Skip to content

Dev #45

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
skaliy merged 2 commits into from
Sep 2, 2025
Merged

Dev #45

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
e33496f
feat: error handling for ZNormalization and added new intensity trans…
skaliy Sep 2, 2025
d8b3ab6
bumped version
skaliy Sep 2, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion fastMONAI/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1 +1 @@
__version__ = "0.5.1"
__version__ = "0.5.2"
12 changes: 12 additions & 0 deletions fastMONAI/_modidx.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -99,6 +99,12 @@
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.CustomDictTransform.encodes': ( 'vision_augment.html#customdicttransform.encodes',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.NormalizeIntensity': ( 'vision_augment.html#normalizeintensity',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.NormalizeIntensity.__init__': ( 'vision_augment.html#normalizeintensity.__init__',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.NormalizeIntensity.encodes': ( 'vision_augment.html#normalizeintensity.encodes',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.OneOf': ( 'vision_augment.html#oneof',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.OneOf.__init__': ( 'vision_augment.html#oneof.__init__',
Expand Down Expand Up @@ -163,6 +169,12 @@
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.RandomSpike.encodes': ( 'vision_augment.html#randomspike.encodes',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.RescaleIntensity': ( 'vision_augment.html#rescaleintensity',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.RescaleIntensity.__init__': ( 'vision_augment.html#rescaleintensity.__init__',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.RescaleIntensity.encodes': ( 'vision_augment.html#rescaleintensity.encodes',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.ZNormalization': ( 'vision_augment.html#znormalization',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.ZNormalization.__init__': ( 'vision_augment.html#znormalization.__init__',
Expand Down
120 changes: 102 additions & 18 deletions fastMONAI/vision_augmentation.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,14 +1,16 @@
# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/03_vision_augment.ipynb.

# %% auto 0
__all__ = ['CustomDictTransform', 'do_pad_or_crop', 'PadOrCrop', 'ZNormalization', 'BraTSMaskConverter', 'BinaryConverter',
'RandomGhosting', 'RandomSpike', 'RandomNoise', 'RandomBiasField', 'RandomBlur', 'RandomGamma',
'RandomMotion', 'RandomElasticDeformation', 'RandomAffine', 'RandomFlip', 'OneOf']
__all__ = ['CustomDictTransform', 'do_pad_or_crop', 'PadOrCrop', 'ZNormalization', 'RescaleIntensity', 'NormalizeIntensity',
'BraTSMaskConverter', 'BinaryConverter', 'RandomGhosting', 'RandomSpike', 'RandomNoise', 'RandomBiasField',
'RandomBlur', 'RandomGamma', 'RandomMotion', 'RandomElasticDeformation', 'RandomAffine', 'RandomFlip',
'OneOf']

# %% ../nbs/03_vision_augment.ipynb 2
from fastai.data.all import *
from .vision_core import *
import torchio as tio
from monai.transforms import NormalizeIntensity as MonaiNormalizeIntensity

# %% ../nbs/03_vision_augment.ipynb 5
class CustomDictTransform(ItemTransform):
Expand Down Expand Up @@ -84,16 +86,98 @@ def __init__(self, masking_method=None, channel_wise=True):
self.channel_wise = channel_wise

def encodes(self, o: MedImage):
if self.channel_wise:
o = torch.stack([self.z_normalization(c[None])[0] for c in o])
else: o = self.z_normalization(o)
try:
if self.channel_wise:
o = torch.stack([self.z_normalization(c[None])[0] for c in o])
else:
o = self.z_normalization(o)
except RuntimeError as e:
if "Standard deviation is 0" in str(e):
# Calculate mean for debugging information
mean = float(o.mean())

error_msg = (
f"Standard deviation is 0 for image (mean={mean:.3f}).\n"
f"This indicates uniform pixel values.\n\n"
f"Possible causes:\n"
f"• Corrupted or blank image\n"
f"• Oversaturated regions\n"
f"• Background-only regions\n"
f"• All-zero mask being processed as image\n\n"
f"Suggested solutions:\n"
f"• Check image quality and acquisition\n"
f"• Verify image vs mask data loading"
)
raise RuntimeError(error_msg) from e

return MedImage.create(o)

def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 10
class RescaleIntensity(DisplayedTransform):
"""Apply TorchIO RescaleIntensity for robust intensity scaling.

Args:
out_min_max (tuple[float, float]): Output intensity range (min, max)
in_min_max (tuple[float, float]): Input intensity range (min, max)

Example for CT images:
# Normalize CT from air (-1000 HU) to bone (1000 HU) into range (-1, 1)
transform = RescaleIntensity(out_min_max=(-1, 1), in_min_max=(-1000, 1000))
"""

order = 0

def __init__(self, out_min_max: tuple[float, float], in_min_max: tuple[float, float]):
self.rescale = tio.RescaleIntensity(out_min_max=out_min_max, in_min_max=in_min_max)

def encodes(self, o: MedImage):
return MedImage.create(self.rescale(o))

def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 11
class NormalizeIntensity(DisplayedTransform):
"""Apply MONAI NormalizeIntensity.

