def cli():
"""
Basic example for the pulsar sphere renderer using the PyTorch3D interface.
Writes to `basic-pt3d.png`.
"""
LOGGER.info("Rendering on GPU...")
torch.manual_seed(1)
n_points = 10
width = 1_000
height = 1_000
device = torch.device("cuda")
# Generate sample data.
vert_pos = torch.rand(n_points, 3, dtype=torch.float32,
device=device) * 10.0
vert_pos[:, 2] += 25.0
vert_pos[:, :2] -= 5.0
vert_col = torch.rand(n_points, 3, dtype=torch.float32, device=device)
pcl = Pointclouds(points=vert_pos[None, ...], features=vert_col[None, ...])
# Alternatively, you can also use the look_at_view_transform to get R and T:
# R, T = look_at_view_transform(
# dist=30.0, elev=0.0, azim=180.0, at=((0.0, 0.0, 30.0),), up=((0, 1, 0),),
# )
cameras = PerspectiveCameras(
# The focal length must be double the size for PyTorch3D because of the NDC
# coordinates spanning a range of two - and they must be normalized by the
# sensor width (see the pulsar example). This means we need here
# 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar.
focal_length=(5.0 * 2.0 / 2.0, ),
R=torch.eye(3, dtype=torch.float32, device=device)[None, ...],
T=torch.zeros((1, 3), dtype=torch.float32, device=device),
image_size=((width, height), ),
device=device,
)
vert_rad = torch.rand(n_points, dtype=torch.float32, device=device)
raster_settings = PointsRasterizationSettings(
image_size=(width, height),
radius=vert_rad,
)
rasterizer = PointsRasterizer(cameras=cameras,
raster_settings=raster_settings)
renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device)
# Render.
image = renderer(
pcl,
gamma=(1.0e-1, ), # Renderer blending parameter gamma, in [1., 1e-5].
znear=(1.0, ),
zfar=(45.0, ),
radius_world=True,
bg_col=torch.ones((3, ), dtype=torch.float32, device=device),
)[0]
LOGGER.info("Writing image to `%s`.", path.abspath("basic-pt3d.png"))
imageio.imsave("basic-pt3d.png",
(image.cpu().detach() * 255.0).to(torch.uint8).numpy())
LOGGER.info("Done.")
def setup(self, device):
if self.renderer is not None: return
R, T = look_at_view_transform(self.opt.viewpoint_distance,
self.opt.viewpoint_elevation,
self.opt.viewpoint_azimuth,
device=device)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
raster_settings = PointsRasterizationSettings(
image_size=self.opt.raster_image_size,
radius=self.opt.raster_radius,
points_per_pixel=self.opt.raster_points_per_pixel,
)
rasterizer = PointsRasterizer(cameras=cameras,
raster_settings=raster_settings)
lights = PointLights(device=device,
location=[self.opt.lights_location])
self.renderer = PulsarPointsRenderer(rasterizer=rasterizer,
n_channels=3).to(device)
def test_pointcloud(self):
data = _CommonData()
clouds = Pointclouds(points=torch.tensor([[data.point]])).extend(2)
colorful_cloud = Pointclouds(points=torch.tensor([[data.point]]),
features=torch.ones(1, 1, 3)).extend(2)
points_per_pixel = 2
# for camera in [data.camera_screen]:
for camera in (data.camera_ndc, data.camera_screen):
rasterizer = PointsRasterizer(
cameras=camera,
raster_settings=PointsRasterizationSettings(
image_size=data.image_size,
radius=0.0001,
points_per_pixel=points_per_pixel,
),
)
# when rasterizing we expect only one pixel to be occupied
rasterizer_output = rasterizer(clouds).idx
self.assertTupleEqual(rasterizer_output.shape, (2, ) +
data.image_size + (points_per_pixel, ))
found = torch.nonzero(rasterizer_output != -1)
self.assertTupleEqual(found.shape, (2, 4))
self.assertListEqual(found[0].tolist(), [0, data.y, data.x, 0])
self.assertListEqual(found[1].tolist(), [1, data.y, data.x, 0])
if camera.in_ndc():
# Pulsar not currently working in screen space.
pulsar_renderer = PulsarPointsRenderer(rasterizer=rasterizer)
pulsar_output = pulsar_renderer(colorful_cloud,
gamma=(0.1, 0.1),
znear=(0.1, 0.1),
zfar=(70, 70))
self.assertTupleEqual(pulsar_output.shape,
(2, ) + data.image_size + (3, ))
# Look for points rendered in the red channel only, expecting our one.
# Check the first batch element only.
# TODO: Something is odd with the second.
found = torch.nonzero(pulsar_output[0, :, :, 0])
self.assertTupleEqual(found.shape, (1, 2))
self.assertListEqual(found[0].tolist(), [data.y, data.x])