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Commit cb77c5f9 authored by Christopher Randolph Rhodes's avatar Christopher Randolph Rhodes
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Expose gaussian blur in ilastik segmentation model

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2 merge requests!50Release 2024.06.03,!42Models are now initialized with pydantic models
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model_server/base/process.py
+ 9
− 0
View file @ cb77c5f9
...@@ -6,6 +6,7 @@ from math import ceil, floor ...@@ -6,6 +6,7 @@ from math import ceil, floor
import numpy as np import numpy as np
import skimage import skimage
from skimage.exposure import rescale_intensity from skimage.exposure import rescale_intensity
from skimage.filters import gaussian
from skimage.measure import find_contours from skimage.measure import find_contours
def is_mask(img): def is_mask(img):
...@@ -128,6 +129,14 @@ def get_safe_contours(mask): ...@@ -128,6 +129,14 @@ def get_safe_contours(mask):
else: else:
return find_contours(mask) return find_contours(mask)
def smooth(img: np.ndarray, sig: float) -> np.ndarray:
"""
Perform Gaussian smoothing on an image
:param img: image data
:param sig: threshold parameter
:return: smoothed image
"""
return gaussian(img, sig)
class Error(Exception): class Error(Exception):
pass pass
......
model_server/extensions/ilastik/models.py
+ 10
− 2
View file @ cb77c5f9
...@@ -4,11 +4,13 @@ from pathlib import Path ...@@ -4,11 +4,13 @@ from pathlib import Path
import numpy as np import numpy as np
from pydantic import BaseModel from pydantic import BaseModel
from skimage.filters import gaussian
import vigra import vigra
import model_server.extensions.ilastik.conf import model_server.extensions.ilastik.conf
from model_server.base.accessors import PatchStack from model_server.base.accessors import PatchStack
from model_server.base.accessors import GenericImageDataAccessor, InMemoryDataAccessor from model_server.base.accessors import GenericImageDataAccessor, InMemoryDataAccessor
from model_server.base.process import smooth
from model_server.base.models import Model, ImageToImageModel, InstanceSegmentationModel, InvalidInputImageError, ParameterExpectedError, SemanticSegmentationModel from model_server.base.models import Model, ImageToImageModel, InstanceSegmentationModel, InvalidInputImageError, ParameterExpectedError, SemanticSegmentationModel
class IlastikParams(BaseModel): class IlastikParams(BaseModel):
...@@ -92,7 +94,8 @@ class IlastikModel(Model): ...@@ -92,7 +94,8 @@ class IlastikModel(Model):
class IlastikPixelClassifierParams(IlastikParams): class IlastikPixelClassifierParams(IlastikParams):
px_class: int = 0 px_class: int = 0
px_prob_threshold = 0.5 px_prob_threshold: float = 0.5
px_smoothing: float = 0.0
class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel): class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
model_id = 'ilastik_pixel_classification' model_id = 'ilastik_pixel_classification'
...@@ -157,7 +160,12 @@ class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel): ...@@ -157,7 +160,12 @@ class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
def label_pixel_class(self, img: GenericImageDataAccessor, **kwargs): def label_pixel_class(self, img: GenericImageDataAccessor, **kwargs):
pxmap, _ = self.infer(img) pxmap, _ = self.infer(img)
mask = pxmap.data[:, :, self.params['px_class'], :] > self.params['px_prob_threshold'] sig = self.params['px_smoothing']
if sig > 0.0:
proc = smooth(img.data, sig)
else:
proc = pxmap.data
mask = proc[:, :, self.params['px_class'], :] > self.params['px_prob_threshold']
return InMemoryDataAccessor(mask) return InMemoryDataAccessor(mask)
......
