Cut cost is computed by Cython code

skimage/graph/_ncut.py

@@ -1,10 +1,11 @@
 import networkx as nx
 import numpy as np
 from scipy import sparse
+from . import _ncut_cy
 
 
-def DW_matrix(graph):
-    """Returns the diagonal and weight matrix of a graph.
+def DW_matrices(graph):
+    """Returns the diagonal and weight matrices of a graph.
 
     Parameters
     ----------
@@ -14,15 +15,15 @@ def DW_matrix(graph):
     Returns
     -------
     D : csc_matrix
-        The diagonal matrix of the graph. `D[i,i]` is the sum of weights of all
-        edges incident on `i`. All other enteries are `0`.
+        The diagonal matrix of the graph. `D[i, i]` is the sum of weights of
+        all edges incident on `i`. All other enteries are `0`.
     W : csc_matrix
-        The weight matrix of the graph. `W[i,j]` is the weight of the edge
+        The weight matrix of the graph. `W[i, j]` is the weight of the edge
         joining `i` to `j`.
     """
     #Cause sparse.eigsh prefers CSC format
     W = nx.to_scipy_sparse_matrix(graph, format='csc')
-    entries = W.sum(0)
+    entries = W.sum(axis=0)
     D = sparse.dia_matrix((entries, 0), shape=W.shape).tocsc()
     return D, W
 
@@ -46,10 +47,9 @@ def ncut_cost(mask, D, W):
         The cost of performing the N-cut.
     """
     mask = np.array(mask)
-    mask_list = [np.logical_xor(mask[i], mask) for i in range(mask.shape[0])]
-    mask_array = np.array(mask_list)
+    cut = _ncut_cy.cut_cost(mask, W)
 
-    cut = float(W[mask_array].sum() / 2.0)
+    # Cause D has elements only along diagonal
     assoc_a = D.data[mask].sum()
     assoc_b = D.data[np.logical_not(mask)].sum()
 

skimage/graph/_ncut_cy.pyx

@@ -38,3 +38,30 @@ def argmin2(cnp.float64_t[:] array):
         i += 1
 
     return i2
+
+
+def cut_cost(mask, W):
+    mask = np.array(mask)
+
+    cdef Py_ssize_t num_rows, num_cols
+    cdef cnp.int32_t row, col
+    cdef cnp.int32_t[:] indices = W.indices
+    cdef cnp.int32_t[:] indptr = W.indptr
+    cdef cnp.float64_t[:] data = W.data
+    cdef cnp.int32_t row_index
+    cdef cnp.double_t cost = 0
+
+    num_rows = W.shape[0]
+    num_cols = W.shape[1]
+
+    col = 0
+    while col < num_cols:
+        row_index = indptr[col]
+        while row_index < indptr[col+1]:
+            row = indices[row_index]
+            if mask[row] != mask[col]:
+                cost += <cnp.double_t>data[row_index]
+            row_index += 1
+        col += 1
+
+    return cost*0.5

skimage/graph/graph_cut.py

@@ -134,7 +134,7 @@ def _ncut_relabel(rag, thresh, num_cuts, map_array):
         The array which maps old labels to new ones. This is modified inside
         the function.
     """
-    d, w = _ncut.DW_matrix(rag)
+    d, w = _ncut.DW_matrices(rag)
     error = False
 
     try: