RayBundle visualization

davnov134 · facebook-github-bot · commit 4426a9d12c16 · 2021-07-01T17:31:01.000-07:00
Summary: Extends plotly_vis to visualize `RayBundle`s.

Reviewed By: patricklabatut

Differential Revision: D29014098

fbshipit-source-id: 4dee426510a1fa53d4afefbe1bcdd003684c9932
diff --git a/pytorch3d/vis/plotly_vis.py b/pytorch3d/vis/plotly_vis.py
@@ -11,12 +11,22 @@
 import plotly.graph_objects as go
 import torch
 from plotly.subplots import make_subplots
-from pytorch3d.renderer import TexturesVertex
+from pytorch3d.renderer import TexturesVertex, RayBundle, ray_bundle_to_ray_points
 from pytorch3d.renderer.camera_utils import camera_to_eye_at_up
 from pytorch3d.renderer.cameras import CamerasBase
 from pytorch3d.structures import Meshes, Pointclouds, join_meshes_as_scene
 
 
+Struct = Union[CamerasBase, Meshes, Pointclouds, RayBundle]
+
+
+def _get_struct_len(struct: Struct):  # pragma: no cover
+    """
+    Returns the length (usually corresponds to the batch size) of the input structure.
+    """
+    return len(struct.directions) if isinstance(struct, RayBundle) else len(struct)
+
+
 def get_camera_wireframe(scale: float = 0.3):  # pragma: no cover
     """
     Returns a wireframe of a 3D line-plot of a camera symbol.
@@ -55,18 +65,22 @@ class Lighting(NamedTuple):  # pragma: no cover
 
 
 def plot_scene(
-    plots: Dict[str, Dict[str, Union[Pointclouds, Meshes, CamerasBase]]],
+    plots: Dict[str, Dict[str, Struct]],
     *,
     viewpoint_cameras: Optional[CamerasBase] = None,
     ncols: int = 1,
     camera_scale: float = 0.3,
     pointcloud_max_points: int = 20000,
     pointcloud_marker_size: int = 1,
+    raybundle_max_rays: int = 20000,
+    raybundle_max_points_per_ray: int = 1000,
+    raybundle_ray_point_marker_size: int = 1,
+    raybundle_ray_line_width: int = 1,
     **kwargs,
 ):  # pragma: no cover
     """
-    Main function to visualize Meshes, Cameras and Pointclouds.
-    Plots input Pointclouds, Meshes, and Cameras data into named subplots,
+    Main function to visualize Cameras, Meshes, Pointclouds, and RayBundle.
+    Plots input Cameras, Meshes, Pointclouds, and RayBundle data into named subplots,
     with named traces based on the dictionary keys. Cameras are
     rendered at the camera center location using a wireframe.
 
@@ -87,6 +101,13 @@ def plot_scene(
             pointcloud_max_points is used.
         pointcloud_marker_size: the size of the points rendered by plotly
             when plotting a pointcloud.
+        raybundle_max_rays: maximum number of rays of a RayBundle to visualize. Randomly
+            subsamples without replacement in case the number of rays is bigger than max_rays.
+        raybundle_max_points_per_ray: the maximum number of points per ray in RayBundle
+            to visualize. If more are present, a random sample of size
+            max_points_per_ray is used.
+        raybundle_ray_point_marker_size: the size of the ray points of a plotted RayBundle
+        raybundle_ray_line_width: the width of the plotted rays of a RayBundle
         **kwargs: Accepts lighting (a Lighting object) and any of the args xaxis,
             yaxis and zaxis which Plotly's scene accepts. Accepts axis_args,
             which is an AxisArgs object that is applied to all 3 axes.
@@ -186,6 +207,18 @@ def plot_scene(
     The above example will render one subplot with the mesh object
     and two cameras.
 
