diff --git a/extensions/ilastik/models.py b/extensions/ilastik/models.py
index 21278461a2d6d949fd54d1abd453f2251f7e39de..8f22c3ad24310af9fb33e17b938a22705a3dbe4e 100644
--- a/extensions/ilastik/models.py
+++ b/extensions/ilastik/models.py
@@ -6,10 +6,10 @@ import vigra
import extensions.ilastik.conf
from model_server.accessors import GenericImageDataAccessor, InMemoryDataAccessor
-from model_server.models import ImageToImageModel, ParameterExpectedError
+from model_server.models import Model, ParameterExpectedError, SemanticSegmentationModel
-class IlastikImageToImageModel(ImageToImageModel):
+class IlastikModel(Model):
def __init__(self, params, autoload=True):
self.project_file = Path(params['project_file'])
@@ -27,7 +27,6 @@ class IlastikImageToImageModel(ImageToImageModel):
self.shell = None
super().__init__(autoload, params)
-
def load(self):
from ilastik import app
from ilastik.applets.dataSelection.opDataSelection import PreloadedArrayDatasetInfo
@@ -51,7 +50,7 @@ class IlastikImageToImageModel(ImageToImageModel):
return True
-class IlastikPixelClassifierModel(IlastikImageToImageModel):
+class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
model_id = 'ilastik_pixel_classification'
operations = ['segment', ]
@@ -78,43 +77,44 @@ class IlastikPixelClassifierModel(IlastikImageToImageModel):
)
return InMemoryDataAccessor(data=yxcz), {'success': True}
- def segment(self, input_img, thresh, channel):
- return InMemoryDataAccessor(
- self.infer(input_img).data[:, :, channel, :] > thresh
+ def segment(self, img: GenericImageDataAccessor, pixel_class: int, pixel_probability_threshold=0.5):
+ pxmap, _ = self.infer(img)
+ mask = pxmap.data[:, :, pixel_class, :] > pixel_probability_threshold
+ return InMemoryDataAccessor(mask)
+
+# TODO: deprecate
+class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel):
+ model_id = 'ilastik_object_classification_from_pixel_predictions'
+
+ @staticmethod
+ def get_workflow():
+ from ilastik.workflows.objectClassification.objectClassificationWorkflow import ObjectClassificationWorkflowPrediction
+ return ObjectClassificationWorkflowPrediction
+
+ def infer(self, input_img: GenericImageDataAccessor, pxmap_img: GenericImageDataAccessor) -> (np.ndarray, dict):
+ tagged_input_data = vigra.taggedView(input_img.data, 'yxcz')
+ tagged_pxmap_data = vigra.taggedView(pxmap_img.data, 'yxcz')
+
+ dsi = [
+ {
+ 'Raw Data': self.PreloadedArrayDatasetInfo(preloaded_array=tagged_input_data),
+ 'Prediction Maps': self.PreloadedArrayDatasetInfo(preloaded_array=tagged_pxmap_data),
+ }
+ ]
+
+ obmaps = self.shell.workflow.batchProcessingApplet.run_export(dsi, export_to_array=True) # [z x h x w x n]
+
+ assert (len(obmaps) == 1, 'ilastik generated more than one object map')
+
+ yxcz = np.moveaxis(
+ obmaps[0],
+ [1, 2, 3, 0],
+ [0, 1, 2, 3]
)
+ return InMemoryDataAccessor(data=yxcz), {'success': True}
+
-# class IlastikObjectClassifierFromPixelPredictionsModel(IlastikImageToImageModel):
-# model_id = 'ilastik_object_classification_from_pixel_predictions'
-#
-# @staticmethod
-# def get_workflow():
-# from ilastik.workflows.objectClassification.objectClassificationWorkflow import ObjectClassificationWorkflowPrediction
-# return ObjectClassificationWorkflowPrediction
-#
-# def infer(self, input_img: GenericImageDataAccessor, pxmap_img: GenericImageDataAccessor) -> (np.ndarray, dict):
-# tagged_input_data = vigra.taggedView(input_img.data, 'yxcz')
-# tagged_pxmap_data = vigra.taggedView(pxmap_img.data, 'yxcz')
-#
-# dsi = [
-# {
-# 'Raw Data': self.PreloadedArrayDatasetInfo(preloaded_array=tagged_input_data),
-# 'Prediction Maps': self.PreloadedArrayDatasetInfo(preloaded_array=tagged_pxmap_data),
-# }
-# ]
-#
-# obmaps = self.shell.workflow.batchProcessingApplet.run_export(dsi, export_to_array=True) # [z x h x w x n]
-#
-# assert (len(obmaps) == 1, 'ilastik generated more than one object map')
-#
-# yxcz = np.moveaxis(
-# obmaps[0],
-# [1, 2, 3, 0],
-# [0, 1, 2, 3]
-# )
-# return InMemoryDataAccessor(data=yxcz), {'success': True}
-
-
-class IlastikObjectClassifierFromSegmentationModel(IlastikImageToImageModel):
+class IlastikObjectClassifierFromSegmentationModel(IlastikModel):
model_id = 'ilastik_object_classification_from_segmentation'
@staticmethod
diff --git a/extensions/ilastik/router.py b/extensions/ilastik/router.py
index 4d9a8b28a0bad08ff4d079e9175193c94d50e047..15a59b283ef6d7746127ba457401fd6d40042b7d 100644
--- a/extensions/ilastik/router.py
+++ b/extensions/ilastik/router.py
@@ -14,7 +14,7 @@ router = APIRouter(
session = Session()
-def load_ilastik_model(model_class: ilm.IlastikImageToImageModel, project_file: str, duplicate=True) -> dict:
+def load_ilastik_model(model_class: ilm.IlastikModel, project_file: str, duplicate=True) -> dict:
"""
Load an ilastik model of a given class and project filename.
