Update app.py

app.py

@@ -17,8 +17,10 @@ def process_video(video):
     frame_height = int(input_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
     fps = input_video.get(cv2.CAP_PROP_FPS)
 
-    # Create an empty list to store processed frames
-    processed_frames = []
+    # Create a VideoWriter object to write processed frames to an output file
+    output_video_path = "processed_output.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for .mp4 format
+    output_video = cv2.VideoWriter(output_video_path, fourcc, fps, (frame_width, frame_height))
 
     while True:
         # Read a frame from the video
@@ -32,22 +34,20 @@ def process_video(video):
         # The results object contains annotations for the frame
         annotated_frame = results[0].plot()  # Plot the frame with bounding boxes
 
-        # Convert the annotated frame to RGB format
-        annotated_frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
-
-        # Append the frame to the list
-        processed_frames.append(annotated_frame_rgb)
+        # Write the annotated frame to the output video
+        output_video.write(annotated_frame)
 
     # Release resources
     input_video.release()
+    output_video.release()
 
-    # Return the processed frames as an output video in Gradio
-    return processed_frames
+    # Return the processed video file path
+    return output_video_path
 
 # Create a Gradio interface for video upload
 iface = gr.Interface(fn=process_video, 
                      inputs=gr.Video(label="Upload Video"),  # Updated line
-                     outputs=gr.Video(),  # This will display the output video directly
+                     outputs=gr.Video(label="Processed Video"),  # This will display the output video directly
                      title="YOLOv8 Object Detection on Video",
                      description="Upload a video for object detection using YOLOv8")