feat: apply Tier4Viewer in Tier4 rendering (#19)

* feat: apply `Tier4Viewer` in `Tier4` rendering Signed-off-by: ktro2828 <[email protected]> * docs: update documentation Signed-off-by: ktro2828 <[email protected]> --------- Signed-off-by: ktro2828 <[email protected]>
tier4 · Oct 22, 2024 · 59a2078 · 59a2078
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diff --git a/docs/tutorials/render.md b/docs/tutorials/render.md
@@ -1,4 +1,8 @@
-## Rendering Scene
+## Rendering with `Tier4`
+
+If you want to visualize annotation results, `Tier4` supports some rendering methods as below.
+
+### Rendering Scene
 
 ```python
 >>> scene_token = t4.scene[0].token
@@ -7,7 +11,7 @@
 
 ![Render Scene GIF](../assets/render_scene.gif)
 
-## Rendering Instance
+### Rendering Instance
 
 ```python
 >>> instance_token = t4.instance[0].token
@@ -16,7 +20,7 @@
 
 ![Render Instance GIF](../assets/render_instance.gif)
 
-## Rendering PointCloud
+### Rendering PointCloud
 
 ```python
 >>> scene_token = t4.scene[0].token
@@ -35,7 +39,7 @@
     ```
 <!-- prettier-ignore-end -->
 
-## Save Recording
+### Save Recording
 
 You can save the rendering result as follows:
 
@@ -48,3 +52,18 @@ If you don't want to spawn the viewer, please specify `show=False` as below:
 ```python
 >>> t4.render_scene(scene_token, save_dir=<DIR_TO_SAVE>, show=False)
 ```
+
+## Rendering with `Tier4Viewer`
+
+If you want to visualize your components, such as boxes that your ML-model estimated, `Tier4Viewer` allows you to visualize these components.  
+For details, please refer to the API references.
+
+```python
+>>> from t4_devkit.viewer import Tier4Viewer
+# You need to specify `cameras` if you want to 2D spaces
+>>> viewer = Tier4Viewer(app_id, cameras=<CAMERA_NAMES:[str;N]>)
+# Rendering 3D boxes
+>>> viewer.render_box3ds(seconds, box3ds)
+# Rendering 2D boxes
+>>> viewer.render_box2ds(seconds, box2ds)
+```
diff --git a/t4_devkit/dataclass/pointcloud.py b/t4_devkit/dataclass/pointcloud.py
@@ -3,7 +3,7 @@
 import struct
 from abc import abstractmethod
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, ClassVar
+from typing import TYPE_CHECKING, ClassVar, TypeVar
 
 import numpy as np
 
@@ -12,7 +12,13 @@
 
     from t4_devkit.typing import NDArrayFloat, NDArrayU8
 
-__all__ = ["PointCloud", "LidarPointCloud", "RadarPointCloud", "SegmentationPointCloud"]
+__all__ = [
+    "PointCloud",
+    "LidarPointCloud",
+    "RadarPointCloud",
+    "SegmentationPointCloud",
+    "PointCloudLike",
+]
 
 
 @dataclass
@@ -196,3 +202,6 @@ def from_file(cls, point_filepath: str, label_filepath: str) -> Self:
         points = scan.reshape((-1, 5))[:, : cls.num_dims()]
         labels = np.fromfile(label_filepath, dtype=np.uint8)
         return cls(points.T, labels)
+
+
+PointCloudLike = TypeVar("PointCloudLike", bound=PointCloud)