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 get_visible_points(point_clouds, cameras, depth_merge_threshold=0.01):
""" Returns packed visibility """
from pytorch3d.renderer import PointsRasterizationSettings, PointsRasterizer
img_size = 256
raster = PointsRasterizer(raster_settings=PointsRasterizationSettings(
image_size=img_size, points_per_pixel=20, radius=3 *2.0 / img_size))
frag = raster(point_clouds, cameras=cameras)
depth_occ_mask = (frag.zbuf[..., 1:] -
frag.zbuf[..., :1]) < depth_merge_threshold
occ_idx = frag.idx[...,1:][~depth_occ_mask]
frag.idx[..., 1:][~depth_occ_mask] = -1
mask = get_per_point_visibility_mask(point_clouds, frag)
occ_idx = occ_idx[occ_idx!=-1]
occ_idx = occ_idx.unique()
mask[occ_idx.long()] = False
return mask
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 _setup_render(self):
# Unpack options ...
opts = self.opts
# Initialize a camera.
# TODO(ycho): Alternatively, specify the intrinsic matrix `K` instead.
cameras = FoVPerspectiveCameras(znear=opts.znear,
zfar=opts.zfar,
aspect_ratio=opts.aspect,
fov=opts.fov,
degrees=True,
device=self.device)
# Define the settings for rasterization and shading.
# As we are rendering images for visualization purposes only we will set faces_per_pixel=1
# and blur_radius=0.0. Refer to raster_points.py for explanations of
# these parameters.
# points_per_pixel (Optional): We will keep track of this many points per
# pixel, returning the nearest points_per_pixel points along the z-axis
# Create a points renderer by compositing points using an alpha compositor (nearer points
# are weighted more heavily). See [1] for an explanation.
if self.opts.use_mesh:
raster_settings = RasterizationSettings(
image_size=opts.image_size,
blur_radius=0.0, # hmm...
faces_per_pixel=1)
rasterizer = MeshRasterizer(cameras=cameras,
raster_settings=raster_settings)
lights = PointLights(device=self.device,
location=[[0.0, 0.0, -3.0]])
renderer = MeshRenderer(rasterizer=rasterizer,
shader=SoftPhongShader(device=self.device,
cameras=cameras,
lights=lights))
else:
raster_settings = PointsRasterizationSettings(
image_size=opts.image_size, radius=0.1, points_per_pixel=8)
rasterizer = PointsRasterizer(cameras=cameras,
raster_settings=raster_settings)
renderer = PointsRenderer(rasterizer=rasterizer,
compositor=AlphaCompositor())
return renderer
def __init__(self, cameras=None, raster_settings=None, frnn_radius=0.2):
"""
cameras: A cameras object which has a `transform_points` method
which returns the transformed points after applying the
world-to-view and view-to-screen transformations.
compositor: Use SurfaceSplatting compositor by default unless user overwrites it, in that case,
issue a warning
raster_settings: the parameters for rasterization. This should be a
named tuple.
All these initial settings can be overridden by passing keyword
arguments to the forward function.
"""
if raster_settings is None:
raster_settings = PointsRasterizationSettings()
super().__init__(cameras=cameras, raster_settings=raster_settings)
self.raster_settings = raster_settings
self.frnn_radius = frnn_radius
def test_points_renderer_to(self):
"""
Test moving all the tensors in the points renderer to a new device.
