def test_simple_sphere_batched(self):
device = torch.device("cuda:0")
sphere_mesh = ico_sphere(1, device)
verts_padded = sphere_mesh.verts_padded()
verts_padded[..., 1] += 0.2
verts_padded[..., 0] += 0.2
pointclouds = Pointclouds(
points=verts_padded, features=torch.ones_like(verts_padded)
)
batch_size = 20
pointclouds = pointclouds.extend(batch_size)
R, T = look_at_view_transform(2.7, 0.0, 0.0)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
raster_settings = PointsRasterizationSettings(
image_size=256, radius=5e-2, points_per_pixel=1
)
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
compositor = NormWeightedCompositor()
renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)
# Load reference image
filename = "simple_pointcloud_sphere.png"
image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
images = renderer(pointclouds)
for i in range(batch_size):
rgb = images[i, ..., :3].squeeze().cpu()
if i == 0 and DEBUG:
filename = "DEBUG_%s" % filename
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
DATA_DIR / filename
)
self.assertClose(rgb, image_ref)
def test_simple_sphere(self):
device = torch.device("cuda:0")
sphere_mesh = ico_sphere(1, device)
verts_padded = sphere_mesh.verts_padded()
# Shift vertices to check coordinate frames are correct.
verts_padded[..., 1] += 0.2
verts_padded[..., 0] += 0.2
pointclouds = Pointclouds(
points=verts_padded, features=torch.ones_like(verts_padded)
)
R, T = look_at_view_transform(2.7, 0.0, 0.0)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
raster_settings = PointsRasterizationSettings(
image_size=256, radius=5e-2, points_per_pixel=1
)
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
compositor = NormWeightedCompositor()
renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)
# Load reference image
filename = "simple_pointcloud_sphere.png"
image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
for bin_size in [0, None]:
# Check both naive and coarse to fine produce the same output.
renderer.rasterizer.raster_settings.bin_size = bin_size
images = renderer(pointclouds)
rgb = images[0, ..., :3].squeeze().cpu()
if DEBUG:
filename = "DEBUG_%s" % filename
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
DATA_DIR / filename
)
self.assertClose(rgb, image_ref)
def test_texture_sampling_cow(self):
# test texture sampling for the cow example by converting
# the cow mesh and its texture uv to a pointcloud with texture
device = torch.device("cuda:0")
obj_dir = get_pytorch3d_dir() / "docs/tutorials/data"
obj_filename = obj_dir / "cow_mesh/cow.obj"
for text_type in ("uv", "atlas"):
# Load mesh + texture
if text_type == "uv":
mesh = load_objs_as_meshes(
[obj_filename], device=device, load_textures=True, texture_wrap=None
)
elif text_type == "atlas":
mesh = load_objs_as_meshes(
[obj_filename],
device=device,
load_textures=True,
create_texture_atlas=True,
texture_atlas_size=8,
texture_wrap=None,
)
points, normals, textures = sample_points_from_meshes(
mesh, num_samples=50000, return_normals=True, return_textures=True
)
pointclouds = Pointclouds(points, normals=normals, features=textures)
for pos in ("front", "back"):
# Init rasterizer settings
if pos == "back":
azim = 0.0
elif pos == "front":
azim = 180
R, T = look_at_view_transform(2.7, 0, azim)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
raster_settings = PointsRasterizationSettings(
image_size=512, radius=1e-2, points_per_pixel=1
)
rasterizer = PointsRasterizer(
cameras=cameras, raster_settings=raster_settings
)
compositor = NormWeightedCompositor()
renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)
images = renderer(pointclouds)
rgb = images[0, ..., :3].squeeze().cpu()
if DEBUG:
filename = "DEBUG_cow_mesh_to_pointcloud_%s_%s.png" % (
text_type,
pos,
)
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
DATA_DIR / filename
)