refactor(navigation): render generated CDR paths and goals

.github/workflows/message-codegen.yml

@@ -26,6 +26,8 @@ on:
       - dimos/navigation/spec.py
       - dimos/navigation/local_planner/**
       - dimos/navigation/trajectory_follower/**
+      - dimos/navigation/global_planner/viz.py
+      - dimos/navigation/global_planner/test_viz.py
       - dimos/navigation/test_tf_pose.py
       - dimos/navigation/movement_manager/**
       - dimos/navigation/go2/replanning_a_star/**
@@ -329,3 +331,8 @@ jobs:
           export DIMOS_NATIVE_FOLLOWER_BINARY="$PWD/dimos/navigation/trajectory_follower/fancy/rust/target/debug/trajectory_follower"
           .native-review/bin/python -I -c 'from dimos.navigation.trajectory_follower.fancy import native; import pytest; import sys; assert "site-packages" in str(native.__file__); raise SystemExit(pytest.main(sys.argv[1:]))' --noconftest --import-mode=importlib -o addopts='' -q "$PWD/dimos/navigation/trajectory_follower/fancy/test_generated_native.py"
           MYPYPATH="$PWD/build/message-source/python" .native-review/bin/python -m mypy --follow-imports=silent dimos/navigation/trajectory_follower/fancy/native.py
+      - name: Verify generated navigation path and goal rendering
+        if: runner.os == 'Linux'
+        run: |
+          .native-review/bin/python -I -c 'from dimos.navigation.local_planner import viz as local; from dimos.navigation.global_planner import viz as global_; import pytest; import sys; assert all("site-packages" in str(value.__file__) for value in (local,global_)); raise SystemExit(pytest.main(sys.argv[1:]))' --noconftest --import-mode=importlib -o addopts='' -q "$PWD/dimos/navigation/local_planner/test_viz.py" "$PWD/dimos/navigation/global_planner/test_viz.py"
+          MYPYPATH="$PWD/build/message-source/python" .native-review/bin/python -m mypy --follow-imports=silent dimos/navigation/local_planner/viz.py dimos/navigation/global_planner/viz.py

dimos/models/embedding/clip.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import CLIPModel as HFCLIPModel, CLIPProcessor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/dino.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import Any, overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import AutoImageProcessor, AutoModel
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/siglip.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import SiglipModel as HFSiglipModel, SiglipProcessor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/siglip2.py

@@ -17,12 +17,12 @@
 from functools import cached_property
 from typing import overload
 
-from dimos.messages.sensor_msgs.msg import Image
 from PIL import Image as PILImage
 import torch
 import torch.nn.functional as functional
 from transformers import SiglipModel as HFSiglipModel, SiglipProcessor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import HuggingFaceModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, HuggingFaceEmbeddingModelConfig
 from dimos.msgs.image import image_to_rgb

