image region in the whole slide image to read from. "
"default=0 0 2000 2000",
) -> callable:
"""Enables --region option for cli."""
return click.option(
"--region",
type=int,
nargs=4,
help=usage_help,
)
[docs]
def cli_units(
usage_help: str = "Image resolution units to read the image.",
default: str = "level",
input_type: click.Choice | None = None,
) -> callable:
"""Enables --units option for cli."""
if input_type is None:
input_type = click.Choice(
["mpp", "power", "level", "baseline"],
case_sensitive=False,
)
return click.option(
"--units",
default=default,
type=input_type,
help=add_default_to_usage_help(usage_help, default),
)
[docs]
def cli_resolution(
usage_help: str = "Image resolution to read the image.",
default: float = 0,
) -> callable:
"""Enables --resolution option for cli."""
return click.option(
"--resolution",
type=float,
default=default,
help=add_default_to_usage_help(usage_help, default),
)
[docs]
def cli_tile_objective(
usage_help: str = "Objective value for the saved tiles.",
default: int = 20,
) -> callable:
"""Enables --tile-objective-value option for cli."""
return click.option(
"--tile-objective-value",
type=int,
default=default,
help=add_default_to_usage_help(usage_help, default),
)
[docs]
def cli_tile_read_size(
usage_help: str = "Width and Height of saved tiles. default=5000 5000",
) -> callable:
"""Enables --tile-read-size option for cli."""
return click.option(
"--tile-read-size",
type=int,
nargs=2,
default=[5000, 5000],
help=usage_help,
)
[docs]
def cli_method(
usage_help: str = "Select method of for tissue masking.",
default: str = "Otsu",
input_type: click.Choice | None = None,
) -> callable:
"""Enables --method option for cli."""
if input_type is None:
input_type = click.Choice(["Otsu", "Morphological"], case_sensitive=True)
return click.option(
"--method",
type=input_type,
default=default,
help=add_default_to_usage_help(usage_help, default),
)
[docs]
def cli_pretrained_model(
usage_help: str = "Name of the predefined model used to process the data. "
"The format is <model_name>_<dataset_trained_on>. For example, "
"`resnet18-kather100K` is a resnet18 model trained on the Kather dataset. "
"Please see "
"https://tia-toolbox.readthedocs.io/en/latest/usage.html#deep-learning-models "
"for a detailed list of available pretrained models."
"By default, the corresponding pretrained weights will also be"
"downloaded. However, you can override with your own set of weights"
"via the `pretrained_weights` argument. Argument is case insensitive.",
default: str = "resnet18-kather100k",
) -> callable:
"""Enables --pretrained-model option for cli."""
return click.option(
"--pretrained-model",
help=add_default_to_usage_help(usage_help, default),
default=default,
)
[docs]
def cli_pretrained_weights(
usage_help: str = "Path to the model weight file. If not supplied, the default "
"pretrained weight will be used.",
default: str | None = None,
) -> callable:
"""Enables --pretrained-weights option for cli."""
return click.option(
"--pretrained-weights",
help=add_default_to_usage_help(usage_help, default),
default=default,
)
[docs]
def cli_return_probabilities(
usage_help: str = "Whether to return raw model probabilities.",
*,
default: bool = False,
) -> callable:
"""Enables --return-probabilities option for cli."""
return click.option(
"--return-probabilities",
type=bool,
help=add_default_to_usage_help(usage_help, default),
default=default,
)
[docs]
def cli_merge_predictions(
usage_help: str = "Whether to merge the predictions to form a 2-dimensional map.",
*,
default: bool = True,
) -> callable:
"""Enables --merge-predictions option for cli."""
return click.option(
"--merge-predictions",
type=bool,
default=default,
help=add_default_to_usage_help(usage_help, default),
)
[docs]
def cli_return_labels(
usage_help: str = "Whether to return raw model output as labels.",
*,
default: bool = True,
) -> callable:
"""Enables --return-labels option for cli."""
return click.option(
"--return-labels",
type=bool,
help=add_default_to_usage_help(usage_help, default),
default=default,
)
[docs]
def cli_batch_size(
usage_help: str = "Number of image patches to feed into the model each time.",
default: int = 1,
) -> callable:
"""Enables --batch-size option for cli."""
return click.option(
"--batch-size",
help=add_default_to_usage_help(usage_help, default),
default=default,
)
[docs]
def cli_masks(
usage_help: str = "Path to the input directory containing masks to process "
"corresponding to image tiles and whole-slide images. "
"Patches are only processed if they are within a masked area. "
"If masks are not provided, then a tissue mask will be "
"automatically generated for whole-slide images or the entire image is "
"processed for image tiles. Supported file types are jpg, png and npy.",
default: str | None = None,
) -> callable:
"""Enables --masks option for cli."""
return click.option(
"--masks",
help=add_default_to_usage_help(usage_help, default),
default=default,
)
[docs]
def cli_auto_generate_mask(
usage_help: str = "Automatically generate tile/WSI tissue mask.",
*,
default: bool = False,
) -> callable:
"""Enables --auto-generate-mask option for cli."""
