code

detect_scene.py

@@ -97,6 +97,14 @@ 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)
@@ -115,7 +123,12 @@ def save_timeline_jpg(frames, scene_change_indices, filename, interval=5, roi_ra
             bboxprops=bboxprops
         )
         ax.add_artist(ab)
-        ax.text(x_positions[i], 0.25, 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 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:
+            pred_val = single_frame_pred[fidx]
+            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:
         ax.text(0, 0.95, title, fontsize=12, ha='left', va='top', color='navy')
@@ -123,24 +136,18 @@ def save_timeline_jpg(frames, scene_change_indices, filename, interval=5, roi_ra
     plt.savefig(filename, dpi=150)
     plt.close(fig)
 
-def load_webp_animation(filepath):
-    from PIL import Image
+def frames_to_video_tensor(frames):
+    """Convert a list of PIL frames to a torch tensor of shape (num_frames, 27, 48, 3) and dtype uint8."""
     import numpy as np
-    im = Image.open(filepath)
-    frames = []
-    try:
-        while True:
-            frame = im.convert("RGB").resize((48, 27), resample=Image.Resampling.BILINEAR)  # resize to 48x27 (W x H)
-            arr = np.array(frame, dtype=np.uint8)
-            frames.append(torch.from_numpy(arr))
-            im.seek(im.tell() + 1)
-    except EOFError:
-        pass
-    video_tensor = torch.stack(frames)  # shape: num_frames x 27 x 48 x 3
-    return video_tensor
-
-def detect_scene_changes(filepath):
-    video_tensor = load_webp_animation(filepath)
+    from PIL import Image
+    processed = []
+    for frame in frames:
+        arr = np.array(frame.resize((48, 27), resample=Image.Resampling.BILINEAR), dtype=np.uint8)
+        processed.append(torch.from_numpy(arr))
+    return torch.stack(processed)
+
+def detect_scene_changes(frames):
+    video_tensor = frames_to_video_tensor(frames)
     video_tensor = video_tensor.unsqueeze(0).to(device)  # shape: 1 x num_frames x H x W x 3
     with torch.no_grad():
         single_frame_pred, all_frame_pred = model(video_tensor)
@@ -155,6 +162,7 @@ def detect_scene_changes(filepath):
         "num_frames": video_tensor.shape[1]
     }
 
+
 if __name__ == "__main__":
     device = get_best_device()
     print(f"Using device: {device}")
@@ -165,15 +173,20 @@ if __name__ == "__main__":
     for file in files:
         match = re.search(r"sample-(\d+)", file.name)
         sample_num = match.group(1) if match else "unknown"
-        result = detect_scene_changes(file)
-        # Save results to JSON
+        original_frames = load_original_frames(file)
+        result = detect_scene_changes(original_frames)
+        # Save results to JSON (include threshold and predictions)
         json_filename = file.parent / f"sample-{sample_num}.json"
         with open(json_filename, "w") as f:
-            json.dump({"scene_change_indices": result["scene_change_indices"]}, f, indent=2)
+            json.dump({
+                "scene_change_indices": result["scene_change_indices"],
+                "threshold": SCENE_CUT_THRESHOLD,
+                "single_frame_pred": result["single_frame_pred"].tolist(),
+                "all_frame_pred": result["all_frame_pred"].tolist()
+            }, f, indent=2)
         # Save timeline JPG
         timeline_filename = file.parent / f"sample-{sample_num}.timeline.jpg"
         plot_filename = file.parent / f"sample-{sample_num}.plot.jpg"
-        original_frames = load_original_frames(file)
         save_timeline_jpg(
             frames=original_frames,
             scene_change_indices=result["scene_change_indices"],