[Feature] Add BioMedical data loading (#2176)

* [WIP] Add BioMedical data loading * add depends nibabel * fix bug * fix ut * fix * add test data * xyz2zyx zyx2xyz * format * remove ignore empty * remove ignore empty * remove with seg in LoadBiomedicalAnnotation * float32 * docstring * toxyz * docstring
2022-10-18 16:30:08 +08:00 · 2022-10-18 16:30:08 +08:00 · 20c7dc689c
parent 2ea4034014
commit 20c7dc689c
12 changed files with 416 additions and 9 deletions
--- a/mmseg/datasets/init.py
+++ b/mmseg/datasets/init.py
@ -16,10 +16,11 @@ from .pascal_context import PascalContextDataset, PascalContextDataset59
 from .potsdam import PotsdamDataset
 from .stare import STAREDataset
 from .transforms import (CLAHE, AdjustGamma, LoadAnnotations,
-                         LoadImageFromNDArray, PackSegInputs,
-                         PhotoMetricDistortion, RandomCrop, RandomCutOut,
-                         RandomMosaic, RandomRotate, Rerange, ResizeToMultiple,
-                         RGB2Gray, SegRescale)
+                         LoadBiomedicalAnnotation, LoadBiomedicalData,
+                         LoadBiomedicalImageFromFile, LoadImageFromNDArray,
+                         PackSegInputs, PhotoMetricDistortion, RandomCrop,
+                         RandomCutOut, RandomMosaic, RandomRotate, Rerange,
+                         ResizeToMultiple, RGB2Gray, SegRescale)
 from .voc import PascalVOCDataset

 __all__ = [
@ -31,5 +32,6 @@ __all__ = [
    'LoadAnnotations', 'RandomCrop', 'SegRescale', 'PhotoMetricDistortion',
    'RandomRotate', 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray',
    'RandomCutOut', 'RandomMosaic', 'PackSegInputs', 'ResizeToMultiple',
-    'LoadImageFromNDArray'
+    'LoadImageFromNDArray', 'LoadBiomedicalImageFromFile',
+    'LoadBiomedicalAnnotation', 'LoadBiomedicalData'
 ]
--- a/mmseg/datasets/transforms/init.py
+++ b/mmseg/datasets/transforms/init.py
@ -1,6 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .formatting import PackSegInputs
-from .loading import LoadAnnotations, LoadImageFromNDArray
+from .loading import (LoadAnnotations, LoadBiomedicalAnnotation,
+                      LoadBiomedicalData, LoadBiomedicalImageFromFile,
+                      LoadImageFromNDArray)
 from .transforms import (CLAHE, AdjustGamma, PhotoMetricDistortion, RandomCrop,
                         RandomCutOut, RandomMosaic, RandomRotate, Rerange,
                         ResizeToMultiple, RGB2Gray, SegRescale)
@ -9,5 +11,6 @@ __all__ = [
    'LoadAnnotations', 'RandomCrop', 'SegRescale', 'PhotoMetricDistortion',
    'RandomRotate', 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray',
    'RandomCutOut', 'RandomMosaic', 'PackSegInputs', 'ResizeToMultiple',
-    'LoadImageFromNDArray'
+    'LoadImageFromNDArray', 'LoadBiomedicalImageFromFile',
+    'LoadBiomedicalAnnotation', 'LoadBiomedicalData'
 ]
--- a/mmseg/datasets/transforms/loading.py
+++ b/mmseg/datasets/transforms/loading.py
@ -1,12 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from typing import Dict

 import mmcv
+import mmengine
 import numpy as np
+from mmcv.transforms import BaseTransform
 from mmcv.transforms import LoadAnnotations as MMCV_LoadAnnotations
 from mmcv.transforms import LoadImageFromFile