:param model_class:
@@ -35,10 +35,6 @@ def load_ilastik_model(model_class: ilm.IlastikImageToImageModel, project_file:
)
return {'model_id': result}
-# @router.put('/px/load/')
-# def load_px_model(project_file: str, duplicate: bool = True) -> dict:
-# return load_ilastik_model(ilm.IlastikPixelClassifierModel, project_file, duplicate=duplicate)
-
@router.put('/seg/load/')
def load_px_model(project_file: str, duplicate: bool = True) -> dict:
return load_ilastik_model(ilm.IlastikPixelClassifierModel, project_file, duplicate=duplicate)
diff --git a/extensions/ilastik/tests/test_ilastik.py b/extensions/ilastik/tests/test_ilastik.py
index 79cbe3b49630468c8c97558e22923cd522b26cf3..f4f400a60ebab7a485e48d8876d4ce428a3dd81a 100644
--- a/extensions/ilastik/tests/test_ilastik.py
+++ b/extensions/ilastik/tests/test_ilastik.py
@@ -113,7 +113,7 @@ class TestIlastikOverApi(TestServerBaseClass):
def test_httpexception_if_incorrect_project_file_loaded(self):
resp_load = requests.put(
- self.uri + 'ilastik/px/load/',
+ self.uri + 'ilastik/seg/load/',
params={'project_file': 'improper.ilp'},
)
self.assertEqual(resp_load.status_code, 404)
@@ -121,7 +121,7 @@ class TestIlastikOverApi(TestServerBaseClass):
def test_load_ilastik_pixel_model(self):
resp_load = requests.put(
- self.uri + 'ilastik/px/load/',
+ self.uri + 'ilastik/seg/load/',
params={'project_file': str(ilastik_classifiers['px'])},
)
self.assertEqual(resp_load.status_code, 200, resp_load.json())
@@ -137,7 +137,7 @@ class TestIlastikOverApi(TestServerBaseClass):
resp_list_1st = requests.get(self.uri + 'models').json()
self.assertEqual(len(resp_list_1st), 1, resp_list_1st)
resp_load_2nd = requests.put(
- self.uri + 'ilastik/px/load/',
+ self.uri + 'ilastik/seg/load/',
params={
'project_file': str(ilastik_classifiers['px']),
'duplicate': True,
@@ -146,7 +146,7 @@ class TestIlastikOverApi(TestServerBaseClass):
resp_list_2nd = requests.get(self.uri + 'models').json()
self.assertEqual(len(resp_list_2nd), 2, resp_list_2nd)
resp_load_3rd = requests.put(
- self.uri + 'ilastik/px/load/',
+ self.uri + 'ilastik/seg/load/',
params={
'project_file': str(ilastik_classifiers['px']),
'duplicate': False,
@@ -172,14 +172,14 @@ class TestIlastikOverApi(TestServerBaseClass):
# load models with these paths
resp1 = requests.put(
- self.uri + 'ilastik/px/load/',
+ self.uri + 'ilastik/seg/load/',
params={
'project_file': ilp_win,
'duplicate': False,
},
)
resp2 = requests.put(
- self.uri + 'ilastik/px/load/',
+ self.uri + 'ilastik/seg/load/',
params={
'project_file': ilp_posx,
'duplicate': False,
diff --git a/model_server/models.py b/model_server/models.py
index 4285b49c4d8a55bb3b77135d9e578546ea851d4c..07fc31dfdf2ea0b85ad5e63fa0daeb8bd15242fd 100644
--- a/model_server/models.py
+++ b/model_server/models.py
@@ -36,7 +36,7 @@ class Model(ABC):
pass
@abstractmethod
- def infer(self, img: GenericImageDataAccessor) -> (object, dict): # return json describing inference result
+ def infer(self, *args) -> (object, dict): # return json describing inference result
pass
def reload(self):
@@ -52,6 +52,15 @@ class ImageToImageModel(Model):
def infer(self, img: GenericImageDataAccessor) -> (GenericImageDataAccessor, dict):
pass
+class SemanticSegmentationModel(ImageToImageModel):
+ """
+ Model that exposes a method that returns a binary mask for a given input image and pixel class
+ """
+
+ @abstractmethod
+ def segment(self, img: GenericImageDataAccessor, pixel_class: int, **kwargs) -> (GenericImageDataAccessor, dict):
+ pass
+
class DummyImageToImageModel(ImageToImageModel):
model_id = 'dummy_make_white_square'
@@ -67,6 +76,7 @@ class DummyImageToImageModel(ImageToImageModel):
result[floor(0.25 * h) : floor(0.75 * h), floor(0.25 * w) : floor(0.75 * w)] = 255
return InMemoryDataAccessor(data=result), {'success': True}
+
class Error(Exception):
pass