Speeds-up the evaluation of vein probabilities

bob/bio/vein/extractor/MaximumCurvature.py

@@ -217,7 +217,7 @@ class MaximumCurvature (Extractor):
 
     '''
 
-    V = numpy.zeros_like(k)
+    V = numpy.zeros(k.shape[:2], dtype='float64')
 
     def _prob_1d(a):
       '''Finds "vein probabilities" in a 1-D signal
@@ -289,17 +289,17 @@ class MaximumCurvature (Extractor):
 
     # Horizontal direction
     for index in range(k.shape[0]):
-      V[index,:,0] += _prob_1d(k[index,:,0])
+      V[index,:] += _prob_1d(k[index,:,0])
 
     # Vertical direction
     for index in range(k.shape[1]):
-      V[:,index,1] += _prob_1d(k[:,index,1])
+      V[:,index] += _prob_1d(k[:,index,1])
 
     # Direction: 45 degrees (\)
     curv = k[:,:,2]
     i,j = numpy.indices(curv.shape)
     for index in range(-curv.shape[0]+1, curv.shape[1]):
-      V[i==(j-index),2] += _prob_1d(curv.diagonal(index))
+      V[i==(j-index)] += _prob_1d(curv.diagonal(index))
 
     # Direction: -45 degrees (/)
     # NOTE: due to the way the access to the diagonals are implemented, in this
@@ -308,7 +308,7 @@ class MaximumCurvature (Extractor):
     curv = numpy.flipud(k[:,:,3]) #required so we get "/" diagonals correctly
     Vud = numpy.flipud(V) #match above inversion
     for index in reversed(range(curv.shape[1]-1, -curv.shape[0], -1)):
-      Vud[i==(j-index),3] += _prob_1d(curv.diagonal(index))
+      Vud[i==(j-index)] += _prob_1d(curv.diagonal(index))
 
     return V
 
@@ -472,36 +472,36 @@ class MaximumCurvature (Extractor):
     finger_image = image[0]
     finger_mask = image[1]
 
-    import time
-    start = time.time()
+    #import time
+    #start = time.time()
 
     kappa = self.detect_valleys(finger_image, finger_mask)
 
     #self._view_four(kappa, "Valley Detectors - $\kappa$")
 
-    print('filtering took %.2f seconds' % (time.time() - start))
-    start = time.time()
+    #print('filtering took %.2f seconds' % (time.time() - start))
+    #start = time.time()
 
     V = self.eval_vein_probabilities(kappa)
 
     #self._view_four(V, "Center Probabilities - $V_i$")
     #self._view_single(V.sum(axis=2), "Accumulated Probabilities - V")
 
-    print('probabilities took %.2f seconds' % (time.time() - start))
-    start = time.time()
+    #print('probabilities took %.2f seconds' % (time.time() - start))
+    #start = time.time()
 
-    Cd = self.connect_centres(V.sum(axis=2))
+    Cd = self.connect_centres(V)
 
     #self._view_four(Cd, "Connected Centers - $C_{di}$")
     #self._view_single(numpy.amax(Cd, axis=2), "Connected Centers - G")
 
-    print('connections took %.2f seconds' % (time.time() - start))
-    start = time.time()
+    #print('connections took %.2f seconds' % (time.time() - start))
+    #start = time.time()
 
     retval = self.binarise(numpy.amax(Cd, axis=2))
 
     #self._view_single(retval, "Final Binarised Image")
 
-    print('binarization took %.2f seconds' % (time.time() - start))
+    #print('binarization took %.2f seconds' % (time.time() - start))
 
     return retval

bob/bio/vein/tests/test.py

@@ -86,8 +86,7 @@ def test_max_curvature():
   MC = MaximumCurvature(3) #value used to create references
 
   kappa = MC.detect_valleys(image, mask)
-  V = MC.eval_vein_probabilities(kappa)
-  Vt = V.sum(axis=2)
+  Vt = MC.eval_vein_probabilities(kappa)
   Cd = MC.connect_centres(Vt)
   G = numpy.amax(Cd, axis=2)
   bina = MC.binarise(G)

matlab/compare.py

@@ -32,8 +32,7 @@ from bob.bio.vein.extractor.MaximumCurvature import MaximumCurvature
 MC = MaximumCurvature(3)
 
 kappa = MC.detect_valleys(image, region) #OK
-V = MC.eval_vein_probabilities(kappa) #OK
-Vt = V.sum(axis=2) #OK
+Vt = MC.eval_vein_probabilities(kappa) #OK
 Cd = MC.connect_centres(Vt) #OK
 G = numpy.amax(Cd, axis=2) #OK
 
@@ -42,9 +41,6 @@ for k in range(4):
   print('Comparing kappa[%d]: %s' % (k,
     numpy.abs(kappa[...,k]-kappa_matlab[...,k]).sum()))
 
-for k in range(4):
-  print('Comparing V[%d]: %s' % (k, numpy.abs(V[...,k]-V_matlab[...,k]).sum()))
-
 print('Comparing Vt: %s' % numpy.abs(Vt-Vt_matlab).sum())
 
 for k in range(4):