From 50fce2cda942a97dfbbe4eaf9ff401af78736ef5 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Thu, 20 Jul 2023 23:26:54 +0200
Subject: [PATCH] [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 | 44 +++++++++------------------
src/ptbench/data/shenzhen/loader.py | 39 +++++++++++-------------
2 files changed, 32 insertions(+), 51 deletions(-)
diff --git a/src/ptbench/data/montgomery/loader.py b/src/ptbench/data/montgomery/loader.py
index 45733dad..ad856d5f 100644
--- a/src/ptbench/data/montgomery/loader.py
+++ b/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]
diff --git a/src/ptbench/data/shenzhen/loader.py b/src/ptbench/data/shenzhen/loader.py
index df4d4afb..3409fed2 100644
--- a/src/ptbench/data/shenzhen/loader.py
+++ b/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]
--
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