diff --git a/model_server/base/process.py b/model_server/base/process.py
index 3ee227d77b5355725ca130a4c4ea8567c3c9c3a9..992c9fb93140ab49859f0807b55a1f8e321cd80d 100644
--- a/model_server/base/process.py
+++ b/model_server/base/process.py
@@ -4,6 +4,7 @@ Image processing utility functions
from math import ceil, floor
import numpy as np
+import skimage
from skimage.exposure import rescale_intensity
@@ -85,3 +86,43 @@ def make_rgb(nda):
outdata = np.zeros((h, w, 3, nz), dtype=nda.dtype)
outdata[:, :, 0:c, :] = nda[:, :, :, :]
return outdata
+
+
+def mask_largest_object(
+ img: np.ndarray,
+ max_allowed: int = 10,
+ verbose: bool = True
+) -> np.ndarray:
+ """
+ Where more than one connected component is found in an image, return the largest object by area
+ :param img: (np.ndarray) containing object labels or binary mask
+ :param max_allowed: raise an error if more than this number of objects is found
+ :param verbose: print a message each time more than one object is found
+ :return: np.ndarray of same size as img
+ """
+ if is_mask(img): # assign object labels
+ ob_id = skimage.measure.label(img)
+ else: # assume img is contains object labels
+ ob_id = img
+
+ num_obj = len(np.unique(ob_id)) - 1
+ if num_obj > max_allowed:
+ raise TooManyObjectError(f'Found {num_obj} objects in frame')
+ if num_obj > 1:
+ if verbose:
+ print(f'Found {num_obj} nonzero unique values in object map; keeping the one with the largest area')
+ val, cts = np.unique(ob_id, return_counts=True)
+ mask = ob_id == val[1 + cts[1:].argmax()]
+ return mask * img
+ else:
+ return img
+
+
+class Error(Exception):
+ pass
+
+
+class TooManyObjectError(Exception):
+ pass
+
+
diff --git a/model_server/base/roiset.py b/model_server/base/roiset.py
index bf40227a0629c2d3747e1faf20360565fa664a63..4aecae346e71ef56dc805b0b2549df589038563a 100644
--- a/model_server/base/roiset.py
+++ b/model_server/base/roiset.py
@@ -18,7 +18,8 @@ from model_server.base.models import InstanceSegmentationModel
from model_server.base.process import pad, rescale, resample_to_8bit, make_rgb
from model_server.extensions.chaeo.annotators import draw_box_on_patch, draw_contours_on_patch, draw_boxes_on_3d_image
from model_server.extensions.chaeo.accessors import write_patch_to_file, MonoPatchStack, Multichannel3dPatchStack
-from model_server.extensions.chaeo.process import mask_largest_object
+from base.process import mask_largest_object
+
class PatchParams(BaseModel):
draw_bounding_box: bool = False
diff --git a/model_server/extensions/chaeo/process.py b/model_server/extensions/chaeo/process.py
deleted file mode 100644
index e41578eee35f7849e7d714326c59ca68e34042b0..0000000000000000000000000000000000000000
--- a/model_server/extensions/chaeo/process.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import numpy as np
-import skimage
-
-from model_server.base.process import is_mask
-
-def mask_largest_object(
- img: np.ndarray,
- max_allowed: int = 10,
- verbose: bool = True
-) -> np.ndarray:
- """
- Where more than one connected component is found in an image, return the largest object by area
- :param img: (np.ndarray) containing object labels or binary mask
- :param max_allowed: raise an error if more than this number of objects is found
- :param verbose: print a message each time more than one object is found
- :return: np.ndarray of same size as img
- """
- if is_mask(img): # assign object labels
- ob_id = skimage.measure.label(img)
- else: # assume img is contains object labels
- ob_id = img
-
- # import skimage
- # from pathlib import Path
- # where = Path('c:/Users/rhodes/projects/proj0011-plankton-seg/tmp')
- # skimage.io.imsave(where / 'raw.png', img)
- num_obj = len(np.unique(ob_id)) - 1
- if num_obj > max_allowed:
- raise TooManyObjectError(f'Found {num_obj} objects in frame')
- if num_obj > 1:
- if verbose:
- print(f'Found {num_obj} nonzero unique values in object map; keeping the one with the largest area')
- # pr = regionprops_table(ob_id, properties=['label', 'area'])
