GUI prop refactor

docs/requirements.txt

@@ -1,5 +1,5 @@
-sphinx==7.2.6
-furo==2023.9.10
+sphinx==8.0.2
+furo==2024.8.6
 docutils==0.20.1
 m2r2==0.3.3.post2
 toml==0.10.2

docs/source/conf.py

@@ -267,6 +267,6 @@ def setup(app):
 napoleon_use_ivar = False
 napoleon_use_param = True
 napoleon_use_rtype = True
-napoleon_preprocess_types = False
+napoleon_preprocess_types = True
 napoleon_type_aliases = None
 napoleon_attr_annotations = True

docs/source/examples/01_image.rst

@@ -20,7 +20,7 @@ NeRFs), or images to render as 3D textures.
         from pathlib import Path
 
         import imageio.v3 as iio
-        import numpy as onp
+        import numpy as np
 
         import viser
 
@@ -47,12 +47,7 @@ NeRFs), or images to render as 3D textures.
             while True:
                 server.scene.add_image(
                     "/noise",
-                    onp.random.randint(
-                        0,
-                        256,
-                        size=(400, 400, 3),
-                        dtype=onp.uint8,
-                    ),
+                    np.random.randint(0, 256, size=(400, 400, 3), dtype=np.uint8),
                     4.0,
                     4.0,
                     format="jpeg",

docs/source/examples/02_gui.rst

@@ -15,7 +15,7 @@ Examples of basic GUI elements that we can create, read from, and write to.
 
         import time
 
-        import numpy as onp
+        import numpy as np
 
         import viser
 
@@ -96,8 +96,8 @@ Examples of basic GUI elements that we can create, read from, and write to.
                 print(file.name, len(file.content), "bytes")
 
             # Pre-generate a point cloud to send.
-            point_positions = onp.random.uniform(low=-1.0, high=1.0, size=(5000, 3))
-            color_coeffs = onp.random.uniform(0.4, 1.0, size=(point_positions.shape[0]))
+            point_positions = np.random.uniform(low=-1.0, high=1.0, size=(5000, 3))
+            color_coeffs = np.random.uniform(0.4, 1.0, size=(point_positions.shape[0]))
 
             counter = 0
             while True:
@@ -111,11 +111,11 @@ Examples of basic GUI elements that we can create, read from, and write to.
                 # connected clients.
                 server.scene.add_point_cloud(
                     "/point_cloud",
-                    points=point_positions * onp.array(gui_vector3.value, dtype=onp.float32),
+                    points=point_positions * np.array(gui_vector3.value, dtype=np.float32),
                     colors=(
-                        onp.tile(gui_rgb.value, point_positions.shape[0]).reshape((-1, 3))
+                        np.tile(gui_rgb.value, point_positions.shape[0]).reshape((-1, 3))
                         * color_coeffs[:, None]
-                    ).astype(onp.uint8),
+                    ).astype(np.uint8),
                     position=gui_vector2.value + (0,),
                     point_shape="circle",
                 )
@@ -131,8 +131,8 @@ Examples of basic GUI elements that we can create, read from, and write to.
 
                 # Update the number of handles in the multi-slider.
                 if gui_slider_positions.value != len(gui_multi_slider.value):
-                    gui_multi_slider.value = onp.linspace(
-                        0, 100, gui_slider_positions.value, dtype=onp.int64
+                    gui_multi_slider.value = np.linspace(
+                        0, 100, gui_slider_positions.value, dtype=np.int64
                     )
 
                 counter += 1

docs/source/examples/03_gui_callbacks.rst

@@ -16,7 +16,7 @@ we get updates.
 
         import time
 
-        import numpy as onp
+        import numpy as np
         from typing_extensions import assert_never
 
         import viser
@@ -86,8 +86,8 @@ we get updates.
                 num_points = gui_num_points.value
                 server.scene.add_point_cloud(
                     "/frame/point_cloud",
-                    points=onp.random.normal(size=(num_points, 3)),
-                    colors=onp.random.randint(0, 256, size=(num_points, 3)),
+                    points=np.random.normal(size=(num_points, 3)),
+                    colors=np.random.randint(0, 256, size=(num_points, 3)),
                 )
 
