2743 - no extension check for user specified reader

docs/source/data.rst

@@ -74,7 +74,7 @@ Generic Interfaces
 .. autoclass:: ImageDataset
   :members:
   :special-members: __getitem__
-  
+
 `NPZDictItemDataset`
 ~~~~~~~~~~~~~~~~~~~~
 .. autoclass:: NPZDictItemDataset
@@ -108,6 +108,11 @@ Patch-based dataset
 Image reader
 ------------
 
+ImageReader
+~~~~~~~~~~~
+.. autoclass:: ImageReader
+  :members:
+
 ITKReader
 ~~~~~~~~~
 .. autoclass:: ITKReader

monai/data/image_reader.py

@@ -45,16 +45,30 @@
 
 
 class ImageReader(ABC):
-    """Abstract class to define interface APIs to load image files.
-    users need to call `read` to load image and then use `get_data`
-    to get the image data and properties from meta data.
+    """
+    An abstract class defines APIs to load image files.
+
+    Typical usage of an implementation of this class is:
+
+    .. code-block:: python
+
+        image_reader = MyImageReader()
+        img_obj = image_reader.read(path_to_image)
+        img_data, meta_data = image_reader.get_data(img_obj)
+
+    - The `read` call converts image filenames into image objects,
+    - The `get_data` call fetches the image data, as well as meta data.
+    - A reader should implement `verify_suffix` with the logic of checking the input filename
+      by the filename extensions.
 
     """
 
     @abstractmethod
     def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
         """
-        Verify whether the specified file or files format is supported by current reader.
+        Verify whether the specified `filename` is supported by the current reader.
+        This method should return True if the reader is able to read the format suggested by the
+        `filename`.
 
         Args:
             filename: file name or a list of file names to read.
@@ -67,7 +81,7 @@ def verify_suffix(self, filename: Union[Sequence[str], str]) -> bool:
     def read(self, data: Union[Sequence[str], str], **kwargs) -> Union[Sequence[Any], Any]:
         """
         Read image data from specified file or files.
-        Note that it returns the raw data, so different readers return different image data type.
+        Note that it returns a data object or a sequence of data objects.
 
         Args:
             data: file name or a list of file names to read.
@@ -80,7 +94,8 @@ def read(self, data: Union[Sequence[str], str], **kwargs) -> Union[Sequence[Any]
     def get_data(self, img) -> Tuple[np.ndarray, Dict]:
         """
         Extract data array and meta data from loaded image and return them.
-        This function must return 2 objects, first is numpy array of image data, second is dict of meta data.
+        This function must return two objects, the first is a numpy array of image data,
+        the second is a dictionary of meta data.
 
         Args:
             img: an image object loaded from an image file or a list of image objects.
@@ -124,7 +139,7 @@ def _stack_images(image_list: List, meta_dict: Dict):
 class ITKReader(ImageReader):
     """
     Load medical images based on ITK library.
-    All the supported image formats can be found:
+    All the supported image formats can be found at:
     https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO
     The loaded data array will be in C order, for example, a 3D image NumPy
     array index order will be `CDWH`.
@@ -396,7 +411,10 @@ def _get_meta_dict(self, img) -> Dict:
 
         """
         # swap to little endian as PyTorch doesn't support big endian
-        header = img.header.as_byteswapped("<")
+        try:
+            header = img.header.as_byteswapped("<")
+        except ValueError:
+            header = img.header
         return dict(header)
 
     def _get_affine(self, img):
@@ -419,11 +437,18 @@ def _get_spatial_shape(self, img):
 
         """
         # swap to little endian as PyTorch doesn't support big endian
-        header = img.header.as_byteswapped("<")
-        ndim = header["dim"][0]
+        try:
+            header = img.header.as_byteswapped("<")
+        except ValueError:
+            header = img.header
+        dim = header.get("dim", None)
+        if dim is None:
+            dim = header.get("dims")  # mgh format?
+            dim = np.insert(dim, 0, 3)
+        ndim = dim[0]
         spatial_rank = min(ndim, 3)
         # the img data should have no channel dim or the last dim is channel
-        return np.asarray(header["dim"][1 : spatial_rank + 1])
+        return np.asarray(dim[1 : spatial_rank + 1])
 
     def _get_array_data(self, img):
         """

monai/data/nifti_saver.py

@@ -9,6 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from pathlib import Path
 from typing import Dict, Optional, Union
 
 import numpy as np
@@ -36,7 +37,7 @@ class NiftiSaver:
 
     def __init__(
         self,
-        output_dir: str = "./",
+        output_dir: Union[Path, str] = "./",
         output_postfix: str = "seg",
         output_ext: str = ".nii.gz",
         resample: bool = True,

monai/data/png_saver.py

@@ -9,6 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from pathlib import Path
 from typing import Dict, Optional, Union
 
 import numpy as np
@@ -33,7 +34,7 @@ class PNGSaver:
 
     def __init__(
         self,
-        output_dir: str = "./",
+        output_dir: Union[Path, str] = "./",
         output_postfix: str = "seg",
         output_ext: str = ".png",
         resample: bool = True,

monai/data/utils.py

@@ -19,7 +19,7 @@
 from copy import deepcopy
 from functools import reduce
 from itertools import product, starmap
-from pathlib import PurePath
+from pathlib import Path, PurePath
 from typing import Any, Dict, Generator, Iterable, List, Mapping, Optional, Sequence, Tuple, Union
 
 import numpy as np
@@ -492,6 +492,8 @@ def correct_nifti_header_if_necessary(img_nii):
     Args:
         img_nii: nifti image object
     """
+    if img_nii.header.get("dim") is None:
+        return img_nii  # not nifti?
     dim = img_nii.header["dim"][0]
     if dim >= 5:
         return img_nii  # do nothing for high-dimensional array
@@ -677,7 +679,7 @@ def to_affine_nd(r: Union[np.ndarray, int], affine: np.ndarray) -> np.ndarray:
 def create_file_basename(
     postfix: str,
     input_file_name: str,
-    folder_path: str,
+    folder_path: Union[Path, str],
     data_root_dir: str = "",
     separate_folder: bool = True,
     patch_index: Optional[int] = None,

monai/transforms/__init__.py

@@ -193,7 +193,7 @@
 )
 from .inverse import InvertibleTransform
 from .inverse_batch_transform import BatchInverseTransform, Decollated
-from .io.array import LoadImage, SaveImage
+from .io.array import SUPPORTED_READERS, LoadImage, SaveImage
 from .io.dictionary import LoadImaged, LoadImageD, LoadImageDict, SaveImaged, SaveImageD, SaveImageDict
 from .nvtx import (
     Mark,