--- a
+++ b/src/LiviaNet/Modules/General/Evaluation.py
@@ -0,0 +1,90 @@
+""" 
+Copyright (c) 2016, Jose Dolz .All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+    1. Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimer.
+    2. Redistributions in binary form must reproduce the above copyright notice,
+       this list of conditions and the following disclaimer in the documentation
+       and/or other materials provided with the distribution.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+    WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+Jose Dolz. Dec, 2016.
+email: jose.dolz.upv@gmail.com
+LIVIA Department, ETS, Montreal.
+"""
+
+import pdb
+import numpy as np
+
+# ----- Dice Score -----
+def computeDice(autoSeg, groundTruth):
+    """ Returns
+    -------
+    DiceArray : floats array
+          
+          Dice coefficient as a float on range [0,1].
+          Maximum similarity = 1
+          No similarity = 0 """
+          
+    n_classes = int( np.max(groundTruth) + 1)
+   
+    DiceArray = []
+    
+    
+    for c_i in xrange(1,n_classes):
+        idx_Auto = np.where(autoSeg.flatten() == c_i)[0]
+        idx_GT   = np.where(groundTruth.flatten() == c_i)[0]
+        
+        autoArray = np.zeros(autoSeg.size,dtype=np.bool)
+        autoArray[idx_Auto] = 1
+        
+        gtArray = np.zeros(autoSeg.size,dtype=np.bool)
+        gtArray[idx_GT] = 1
+        
+        dsc = dice(autoArray, gtArray)
+
+        #dice = np.sum(autoSeg[groundTruth==c_i])*2.0 / (np.sum(autoSeg) + np.sum(groundTruth))
+        DiceArray.append(dsc)
+        
+    return DiceArray
+
+
+def dice(im1, im2):
+    """
+    Computes the Dice coefficient
+    ----------
+    im1 : boolean array
+    im2 : boolean array
+    
+    If they are not boolean, they will be converted.
+    
+    -------
+    It returns the Dice coefficient as a float on the range [0,1].
+        1: Perfect overlapping 
+        0: Not overlapping 
+    """
+    im1 = np.asarray(im1).astype(np.bool)
+    im2 = np.asarray(im2).astype(np.bool)
+
+    if im1.size != im2.size:
+        raise ValueError("Size mismatch between input arrays!!!")
+
+    im_sum = im1.sum() + im2.sum()
+    if im_sum == 0:
+        return 1.0
+
+    # Compute Dice 
+    intersection = np.logical_and(im1, im2)
+
+    return 2. * intersection.sum() / im_sum