             # We can (optionally) also attach callbacks!

docs/source/examples/05_camera_commands.rst

@@ -16,7 +16,7 @@ corresponding client automatically.
 
         import time
 
-        import numpy as onp
+        import numpy as np
 
         import viser
         import viser.transforms as tf
@@ -32,12 +32,12 @@ corresponding client automatically.
             When a frame is clicked, we move the camera to the corresponding frame.
             """
 
-            rng = onp.random.default_rng(0)
+            rng = np.random.default_rng(0)
 
             def make_frame(i: int) -> None:
                 # Sample a random orientation + position.
                 wxyz = rng.normal(size=4)
-                wxyz /= onp.linalg.norm(wxyz)
+                wxyz /= np.linalg.norm(wxyz)
                 position = rng.uniform(-3.0, 3.0, size=(3,))
 
                 # Create a coordinate frame and label.
@@ -52,7 +52,7 @@ corresponding client automatically.
                     )
                     T_world_target = tf.SE3.from_rotation_and_translation(
                         tf.SO3(frame.wxyz), frame.position
-                    ) @ tf.SE3.from_translation(onp.array([0.0, 0.0, -0.5]))
+                    ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5]))
 
                     T_current_target = T_world_current.inverse() @ T_world_target
 

docs/source/examples/06_mesh.rst

@@ -16,7 +16,7 @@ Visualize a mesh. To get the demo data, see ``./assets/download_dragon_mesh.sh``
         import time
         from pathlib import Path
 
-        import numpy as onp
+        import numpy as np
         import trimesh
 
         import viser
@@ -35,13 +35,13 @@ Visualize a mesh. To get the demo data, see ``./assets/download_dragon_mesh.sh``
             name="/simple",
             vertices=vertices,
             faces=faces,
-            wxyz=tf.SO3.from_x_radians(onp.pi / 2).wxyz,
+            wxyz=tf.SO3.from_x_radians(np.pi / 2).wxyz,
             position=(0.0, 0.0, 0.0),
         )
         server.scene.add_mesh_trimesh(
             name="/trimesh",
             mesh=mesh.smoothed(),
-            wxyz=tf.SO3.from_x_radians(onp.pi / 2).wxyz,
+            wxyz=tf.SO3.from_x_radians(np.pi / 2).wxyz,
             position=(0.0, 5.0, 0.0),
         )
 

docs/source/examples/07_record3d_visualizer.rst

@@ -16,7 +16,7 @@ Parse and stream record3d captures. To get the demo data, see ``./assets/downloa
         import time
         from pathlib import Path
 
-        import numpy as onp
+        import numpy as np
         import tyro
         from tqdm.auto import tqdm
 
@@ -96,7 +96,7 @@ Parse and stream record3d captures. To get the demo data, see ``./assets/downloa
             # Load in frames.
             server.scene.add_frame(
                 "/frames",
-                wxyz=tf.SO3.exp(onp.array([onp.pi / 2.0, 0.0, 0.0])).wxyz,
+                wxyz=tf.SO3.exp(np.array([np.pi / 2.0, 0.0, 0.0])).wxyz,
                 position=(0, 0, 0),
                 show_axes=False,
             )
@@ -118,7 +118,7 @@ Parse and stream record3d captures. To get the demo data, see ``./assets/downloa
                 )
 
                 # Place the frustum.
-                fov = 2 * onp.arctan2(frame.rgb.shape[0] / 2, frame.K[0, 0])
+                fov = 2 * np.arctan2(frame.rgb.shape[0] / 2, frame.K[0, 0])
                 aspect = frame.rgb.shape[1] / frame.rgb.shape[0]
                 server.scene.add_camera_frustum(
                     f"/frames/t{i}/frustum",

docs/source/examples/08_smpl_visualizer.rst

@@ -23,7 +23,6 @@ See here for download instructions:
         from pathlib import Path
 
         import numpy as np
-        import numpy as onp
         import tyro
 
         import viser
@@ -43,7 +42,7 @@ See here for download instructions:
 
