[data.montgomery/shenzhen.loader] Simplify loaders, adjust some variable...

[data.montgomery/shenzhen.loader] Simplify loaders, adjust some variable names, comments and add some (commented out) test code to visualize generated images

src/ptbench/data/montgomery/loader.py

@@ -2,37 +2,16 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""Montgomery datamodule for TB detection (default protocol)
-
-* See :py:mod:`ptbench.data.montgomery` for more database details.
-
-This configuration:
-
-* Raw data input (on disk):
-
-    * PNG images 8 bit grayscale
-    * resolution: 4020 x 4892 px or 4892 x 4020 px
-
-* Output image:
-
-    * Transforms:
-
-        * Load raw PNG with :py:mod:`PIL`
-        * Remove black borders
-        * Torch center cropping to get square image
-
-    * Final specifications
-
-        * Grayscale (single channel), 8 bits
-        * Varying resolutions
-"""
+"""Specialized raw-data loaders for the Montgomery dataset."""
 
 import os
 
+import PIL.Image
+
 from torchvision.transforms.functional import center_crop, to_tensor
 
 from ...utils.rc import load_rc
-from ..image_utils import load_pil, remove_black_borders
+from ..image_utils import remove_black_borders
 from ..typing import RawDataLoader as _BaseRawDataLoader
 from ..typing import Sample
 
@@ -73,10 +52,17 @@ class RawDataLoader(_BaseRawDataLoader):
         sample
             The sample representation
         """
-        tensor = load_pil(os.path.join(self.datadir, sample[0]))
-        tensor = remove_black_borders(tensor)
-        tensor = center_crop(tensor, min(tensor.size[0], tensor.size[1]))
-        tensor = to_tensor(tensor)
+        # N.B.: Montgomery images are encoded as grayscale PNGs, so no need to
+        # convert them again with Image.convert("L").
+        image = PIL.Image.open(os.path.join(self.datadir, sample[0]))
+        image = remove_black_borders(image)
+        tensor = to_tensor(image)
+        tensor = center_crop(tensor, min(*tensor.shape[1:]))
+
+        # use the code below to view generated images
+        # from torchvision.transforms.functional import to_pil_image
+        # to_pil_image(tensor).show()
+        # __import__("pdb").set_trace()
 
         return tensor, dict(label=sample[1], name=sample[0])  # type: ignore[arg-type]
 

src/ptbench/data/shenzhen/loader.py

@@ -2,30 +2,16 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""Shenzhen dataset for computer-aided diagnosis.
-
-The standard digital image database for Tuberculosis is created by the National
-Library of Medicine, Maryland, USA in collaboration with Shenzhen No.3 People’s
-Hospital, Guangdong Medical College, Shenzhen, China. The Chest X-rays are from
-out-patient clinics, and were captured as part of the daily routine using
-Philips DR Digital Diagnose systems.
-
-* Reference: [MONTGOMERY-SHENZHEN-2014]_
-* Original resolution (height x width or width x height): 3000 x 3000 or less
-* Split reference: none
-* Protocol ``default``:
-
-  * Training samples: 64% of TB and healthy CXR (including labels)
-  * Validation samples: 16% of TB and healthy CXR (including labels)
-  * Test samples: 20% of TB and healthy CXR (including labels)
-"""
+"""Specialized raw-data loaders for the Shenzen dataset."""
 
 import os
 
+import PIL.Image
+
 from torchvision.transforms.functional import center_crop, to_tensor
 
 from ...utils.rc import load_rc
-from ..image_utils import load_pil_rgb, remove_black_borders
+from ..image_utils import remove_black_borders
 from ..typing import RawDataLoader as _BaseRawDataLoader
 from ..typing import Sample
 
@@ -69,10 +55,19 @@ class RawDataLoader(_BaseRawDataLoader):
         sample
             The sample representation
         """
-        tensor = load_pil_rgb(os.path.join(self.datadir, sample[0]))
-        tensor = remove_black_borders(tensor)
-        tensor = center_crop(tensor, min(tensor.size[0], tensor.size[1]))
-        tensor = to_tensor(tensor)
+        # N.B.: Image.convert("L") is required to normalize grayscale back to
+        # normal (instead of inverted).
+        image = PIL.Image.open(os.path.join(self.datadir, sample[0])).convert(
+            "L"
+        )
+        image = remove_black_borders(image)
+        tensor = to_tensor(image)
+        tensor = center_crop(tensor, min(*tensor.shape[1:]))
+
+        # use the code below to view generated images
+        # from torchvision.transforms.functional import to_pil_image
+        # to_pil_image(tensor).show()
+        # __import__("pdb").set_trace()
 
         return tensor, dict(label=sample[1], name=sample[0])  # type: ignore[arg-type]