return click.option(
"--auto-generate-mask",
help=add_default_to_usage_help(usage_help, default),
type=bool,
default=default,
)
[docs]
def cli_yaml_config_path(
usage_help: str = "Path to ioconfig file. Sample yaml file can be viewed in "
"tiatoolbox.data.pretrained_model.yaml. "
"if pretrained_model is used the ioconfig is automatically set.",
default: str | None = None,
) -> callable:
"""Enables --yaml-config-path option for cli."""
return click.option(
"--yaml-config-path",
help=add_default_to_usage_help(usage_help, default),
default=default,
)
[docs]
def cli_on_gpu(
usage_help: str = "Run the model on GPU.",
*,
default: bool = False,
) -> callable:
"""Enables --on-gpu option for cli."""
return click.option(
"--on-gpu",
type=bool,
default=default,
help=add_default_to_usage_help(usage_help, default),
)
[docs]
def cli_num_loader_workers(
usage_help: str = "Number of workers to load the data. Please note that they will "
"also perform preprocessing.",
default: int = 0,
) -> callable:
"""Enables --num-loader-workers option for cli."""
return click.option(
"--num-loader-workers",
help=add_default_to_usage_help(usage_help, default),
type=int,
default=default,
)
[docs]
def cli_num_postproc_workers(
usage_help: str = "Number of workers to post-process the network output.",
default: int = 0,
) -> callable:
"""Enables --num-postproc-workers option for cli."""
return click.option(
"--num-postproc-workers",
help=add_default_to_usage_help(usage_help, default),
type=int,
default=default,
)
[docs]
def cli_verbose(
usage_help: str = "Prints the console output.",
*,
default: bool = True,
) -> callable:
"""Enables --verbose option for cli."""
return click.option(
"--verbose",
type=bool,
help=add_default_to_usage_help(usage_help, str(default)),
default=default,
)
[docs]
def prepare_file_dir_cli(
img_input: str or Path,
output_path: str or Path,
file_types: str,
mode: str,
sub_dirname: str,
) -> [list, Path]:
"""Prepares CLI for running code on multiple files or a directory.
Checks for existing directories to run tests.
Converts file path to list of file paths or
creates list of file paths if input is a directory.
Args:
img_input (str or Path):
File path to images.
output_path (str or Path):
Output directory path.
file_types (str):
File types to process using cli.
mode (str):
wsi or tile mode.
sub_dirname (str):
Name of subdirectory to save output.
Returns:
list: list of file paths to process.
pathlib.Path: updated output path.
"""
from tiatoolbox.utils.misc import grab_files_from_dir, string_to_tuple
img_input = no_input_message(input_file=img_input)
file_types = string_to_tuple(in_str=file_types)
if isinstance(output_path, str):
output_path = Path(output_path)
if not Path.exists(img_input):
raise FileNotFoundError
files_all = [
img_input,
]
if Path.is_dir(img_input):
files_all = grab_files_from_dir(input_path=img_input, file_types=file_types)
if output_path is None and mode == "save":
input_dir = Path(img_input).parent
output_path = input_dir / sub_dirname
if mode == "save":
output_path.mkdir(parents=True, exist_ok=True)
return [files_all, output_path]
[docs]
def prepare_model_cli(
img_input: str or Path,
output_path: str or Path,
masks: str or Path,
file_types: str,
) -> [list, list, Path]:
"""Prepares cli for running models.
Checks for existing directories to run tests.
Converts file path to list of file paths or
creates list of file paths if input is a directory.
Args:
img_input (str or Path):
File path to images.
output_path (str or Path):
Output directory path.
masks (str or Path):
File path to masks.
file_types (str):
File types to process using cli.
Returns:
list:
List of file paths to process.
list:
List of masks corresponding to input files.
Path:
Output path.
"""
from tiatoolbox.utils.misc import grab_files_from_dir, string_to_tuple
img_input = no_input_message(input_file=img_input)
output_path = Path(output_path)
file_types = string_to_tuple(in_str=file_types)
if output_path.exists():
msg = "Path already exists."
raise FileExistsError(msg)
if not Path.exists(img_input):
raise FileNotFoundError
files_all = [
img_input,
]
masks_all = None
if masks is not None:
masks = Path(masks)
if masks.is_file():
masks_all = [masks]
if masks.is_dir():
masks_all = grab_files_from_dir(
input_path=masks,
file_types=("*.jpg", "*.png"),
)
if Path.is_dir(img_input):
files_all = grab_files_from_dir(input_path=img_input, file_types=file_types)
return [files_all, masks_all, output_path]
tiatoolbox_cli = TIAToolboxCLI()
[docs]
def prepare_ioconfig_seg(
segment_config_class: IOConfigABC,
pretrained_weights: str | Path,
yaml_config_path: str | Path,
) -> IOConfigABC | None:
"""Prepare ioconfig for segmentation."""
import yaml
if pretrained_weights is not None:
with Path(yaml_config_path).open() as registry_handle:
ioconfig = yaml.safe_load(registry_handle)
return segment_config_class(**ioconfig)
return None