bug fixed

pages/3_Training the Model.py

@@ -1,5 +1,6 @@
 import streamlit as st
 from PIL import Image
+import time
 from utils.levels import complete_level, render_page, initialize_level
 from utils.login import get_login, initialize_login
 from utils.database import get_database
@@ -64,6 +65,7 @@ def step3_page():
         if len(images) > 0:
             database = get_database(PKL_PATH)
             for i in range(100):
+                time.sleep(0.1)
                 my_bar.progress(i, text="Training....")
             my_bar.progress(100, text="Successfully Trained!")
             st.success("Model trained successfully!")
@@ -85,7 +87,7 @@ def step3_page():
                                    'encoding': my_face_encoding}
                     with open(PKL_PATH, 'wb') as f:
                         pkl.dump(database, f)
-
+                time.sleep(0.5)
                 my_bar.progress(int((i + 1) / len(images) * 100), text="Generating face encodings...")
             my_bar.progress(100, text="Successfully encoded all the known faces!")
             st.success("Face encoding completed successfully!")

pages/4_Trying It Out.py

@@ -32,9 +32,10 @@ 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 = 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.")
+    tolerance = 0.14
+    # 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.")
 
     if input_type == "Image upload":
         st.title("Face Recognition App")

utils/inference.py

@@ -17,7 +17,7 @@ def recognize(image,tolerance):
     face_encodings = face_recognition.face_encodings(image,face_locations)
     for (top,right,bottom,left), face_encoding in zip(face_locations,face_encodings):
         matches = face_recognition.compare_faces(known_encoding,face_encoding,tolerance=tolerance)
-        distance = face_recognition.face_distance(known_encoding,face_encoding)
+        # distance = face_recognition.face_distance(known_encoding,face_encoding)
         name = 'Unknown'
         face_id = 'Unknown'
         for i in range(len(matches)):
@@ -25,8 +25,8 @@ def recognize(image,tolerance):
                 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)
+                # 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
         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)