Update

.gitignore

@@ -4,4 +4,5 @@ tmp/*
 !tmp/.gitkeep
 *.mp4
 .DS_Store
-diary.csv
+diary.csv
+diary.xlsx

run_opencv.py

@@ -1,5 +1,6 @@
 import json
 from datetime import datetime
+from time import time
 from typing import List, Optional, Tuple
 
 import cv2
@@ -50,11 +51,16 @@ class ActivityModel:
 
         self.load_json()
 
-        self.diary: List[Tuple[str, str, float]] = []  # [time, activity, confidence]
+        self.diary: List[
+            Tuple[str, int, str, float]
+        ] = []  # [time, activity, confidence]
 
     def save_diary(self):
-        df = pd.DataFrame(self.diary, columns=["time", "activity", "confidence"])
+        df = pd.DataFrame(
+            self.diary, columns=["time", "timestamp", "activity", "confidence"]
+        )
         df.to_csv("diary.csv")
+        df.to_excel("diary.xlsx")
 
     def load_json(self):
         if args.id2label is not None:
@@ -93,13 +99,15 @@ class ActivityModel:
             return
         predicted_label = self.model.config.id2label[max_index]
 
-        confidence = logits[0][max_index]
+        confidence = logits[0][max_index].item()
 
         if (self.args.threshold is None) or (
             self.args.threshold is not None and confidence >= self.args.threshold
         ):
             img_container.frame_rate.label = f"{predicted_label}_{confidence:.2f}%"
-            self.diary.append((str(datetime.now()), predicted_label, confidence))
+            self.diary.append(
+                (str(datetime.now()), int(time()), predicted_label, confidence)
+            )
 
         # logits = np.squeeze(logits)
         # logits = logits.squeeze().numpy()