Args:
nonzero (bool): Only normalize non-zero values (default: True)
channel_wise (bool): Apply normalization per channel (default: True)
subtrahend (float, optional): Value to subtract
divisor (float, optional): Value to divide by
"""

order = 0

def __init__(self, nonzero: bool = True, channel_wise: bool = True,
subtrahend: float = None, divisor: float = None):
self.nonzero = nonzero
self.channel_wise = channel_wise
self.subtrahend = subtrahend
self.divisor = divisor

self.transform = MonaiNormalizeIntensity(
nonzero=nonzero,
channel_wise=False, # Always 'False', we handle channel-wise manually
subtrahend=subtrahend,
divisor=divisor
)

def encodes(self, o: MedImage):
if self.channel_wise:
result = torch.stack([self.transform(c[None])[0] for c in o])
else:
result = torch.Tensor(self.transform(o))

return MedImage.create(result)

def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 12
class BraTSMaskConverter(DisplayedTransform):
'''Convert BraTS masks.'''

Expand All @@ -105,7 +189,7 @@ def encodes(self, o:(MedMask)):
o = torch.where(o==4, 3., o)
return MedMask.create(o)

# %% ../nbs/03_vision_augment.ipynb 11
# %% ../nbs/03_vision_augment.ipynb 13
class BinaryConverter(DisplayedTransform):
'''Convert to binary mask.'''

Expand All @@ -118,7 +202,7 @@ def encodes(self, o: MedMask):
o = torch.where(o>0, 1., 0)
return MedMask.create(o)

# %% ../nbs/03_vision_augment.ipynb 12
# %% ../nbs/03_vision_augment.ipynb 14
class RandomGhosting(DisplayedTransform):
"""Apply TorchIO `RandomGhosting`."""

Expand All @@ -133,7 +217,7 @@ def encodes(self, o: MedImage):
def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 13
# %% ../nbs/03_vision_augment.ipynb 15
class RandomSpike(DisplayedTransform):
'''Apply TorchIO `RandomSpike`.'''

Expand All @@ -148,7 +232,7 @@ def encodes(self, o:MedImage):
def encodes(self, o:MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 14
# %% ../nbs/03_vision_augment.ipynb 16
class RandomNoise(DisplayedTransform):
'''Apply TorchIO `RandomNoise`.'''

Expand All @@ -163,7 +247,7 @@ def encodes(self, o: MedImage):
def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 15
# %% ../nbs/03_vision_augment.ipynb 17
class RandomBiasField(DisplayedTransform):
'''Apply TorchIO `RandomBiasField`.'''

Expand All @@ -178,7 +262,7 @@ def encodes(self, o: MedImage):
def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 16
# %% ../nbs/03_vision_augment.ipynb 18
class RandomBlur(DisplayedTransform):
'''Apply TorchIO `RandomBiasField`.'''

Expand All @@ -193,7 +277,7 @@ def encodes(self, o: MedImage):
def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 17
# %% ../nbs/03_vision_augment.ipynb 19
class RandomGamma(DisplayedTransform):
'''Apply TorchIO `RandomGamma`.'''

Expand All @@ -209,7 +293,7 @@ def encodes(self, o: MedImage):
def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 18
# %% ../nbs/03_vision_augment.ipynb 20
class RandomMotion(DisplayedTransform):
"""Apply TorchIO `RandomMotion`."""

Expand Down Expand Up @@ -237,7 +321,7 @@ def encodes(self, o: MedImage):
def encodes(self, o: MedMask):
return o

# %% ../nbs/03_vision_augment.ipynb 20
# %% ../nbs/03_vision_augment.ipynb 22
class RandomElasticDeformation(CustomDictTransform):
"""Apply TorchIO `RandomElasticDeformation`."""

Expand All @@ -250,7 +334,7 @@ def __init__(self, num_control_points=7, max_displacement=7.5,
image_interpolation=image_interpolation,
p=p))

# %% ../nbs/03_vision_augment.ipynb 21
# %% ../nbs/03_vision_augment.ipynb 23
class RandomAffine(CustomDictTransform):
"""Apply TorchIO `RandomAffine`."""

Expand All @@ -266,14 +350,14 @@ def __init__(self, scales=0, degrees=10, translation=0, isotropic=False,
default_pad_value=default_pad_value,
p=p))

# %% ../nbs/03_vision_augment.ipynb 22
# %% ../nbs/03_vision_augment.ipynb 24
class RandomFlip(CustomDictTransform):
"""Apply TorchIO `RandomFlip`."""

def __init__(self, axes='LR', p=0.5):
super().__init__(tio.RandomFlip(axes=axes, flip_probability=p))

# %% ../nbs/03_vision_augment.ipynb 23
# %% ../nbs/03_vision_augment.ipynb 25
class OneOf(CustomDictTransform):
"""Apply only one of the given transforms using TorchIO `OneOf`."""

Expand Down
105 changes: 101 additions & 4 deletions nbs/03_vision_augment.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,8 @@
"#| export\n",
"from fastai.data.all import *\n",
"from fastMONAI.vision_core import *\n",
"import torchio as tio"
"import torchio as tio\n",
"from monai.transforms import NormalizeIntensity as MonaiNormalizeIntensity"
]
},
{
Expand Down Expand Up @@ -153,16 +154,112 @@
" self.channel_wise = channel_wise\n",
"\n",
" def encodes(self, o: MedImage):\n",
" if self.channel_wise:\n",
" o = torch.stack([self.z_normalization(c[None])[0] for c in o])\n",
" else: o = self.z_normalization(o) \n",
" try:\n",
" if self.channel_wise:\n",
" o = torch.stack([self.z_normalization(c[None])[0] for c in o])\n",
" else: \n",
" o = self.z_normalization(o)\n",
" except RuntimeError as e:\n",
" if \"Standard deviation is 0\" in str(e):\n",
" # Calculate mean for debugging information\n",
" mean = float(o.mean())\n",
" \n",
" error_msg = (\n",
" f\"Standard deviation is 0 for image (mean={mean:.3f}).\\n\"\n",
" f\"This indicates uniform pixel values.\\n\\n\"\n",
" f\"Possible causes:\\n\"\n",
" f\"• Corrupted or blank image\\n\"\n",
" f\"• Oversaturated regions\\n\" \n",
" f\"• Background-only regions\\n\"\n",
" f\"• All-zero mask being processed as image\\n\\n\"\n",
" f\"Suggested solutions:\\n\"\n",
" f\"• Check image quality and acquisition\\n\"\n",
" f\"• Verify image vs mask data loading\"\n",
" )\n",
" raise RuntimeError(error_msg) from e\n",
"\n",
" return MedImage.create(o)\n",
"\n",
" def encodes(self, o: MedMask):\n",
" return o"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# | export\n",
"class RescaleIntensity(DisplayedTransform):\n",
" \"\"\"Apply TorchIO RescaleIntensity for robust intensity scaling.\n",
" \n",
" Args:\n",
" out_min_max (tuple[float, float]): Output intensity range (min, max)\n",
" in_min_max (tuple[float, float]): Input intensity range (min, max) \n",
" \n",
" Example for CT images:\n",
" # Normalize CT from air (-1000 HU) to bone (1000 HU) into range (-1, 1)\n",
" transform = RescaleIntensity(out_min_max=(-1, 1), in_min_max=(-1000, 1000))\n",
" \"\"\"\n",
" \n",
" order = 0\n",
" \n",
" def __init__(self, out_min_max: tuple[float, float], in_min_max: tuple[float, float]):\n",
" self.rescale = tio.RescaleIntensity(out_min_max=out_min_max, in_min_max=in_min_max)\n",
" \n",
" def encodes(self, o: MedImage):\n",
" return MedImage.create(self.rescale(o))\n",
" \n",
" def encodes(self, o: MedMask):\n",
" return o"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# | export\n",
"class NormalizeIntensity(DisplayedTransform):\n",
" \"\"\"Apply MONAI NormalizeIntensity.\n",
" \n",
" Args:\n",
" nonzero (bool): Only normalize non-zero values (default: True)\n",
" channel_wise (bool): Apply normalization per channel (default: True)\n",
" subtrahend (float, optional): Value to subtract \n",
" divisor (float, optional): Value to divide by\n",
" \"\"\"\n",
" \n",
" order = 0\n",
" \n",
" def __init__(self, nonzero: bool = True, channel_wise: bool = True, \n",
" subtrahend: float = None, divisor: float = None):\n",
" self.nonzero = nonzero\n",
" self.channel_wise = channel_wise\n",
" self.subtrahend = subtrahend\n",
" self.divisor = divisor\n",
" \n",
" self.transform = MonaiNormalizeIntensity(\n",
" nonzero=nonzero,\n",
" channel_wise=False, # Always 'False', we handle channel-wise manually\n",
" subtrahend=subtrahend,\n",
" divisor=divisor\n",
" )\n",
" \n",
" def encodes(self, o: MedImage):\n",
" if self.channel_wise:\n",
" result = torch.stack([self.transform(c[None])[0] for c in o])\n",
" else:\n",
" result = torch.Tensor(self.transform(o))\n",
" \n",
" return MedImage.create(result)\n",
" \n",
" def encodes(self, o: MedMask):\n",
" return o"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down
Loading