Merge branch 'evaluation' into 'dev'

Debugged version of evaluation with scipy ndimage

See merge request !43

Author: wyli

niftynet/evaluation/compute_ROI_statistics.py

@@ -68,19 +68,19 @@ def run(param, csv_dict):
             elif len(np.unique(seg_d)) == 2:
                 type_str = "Binary"
                 seg_d = MorphologyOps(seg_d, 24).foreground_component()
-                threshold_steps = np.arange(1, np.max(seg_d))
+                threshold_steps = np.arange(1, seg_d[1])
             else:
                 pass
 
             for n, i in enumerate(threshold_steps):
                 print('{} of {} thresholding steps'.format(
                     n, len(threshold_steps)))
                 if type_str == "Labels" or type_str == "Binary":
-                    seg_d_binary = (seg_d == i)
+                    seg_d_binary = (seg_d[0] == i)
                 else:
-                    seg_d_binary = np.copy(seg_d)
-                    seg_d_binary[seg_d < i] = 0  # threshold prob.
-                if np.count_nonzero(seg_d) == 0:
+                    seg_d_binary = np.copy(seg_d[1])
+                    seg_d_binary[seg_d[0] < i] = 0  # threshold prob.
+                if np.count_nonzero(seg_d_binary) == 0:
                     print("Empty foreground in thresholded image")
                     continue
                 roi_stats = RegionProperties(seg_d_binary, img, MEASURES)

niftynet/evaluation/pairwise_measures.py

@@ -196,10 +196,10 @@ def _connected_components(self):
 
     def connected_elements(self):
         blobs_ref, blobs_seg, init = self._connected_components()
-        list_blobs_ref = np.unique(blobs_ref[blobs_ref > 0])
-        list_blobs_seg = np.unique(blobs_seg[blobs_seg > 0])
-        mul_blobs_ref = np.multiply(blobs_ref, init)
-        mul_blobs_seg = np.multiply(blobs_seg, init)
+        list_blobs_ref = range(1, blobs_ref[1])
+        list_blobs_seg = range(1, blobs_seg[1])
+        mul_blobs_ref = np.multiply(blobs_ref[0], init)
+        mul_blobs_seg = np.multiply(blobs_seg[0], init)
         list_TP_ref = np.unique(mul_blobs_ref[mul_blobs_ref > 0])
         list_TP_seg = np.unique(mul_blobs_seg[mul_blobs_seg > 0])
 
@@ -210,27 +210,27 @@ def connected_elements(self):
     @CacheFunctionOutput
     def connected_errormaps(self):
         blobs_ref, blobs_seg, init = self._connected_components()
-        list_blobs_ref = np.unique(blobs_ref[blobs_ref > 0])
-        list_blobs_seg = np.unique(blobs_seg[blobs_seg > 0])
-        mul_blobs_ref = np.multiply(blobs_ref, init)
-        mul_blobs_seg = np.multiply(blobs_seg, init)
+        list_blobs_ref = range(1, blobs_ref[1])
+        list_blobs_seg = range(1, blobs_seg[1])
+        mul_blobs_ref = np.multiply(blobs_ref[0], init)
+        mul_blobs_seg = np.multiply(blobs_seg[0], init)
         list_TP_ref = np.unique(mul_blobs_ref[mul_blobs_ref > 0])
         list_TP_seg = np.unique(mul_blobs_seg[mul_blobs_seg > 0])
 
         list_FN = [x for x in list_blobs_ref if x not in list_TP_ref]
         list_FP = [x for x in list_blobs_seg if x not in list_TP_seg]
         # print(np.max(blobs_ref),np.max(blobs_seg))
-        tpc_map = np.zeros_like(blobs_ref)
-        fpc_map = np.zeros_like(blobs_ref)
-        fnc_map = np.zeros_like(blobs_ref)
+        tpc_map = np.zeros_like(blobs_ref[0])
+        fpc_map = np.zeros_like(blobs_ref[0])
+        fnc_map = np.zeros_like(blobs_ref[0])
         for i in list_TP_ref:
-            tpc_map[blobs_ref == i] = 1
+            tpc_map[blobs_ref[0] == i] = 1
         for i in list_TP_seg:
-            tpc_map[blobs_seg == i] = 1
+            tpc_map[blobs_seg[0] == i] = 1
         for i in list_FN:
-            fnc_map[blobs_ref == i] = 1
+            fnc_map[blobs_ref[0] == i] = 1
         for i in list_FP:
-            fpc_map[blobs_seg == i] = 1
+            fpc_map[blobs_seg[0] == i] = 1
         return tpc_map, fnc_map, fpc_map
 
     def outline_error(self):

niftynet/utilities/misc_common.py

@@ -109,7 +109,7 @@ def border_map(self):
         return border
 
     def foreground_component(self):
-        return ndimage.label(self.binary_map, background=0)
+        return ndimage.label(self.binary_map)
 
 
 class CacheFunctionOutput(object):