Fix issue in table computation and add tests

Author: constantinpape

flamingo_tools/segmentation/postprocessing.py

@@ -6,7 +6,6 @@
 import numpy as np
 import nifty.tools as nt
 import pandas as pd
-import vigra
 
 from elf.io import open_file
 from scipy.spatial import distance
@@ -113,15 +112,14 @@ def neighbors_in_radius(table: pd.DataFrame, radius: float = 15) -> np.ndarray:
 # Filter the segmentation based on a spatial statistics from above.
 #
 
-# FIXME: functions causes ValueError by using arrays of different lengths
-
 
+# FIXME: this computes the distance in pixels, but the MoBIE table contains it in physical units (=nm)
+# This is inconsistent.
 def _compute_table(segmentation):
-    segmentation, n_ids, _ = vigra.analysis.relabelConsecutive(segmentation[:], start_label=1, keep_zeros=True)
     props = measure.regionprops(segmentation)
-    coordinates = np.array([prop.centroid for prop in props])[1:]
-    label_ids = np.unique(segmentation)[1:]
-    sizes = np.array([prop.area for prop in props])[1:]
+    label_ids = np.array([prop.label for prop in props])
+    coordinates = np.array([prop.centroid for prop in props])
+    sizes = np.array([prop.area for prop in props])
     table = pd.DataFrame({
         "label_id": label_ids,
         "n_pixels": sizes,

test/test_segmentation/test_postprocessing.py

@@ -0,0 +1,49 @@
+import os
+import tempfile
+import unittest
+
+from elf.io import open_file
+from skimage.data import binary_blobs
+from skimage.measure import label
+
+
+class TestPostprocessing(unittest.TestCase):
+    def _create_example_seg(self, tmp_dir):
+        seg = binary_blobs(256, n_dim=3, volume_fraction=0.2)
+        seg = label(seg)
+        return seg
+
+    def _test_postprocessing(self, spatial_statistics, threshold, **spatial_statistics_kwargs):
+        from flamingo_tools.segmentation.postprocessing import filter_segmentation
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            example_seg = self._create_example_seg(tmp_dir)
+            output_path = os.path.join(tmp_dir, "test-output.zarr")
+            output_key = "seg-filtered"
+            filter_segmentation(
+                example_seg, output_path, spatial_statistics, threshold,
+                output_key=output_key, **spatial_statistics_kwargs
+            )
+            self.assertTrue(os.path.exists(output_path))
+            with open_file(output_path, "r") as f:
+                filtered_seg = f[output_key][:]
+            self.assertEqual(filtered_seg.shape, example_seg.shape)
+
+    def test_nearest_neighbor_distance(self):
+        from flamingo_tools.segmentation.postprocessing import nearest_neighbor_distance
+
+        self._test_postprocessing(nearest_neighbor_distance, threshold=5)
+
+    def test_local_ripleys_k(self):
+        from flamingo_tools.segmentation.postprocessing import local_ripleys_k
+
+        self._test_postprocessing(local_ripleys_k, threshold=0.5)
+
+    def test_neighbors_in_radius(self):
+        from flamingo_tools.segmentation.postprocessing import neighbors_in_radius
+
+        self._test_postprocessing(neighbors_in_radius, threshold=5)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/test_segmentation/test_unet_prediction.py

@@ -1,5 +1,4 @@
 import os
-import sys
 import tempfile
 import unittest
 
@@ -9,8 +8,6 @@
 import z5py
 from torch_em.model import UNet3d
 
-sys.path.append("../..")
-
 
 class TestUnetPrediction(unittest.TestCase):
     shape = (64, 128, 128)