dimos/models/embedding/test_generated_images.py

@@ -29,20 +29,25 @@
 from dimos.msgs.image import image_from_array, image_view
 
 
-@pytest.mark.parametrize("model_type", [CLIPModel,DINOModel,SigLIPModel,SigLIP2Model])
-def test_generated_embeddings_keep_zero_source_time_and_convert_rgb(mocker,model_type):
-    processor,network=mocker.Mock(),mocker.Mock()
-    features=torch.tensor([[3.0,4.0]])
-    processor.return_value=BatchFeature(data={"pixel_values":torch.zeros(1,3,4,8)})
-    network.get_image_features.return_value=features
-    network.return_value=BaseModelOutputWithPooling(last_hidden_state=features[:,None,:],pooler_output=features)
-    mocker.patch.object(model_type,"_processor",new_callable=PropertyMock,return_value=processor)
-    mocker.patch.object(model_type,"_model",new_callable=PropertyMock,return_value=network)
-    image=image_from_array(np.full((4,8,3),[0,0,255],dtype=np.uint8),encoding="bgr8",
-        header=Header(frame_id="camera",stamp=Time(sec=0,nanosec=0)))
-    model=model_type(device="cpu")
-    result=model.embed(image)
+@pytest.mark.parametrize("model_type", [CLIPModel, DINOModel, SigLIPModel, SigLIP2Model])
+def test_generated_embeddings_keep_zero_source_time_and_convert_rgb(mocker, model_type):
+    processor, network = mocker.Mock(), mocker.Mock()
+    features = torch.tensor([[3.0, 4.0]])
+    processor.return_value = BatchFeature(data={"pixel_values": torch.zeros(1, 3, 4, 8)})
+    network.get_image_features.return_value = features
+    network.return_value = BaseModelOutputWithPooling(
+        last_hidden_state=features[:, None, :], pooler_output=features
+    )
+    mocker.patch.object(model_type, "_processor", new_callable=PropertyMock, return_value=processor)
+    mocker.patch.object(model_type, "_model", new_callable=PropertyMock, return_value=network)
+    image = image_from_array(
+        np.full((4, 8, 3), [0, 0, 255], dtype=np.uint8),
+        encoding="bgr8",
+        header=Header(frame_id="camera", stamp=Time(sec=0, nanosec=0)),
+    )
+    model = model_type(device="cpu")
+    result = model.embed(image)
     assert result.timestamp == 0.0
-    torch.testing.assert_close(result.vector,torch.tensor([0.6,0.8]))
-    assert processor.call_args.kwargs["images"][0].getpixel((0,0)) == (255,0,0)
-    np.testing.assert_array_equal(image_view(image)[0,0],[0,0,255])
+    torch.testing.assert_close(result.vector, torch.tensor([0.6, 0.8]))
+    assert processor.call_args.kwargs["images"][0].getpixel((0, 0)) == (255, 0, 0)
+    np.testing.assert_array_equal(image_view(image)[0, 0], [0, 0, 255])

dimos/models/embedding/treid.py

@@ -19,11 +19,11 @@
 
 from functools import cached_property
 
-from dimos.messages.sensor_msgs.msg import Image
 import torch
 import torch.nn.functional as functional
 from torchreid import utils as torchreid_utils
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import LocalModel
 from dimos.models.embedding.base import Embedding, EmbeddingModel, EmbeddingModelConfig
 from dimos.msgs.image import image_to_bgr

dimos/models/segmentation/edge_tam.py

@@ -20,14 +20,14 @@
 import tempfile
 from typing import TYPE_CHECKING, Any, Protocol, TypedDict
 
-from dimos.messages.sensor_msgs.msg import Image
 from hydra.utils import instantiate
 import numpy as np
 from numpy.typing import NDArray
 from omegaconf import OmegaConf
 from PIL import Image as PILImage
 import torch
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.models.base import default_torch_device
 from dimos.msgs.image import image_to_bgr, image_to_jpeg
 from dimos.perception.detection.detectors.base import Detector

dimos/models/segmentation/test_yoloe.py

@@ -16,11 +16,11 @@
 from typing import Any
 from unittest.mock import MagicMock
 
-from dimos.messages.sensor_msgs.msg import Image
-from dimos.messages.std_msgs.msg import Header
 import numpy as np
 from ultralytics.models.yolo.yoloe import YOLOEVPSegPredictor
 
+from dimos.messages.sensor_msgs.msg import Image
+from dimos.messages.std_msgs.msg import Header
 from dimos.models.segmentation.yoloe import YoloeBoxSegmenter
 from dimos.msgs.image import image_from_array, image_to_bgr
 from dimos.msgs.time import time_from_seconds, to_seconds

dimos/models/segmentation/yoloe.py

@@ -19,11 +19,11 @@
 from dataclasses import replace
 from typing import Protocol
 
-from dimos.messages.sensor_msgs.msg import Image
 import numpy as np
 from numpy.typing import NDArray
 from ultralytics.models.yolo.yoloe import YOLOEVPSegPredictor
 
+from dimos.messages.sensor_msgs.msg import Image
 from dimos.msgs.image import image_to_bgr
 from dimos.perception.detection.detectors.yoloe import Yoloe2DDetector, YoloePromptMode
 from dimos.perception.detection.type.detection2d.bbox import Bbox, Detection2DBBox