 from mmseg.registry import TRANSFORMS
+from mmseg.utils import datafrombytes


@TRANSFORMS.register_module()
@ -168,3 +172,273 @@ class LoadImageFromNDArray(LoadImageFromFile):
        results['img_shape'] = img.shape[:2]
        results['ori_shape'] = img.shape[:2]
        return results
+
+
+@TRANSFORMS.register_module()
+class LoadBiomedicalImageFromFile(BaseTransform):
+    """Load an biomedical mage from file.
+
+    Required Keys:
+
+    - img_path
+
+    Added Keys:
+
+    - img (np.ndarray): Biomedical image with shape (N, Z, Y, X) by default,
+        N is the number of modalities, and data type is float32
+        if set to_float32 = True, or float64 if decode_backend is 'nifti' and
+        to_float32 is False.
+    - img_shape
+    - ori_shape
+
+    Args:
+        decode_backend (str): The data decoding backend type. Options are
+            'numpy'and 'nifti', and there is a convention that when backend is
+            'nifti' the axis of data loaded is XYZ, and when backend is
+            'numpy', the the axis is ZYX. The data will be transposed if the
+            backend is 'nifti'. Defaults to 'nifti'.
+        to_xyz (bool): Whether transpose data from Z, Y, X to X, Y, Z.
+            Defaults to False.
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an float64 array.
+            Defaults to True.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmengine.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(
+        self,
+        decode_backend: str = 'nifti',
+        to_xyz: bool = False,
+        to_float32: bool = True,
+        file_client_args: dict = dict(backend='disk')
+    ) -> None:
+        self.decode_backend = decode_backend
+        self.to_xyz = to_xyz
+        self.to_float32 = to_float32
+        self.file_client_args = file_client_args.copy()
+        self.file_client = mmengine.FileClient(**self.file_client_args)
+
+    def transform(self, results: Dict) -> Dict:
+        """Functions to load image.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+
+        filename = results['img_path']
+
+        data_bytes = self.file_client.get(filename)
+        img = datafrombytes(data_bytes, backend=self.decode_backend)
+
+        if self.to_float32:
+            img = img.astype(np.float32)
+
+        if len(img.shape) == 3:
+            img = img[None, ...]
+
+        if self.decode_backend == 'nifti':
+            img = img.transpose(0, 3, 2, 1)
+
+        if self.to_xyz:
+            img = img.transpose(0, 3, 2, 1)
+
+        results['img'] = img
+        results['img_shape'] = img.shape[1:]
+        results['ori_shape'] = img.shape[1:]
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f"decode_backend='{self.decode_backend}', "
+                    f'to_xyz={self.to_xyz}, '
+                    f'to_float32={self.to_float32}, '
+                    f'file_client_args={self.file_client_args})')
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class LoadBiomedicalAnnotation(BaseTransform):
+    """Load ``seg_map`` annotation provided by biomedical dataset.
+
+    The annotation format is as the following:
+
+    .. code-block:: python
+
+        {
+            'gt_seg_map': np.ndarray (X, Y, Z) or (Z, Y, X)
+        }
+
+    Required Keys:
+
+    - seg_map_path
+
+    Added Keys:
+
+    - gt_seg_map (np.ndarray): Biomedical seg map with shape (Z, Y, X) by
+        default, and data type is float32 if set to_float32 = True, or
+        float64 if decode_backend is 'nifti' and to_float32 is False.
+
+    Args:
+        decode_backend (str): The data decoding backend type. Options are
+            'numpy'and 'nifti', and there is a convention that when backend is
+            'nifti' the axis of data loaded is XYZ, and when backend is
+            'numpy', the the axis is ZYX. The data will be transposed if the
+            backend is 'nifti'. Defaults to 'nifti'.
+        to_xyz (bool): Whether transpose data from Z, Y, X to X, Y, Z.
+            Defaults to False.
+        to_float32 (bool): Whether to convert the loaded seg map to a float32
+            numpy array. If set to False, the loaded image is an float64 array.
+            Defaults to True.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmengine.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(
+        self,
+        decode_backend: str = 'nifti',
+        to_xyz: bool = False,
+        to_float32: bool = True,
+        file_client_args: dict = dict(backend='disk')
+    ) -> None:
+        super().__init__()
+        self.decode_backend = decode_backend
+        self.to_xyz = to_xyz
+        self.to_float32 = to_float32
+        self.file_client_args = file_client_args.copy()
+        self.file_client = mmengine.FileClient(**self.file_client_args)
+
+    def transform(self, results: Dict) -> Dict:
+        """Functions to load image.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+        data_bytes = self.file_client.get(results['seg_map_path'])
+        gt_seg_map = datafrombytes(data_bytes, backend=self.decode_backend)
+
+        if self.to_float32:
+            gt_seg_map = gt_seg_map.astype(np.float32)
+
+        if self.decode_backend == 'nifti':
+            gt_seg_map = gt_seg_map.transpose(2, 1, 0)
+
+        if self.to_xyz:
+            gt_seg_map = gt_seg_map.transpose(2, 1, 0)
+
+        results['gt_seg_map'] = gt_seg_map
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f"decode_backend='{self.decode_backend}', "
+                    f'to_xyz={self.to_xyz}, '
+                    f'to_float32={self.to_float32}, '
+                    f'file_client_args={self.file_client_args})')
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class LoadBiomedicalData(BaseTransform):
+    """Load an biomedical image and annotation from file.
