cpu conversion fixed

src/pixel3dmm/tracking/tracker.py

@@ -1280,37 +1280,25 @@ class Tracker(object):
                                      )
             # if they asked *only* for the pure shape mask:
             if visualizations == [[View.SHAPE]]:
-                # ops['normal_images'] is [1,3,H,W] in world‐space ∈ [-1,1]
+                # build your normal‐map preview as before
                 normals = ops['normal_images'][0].cpu().numpy()     # [3,H,W]
-                # remap to [0,1]
-                normals = (normals + 1.0) / 2.0
-                # H×W×3
-                normals = np.transpose(normals, (1, 2, 0))
-                # scale to uint8
+                normals = (normals + 1.0) / 2.0                      # → [0,1]
+                normals = np.transpose(normals, (1,2,0))            # H×W×3
                 arr = (normals * 255).clip(0,255).astype(np.uint8)
 
-                if visualizations == [[View.SHAPE]]:
-                    # build your normal‐map preview as before
-                    normals = ops['normal_images'][0].cpu().numpy()     # [3,H,W]
-                    normals = (normals + 1.0) / 2.0                      # → [0,1]
-                    normals = np.transpose(normals, (1,2,0))            # H×W×3
-                    arr = (normals * 255).clip(0,255).astype(np.uint8)
-
-                    # --- new: export the *non*-canonical mesh ---
-                    os.makedirs(self.mesh_folder, exist_ok=True)
-                    frame_id = str(self.frame).zfill(5)
-                    ply_path = os.path.join(self.mesh_folder, f"{frame_id}.ply")
+                # --- export the posed mesh, using the correct face indices field ---
+                os.makedirs(self.mesh_folder, exist_ok=True)
+                frame_id = str(self.frame).zfill(5)
+                ply_path = os.path.join(self.mesh_folder, f"{frame_id}.ply")
 
-                    # faces is the same face list you loaded in setup_renderer
-                    faces_np = self.faces.cpu().numpy()  
-                    # vertices is your posed mesh from above:
-                    #    vertices = …  # torch.Tensor of shape (1, V, 3)
-                    verts_np = vertices[0].detach().cpu().numpy()
+                # pull out the face index tensor
+                faces_np = self.faces.verts_idx.cpu().numpy()
+                # `vertices` is your posed mesh: shape (1, V, 3)
+                verts_np  = vertices[0].detach().cpu().numpy()
 
-                    trimesh.Trimesh(faces=faces_np, vertices=verts_np, process=False) \
-                        .export(ply_path)
+                trimesh.Trimesh(faces=faces_np, vertices=verts_np, process=False).export(ply_path)
 
-                return arr
+            return arr
 
             mask = (self.parse_mask(ops, batch, visualization=True) > 0).float()
             grabbed_depth = ops['actual_rendered_depth'][0, 0,