dimos/navigation/global_planner/test_viz.py

@@ -19,25 +19,58 @@
 import rerun as rr
 
 from dimos.mapping.ray_tracing.viz import render_map_region
-from dimos.msgs.geometry_msgs.PointStamped import PointStamped
-from dimos.msgs.geometry_msgs.PoseStamped import PoseStamped
-from dimos.msgs.nav_msgs.Path import Path
+from dimos.messages.geometry_msgs.msg import Point, PointStamped, Pose, PoseStamped
+from dimos.messages.nav_msgs.msg import Path
+from dimos.messages.std_msgs.msg import Header
+from dimos.msgs.geometry import quaternion_from_euler
 from dimos.msgs.sensor_msgs.PointCloud2 import PointCloud2
+from dimos.msgs.time import time_from_nanoseconds
 from dimos.navigation.global_planner import viz
 from dimos.navigation.global_planner.mls_planner.viz import render_surface_region
 from dimos.visualization.rerun.bridge import is_keyed_by_seq
 
 
 def test_goal_placeholder_is_not_drawn() -> None:
-    assert viz.render_goal(PointStamped(x=float("nan"), y=0.0, z=0.0)) is None
-    goal = viz.render_goal(PointStamped(x=1.0, y=2.0, z=0.0))
+    assert (
+        viz.render_goal(
+            PointStamped(
+                header=Header(frame_id="odom", stamp=time_from_nanoseconds(0)),
+                point=Point(x=float("nan"), y=0.0, z=0.0),
+            )
+        )
+        is None
+    )
+    goal = viz.render_goal(
+        PointStamped(
+            header=Header(frame_id="odom", stamp=time_from_nanoseconds(0)),
+            point=Point(x=1.0, y=2.0, z=0.0),
+        )
+    )
     assert goal is not None
     assert goal.positions.as_arrow_array().to_pylist() == [[1.0, 2.0, 0.0]]
 
 
 def test_empty_path_keeps_the_last_one_drawn() -> None:
-    assert viz.render_path(Path(poses=[])) is None
-    path = viz.render_path(Path(poses=[PoseStamped(1.0, 0.0, 0.0), PoseStamped(2.0, 0.0, 0.0)]))
+    assert (
+        viz.render_path(
+            Path(header=Header(frame_id="odom", stamp=time_from_nanoseconds(0)), poses=[])
+        )
+        is None
+    )
+    path = viz.render_path(
+        Path(
+            header=Header(frame_id="odom", stamp=time_from_nanoseconds(0)),
+            poses=[
+                PoseStamped(
+                    header=Header(frame_id="odom", stamp=time_from_nanoseconds(0)),
+                    pose=Pose(
+                        position=Point(x=x, y=0, z=0), orientation=quaternion_from_euler(0, 0, 0)
+                    ),
+                )
+                for x in [1.0, 2.0]
+            ],
+        )
+    )
     assert path is not None
     lift = viz.PATH_Z_LIFT
     np.testing.assert_allclose(

dimos/navigation/global_planner/viz.py

@@ -32,8 +32,8 @@
     from numpy.typing import NDArray
     from rerun._baseclasses import Archetype
 
-    from dimos.msgs.geometry_msgs.PointStamped import PointStamped
-    from dimos.msgs.nav_msgs.Path import Path
+    from dimos.messages.geometry_msgs.msg import PointStamped
+    from dimos.messages.nav_msgs.msg import Path
     from dimos.visualization.rerun.bridge import VisualOverride
 
 # rerun is imported inside the renderers: it is heavy and only viewers load it.
@@ -66,7 +66,12 @@ def render_path(msg: Path) -> Archetype | None:
     """The planned route. An empty path means no route, so the last one stays drawn."""
     if len(msg.poses) == 0:
         return None
-    return path_strip(np.array([[p.x, p.y, p.z] for p in msg.poses], dtype=np.float32))
+    return path_strip(
+        np.array(
+            [[p.pose.position.x, p.pose.position.y, p.pose.position.z] for p in msg.poses],
+            dtype=np.float32,
+        )
+    )
 
 
 def goal_point(xyz: tuple[float, float, float]) -> Archetype:
@@ -77,9 +82,9 @@ def goal_point(xyz: tuple[float, float, float]) -> Archetype:
 
 def render_goal(msg: PointStamped) -> Archetype | None:
     """The active goal. NaN is the movement manager's placeholder for none."""
-    if any(math.isnan(v) for v in (msg.x, msg.y, msg.z)):
+    if any(math.isnan(v) for v in (msg.point.x, msg.point.y, msg.point.z)):
         return None
-    return goal_point((msg.x, msg.y, msg.z))
+    return goal_point((msg.point.x, msg.point.y, msg.point.z))
 
 
 def robot_body_box(length: float, width: float, height: float) -> Archetype: