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T 371 segmentation loader with multiple wavelengths #372

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merged 4 commits into from
Aug 15, 2024
Merged

T 371 segmentation loader with multiple wavelengths #372

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db59c9f
Change the segmentation loader to know which keys to set
frisograce Jul 31, 2024
a6ed043
remove unnecessary imports
frisograce Jul 31, 2024
2d91eed
Merge branch 'refs/heads/develop' into T_371_seg_many_wvs
frisograce Aug 15, 2024
88f5627
Merge branch 'develop' into T_371_seg_many_wvs
frisograce Aug 15, 2024
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18 changes: 7 additions & 11 deletions simpa/core/simulation_modules/volume_creation_module/segmentation_based_adapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,6 @@

from simpa.core.simulation_modules.volume_creation_module import VolumeCreationAdapterBase
from simpa.utils import Tags
from simpa.utils.constants import property_tags
from simpa.io_handling import save_hdf5
import numpy as np
import torch

Expand Down Expand Up @@ -44,21 +42,19 @@ def create_simulation_volume(self) -> dict:

for seg_class in segmentation_classes:
class_properties = class_mapping[seg_class].get_properties_for_wavelength(self.global_settings, wavelength)
for prop_tag in property_tags:
if isinstance(class_properties[prop_tag], (int, float)) or class_properties[prop_tag] == None: # scalar
assigned_prop = class_properties[prop_tag]
for volume_key in volumes.keys():
if isinstance(class_properties[volume_key], (int, float)) or class_properties[volume_key] == None: # scalar
assigned_prop = class_properties[volume_key]
if assigned_prop is None:
assigned_prop = torch.nan
volumes[prop_tag][segmentation_volume == seg_class] = assigned_prop
elif len(torch.Tensor.size(class_properties[prop_tag])) == 3: # 3D map
assigned_prop = class_properties[prop_tag][torch.tensor(segmentation_volume == seg_class)]
volumes[volume_key][segmentation_volume == seg_class] = assigned_prop
elif len(torch.Tensor.size(class_properties[volume_key])) == 3: # 3D map
assigned_prop = class_properties[volume_key][torch.tensor(segmentation_volume == seg_class)]
assigned_prop[assigned_prop is None] = torch.nan
volumes[prop_tag][torch.tensor(segmentation_volume == seg_class)] = assigned_prop
volumes[volume_key][torch.tensor(segmentation_volume == seg_class)] = assigned_prop
else:
raise AssertionError("Properties need to either be a scalar or a 3D map.")

save_hdf5(self.global_settings, self.global_settings[Tags.SIMPA_OUTPUT_PATH], "/settings/")

# convert volumes back to CPU
for key in volumes.keys():
volumes[key] = volumes[key].numpy().astype(np.float64, copy=False)
Expand Down
2 changes: 1 addition & 1 deletion simpa_examples/segmentation_loader.py
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Expand Up @@ -66,7 +66,7 @@ def segmentation_class_mapping():
settings[Tags.SIMULATION_PATH] = path_manager.get_hdf5_file_save_path()
settings[Tags.VOLUME_NAME] = "SegmentationTest"
settings[Tags.RANDOM_SEED] = 1234
settings[Tags.WAVELENGTHS] = [700]
settings[Tags.WAVELENGTHS] = [700, 800]
settings[Tags.SPACING_MM] = spacing
settings[Tags.DIM_VOLUME_X_MM] = segmentation_volume_mask.shape[0] * spacing
settings[Tags.DIM_VOLUME_Y_MM] = segmentation_volume_mask.shape[1] * spacing
Expand Down
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