+    RayBundle visualization is also supproted:
+    ..code-block::python
+        cameras = PerspectiveCameras(...)
+        ray_bundle = RayBundle(origins=..., lengths=..., directions=..., xys=...)
+        fig = plot_scene({
+            "subplot1_title": {
+                "ray_bundle_trace_title": ray_bundle,
+                "cameras_trace_title": cameras,
+            },
+        })
+        fig.show()
+
     For an example of using kwargs, see below:
     ..code-block::python
         mesh = ...
@@ -264,11 +297,22 @@ def plot_scene(
                 _add_camera_trace(
                     fig, struct, trace_name, subplot_idx, ncols, camera_scale
                 )
+            elif isinstance(struct, RayBundle):
+                _add_ray_bundle_trace(
+                    fig,
+                    struct,
+                    trace_name,
+                    subplot_idx,
+                    ncols,
+                    raybundle_max_rays,
+                    raybundle_max_points_per_ray,
+                    raybundle_ray_point_marker_size,
+                    raybundle_ray_line_width,
+                )
             else:
                 raise ValueError(
-                    "struct {} is not a Cameras, Meshes or Pointclouds object".format(
-                        struct
-                    )
+                    "struct {} is not a Cameras, Meshes, Pointclouds,".format(struct)
+                    + " or RayBundle object."
                 )
 
         # Ensure update for every subplot.
@@ -329,7 +373,8 @@ def plot_scene(
 
 def plot_batch_individually(
     batched_structs: Union[
-        List[Union[Meshes, Pointclouds, CamerasBase]], Meshes, Pointclouds, CamerasBase
+        List[Struct],
+        Struct,
     ],
     *,
     viewpoint_cameras: Optional[CamerasBase] = None,
@@ -340,26 +385,27 @@ def plot_batch_individually(
 ):  # pragma: no cover
     """
     This is a higher level plotting function than plot_scene, for plotting
-    Cameras, Meshes and Pointclouds in simple cases. The simplest use is to plot a
-    single Cameras, Meshes or Pointclouds object, where you just pass it in as a
-    one element list. This will plot each batch element in a separate subplot.
+    Cameras, Meshes, Pointclouds, and RayBundle in simple cases. The simplest use
+    is to plot a single Cameras, Meshes, Pointclouds, or a RayBundle object,
+    where you just pass it in as a one element list. This will plot each batch
+    element in a separate subplot.
 
-    More generally, you can supply multiple Cameras, Meshes or Pointclouds
+    More generally, you can supply multiple Cameras, Meshes, Pointclouds, or RayBundle
     having the same batch size `n`. In this case, there will be `n` subplots,
     each depicting the corresponding batch element of all the inputs.
 
-    In addition, you can include Cameras, Meshes and Pointclouds of size 1 in
+    In addition, you can include Cameras, Meshes, Pointclouds, or RayBundle of size 1 in
     the input. These will either be rendered in the first subplot
     (if extend_struct is False), or in every subplot.
 
     Args:
-        batched_structs: a list of Cameras, Meshes and/or Pointclouds to be rendered.
-            Each structure's corresponding batch element will be plotted in
-            a single subplot, resulting in n subplots for a batch of size n.
+        batched_structs: a list of Cameras, Meshes, Pointclouds, and RayBundle
+            to be rendered. Each structure's corresponding batch element will be
+            plotted in a single subplot, resulting in n subplots for a batch of size n.
             Every struct should either have the same batch size or be of batch size 1.
             See extend_struct and the description above for how batch size 1 structs
-            are handled. Also accepts a single Cameras, Meshes or Pointclouds object,
-            which will have each individual element plotted in its own subplot.
+            are handled. Also accepts a single Cameras, Meshes, Pointclouds, and RayBundle
+            object, which will have each individual element plotted in its own subplot.
         viewpoint_cameras: an instance of a Cameras object providing a location
             to view the plotly plot from. If the batch size is equal
             to the number of subplots, it is a one to one mapping.
@@ -407,13 +453,14 @@ def plot_batch_individually(
         return
     max_size = 0
     if isinstance(batched_structs, list):
-        max_size = max(len(s) for s in batched_structs)
+        max_size = max(_get_struct_len(s) for s in batched_structs)
         for struct in batched_structs:
-            if len(struct) not in (1, max_size):
-                msg = "invalid batch size {} provided: {}".format(len(struct), struct)
+            struct_len = _get_struct_len(struct)
+            if struct_len not in (1, max_size):
+                msg = "invalid batch size {} provided: {}".format(struct_len, struct)
                 raise ValueError(msg)
     else:
-        max_size = len(batched_structs)
+        max_size = _get_struct_len(batched_structs)
 
