Update src/pixel3dmm/tracking/tracker.py

src/pixel3dmm/tracking/tracker.py

@@ -1714,13 +1714,25 @@ class Tracker(object):
             batches = {k: torch.cat([x[k] for x in batches], dim=0) for k in batch.keys()}
             selected_frames = torch.from_numpy(np.array(selected_frames)).long().cuda()
 
-            result_rendering = self.render_and_save(batches, visualizations=[[View.GROUND_TRUTH, View.COLOR_OVERLAY, View.LANDMARKS, View.SHAPE]],
+            result_rendering = self.render_and_save(batches, visualizations=[[View.SHAPE]],
                                                     frame_dst='/joint_initialization', outer_iter=0, timestep=timestep, is_final=True, selected_frames=selected_frames)
             video_frames.append(np.array(result_rendering))
             self.frame += 1
 
-        mediapy.write_video(f'{self.save_folder}/{self.actor_name}/result.mp4', images=video_frames, crf=15, fps=25)
+        out_dir = f"{self.save_folder}/{self.config.video_name}/frames"
+        os.makedirs(out_dir, exist_ok=True)
 
+        for i, frame in enumerate(video_frames):
+            # If float in [0,1], convert:
+            if frame.dtype != np.uint8:
+                frame_uint8 = (frame * 255).astype(np.uint8)
+            else:
+                frame_uint8 = frame
+            # OpenCV expects BGR ordering:
+            bgr = cv2.cvtColor(frame_uint8, cv2.COLOR_RGB2BGR)
+            cv2.imwrite(os.path.join(out_dir, f"{i:05d}.jpg"), bgr)
+
+        print(f"✅ Saved {len(video_frames)} frames to `{out_dir}`")
 
         # Optionally delete all preoprocessing artifacts, once tracking is done (only keep cropped images)
         if self.config.delete_preprocessing: