Update dino_processor.py

dino_processor.py

@@ -111,21 +111,28 @@ def generate_attention_maps(frame_path, model, device, output_dir, frame_name):
     return overlay_path, attn_path
 
 # --- Main orchestrator function ---
-def process_video_with_dino(video_path, output_dir="dino_output"):
+def process_video_with_dino(video_path):
     """
     Main function to process a video and generate DINO attention maps.
+    Saves all outputs to a permanent, timestamped folder.
     
     Args:
         video_path (str): Path to the input video.
-        output_dir (str): Directory to save all intermediate and final files.
         
     Returns:
         list: A list of tuples, where each tuple contains (overlay_path, attention_map_path).
     """
-    # Clean up previous runs and create output directory
-    if os.path.exists(output_dir):
-        shutil.rmtree(output_dir)
+    # --- MODIFICATION START ---
+    # 1. Define a permanent archive directory.
+    archive_dir = "dino_archive"
+    os.makedirs(archive_dir, exist_ok=True)
+
+    # 2. Create a unique, timestamped directory for this specific run.
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    output_dir = os.path.join(archive_dir, timestamp)
     os.makedirs(output_dir, exist_ok=True)
+    print(f"Results for this run will be saved in: {output_dir}")
+    # --- MODIFICATION END ---
 
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     
@@ -143,7 +150,7 @@ def process_video_with_dino(video_path, output_dir="dino_output"):
     model.load_state_dict(state_dict, strict=True)
     print("DINO weights loaded successfully from torch.hub.")
 
-    # Step 1: Extract frames
+    # Step 1: Extract frames (saves into the new unique output_dir)
     frame_paths = extract_frames(video_path, output_dir)
     if not frame_paths:
         raise ValueError("No frames were extracted from the video.")
@@ -160,7 +167,13 @@ def process_video_with_dino(video_path, output_dir="dino_output"):
     for frame_name in selected_frames:
         frame_path = os.path.join(frames_dir, frame_name)
         frame_name_no_ext = os.path.splitext(frame_name)[0]
+        # The generated images will now be saved inside the unique timestamped folder
         overlay_path, attn_path = generate_attention_maps(frame_path, model, device, output_dir, frame_name_no_ext)
         results.append((overlay_path, attn_path))
         
+    # We no longer need the temporary frames, so we can clean them up to save space.
+    # The final images (overlays and heatmaps) will remain.
+    shutil.rmtree(frames_dir)
+    print(f"Cleaned up temporary frames directory: {frames_dir}")
+        
     return results