diff --git a/model_server/base/process.py b/model_server/base/process.py
index 94c6cf6206d4a0f1dcc4cfb78635807d58efd0c7..d475b063ca641922e4c08707bdc2bfc36049f9ea 100644
--- a/model_server/base/process.py
+++ b/model_server/base/process.py
@@ -6,6 +6,7 @@ from math import ceil, floor
import numpy as np
import skimage
from skimage.exposure import rescale_intensity
+from skimage.filters import gaussian
from skimage.measure import find_contours
def is_mask(img):
@@ -128,6 +129,14 @@ def get_safe_contours(mask):
else:
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):
pass
diff --git a/model_server/extensions/ilastik/models.py b/model_server/extensions/ilastik/models.py
index 15cbd4f0272b29feb263b7e8009acf5bda48d6de..4a1c4c0ab785e7b2eac3b481447390d2ad28749d 100644
--- a/model_server/extensions/ilastik/models.py
+++ b/model_server/extensions/ilastik/models.py
@@ -4,11 +4,13 @@ from pathlib import Path
import numpy as np
from pydantic import BaseModel
+from skimage.filters import gaussian
import vigra
import model_server.extensions.ilastik.conf
from model_server.base.accessors import PatchStack
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
class IlastikParams(BaseModel):
@@ -92,7 +94,8 @@ class IlastikModel(Model):
class IlastikPixelClassifierParams(IlastikParams):
px_class: int = 0
- px_prob_threshold = 0.5
+ px_prob_threshold: float = 0.5
+ px_smoothing: float = 0.0
class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
model_id = 'ilastik_pixel_classification'
@@ -157,7 +160,12 @@ class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
def label_pixel_class(self, img: GenericImageDataAccessor, **kwargs):
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)
diff --git a/model_server/extensions/ilastik/tests/test_ilastik.py b/model_server/extensions/ilastik/tests/test_ilastik.py
index 40719563777d5f6629a9bfdef6d98d50ef30c5bf..a48867c0e689aa4eb26589aa4a2d2e040ae7f81a 100644
--- a/model_server/extensions/ilastik/tests/test_ilastik.py
+++ b/model_server/extensions/ilastik/tests/test_ilastik.py
@@ -1,5 +1,4 @@
import pathlib
-import requests
import unittest
import numpy as np
@@ -19,6 +18,11 @@ def _random_int(*args):
class TestIlastikPixelClassification(unittest.TestCase):
def setUp(self) -> None:
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
@@ -45,59 +49,61 @@ class TestIlastikPixelClassification(unittest.TestCase):
def test_run_pixel_classifier_on_random_data(self):
- model = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()),
- )
w = 512
h = 256
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))
def test_run_pixel_classifier(self):
- channel = 0
- model = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__())
- )
- cf = CziImageFileAccessor(
- czifile['path']
- )
- mono_image = cf.get_one_channel_data(channel)
+ self.assertEqual(self.mono_image.shape_dict['X'], czifile['w'])
+ self.assertEqual(self.mono_image.shape_dict['Y'], czifile['h'])
+ self.assertEqual(self.mono_image.shape_dict['C'], 1)
+ self.assertEqual(self.mono_image.shape_dict['Z'], 1)
- self.assertEqual(mono_image.shape_dict['X'], czifile['w'])
- 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)
+ mask = self.model.label_pixel_class(self.mono_image)
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['Z'], 1)
self.assertTrue(
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
)
)
- self.mono_image = mono_image
- self.mask = mask
+ def test_label_pixels_with_params(self):
+ 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):
- model = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__())
- )
- self.assertEqual(model.model_chroma, 1)
- self.assertEqual(model.model_3d, False)
+ self.assertEqual(self.model.model_chroma, 1)
+ self.assertEqual(self.model.model_3d, False)
# correct data
self.assertIsInstance(
- model.label_pixel_class(
+ self.model.label_pixel_class(
InMemoryDataAccessor(
_random_int(512, 256, 1, 1)
)
@@ -107,7 +113,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
# raise except with input of multiple channels
with self.assertRaises(ilm.IlastikInputShapeError):
- mask = model.label_pixel_class(
+ mask = self.model.label_pixel_class(
InMemoryDataAccessor(
_random_int(512, 256, 3, 1)
)
@@ -115,7 +121,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
# raise except with input of multiple channels
with self.assertRaises(ilm.IlastikInputShapeError):
- mask = model.label_pixel_class(
+ mask = self.model.label_pixel_class(
InMemoryDataAccessor(
_random_int(512, 256, 1, 15)
)
@@ -130,20 +136,16 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.assertEqual(acc.hw, (512, 512))
self.assertEqual(acc.iat(0, crop=True).hw, (256, 512))
- model = ilm.IlastikPixelClassifierModel(
- ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()),
- )
-
- mask = model.label_patch_stack(acc)
+ mask = self.model.label_patch_stack(acc)
self.assertEqual(mask.dtype, bool)
self.assertEqual(mask.chroma, 1)
self.assertEqual(mask.hw, acc.hw)
self.assertEqual(mask.nz, acc.nz)
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.chroma, len(model.labels))
+ self.assertEqual(pxmap.chroma, len(self.model.labels))
self.assertEqual(pxmap.hw, acc.hw)
self.assertEqual(pxmap.nz, acc.nz)
self.assertEqual(pxmap.count, acc.count)
@@ -154,7 +156,8 @@ class TestIlastikPixelClassification(unittest.TestCase):
model = ilm.IlastikObjectClassifierFromPixelPredictionsModel(
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(
write_accessor_data_to_file(
@@ -171,7 +174,8 @@ class TestIlastikPixelClassification(unittest.TestCase):
model = ilm.IlastikObjectClassifierFromSegmentationModel(
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(
write_accessor_data_to_file(