             def __init__(self, model_path: Path) -> None:
                 assert model_path.suffix.lower() == ".npz", "Model should be an .npz file!"
-                body_dict = dict(**onp.load(model_path, allow_pickle=True))
+                body_dict = dict(**np.load(model_path, allow_pickle=True))
 
                 self._J_regressor = body_dict["J_regressor"]
                 self._weights = body_dict["weights"]
@@ -180,7 +179,7 @@ See here for download instructions:
                 @gui_random_shape.on_click
                 def _(_):
                     for beta in gui_betas:
-                        beta.value = onp.random.normal(loc=0.0, scale=1.0)
+                        beta.value = np.random.normal(loc=0.0, scale=1.0)
 
                 gui_betas = []
                 for i in range(num_betas):
@@ -205,8 +204,8 @@ See here for download instructions:
                     for joint in gui_joints:
                         # It's hard to uniformly sample orientations directly in so(3), so we
                         # first sample on S^3 and then convert.
-                        quat = onp.random.normal(loc=0.0, scale=1.0, size=(4,))
-                        quat /= onp.linalg.norm(quat)
+                        quat = np.random.normal(loc=0.0, scale=1.0, size=(4,))
+                        quat /= np.linalg.norm(quat)
                         joint.value = tf.SO3(wxyz=quat).log()
 
                 gui_joints: list[viser.GuiInputHandle[tuple[float, float, float]]] = []

docs/source/examples/09_urdf_visualizer.rst

@@ -25,7 +25,7 @@ and viser. It can also take a path to a local URDF file as input.
         import time
         from typing import Literal
 
-        import numpy as onp
+        import numpy as np
         import tyro
         from robot_descriptions.loaders.yourdfpy import load_robot_description
 
@@ -44,8 +44,8 @@ and viser. It can also take a path to a local URDF file as input.
                 lower,
                 upper,
             ) in viser_urdf.get_actuated_joint_limits().items():
-                lower = lower if lower is not None else -onp.pi
-                upper = upper if upper is not None else onp.pi
+                lower = lower if lower is not None else -np.pi
+                upper = upper if upper is not None else np.pi
                 initial_pos = 0.0 if lower < 0 and upper > 0 else (lower + upper) / 2.0
                 slider = server.gui.add_slider(
                     label=joint_name,
@@ -56,7 +56,7 @@ and viser. It can also take a path to a local URDF file as input.
                 )
                 slider.on_update(  # When sliders move, we update the URDF configuration.
                     lambda _: viser_urdf.update_cfg(
-                        onp.array([slider.value for slider in slider_handles])
+                        np.array([slider.value for slider in slider_handles])
                     )
                 )
                 slider_handles.append(slider)
@@ -97,7 +97,7 @@ and viser. It can also take a path to a local URDF file as input.
                 )
 
             # Set initial robot configuration.
-            viser_urdf.update_cfg(onp.array(initial_config))
+            viser_urdf.update_cfg(np.array(initial_config))
 
             # Create joint reset button.
             reset_button = server.gui.add_button("Reset")

docs/source/examples/11_colmap_visualizer.rst

@@ -19,7 +19,7 @@ Visualize COLMAP sparse reconstruction outputs. To get demo data, see ``./assets
         from typing import List
 
         import imageio.v3 as iio
-        import numpy as onp
+        import numpy as np
         import tyro
         from tqdm.auto import tqdm
 
@@ -60,7 +60,7 @@ Visualize COLMAP sparse reconstruction outputs. To get demo data, see ``./assets
             def _(event: viser.GuiEvent) -> None:
                 client = event.client
                 assert client is not None
-                client.camera.up_direction = tf.SO3(client.camera.wxyz) @ onp.array(
+                client.camera.up_direction = tf.SO3(client.camera.wxyz) @ np.array(
                     [0.0, -1.0, 0.0]
                 )
 
@@ -82,10 +82,10 @@ Visualize COLMAP sparse reconstruction outputs. To get demo data, see ``./assets
                 "Point size", min=0.01, max=0.1, step=0.001, initial_value=0.05
             )
 
