sdsad

pages/4_Face Recognition.py

@@ -30,7 +30,6 @@ def step4_page():
     )
     face_encodings_dir = os.path.join(".sessions", get_login()["username"], "face_encodings")
     face_encodings = os.listdir(face_encodings_dir)
-    st.write(face_encodings)
     known_face_encodings = []
     known_face_names = []
     if len(face_encodings) > 0:
@@ -52,33 +51,33 @@ def step4_page():
         face_locations = face_recognition.face_locations(image)
         face_encodings = face_recognition.face_encodings(image, face_locations)
 
+        st.image(image)
         # Loop through each face in this image
         cols = st.columns(len(face_encodings))
         i = 0
-        st.image(image)
-        st.info("Select the tolerance level you want for your model! (How much distance between faces to consider it a match. "
-                "Lower is more strict. 0.6 is typical best performance.)")
-        tolerance = st.slider('Select tolerance level', 0.0, 1.0, 0.3, 0.1)
-        if tolerance:
-            for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
-                # See if the face is a match for the known face(s)
-                # matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+        # st.info("Select the tolerance level you want for your model! (How much distance between faces to consider it a match. "
+        #         "Lower is more strict. 0.6 is typical best performance.)")
+        # tolerance = st.slider('Select tolerance level', 0.0, 1.0, 0.3, 0.1)
+        # if tolerance:
+        for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+            # See if the face is a match for the known face(s)
+            # matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
 
-                name = "Unknown"
-                # If a match was found in known_face_encodings, just use the first one.
-                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
+            name = "Unknown"
+            # If a match was found in known_face_encodings, just use the first one.
+            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
 
-                # Calculate the row sums
-                row_sums = np.sum(face_distances, axis=1)
-                best_match_index = np.argmin(row_sums)
-                if best_match_index is not None:
-                    name = known_face_names[best_match_index]
+            # Calculate the row sums
+            row_sums = np.sum(face_distances, axis=1)
+            best_match_index = np.argmin(row_sums)
+            if best_match_index is not None:
+                name = known_face_names[best_match_index]
 
-                face_image = image[top:bottom, left:right]
-                pil_image = Image.fromarray(face_image)
-                cols[i].image(pil_image, use_column_width=True)
-                cols[i].write("Person name: " +name)
-                i+=1
+            face_image = image[top:bottom, left:right]
+            pil_image = Image.fromarray(face_image)
+            cols[i].image(pil_image, use_column_width=True)
+            cols[i].write("Person name: " +name)
+            i+=1
 
     st.info("Click on the button below to complete this level!")
     if st.button("Complete Level"):