model_server/extensions/ilastik/tests/test_ilastik.py
+ 44
− 40
View file @ cb77c5f9
import pathlib import pathlib
import requests
import unittest import unittest
import numpy as np import numpy as np
...@@ -19,6 +18,11 @@ def _random_int(*args): ...@@ -19,6 +18,11 @@ def _random_int(*args):
class TestIlastikPixelClassification(unittest.TestCase): class TestIlastikPixelClassification(unittest.TestCase):
def setUp(self) -> None: def setUp(self) -> None:
self.cf = CziImageFileAccessor(czifile['path']) self.cf = CziImageFileAccessor(czifile['path'])
self.channel = 0
self.model = ilm.IlastikPixelClassifierModel(
params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__())
)
self.mono_image = self.cf.get_one_channel_data(self.channel)
def test_faulthandler(self): # recreate error that is messing up ilastik def test_faulthandler(self): # recreate error that is messing up ilastik
...@@ -45,59 +49,61 @@ class TestIlastikPixelClassification(unittest.TestCase): ...@@ -45,59 +49,61 @@ class TestIlastikPixelClassification(unittest.TestCase):
def test_run_pixel_classifier_on_random_data(self): def test_run_pixel_classifier_on_random_data(self):
model = ilm.IlastikPixelClassifierModel(
params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()),
)
w = 512 w = 512
h = 256 h = 256
input_img = InMemoryDataAccessor(data=np.random.rand(h, w, 1, 1)) input_img = InMemoryDataAccessor(data=np.random.rand(h, w, 1, 1))
mask = model.label_pixel_class(input_img) mask = self.model.label_pixel_class(input_img)
self.assertEqual(mask.shape, (h, w, 1, 1)) self.assertEqual(mask.shape, (h, w, 1, 1))
def test_run_pixel_classifier(self): def test_run_pixel_classifier(self):
channel = 0 self.assertEqual(self.mono_image.shape_dict['X'], czifile['w'])
model = ilm.IlastikPixelClassifierModel( self.assertEqual(self.mono_image.shape_dict['Y'], czifile['h'])
params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()) self.assertEqual(self.mono_image.shape_dict['C'], 1)
) self.assertEqual(self.mono_image.shape_dict['Z'], 1)
cf = CziImageFileAccessor(
czifile['path']
)
mono_image = cf.get_one_channel_data(channel)
self.assertEqual(mono_image.shape_dict['X'], czifile['w']) mask = self.model.label_pixel_class(self.mono_image)
self.assertEqual(mono_image.shape_dict['Y'], czifile['h'])
self.assertEqual(mono_image.shape_dict['C'], 1)
self.assertEqual(mono_image.shape_dict['Z'], 1)
mask = model.label_pixel_class(mono_image)
self.assertTrue(mask.is_mask()) self.assertTrue(mask.is_mask())
self.assertEqual(mask.shape[0:2], cf.shape[0:2]) self.assertEqual(mask.shape[0:2], self.cf.shape[0:2])
self.assertEqual(mask.shape_dict['C'], 1) self.assertEqual(mask.shape_dict['C'], 1)
self.assertEqual(mask.shape_dict['Z'], 1) self.assertEqual(mask.shape_dict['Z'], 1)
self.assertTrue( self.assertTrue(
write_accessor_data_to_file( write_accessor_data_to_file(
output_path / f'pxmap_{cf.fpath.stem}_ch{channel}.tif', output_path / 'seg' / f'seg_{self.cf.fpath.stem}_ch{self.channel}.tif',
mask mask
) )
) )
self.mono_image = mono_image def test_label_pixels_with_params(self):
self.mask = mask def _run_seg(tr, sig):
mod = ilm.IlastikPixelClassifierModel(
params=ilm.IlastikPixelClassifierParams(
project_file=ilastik_classifiers['px'].__str__(),
px_prob_threshold=tr,
px_smoothing=sig,
),
)
mask = mod.label_pixel_class(self.mono_image)
write_accessor_data_to_file(
output_path / 'seg' / f'seg_tr{int(10*tr)}_sig{int(10*sig)}.tif',
mask
)
return mask
mask1 = _run_seg(0.5, 0.0)
mask2 = _run_seg(0.5, 0.2)
self.assertEqual(mask1.shape, mask2.shape)
def test_pixel_classifier_enforces_input_shape(self): def test_pixel_classifier_enforces_input_shape(self):
model = ilm.IlastikPixelClassifierModel( self.assertEqual(self.model.model_chroma, 1)
params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()) self.assertEqual(self.model.model_3d, False)
)
self.assertEqual(model.model_chroma, 1)
self.assertEqual(model.model_3d, False)
# correct data # correct data
self.assertIsInstance( self.assertIsInstance(
