face similarity

app/Hackathon_setup/face_recognition.py

@@ -94,15 +94,14 @@ def get_similarity(img1, img2):
     print('face1.type', type(face1))
 
     output1,output2 = feature_net(face1,face2)
-    normalized_face1 = F.normalize(output1, dim=1)
-    normalized_face2 = F.normalize(output2, dim=1)
-    euclidean_distance = F.pairwise_distance(normalized_face1, normalized_face2)
+    #normalized_face1 = F.normalize(output1, dim=1)
+    #normalized_face2 = F.normalize(output2, dim=1)
+    #euclidean_distance = F.pairwise_distance(normalized_face1, normalized_face2)
+    euclidean_distance = F.pairwise_distance(face1, face2)
     #pairwise - more distance means less similarity
     #cosine similarity - more means more similarity btwn 2 arrays
     # Use euclidean similarity to measure the similarity between given two images
     euc_similarity = euclidean_distance.item()
-    # print('output1:::',output1)
-    # print('output1[0]:::',output1[0])
     # cos_similarity1 = torch.nn.functional.cosine_similarity(output1, output2)
     # print('cos_similarity1',cos_similarity1)
     # cos_similarity = cos_similarity1.item()