add normal rerun viz

b3d/utils.py

@@ -395,6 +395,22 @@ def update_choices_get_score(trace, key, addr_const, *values):
     enumerate_choices_get_scores, static_argnums=(2,)
 )
 
+def unproject_depth(depth, renderer):
+    """Unprojects a depth image into a point cloud.
+
+    Args:
+        depth (jnp.ndarray): The depth image. Shape (H, W)
+        intrinsics (b.camera.Intrinsics): The camera intrinsics.
+    Returns:
+        jnp.ndarray: The point cloud. Shape (H, W, 3)
+    """
+    mask = (depth < renderer.far) * (depth > renderer.near)
+    depth = depth * mask + renderer.far * (1.0 - mask)
+    y, x = jnp.mgrid[: depth.shape[0], : depth.shape[1]]
+    x = (x - renderer.cx) / renderer.fx
+    y = (y - renderer.cy) / renderer.fy
+    point_cloud_image = jnp.stack([x, y, jnp.ones_like(x)], axis=-1) * depth[:, :, None]
+    return point_cloud_image
 
 def nn_background_segmentation(images):
     import torch

test/test_render_ycb_model.py

@@ -2,7 +2,11 @@
 import jax.numpy as jnp
 import trimesh
 import b3d
+import rerun as rr
 
+PORT = 8812
+rr.init("real")
+rr.connect(addr=f"127.0.0.1:{PORT}")
 
 def test_renderer_full(renderer):
     mesh_path = os.path.join(
@@ -15,7 +19,7 @@ def test_renderer_full(renderer):
     object_library.add_trimesh(mesh)
 
     pose = b3d.Pose.from_position_and_target(
-        jnp.array([0.2, 0.2, 0.0]), jnp.array([0.0, 0.0, 0.0])
+        jnp.array([0.2, 0.2, 0.2]), jnp.array([0.0, 0.0, 0.0])
     ).inv()
 
     rgb, depth = renderer.render_attribute(
@@ -39,17 +43,21 @@ def test_renderer_normal_full(renderer):
     object_library.add_trimesh(mesh)
 
     pose = b3d.Pose.from_position_and_target(
-        jnp.array([0.2, 0.2, 0.0]), jnp.array([0.0, 0.0, 0.0])
+        jnp.array([0.2, 0.2, 0.2]), jnp.array([0.0, 0.0, 0.0])
     ).inv()
 
-    _, _, normal = renderer.render_attribute_normal(
+    rgb, depth, normal = renderer.render_attribute_normal(
         pose[None, ...],
         object_library.vertices,
         object_library.faces,
         jnp.array([[0, len(object_library.faces)]]),
         object_library.attributes,
     )
 
-    normal = jnp.abs(normal)
-    b3d.get_rgb_pil_image(normal).save(b3d.get_root_path() / "assets/test_results/test_ycb_normal.png")
-    assert normal.sum() > 0
+    b3d.get_rgb_pil_image((normal+1)/2).save(b3d.get_root_path() / "assets/test_results/test_ycb_normal.png")
+
+    point_im = b3d.utils.unproject_depth(depth, renderer)
+    rr.log("pc", rr.Points3D(point_im.reshape(-1,3), colors=rgb.reshape(-1,3)))
+    rr.log("arrows", rr.Arrows3D(origins=point_im[::5,::5,:].reshape(-1,3), vectors=normal[::5,::5,:].reshape(-1,3)/100))
+
+    assert jnp.abs(normal).sum() > 0