model.label_pixel_class( self.model.label_pixel_class(
InMemoryDataAccessor( InMemoryDataAccessor(
_random_int(512, 256, 1, 1) _random_int(512, 256, 1, 1)
) )
...@@ -107,7 +113,7 @@ class TestIlastikPixelClassification(unittest.TestCase): ...@@ -107,7 +113,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
# raise except with input of multiple channels # raise except with input of multiple channels
with self.assertRaises(ilm.IlastikInputShapeError): with self.assertRaises(ilm.IlastikInputShapeError):
mask = model.label_pixel_class( mask = self.model.label_pixel_class(
InMemoryDataAccessor( InMemoryDataAccessor(
_random_int(512, 256, 3, 1) _random_int(512, 256, 3, 1)
) )
...@@ -115,7 +121,7 @@ class TestIlastikPixelClassification(unittest.TestCase): ...@@ -115,7 +121,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
# raise except with input of multiple channels # raise except with input of multiple channels
with self.assertRaises(ilm.IlastikInputShapeError): with self.assertRaises(ilm.IlastikInputShapeError):
mask = model.label_pixel_class( mask = self.model.label_pixel_class(
InMemoryDataAccessor( InMemoryDataAccessor(
_random_int(512, 256, 1, 15) _random_int(512, 256, 1, 15)
) )
...@@ -130,20 +136,16 @@ class TestIlastikPixelClassification(unittest.TestCase): ...@@ -130,20 +136,16 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.assertEqual(acc.hw, (512, 512)) self.assertEqual(acc.hw, (512, 512))
self.assertEqual(acc.iat(0, crop=True).hw, (256, 512)) self.assertEqual(acc.iat(0, crop=True).hw, (256, 512))
model = ilm.IlastikPixelClassifierModel( mask = self.model.label_patch_stack(acc)
ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()),
)
mask = model.label_patch_stack(acc)
self.assertEqual(mask.dtype, bool) self.assertEqual(mask.dtype, bool)
self.assertEqual(mask.chroma, 1) self.assertEqual(mask.chroma, 1)
self.assertEqual(mask.hw, acc.hw) self.assertEqual(mask.hw, acc.hw)
self.assertEqual(mask.nz, acc.nz) self.assertEqual(mask.nz, acc.nz)
self.assertEqual(mask.count, acc.count) self.assertEqual(mask.count, acc.count)
pxmap, _ = model.infer_patch_stack(acc) pxmap, _ = self.model.infer_patch_stack(acc)
self.assertEqual(pxmap.dtype, float) self.assertEqual(pxmap.dtype, float)
self.assertEqual(pxmap.chroma, len(model.labels)) self.assertEqual(pxmap.chroma, len(self.model.labels))
self.assertEqual(pxmap.hw, acc.hw) self.assertEqual(pxmap.hw, acc.hw)
self.assertEqual(pxmap.nz, acc.nz) self.assertEqual(pxmap.nz, acc.nz)
self.assertEqual(pxmap.count, acc.count) self.assertEqual(pxmap.count, acc.count)
...@@ -154,7 +156,8 @@ class TestIlastikPixelClassification(unittest.TestCase): ...@@ -154,7 +156,8 @@ class TestIlastikPixelClassification(unittest.TestCase):
model = ilm.IlastikObjectClassifierFromPixelPredictionsModel( model = ilm.IlastikObjectClassifierFromPixelPredictionsModel(
params=ilm.IlastikParams(project_file=ilastik_classifiers['pxmap_to_obj'].__str__()) params=ilm.IlastikParams(project_file=ilastik_classifiers['pxmap_to_obj'].__str__())
) )
objmap, _ = model.infer(self.mono_image, self.mask) mask = self.model.label_pixel_class(self.mono_image)
objmap, _ = model.infer(self.mono_image, mask)
self.assertTrue( self.assertTrue(
write_accessor_data_to_file( write_accessor_data_to_file(
...@@ -171,7 +174,8 @@ class TestIlastikPixelClassification(unittest.TestCase): ...@@ -171,7 +174,8 @@ class TestIlastikPixelClassification(unittest.TestCase):
model = ilm.IlastikObjectClassifierFromSegmentationModel( model = ilm.IlastikObjectClassifierFromSegmentationModel(
params=ilm.IlastikParams(project_file=ilastik_classifiers['seg_to_obj'].__str__()) params=ilm.IlastikParams(project_file=ilastik_classifiers['seg_to_obj'].__str__())
) )
objmap = model.label_instance_class(self.mono_image, self.mask) mask = self.model.label_pixel_class(self.mono_image)
objmap = model.label_instance_class(self.mono_image, mask)
self.assertTrue( self.assertTrue(
write_accessor_data_to_file( write_accessor_data_to_file(
......
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