ssf

pages/4_Trying It Out.py

@@ -32,7 +32,7 @@ def step4_page():
     st.info("Select your input type to analyze!")
     input_type = st.radio("Select the Input Type", ["Image upload", "Camera"])
     # Put slide to adjust tolerance
-    tolerance = 0.14
+    tolerance = 0.2
     # tolerance = st.slider("Tolerance", 0.0, 1.0, 0.15, 0.01)
     # st.info(
     #     "Tolerance is the threshold for face recognition. The lower the tolerance, the more strict the face recognition. The higher the tolerance, the more loose the face recognition.")

utils/inference.py

@@ -16,23 +16,20 @@ def recognize(image,tolerance):
     face_locations = face_recognition.face_locations(image)
     face_encodings = face_recognition.face_encodings(image,face_locations)
     for (top,right,bottom,left), face_encoding in zip(face_locations,face_encodings):
-        st.write("face_encoding")
-        st.write(face_encoding.shape)
-        st.write("known_encoding")
-        st.write(len(known_encoding))
-        st.write(known_encoding[0].shape)
         matches = face_recognition.compare_faces(known_encoding,face_encoding,tolerance=tolerance)
-        # distance = face_recognition.face_distance(known_encoding,face_encoding)
+        distances = face_recognition.face_distance(known_encoding,face_encoding)
         name = 'Unknown'
         face_id = 'Unknown'
+        distance = 100000
         for i in range(len(matches)):
             if matches[i].all():
                 match_index = i
-                name = database[match_index]['name']
-                face_id = database[match_index]['face_id'].split("_")[1]
-                # distance = round(np.sum(distance[match_index]),2)
-                # cv2.putText(image,str(distance),(left,top-30),cv2.FONT_HERSHEY_SIMPLEX,0.75,(0,255,0),2)
-                break
+                temp_dist = round(np.sum(distances[match_index]),2)
+                if temp_dist < distance:
+                    distance = temp_dist
+                    name = database[match_index]['name']
+                    face_id = database[match_index]['face_id'].split("_")[1]
+                    # cv2.putText(image,str(distance),(left,top-30),cv2.FONT_HERSHEY_SIMPLEX,0.75,(0,255,0),2)
         cv2.rectangle(image,(left,top),(right,bottom),(0,255,0),2)
         cv2.putText(image,name,(left,top-10),cv2.FONT_HERSHEY_SIMPLEX,0.75,(0,255,0),2)
     return image, name, face_id