     if max_size == 0:
         msg = "No data is provided with at least one element"
@@ -437,7 +484,8 @@ def plot_batch_individually(
         if isinstance(batched_structs, list):
             for i, batched_struct in enumerate(batched_structs):
                 # check for whether this struct needs to be extended
-                if i >= len(batched_struct) and not extend_struct:
+                batched_struct_len = _get_struct_len(batched_struct)
+                if i >= batched_struct_len and not extend_struct:
                     continue
                 _add_struct_from_batch(
                     batched_struct, scene_num, subplot_title, scene_dictionary, i + 1
@@ -453,10 +501,10 @@ def plot_batch_individually(
 
 
 def _add_struct_from_batch(
-    batched_struct: Union[CamerasBase, Meshes, Pointclouds],
+    batched_struct: Struct,
     scene_num: int,
     subplot_title: str,
-    scene_dictionary: Dict[str, Dict[str, Union[CamerasBase, Meshes, Pointclouds]]],
+    scene_dictionary: Dict[str, Dict[str, Struct]],
     trace_idx: int = 1,
 ):  # pragma: no cover
     """
@@ -492,6 +540,15 @@ def _add_struct_from_batch(
         #  torch.Tensor, torch.nn.Module]` is not a function.
         T = T[t_idx].unsqueeze(0)
         struct = CamerasBase(device=batched_struct.device, R=R, T=T)
+    elif isinstance(batched_struct, RayBundle):
+        # for RayBundle we treat the 1st dim as the batch index
+        struct_idx = min(scene_num, len(batched_struct.lengths) - 1)
+        struct = RayBundle(
+            **{
+                attr: getattr(batched_struct, attr)[struct_idx]
+                for attr in ["origins", "directions", "lengths", "xys"]
+            }
+        )
     else:  # batched meshes and pointclouds are indexable
         struct_idx = min(scene_num, len(batched_struct) - 1)
         struct = batched_struct[struct_idx]
@@ -702,6 +759,138 @@ def _add_camera_trace(
     _update_axes_bounds(verts_center, max_expand, current_layout)
 
