Add options for exporting mini_npy and npy.

src/depth_anything_3/api.py

@@ -350,13 +350,18 @@ def _align_to_input_extrinsics_intrinsics(
         if extrinsics is None:
             return prediction
         prediction.intrinsics = intrinsics.numpy()
-        _, _, scale, aligned_extrinsics = align_poses_umeyama(
-            prediction.extrinsics,
-            extrinsics.numpy(),
-            ransac=len(extrinsics) >= ransac_view_thresh,
-            return_aligned=True,
-            random_state=42,
-        )
+        try:
+            _, _, scale, aligned_extrinsics = align_poses_umeyama(
+                prediction.extrinsics,
+                extrinsics.numpy(),
+                ransac=len(extrinsics) >= ransac_view_thresh,
+                return_aligned=True,
+                random_state=42,
+            )
+        except Exception as e:
+            logger.error(f"Error aligning poses: {e}, using original extrinsics")
+            aligned_extrinsics = extrinsics.numpy()
+            scale = 1.0
         if align_to_input_ext_scale:
             prediction.extrinsics = extrinsics[..., :3, :].numpy()
             prediction.depth /= scale

src/depth_anything_3/utils/export/__init__.py

@@ -20,6 +20,7 @@
 from .feat_vis import export_to_feat_vis
 from .glb import export_to_glb
 from .npz import export_to_mini_npz, export_to_npz
+from .npy import export_to_mini_npy, export_to_npy
 
 
 def export(
@@ -40,6 +41,10 @@ def export(
         export_to_mini_npz(prediction, export_dir)
     elif export_format == "npz":
         export_to_npz(prediction, export_dir)
+    elif export_format == "mini_npy":
+        export_to_mini_npy(prediction, export_dir)
+    elif export_format == "npy":
+        export_to_npy(prediction, export_dir)
     elif export_format == "feat_vis":
         export_to_feat_vis(prediction, export_dir, **kwargs.get(export_format, {}))
     elif export_format == "depth_vis":

src/depth_anything_3/utils/export/npy.py

@@ -0,0 +1,61 @@
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import numpy as np
+
+from depth_anything_3.specs import Prediction
+from depth_anything_3.utils.parallel_utils import async_call
+
+
+@async_call
+def export_to_mini_npy(
+    prediction: Prediction,
+    export_dir: str,
+):
+    output_dir = os.path.join(export_dir, "exports", "mini_npy")
+    os.makedirs(output_dir, exist_ok=True)
+
+    np.save(os.path.join(output_dir, "depth.npy"), np.round(prediction.depth, 8))
+    if prediction.conf is not None:
+        np.save(os.path.join(output_dir, "conf.npy"), np.round(prediction.conf, 2))
+    if prediction.extrinsics is not None:
+        np.save(os.path.join(output_dir, "extrinsics.npy"), prediction.extrinsics)
+    if prediction.intrinsics is not None:
+        np.save(os.path.join(output_dir, "intrinsics.npy"), prediction.intrinsics)
+
+
+@async_call
+def export_to_npy(
+    prediction: Prediction,
+    export_dir: str,
+):
+    output_dir = os.path.join(export_dir, "exports", "npy")
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Use prediction.processed_images, which is already processed image data
+    if prediction.processed_images is None:
+        raise ValueError("prediction.processed_images is required but not available")
+
+    image = prediction.processed_images  # (N,H,W,3) uint8
+
+    np.save(os.path.join(output_dir, "image.npy"), image)
+    np.save(os.path.join(output_dir, "depth.npy"), np.round(prediction.depth, 8))
+
+    if prediction.conf is not None:
+        np.save(os.path.join(output_dir, "conf.npy"), np.round(prediction.conf, 2))
+    if prediction.extrinsics is not None:
+        np.save(os.path.join(output_dir, "extrinsics.npy"), prediction.extrinsics)
+    if prediction.intrinsics is not None:
+        np.save(os.path.join(output_dir, "intrinsics.npy"), prediction.intrinsics)