code

detect_scene.py

@@ -56,7 +56,7 @@ def load_original_frames(filepath):
         pass
     return frames
 
-def save_timeline_jpg(frames, scene_change_indices, filename, interval=5, roi_radius=2, title=None):
+def save_timeline_jpg(frames, scene_change_indices, filename, interval=5, roi_radius=2, title=None, single_frame_pred=None):
     """
     Save a timeline JPG with thumbnails every `interval` frames and every frame near scene changes.
     Scene change regions are highlighted. Each thumbnail is annotated with its frame index.
@@ -97,14 +97,6 @@ def save_timeline_jpg(frames, scene_change_indices, filename, interval=5, roi_ra
     first_frames = set(idx + 1 for idx in scene_change_indices if idx + 1 < len(frames))
 
     # Draw thumbnails and annotate
-    # Optionally, get single_frame_pred if passed as a kwarg
-    single_frame_pred = None
-    import inspect
-    if 'single_frame_pred' in inspect.signature(save_timeline_jpg).parameters:
-        single_frame_pred = locals().get('single_frame_pred', None)
-
-    # But better: pass single_frame_pred as an argument (see below for main loop update)
-
     for i, fidx in enumerate(frames_to_render):
         thumb = frames[fidx].resize((32, 32))
         imagebox = OffsetImage(np.array(thumb), zoom=0.7)
@@ -126,8 +118,11 @@ def save_timeline_jpg(frames, scene_change_indices, filename, interval=5, roi_ra
         # Draw frame index
         ax.text(x_positions[i], 0.32, str(fidx), ha='center', va='center', fontsize=9, color='black', bbox=dict(facecolor='white', edgecolor='none', alpha=0.8, boxstyle='round,pad=0.2'))
         # Draw prediction value below frame index
-        if 'single_frame_pred' in locals() and single_frame_pred is not None:
+        if single_frame_pred is not None:
             pred_val = single_frame_pred[fidx]
+            # Ensure pred_val is a scalar float for formatting
+            if isinstance(pred_val, np.ndarray):
+                pred_val = float(pred_val.squeeze())
             ax.text(x_positions[i], 0.18, f"{pred_val:.2f}", ha='center', va='center', fontsize=8, color='blue', bbox=dict(facecolor='white', edgecolor='none', alpha=0.7, boxstyle='round,pad=0.2'))
 
     if title:
@@ -193,7 +188,8 @@ if __name__ == "__main__":
             filename=timeline_filename,
             interval=5,
             roi_radius=2,
-            title=f"Timeline: {file.name}"
+            title=f"Timeline: {file.name}",
+            single_frame_pred=result["single_frame_pred"]
         )
         # Save prediction plot with thumbnails
         save_prediction_plot(