- val, cts = np.unique(ob_id, return_counts=True)
- mask = ob_id == val[1 + cts[1:].argmax()]
- # idx_max_area = pr['area'].argmax()
- # mask = ob_id == pr['label'][idx_max_area]
- return mask * img
- else:
- return img
-
-
-class Error(Exception):
- pass
-
-
-class TooManyObjectError(Exception):
- pass
diff --git a/model_server/extensions/chaeo/tests/test_process.py b/model_server/extensions/chaeo/tests/test_process.py
deleted file mode 100644
index 79e6883c5f7099869b9b9901474c5c4cb833d158..0000000000000000000000000000000000000000
--- a/model_server/extensions/chaeo/tests/test_process.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import unittest
-
-import numpy as np
-
-from model_server.extensions.chaeo.process import mask_largest_object
-
-class TestMaskLargestObject(unittest.TestCase):
- def test_mask_largest_touching_object(self):
- arr = np.zeros([5, 5], dtype='uint8')
- arr[0:3, 0:3] = 2
- arr[3:, 2:] = 4
- masked = mask_largest_object(arr)
- self.assertTrue(np.all(np.unique(masked) == [0, 2]))
- self.assertTrue(np.all(masked[4:5, 0:2] == 0))
- self.assertTrue(np.all(masked[0:3, 3:5] == 0))
-
- def test_no_change(self):
- arr = np.zeros([5, 5], dtype='uint8')
- arr[0:3, 0:3] = 2
- masked = mask_largest_object(arr)
- self.assertTrue(np.all(masked == arr))
-
- def test_mask_multiple_objects_in_binary_maks(self):
- arr = np.zeros([5, 5], dtype='uint8')
- arr[0:3, 0:3] = 255
- arr[4, 2:5] = 255
- masked = mask_largest_object(arr)
- print(np.unique(masked))
- self.assertTrue(np.all(np.unique(masked) == [0, 255]))
- self.assertTrue(np.all(masked[:, 3:5] == 0))
- self.assertTrue(np.all(masked[3:5, :] == 0))
-
diff --git a/model_server/extensions/chaeo/workflows.py b/model_server/extensions/chaeo/workflows.py
index b837877979017384effdb840c1f171d7e6d1d808..c651f95ecdf68f4b8f3aa431e6d4e512a2467aa4 100644
--- a/model_server/extensions/chaeo/workflows.py
+++ b/model_server/extensions/chaeo/workflows.py
@@ -9,7 +9,7 @@ from skimage.morphology import dilation
from sklearn.model_selection import train_test_split
from base.roiset import RoiSetMetaParams, RoiSetExportParams
-from model_server.extensions.chaeo.process import mask_largest_object
+from base.process import mask_largest_object
from base.roiset import _get_label_ids, RoiSet
from model_server.base.accessors import generate_file_accessor, InMemoryDataAccessor, write_accessor_data_to_file
diff --git a/tests/test_process.py b/tests/test_process.py
index 6908e9d7c1b779065d4de226b7a203979570fb9a..454151bc4f84156348624318a43837b5e9a370c8 100644
--- a/tests/test_process.py
+++ b/tests/test_process.py
@@ -2,6 +2,7 @@ import unittest
import numpy as np
+from base.process import mask_largest_object
from model_server.base.process import pad
class TestProcessingUtilityMethods(unittest.TestCase):
@@ -27,4 +28,31 @@ class TestProcessingUtilityMethods(unittest.TestCase):
nc = self.data4d.shape[2]
nz = self.data4d.shape[3]
padded = pad(self.data4d, 256)
- self.assertEqual(padded.shape, (256, 256, nc, nz))
\ No newline at end of file
+ self.assertEqual(padded.shape, (256, 256, nc, nz))
+
+
+class TestMaskLargestObject(unittest.TestCase):
+ def test_mask_largest_touching_object(self):
+ arr = np.zeros([5, 5], dtype='uint8')
+ arr[0:3, 0:3] = 2
+ arr[3:, 2:] = 4
+ masked = mask_largest_object(arr)
+ self.assertTrue(np.all(np.unique(masked) == [0, 2]))
+ self.assertTrue(np.all(masked[4:5, 0:2] == 0))
+ self.assertTrue(np.all(masked[0:3, 3:5] == 0))
+
+ def test_no_change(self):
+ arr = np.zeros([5, 5], dtype='uint8')
+ arr[0:3, 0:3] = 2
+ masked = mask_largest_object(arr)
+ self.assertTrue(np.all(masked == arr))
+
+ def test_mask_multiple_objects_in_binary_maks(self):
+ arr = np.zeros([5, 5], dtype='uint8')
+ arr[0:3, 0:3] = 255
+ arr[4, 2:5] = 255
+ masked = mask_largest_object(arr)
+ print(np.unique(masked))
+ self.assertTrue(np.all(np.unique(masked) == [0, 255]))
+ self.assertTrue(np.all(masked[:, 3:5] == 0))
+ self.assertTrue(np.all(masked[3:5, :] == 0))