feat: add support of rendering point cloud coloring with intensity

Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp>

Author: ktro2828

t4_devkit/viewer/color.py

@@ -1,31 +1,59 @@
 from __future__ import annotations
 
+from enum import Enum, unique
 from typing import TYPE_CHECKING
 
 import matplotlib
+import numpy as np
 
 if TYPE_CHECKING:
-    from t4_devkit.typing import ArrayLike, NDArrayF64, ScalarLike
+    from t4_devkit.dataclass import PointCloudLike
+    from t4_devkit.typing import ArrayLike, NDArrayF64
+
+
+@unique
+class PointCloudColorMode(str, Enum):
+    """Color mode of point cloud."""
+
+    DISTANCE = "distance"
+    INTENSITY = "intensity"
+
+
+def pointcloud_color(
+    pointcloud: PointCloudLike,
+    color_mode: PointCloudColorMode = PointCloudColorMode.INTENSITY,
+) -> NDArrayF64:
+    """Return color map depending on the specified color mode.
+
+    Args:
+        pointcloud (PointCloudLike): Any inheritance of `PointCloud` class.
+        color_mode (PointCloudColorMode): Color mode of point cloud.
+    """
+    match color_mode:
+        case PointCloudColorMode.DISTANCE:
+            values = np.linalg.norm(pointcloud.points[:3].T, axis=1)
+        case _:
+            values = pointcloud.points[3]
+
+    return distance_color(values)
 
 
 def distance_color(
-    distances: ScalarLike | ArrayLike,
+    distances: ArrayLike,
     cmap: str | None = None,
-    v_min: float = 3.0,
-    v_max: float = 75.0,
 ) -> tuple[float, float, float] | NDArrayF64:
     """Return color map depending on distance values.
 
     Args:
-        distances (ScalarLike | ArrayLike): Array of distances in the shape of (N,).
+        distances (ArrayLike): Array of distances in the shape of (N,).
         cmap (str | None, optional): Color map name in matplotlib. If None, `turbo_r` will be used.
-        v_min (float, optional): Min value to normalize.
-        v_max (float, optional): Max value to normalize.
 
     Returns:
         Color map in the shape of (N,). If input type is any number, returns a color as
             `tuple[float, float, float]`. Otherwise, returns colors as `NDArrayF64`.
     """
     color_map = matplotlib.colormaps["turbo_r"] if cmap is None else matplotlib.colormaps[cmap]
+    v_min = np.min(distances)
+    v_max = np.max(distances)
     norm = matplotlib.colors.Normalize(v_min, v_max)
     return color_map(norm(distances))

t4_devkit/viewer/viewer.py

@@ -14,7 +14,7 @@
 from t4_devkit.lanelet import LaneletParser
 from t4_devkit.schema import SensorModality
 
-from .color import distance_color
+from .color import pointcloud_color
 from .geography import calculate_geodetic_point
 from .lanelet import (
     render_geographic_borders,
@@ -455,7 +455,7 @@ def render_pointcloud(self, seconds: float, channel: str, pointcloud: PointCloud
         # TODO(ktro2828): add support of rendering pointcloud on images
         rr.set_time_seconds(self.timeline, seconds)
 
-        colors = distance_color(np.linalg.norm(pointcloud.points[:3].T, axis=1))
+        colors = pointcloud_color(pointcloud)
         rr.log(
             format_entity(self.ego_entity, channel),
             rr.Points3D(