+
+    The loading data format is as the following:
+
+    .. code-block:: python
+
+        {
+            'img': np.ndarray data[:-1, X, Y, Z]
+            'seg_map': np.ndarray data[-1, X, Y, Z]
+        }
+
+
+    Required Keys:
+
+    - img_path
+
+    Added Keys:
+
+    - img (np.ndarray): Biomedical image with shape (N, Z, Y, X) by default,
+        N is the number of modalities.
+    - gt_seg_map (np.ndarray, optional): Biomedical seg map with shape
+        (Z, Y, X) by default.
+    - img_shape
+    - ori_shape
+
+    Args:
+        with_seg (bool): Whether to parse and load the semantic segmentation
+            annotation. Defaults to False.
+        decode_backend (str): The data decoding backend type. Options are
+            'numpy'and 'nifti', and there is a convention that when backend is
+            'nifti' the axis of data loaded is XYZ, and when backend is
+            'numpy', the the axis is ZYX. The data will be transposed if the
+            backend is 'nifti'. Defaults to 'nifti'.
+        to_xyz (bool): Whether transpose data from Z, Y, X to X, Y, Z.
+            Defaults to False.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmengine.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(
+        self,
+        with_seg=False,
+        decode_backend: str = 'numpy',
+        to_xyz: bool = False,
+        file_client_args: dict = dict(backend='disk')
+    ) -> None:
+        self.with_seg = with_seg
+        self.decode_backend = decode_backend
+        self.to_xyz = to_xyz
+        self.file_client_args = file_client_args.copy()
+        self.file_client = mmengine.FileClient(**self.file_client_args)
+
+    def transform(self, results: Dict) -> Dict:
+        """Functions to load image.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+        data_bytes = self.file_client.get(results['img_path'])
+        data = datafrombytes(data_bytes, backend=self.decode_backend)
+        # img is 4D data (N, X, Y, Z), N is the number of protocol
+        img = data[:-1, :]
+
+        if self.decode_backend == 'nifti':
+            img = img.transpose(0, 3, 2, 1)
+
+        if self.to_xyz:
+            img = img.transpose(0, 3, 2, 1)
+
+        results['img'] = img
+        results['img_shape'] = img.shape[1:]
+        results['ori_shape'] = img.shape[1:]
+
+        if self.with_seg:
+            gt_seg_map = data[-1, :]
+            if self.decode_backend == 'nifti':
+                gt_seg_map = gt_seg_map.transpose(2, 1, 0)
+
+            if self.to_xyz:
+                gt_seg_map = gt_seg_map.transpose(2, 1, 0)
+            results['gt_seg_map'] = gt_seg_map
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = (f'{self.__class__.__name__}('
+                    f'with_seg={self.with_seg}, '
+                    f"decode_backend='{self.decode_backend}', "
+                    f'to_xyz={self.to_xyz}, '
+                    f'file_client_args={self.file_client_args})')
+        return repr_str
--- a/mmseg/utils/init.py
+++ b/mmseg/utils/init.py
@ -10,6 +10,7 @@ from .class_names import (ade_classes, ade_palette, cityscapes_classes,
                          voc_palette)
 # yapf: enable
 from .collect_env import collect_env
+from .io import datafrombytes
 from .misc import add_prefix, stack_batch
 from .set_env import register_all_modules
 from .typing import (ConfigType, ForwardResults, MultiConfig, OptConfigType,
@ -25,5 +26,6 @@ __all__ = [
    'vaihingen_classes', 'isaid_classes', 'stare_classes',
    'cityscapes_palette', 'ade_palette', 'voc_palette', 'cocostuff_palette',
    'loveda_palette', 'potsdam_palette', 'vaihingen_palette', 'isaid_palette',
-    'stare_palette', 'dataset_aliases', 'get_classes', 'get_palette'
+    'stare_palette', 'dataset_aliases', 'get_classes', 'get_palette',
+    'datafrombytes'
 ]
--- a/mmseg/utils/io.py
+++ b/mmseg/utils/io.py
@ -0,0 +1,38 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import gzip
+import io
+import pickle
+
+import numpy as np
+
+
+def datafrombytes(content: bytes, backend: str = 'numpy') -> np.ndarray:
+    """Data decoding from bytes.
+
+    Args:
+        content (bytes): The data bytes got from files or other streams.
+        backend (str): The data decoding backend type. Options are 'numpy',
+            'nifti' and 'pickle'. Defaults to 'numpy'.
+
+    Returns:
+        numpy.ndarray: Loaded data array.
+    """
+    if backend == 'pickle':
+        data = pickle.loads(content)
+    else:
+        with io.BytesIO(content) as f:
+            if backend == 'nifti':
+                f = gzip.open(f)
+                try:
+                    from nibabel import FileHolder, Nifti1Image
+                except ImportError:
+                    print('nifti files io depends on nibabel, please run'
+                          '`pip install nibabel` to install it')
+                fh = FileHolder(fileobj=f)
+                data = Nifti1Image.from_file_map({'header': fh, 'image': fh})
+                data = Nifti1Image.from_bytes(data.to_bytes()).get_fdata()
+            elif backend == 'numpy':
+                data = np.load(f)
+            else:
+                raise ValueError
+    return data
--- a/requirements/optional.txt
+++ b/requirements/optional.txt
@ -1 +1,2 @@
 cityscapesscripts
+nibabel
--- a/tests/data/biomedical.nii.gz
+++ b/tests/data/biomedical.nii.gz
--- a/tests/data/biomedical.npy
+++ b/tests/data/biomedical.npy
--- a/tests/data/biomedical.pkl
+++ b/tests/data/biomedical.pkl
--- a/tests/data/biomedical_ann.nii.gz
+++ b/tests/data/biomedical_ann.nii.gz
--- a/tests/test_datasets/test_loading.py
+++ b/tests/test_datasets/test_loading.py
@ -7,7 +7,11 @@ import mmcv
 import numpy as np
 from mmcv.transforms import LoadImageFromFile

