diff --git a/voyante.py b/voyante.py
index da59bf0..4e6d072 100644
--- a/voyante.py
+++ b/voyante.py
@@ -5,25 +5,46 @@ img1_location="data/GetPicture1.jpeg"
 img2_location="data/GetPicture2.jpeg"
 img3_location="data/GetPicture3.jpeg"
 
-img1 = cv2.imread(img1_location,cv2.IMREAD_UNCHANGED)
-img1g = cv2.imread(img1_location,0)
-# img2 = cv2.imread(img2_location,cv2.IMREAD_UNCHANGED)
-# img3 = cv2.imread(img3_location,cv2.IMREAD_UNCHANGED)
+img1g = cv2.imread(img1_location,0) # cv2.IMREAD_UNCHANGED
+img2g = cv2.imread(img2_location,0)
+img3g = cv2.imread(img3_location,0)
+
+# Cropping the image to get only the cup. Could be automated, or simply ask the user to crop himself
+height1, width1 = img1g.shape
+cropimg1 = img1g[0:width1-150, 0:height1]
+
+height2, width2 = img2g.shape
+cropimg2 = img2g[0:width2-150, 0:height2]
+
+height3, width3 = img3g.shape
+cropimg3 = img3g[0:width3-150, 0:height3]
 
 
+# Binarization of the image, to get only black and white (2 colors)
+blur1 = cv2.GaussianBlur(cropimg1,(5,5),0)
+ret1,th1 = cv2.threshold(blur1,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
 
-height, width, channels = img1.shape
-cropimg1 = img1g[0:width-150, 0:height]
+blur2 = cv2.GaussianBlur(cropimg2,(5,5),0)
+ret2,th2 = cv2.threshold(blur2,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
 
+blur3 = cv2.GaussianBlur(cropimg3,(5,5),0)
+ret3,th3 = cv2.threshold(blur3,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
 
-blur = cv2.GaussianBlur(cropimg1,(5,5),0)
-ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
-
+# Now we want to search similarities between pictures.
+res = cv2.matchTemplate(th3, th3, cv2.TM_SQDIFF)
+min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+print max_val
 
 cv2.imshow('imageg',img1g)
+cv2.imshow('imageg2',img2g)
+cv2.imshow('imageg3',img3g)
 cv2.imshow('imagecrop',cropimg1)
-cv2.imshow('imageout2',blur)
+cv2.imshow('imagecrop2',cropimg2)
+cv2.imshow('imagecrop3',cropimg3)
+
 cv2.imshow('imageout3',th3)
+cv2.imshow('imageout2',th2)
+cv2.imshow('imageout1',th1)