-            points = onp.array([points3d[p_id].xyz for p_id in points3d])
-            colors = onp.array([points3d[p_id].rgb for p_id in points3d])
+            points = np.array([points3d[p_id].xyz for p_id in points3d])
+            colors = np.array([points3d[p_id].rgb for p_id in points3d])
 
-            point_mask = onp.random.choice(points.shape[0], gui_points.value, replace=False)
+            point_mask = np.random.choice(points.shape[0], gui_points.value, replace=False)
             point_cloud = server.scene.add_point_cloud(
                 name="/colmap/pcd",
                 points=points[point_mask],
@@ -148,7 +148,7 @@ Visualize COLMAP sparse reconstruction outputs. To get demo data, see ``./assets
                     image = image[::downsample_factor, ::downsample_factor]
                     frustum = server.scene.add_camera_frustum(
                         f"/colmap/frame_{img_id}/frustum",
-                        fov=2 * onp.arctan2(H / 2, fy),
+                        fov=2 * np.arctan2(H / 2, fy),
                         aspect=W / H,
                         scale=0.15,
                         image=image,
@@ -159,7 +159,7 @@ Visualize COLMAP sparse reconstruction outputs. To get demo data, see ``./assets
 
             @gui_points.on_update
             def _(_) -> None:
-                point_mask = onp.random.choice(points.shape[0], gui_points.value, replace=False)
+                point_mask = np.random.choice(points.shape[0], gui_points.value, replace=False)
                 point_cloud.points = points[point_mask]
                 point_cloud.colors = colors[point_mask]
 

docs/source/examples/15_gui_in_scene.rst

@@ -18,7 +18,7 @@ performed on them.
         import time
         from typing import Optional
 
-        import numpy as onp
+        import numpy as np
 
         import viser
         import viser.transforms as tf
@@ -35,14 +35,14 @@ performed on them.
             When a frame is clicked, we display a 3D gui node.
             """
 
-            rng = onp.random.default_rng(0)
+            rng = np.random.default_rng(0)
 
             displayed_3d_container: Optional[viser.Gui3dContainerHandle] = None
 
             def make_frame(i: int) -> None:
                 # Sample a random orientation + position.
                 wxyz = rng.normal(size=4)
-                wxyz /= onp.linalg.norm(wxyz)
+                wxyz /= np.linalg.norm(wxyz)
                 position = rng.uniform(-3.0, 3.0, size=(3,))
 
                 # Create a coordinate frame and label.
@@ -72,7 +72,7 @@ performed on them.
                         )
                         T_world_target = tf.SE3.from_rotation_and_translation(
                             tf.SO3(frame.wxyz), frame.position
-                        ) @ tf.SE3.from_translation(onp.array([0.0, 0.0, -0.5]))
+                        ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5]))
 
                         T_current_target = T_world_current.inverse() @ T_world_target
 
@@ -94,7 +94,7 @@ performed on them.
                     @randomize_orientation.on_click
                     def _(_) -> None:
                         wxyz = rng.normal(size=4)
-                        wxyz /= onp.linalg.norm(wxyz)
+                        wxyz /= np.linalg.norm(wxyz)
                         frame.wxyz = wxyz
 
                     @close.on_click

docs/source/examples/17_background_composite.rst

@@ -16,7 +16,7 @@ be useful when we want a 2D image to occlude 3D geometry, such as for NeRF rende
 
         import time
 
-        import numpy as onp
+        import numpy as np
         import trimesh
         import trimesh.creation
 
@@ -25,8 +25,8 @@ be useful when we want a 2D image to occlude 3D geometry, such as for NeRF rende
         server = viser.ViserServer()
 
 
-        img = onp.random.randint(0, 255, size=(1000, 1000, 3), dtype=onp.uint8)
-        depth = onp.ones((1000, 1000, 1), dtype=onp.float32)
+        img = np.random.randint(0, 255, size=(1000, 1000, 3), dtype=np.uint8)
+        depth = np.ones((1000, 1000, 1), dtype=np.float32)
 
         # Make a square middle portal.
         depth[250:750, 250:750, :] = 10.0