added 3D points to test

nerfstudio/data/dataparsers/nerfstudio_dataparser.py

@@ -321,12 +321,12 @@ def _generate_dataparser_outputs(self, split="train"):
 
         # _generate_dataparser_outputs might be called more than once so we check if we already loaded the point cloud
         try:
-            self.loaded_points
+            self.prompted_user
         except AttributeError:
-            self.loaded_points = False
+            self.prompted_user = False
 
         # Load 3D points
-        if self.config.load_3D_points and not self.loaded_points:
+        if self.config.load_3D_points:
             colmap_path = self.config.data / "colmap/sparse/0"
 
             if "ply_file_path" in meta:
@@ -336,9 +336,10 @@ def _generate_dataparser_outputs(self, split="train"):
                 from rich.prompt import Confirm
 
                 # check if user wants to make a point cloud from colmap points
-                self.create_pc = Confirm.ask(
-                    "load_3D_points is true, but the dataset was processed with an outdated ns-process-data that didn't convert colmap points to .ply! Update the colmap dataset automatically?"
-                )
+                if not self.prompted_user:
+                    self.create_pc = Confirm.ask(
+                        "load_3D_points is true, but the dataset was processed with an outdated ns-process-data that didn't convert colmap points to .ply! Update the colmap dataset automatically?"
+                    )
 
                 if self.create_pc:
                     import json
@@ -358,9 +359,10 @@ def _generate_dataparser_outputs(self, split="train"):
                 else:
                     ply_file_path = None
             else:
-                CONSOLE.print(
-                    "[bold yellow]Warning: load_3D_points set to true but no point cloud found. gaussian-splatting models will use random point cloud initialization."
-                )
+                if not self.prompted_user:
+                    CONSOLE.print(
+                        "[bold yellow]Warning: load_3D_points set to true but no point cloud found. gaussian-splatting models will use random point cloud initialization."
+                    )
                 ply_file_path = None
 
             if ply_file_path:

nerfstudio/process_data/colmap_utils.py

@@ -392,6 +392,7 @@ def colmap_to_json(
     camera_mask_path: Optional[Path] = None,
     image_id_to_depth_path: Optional[Dict[int, Path]] = None,
     image_rename_map: Optional[Dict[str, str]] = None,
+    ply_filename="sparse_pc.ply",
 ) -> int:
     """Converts COLMAP's cameras.bin and images.bin to a JSON file.
 
@@ -462,8 +463,8 @@ def colmap_to_json(
     out["applied_transform"] = applied_transform.tolist()
 
     # create ply from colmap
-    create_ply_from_colmap(recon_dir, output_dir)
-    out["ply_file_path"] = "sparse_pc.ply"
+    assert ply_filename.endswith(".ply"), f"ply_filename: {ply_filename} does not end with '.ply'"
+    out["ply_file_path"] = ply_filename
 
     with open(output_dir / "transforms.json", "w", encoding="utf-8") as f:
         json.dump(out, f, indent=4)
@@ -645,10 +646,11 @@ def get_matching_summary(num_initial_frames: int, num_matched_frames: int) -> st
     return f"[bold green]COLMAP found poses for {num_matched_frames / num_initial_frames * 100:.2f}% of the images."
 
 
-def create_ply_from_colmap(recon_dir, output_dir):
+def create_ply_from_colmap(filename: str, recon_dir: Path, output_dir: Path):
     """Writes a ply file from colmap.
 
     Args:
+        filename: file name for .ply
         recon_dir: Directory to grab colmap points
         output_dir: Directory to output .ply
     """
@@ -665,7 +667,7 @@ def create_ply_from_colmap(recon_dir, output_dir):
     points3D_rgb = torch.from_numpy(np.array([p.rgb for p in colmap_points.values()], dtype=np.uint8))
 
     # write ply
-    with open(output_dir / "sparse_pc.ply", "w") as f:
+    with open(output_dir / filename, "w") as f:
         # Header
         f.write("ply\n")
         f.write("format ascii 1.0\n")

tests/process_data/test_process_images.py

@@ -15,6 +15,7 @@
     qvec2rotmat,
     write_cameras_binary,
     write_images_binary,
+    write_points3D_binary,
 )
 from nerfstudio.process_data.images_to_nerfstudio_dataset import ImagesToNerfstudioDataset
 
@@ -50,6 +51,19 @@ def test_process_images_skip_colmap(tmp_path: Path):
         {1: Camera(1, "OPENCV", width, height, [110, 110, 50, 75, 0, 0, 0, 0, 0, 0])},
         sparse_path / "cameras.bin",
     )
+    write_points3D_binary(
+        {
+            1: Point3D(
+                id=1,
+                xyz=np.array(0, 0, 0),
+                rgb=np.array(0, 0, 0),
+                error=np.array(0),
+                image_ids=np.array(1),
+                point2D_idxs=np.array([]),
+            ),
+        },
+        sparse_path / "points3D.bin",
+    )
     frames = {}
     num_frames = 10
     qvecs = random_quaternion(num_frames)