-from mmseg.datasets.transforms import LoadAnnotations, LoadImageFromNDArray
+from mmseg.datasets.transforms import (LoadAnnotations,
+                                       LoadBiomedicalAnnotation,
+                                       LoadBiomedicalData,
+                                       LoadBiomedicalImageFromFile,
+                                       LoadImageFromNDArray)


 class TestLoading:
@ -185,3 +189,53 @@ class TestLoading:
                                   "color_type='color', "
                                   "imdecode_backend='cv2', "
                                   "file_client_args={'backend': 'disk'})")
+
+    def test_load_biomedical_img(self):
+        results = dict(
+            img_path=osp.join(self.data_prefix, 'biomedical.nii.gz'))
+        transform = LoadBiomedicalImageFromFile()
+        results = transform(copy.deepcopy(results))
+        assert results['img_path'] == osp.join(self.data_prefix,
+                                               'biomedical.nii.gz')
+        assert len(results['img'].shape) == 4
+        assert results['img'].dtype == np.float32
+        assert results['ori_shape'] == results['img'].shape[1:]
+        assert repr(transform) == ('LoadBiomedicalImageFromFile('
+                                   "decode_backend='nifti', "
+                                   'to_xyz=False, '
+                                   'to_float32=True, '
+                                   "file_client_args={'backend': 'disk'})")
+
+    def test_load_biomedical_annotation(self):
+        results = dict(
+            seg_map_path=osp.join(self.data_prefix, 'biomedical_ann.nii.gz'))
+        transform = LoadBiomedicalAnnotation()
+        results = transform(copy.deepcopy(results))
+        assert len(results['gt_seg_map'].shape) == 3
+        assert results['gt_seg_map'].dtype == np.float32
+
+    def test_load_biomedical_data(self):
+        input_results = dict(
+            img_path=osp.join(self.data_prefix, 'biomedical.npy'))
+        transform = LoadBiomedicalData(with_seg=True)
+        results = transform(copy.deepcopy(input_results))
+        assert results['img_path'] == osp.join(self.data_prefix,
+                                               'biomedical.npy')
+        assert results['img'][0].shape == results['gt_seg_map'].shape
+        assert results['img'].dtype == np.float32
+        assert results['ori_shape'] == results['img'].shape[1:]
+        assert repr(transform) == ('LoadBiomedicalData('
+                                   'with_seg=True, '
+                                   "decode_backend='numpy', "
+                                   'to_xyz=False, '
+                                   "file_client_args={'backend': 'disk'})")
+
+        transform = LoadBiomedicalData(with_seg=False)
+        results = transform(copy.deepcopy(input_results))
+        assert len(results['img'].shape) == 4
+        assert results.get('gt_seg_map') is None
+        assert repr(transform) == ('LoadBiomedicalData('
+                                   'with_seg=False, '
+                                   "decode_backend='numpy', "
+                                   'to_xyz=False, '
+                                   "file_client_args={'backend': 'disk'})")
--- a/tests/test_utils/test_io.py
+++ b/tests/test_utils/test_io.py
@ -0,0 +1,33 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import numpy as np
+import pytest
+from mmengine import FileClient
+
+from mmseg.utils import datafrombytes
+
+
+@pytest.mark.parametrize(
+    ['backend', 'suffix'],
+    [['nifti', '.nii.gz'], ['numpy', '.npy'], ['pickle', '.pkl']])
+def test_datafrombytes(backend, suffix):
+
+    file_client = FileClient('disk')
+    file_path = osp.join(osp.dirname(__file__), '../data/biomedical' + suffix)
+    bytes = file_client.get(file_path)
+    data = datafrombytes(bytes, backend)
+
+    if backend == 'pickle':
+        # test pickle loading
+        assert isinstance(data, dict)
+    else:
+        assert isinstance(data, np.ndarray)
+        if backend == 'nifti':
+            # test nifti file loading
+            assert len(data.shape) == 3
+        else:
+            # test npy file loading
+            # testing data biomedical.npy includes data and label
+            assert len(data.shape) == 4
+            assert data.shape[0] == 2