 
+def _add_ray_bundle_trace(
+    fig: go.Figure,
+    ray_bundle: RayBundle,
+    trace_name: str,
+    subplot_idx: int,
+    ncols: int,
+    max_rays: int,
+    max_points_per_ray: int,
+    marker_size: int,
+    line_width: int,
+):  # pragma: no cover
+    """
+    Adds a trace rendering a RayBundle object to the passed in figure, with
+    a given name and in a specific subplot.
+
+    Args:
+        fig: plotly figure to add the trace within.
+        cameras: the Cameras object to render. It can be batched.
+        trace_name: name to label the trace with.
+        subplot_idx: identifies the subplot, with 0 being the top left.
+        ncols: the number of subplots per row.
+        max_rays: maximum number of plotted rays in total. Randomly subsamples
+            without replacement in case the number of rays is bigger than max_rays.
+        max_points_per_ray: maximum number of points plotted per ray.
+        marker_size: the size of the ray point markers.
+        line_width: the width of the ray lines.
+    """
+
+    n_pts_per_ray = ray_bundle.lengths.shape[-1]
+    n_rays = ray_bundle.lengths.shape[:-1].numel()  # pyre-ignore[16]
+
+    # flatten all batches of rays into a single big bundle
+    ray_bundle_flat = RayBundle(
+        **{
+            attr: torch.flatten(getattr(ray_bundle, attr), start_dim=0, end_dim=-2)
+            for attr in ["origins", "directions", "lengths", "xys"]
+        }
+    )
+
+    # subsample the rays (if needed)
+    if n_rays > max_rays:
+        indices_rays = torch.randperm(n_rays)[:max_rays]
+        ray_bundle_flat = RayBundle(
+            **{
+                attr: getattr(ray_bundle_flat, attr)[indices_rays]
+                for attr in ["origins", "directions", "lengths", "xys"]
+            }
+        )
+
+    # make ray line endpoints
+    min_max_ray_depth = torch.stack(
+        [
+            ray_bundle_flat.lengths.min(dim=1).values,  # pyre-ignore[16]
+            ray_bundle_flat.lengths.max(dim=1).values,
+        ],
+        dim=-1,
+    )
+    ray_lines_endpoints = ray_bundle_to_ray_points(
+        ray_bundle_flat._replace(lengths=min_max_ray_depth)
+    )
+
+    # make the ray lines for plotly plotting
+    nan_tensor = torch.Tensor([[float("NaN")] * 3])
+    ray_lines = torch.empty(size=(1, 3))
+    for ray_line in ray_lines_endpoints:
+        # We combine the ray lines into a single tensor to plot them in a
+        # single trace. The NaNs are inserted between sets of ray lines
+        # so that the lines drawn by Plotly are not drawn between
+        # lines that belong to different rays.
+        ray_lines = torch.cat((ray_lines, nan_tensor, ray_line))
+    x, y, z = ray_lines.detach().cpu().numpy().T.astype(float)
+    row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
+    fig.add_trace(
+        go.Scatter3d(
+            x=x,
+            y=y,
+            z=z,
+            marker={"size": 0.1},
+            line={"width": line_width},
+            name=trace_name,
+        ),
+        row=row,
+        col=col,
+    )
+
+    # subsample the ray points (if needed)
+    if n_pts_per_ray > max_points_per_ray:
+        indices_ray_pts = torch.cat(
+            [
+                torch.randperm(n_pts_per_ray)[:max_points_per_ray] + ri * n_pts_per_ray
+                for ri in range(ray_bundle_flat.lengths.shape[0])
+            ]
+        )
+        ray_bundle_flat = ray_bundle_flat._replace(
+            lengths=ray_bundle_flat.lengths.reshape(-1)[indices_ray_pts].reshape(
+                ray_bundle_flat.lengths.shape[0], -1
+            )
+        )
+
+    # plot the ray points
+    ray_points = (
+        ray_bundle_to_ray_points(ray_bundle_flat)
+        .view(-1, 3)
+        .detach()
+        .cpu()
+        .numpy()
+        .astype(float)
+    )
+    fig.add_trace(
+        go.Scatter3d(
+            x=ray_points[:, 0],
+            y=ray_points[:, 1],
+            z=ray_points[:, 2],
+            mode="markers",
+            name=trace_name + "_points",
+            marker={"size": marker_size},
+        ),
+        row=row,
+        col=col,
+    )
+
+    # Access the current subplot's scene configuration
+    plot_scene = "scene" + str(subplot_idx + 1)
+    current_layout = fig["layout"][plot_scene]
+
+    # update the bounds of the axes for the current trace
+    all_ray_points = ray_bundle_to_ray_points(ray_bundle).view(-1, 3)
+    ray_points_center = all_ray_points.mean(dim=0)
+    max_expand = (all_ray_points.max(0)[0] - all_ray_points.min(0)[0]).max().item()
+    _update_axes_bounds(ray_points_center, float(max_expand), current_layout)
+
+
 def _gen_fig_with_subplots(
     batch_size: int, ncols: int, subplot_titles: List[str]
 ):  # pragma: no cover
