Update app.py

app.py

@@ -47,17 +47,22 @@ def generate_journal_with_images(video_path, frame_interval=30,confidence_thresh
     journal_entries = []
     image_paths = []
     frame_count = 0
-    last_processed_frame = None
     output_folder = "detected_frames"
     os.makedirs(output_folder, exist_ok=True)  # Create folder to store images
+
+    last_processed_second = -1  # Keep track of the last processed second
     
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
         
-        # Process every Nth frame or if the current frame is different from the last processed frame
-        if frame_count % frame_interval == 0 or (last_processed_frame is not None and is_frame_different(last_processed_frame, frame)):
+        # Get the current timestamp in the video
+        current_time = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000  # Convert ms to seconds
+        current_second = int(current_time)  # Round down to the nearest second
+
+        # Process only one frame per second
+        if current_second > last_processed_second:
             frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
             
             # Make predictions using YOLOv10 on the current frame
@@ -80,23 +85,20 @@ def generate_journal_with_images(video_path, frame_interval=30,confidence_thresh
                 cv2.imwrite(frame_filename, annotated_frame[:, :, ::-1])  # Convert back to BGR for saving
                 image_paths.append(frame_filename)
                 
-                # Get current timestamp in the video
-                timestamp = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000  # Convert ms to seconds
-                
                 # Categorize the detected objects into activities
                 activity_summary = categorize_activity(detected_objects)
                 
                 # Store the activities with their timestamp
                 for activity, objects in activity_summary.items():
-                    journal_entries.append(f"At {timestamp:.2f} seconds: {', '.join(objects[0])}")
+                    journal_entries.append(f"At {current_time:.2f} seconds: {', '.join(objects[0])}")
             
-            last_processed_frame = frame  # Update the last processed frame
+            last_processed_second = current_second  # Update the last processed second
         
         frame_count += 1
     
     cap.release()
     
-    return journal_entries, image_paths 
+    return journal_entries, image_paths  
 
 
 def display_journal_with_images(video):