Source code for mednet.data.segment.avdrive

# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
"""AV-DRIVE dataset for Vessel Segmentation.

The DRIVE database has been established to enable comparative studies on
segmentation of blood vessels in retinal images.

* Reference: :cite:p:`staal_ridge-based_2004`
* Original resolution (height x width): 584 x 565
* Split reference: :cite:p:`staal_ridge-based_2004`
* Protocol ``default``:

  * Training samples: 20 (including labels and masks)
  * Test samples: 20 (including labels from annotator 1 and masks)

This module contains the base declaration of common data modules and raw-data
loaders for this database. All configured splits inherit from this definition.
"""

import importlib.resources.abc
import os
import pathlib
import typing

import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms.v2.functional import to_dtype, to_image

from ...models.transforms import crop_image_to_mask
from ...utils.rc import load_rc
from ..datamodule import CachingDataModule
from ..split import JSONDatabaseSplit
from ..typing import RawDataLoader as BaseDataLoader
from ..typing import Sample

DATABASE_SLUG = __name__.rsplit(".", 1)[-1]
"""Pythonic name of this database."""

CONFIGURATION_KEY_DATADIR = "datadir." + DATABASE_SLUG
"""Key to search for in the configuration file for the root directory of this
database."""




class RawDataLoader(BaseDataLoader):
    """A specialized raw-data-loader for the avdrive dataset."""

    datadir: pathlib.Path
    """This variable contains the base directory where the database raw data is
    stored."""

    def __init__(self):
        self.drive_datadir = pathlib.Path(
            load_rc().get("datadir.drive", os.path.realpath(os.curdir))
        )
        self.av_drive_datadir = pathlib.Path(
            load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
        )



    def sample(self, sample: typing.Any) -> Sample:
        """Load a single image sample from the disk.

        Parameters
        ----------
        sample
            A tuple containing path suffixes to the sample image, target, and mask
            to be loaded, within the dataset root folder.

        Returns
        -------
            The sample representation.
        """

        image = PIL.Image.open(self.drive_datadir / sample[0]).convert(mode="RGB")
        image = to_dtype(to_image(image), torch.float32, scale=True)

        target = self.target(sample)

        mask = PIL.Image.open(self.drive_datadir / sample[2]).convert(
            mode="1", dither=None
        )
        mask = to_dtype(to_image(mask), torch.float32, scale=True)

        image = tv_tensors.Image(crop_image_to_mask(image, mask))
        target = tv_tensors.Mask(crop_image_to_mask(target, mask))
        mask = tv_tensors.Mask(mask)

        return dict(image=image, target=target, mask=mask, name=sample[0])




    def target(self, sample: typing.Any) -> torch.Tensor:
        """Load only sample target from its raw representation.

        Parameters
        ----------
        sample
            A tuple containing the path suffix, within the dataset root folder,
            where to find the image to be loaded, and an integer, representing
            the sample target.

        Returns
        -------
            The label corresponding to the specified sample, encapsulated as a
            torch float tensor.
        """

        target = PIL.Image.open(self.av_drive_datadir / sample[1]).convert(
            mode="1", dither=None
        )
        return to_dtype(to_image(target), torch.float32, scale=True)






class DataModule(CachingDataModule):
    """AV_DRIVE dataset for Vessel Segmentation.

    Parameters
    ----------
    split_path
        Path or traversable (resource) with the JSON split description to load.
    """

    def __init__(self, split_path: pathlib.Path | importlib.resources.abc.Traversable):
        super().__init__(
            database_split=JSONDatabaseSplit(split_path),
            raw_data_loader=RawDataLoader(),
            database_name=DATABASE_SLUG,
            split_name=split_path.name.rsplit(".", 2)[0],
            task="segmentation",
            num_classes=1,
        )