"""
device1 = torch.device("cpu")
R, T = look_at_view_transform(1500, 0.0, 0.0)
raster_settings = PointsRasterizationSettings(image_size=256,
radius=0.001,
points_per_pixel=1)
cameras = FoVPerspectiveCameras(device=device1,
R=R,
T=T,
aspect_ratio=1.0,
fov=60.0,
zfar=100)
rasterizer = PointsRasterizer(cameras=cameras,
raster_settings=raster_settings)
renderer = PointsRenderer(rasterizer=rasterizer,
compositor=AlphaCompositor())
mesh = ico_sphere(2, device1)
verts_padded = mesh.verts_padded()
pointclouds = Pointclouds(points=verts_padded,
features=torch.randn_like(verts_padded))
self._check_points_renderer_props_on_device(renderer, device1)
# Test rendering on cpu
output_images = renderer(pointclouds)
self.assertEqual(output_images.device, device1)
# Move renderer and pointclouds to another device and re render
device2 = torch.device("cuda:0")
renderer = renderer.to(device2)
pointclouds = pointclouds.to(device2)
self._check_points_renderer_props_on_device(renderer, device2)
output_images = renderer(pointclouds)
self.assertEqual(output_images.device, device2)
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])
def __init__(self, cfgs):
super(Renderer, self).__init__()
self.device = cfgs.get('device', 'cuda:0')
self.image_size = cfgs.get('image_size', 64)
self.min_depth = cfgs.get('min_depth', 0.9)
self.max_depth = cfgs.get('max_depth', 1.1)
self.rot_center_depth = cfgs.get('rot_center_depth',
(self.min_depth + self.max_depth) / 2)
# todo: FoV (Field of View) was set to be an fixed value of 10 degree (according to the paper).
self.fov = cfgs.get('fov', 10)
self.tex_cube_size = cfgs.get('tex_cube_size', 2)
self.renderer_min_depth = cfgs.get('renderer_min_depth', 0.1)
self.renderer_max_depth = cfgs.get('renderer_max_depth', 10.)
#### camera intrinsics
# (u) (x)
# d * K^-1 (v) = (y)
# (1) (z)
## renderer for visualization
R = [[[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]]
R = torch.FloatTensor(R).to(self.device)
t = torch.zeros(1, 3, dtype=torch.float32).to(self.device)
## todo: K is the camera intrinsic matrix.
fx = (self.image_size - 1) / 2 / (math.tan(
self.fov / 2 * math.pi / 180))
fy = (self.image_size - 1) / 2 / (math.tan(
self.fov / 2 * math.pi / 180))
cx = (self.image_size - 1) / 2
cy = (self.image_size - 1) / 2
K = [[fx, 0., cx], [0., fy, cy], [0., 0., 1.]]
K = torch.FloatTensor(K).to(self.device)
self.inv_K = torch.inverse(K).unsqueeze(0)
self.K = K.unsqueeze(0)
## todo: define Renderer.
## use renderer from pytorch3d.
# fixme: znear and zfar is equivalent to the neural renderer default settings.
cameras = OpenGLOrthographicCameras(device=self.device,
R=R,
T=t,
znear=0.01,
zfar=100)
# cameras = OpenGLPerspectiveCameras(device=self.device, R=R, T=t,
# znear=self.renderer_min_depth,
# zfar=self.renderer_max_depth,
# fov=self.fov)
raster_settings = PointsRasterizationSettings(
image_size=self.image_size,
radius=0.003,
points_per_pixel=10,
bin_size=None,
max_points_per_bin=None)
self.renderer = PointsRenderer(
rasterizer=PointsRasterizer(cameras=cameras,
raster_settings=raster_settings),
compositor=AlphaCompositor(composite_params=None))
"/home/dari/Projects/pointrendering2/Code/scenes/tt_train_colmap_undis/"
)
cameras = scene.GetCamera(1)
# cameras = FoVOrthographicCameras(device=device, R=R, T=T, znear=0.01)
# print("Num Cameras", len(cameras))
# assert len(cameras) == 1
# Define the settings for rasterization and shading. Here we set the output image to be of size
# 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1
# and blur_radius=0.0. Refer to raster_points.py for explanations of these parameters.
raster_settings = PointsRasterizationSettings(
image_size=scene.image_size,
# radius=0.003,
radius=0.005,
points_per_pixel=10)
# Create a points renderer by compositing points using an alpha compositor (nearer points
# are weighted more heavily). See [1] for an explanation.
rasterizer = PointsRasterizer(cameras=cameras,
raster_settings=raster_settings)
renderer = PointsRenderer(rasterizer=rasterizer,
compositor=AlphaCompositor())
images = renderer(scene.point_cloud)
PrintTensorInfo(images)
save_image(images[0].permute((2, 0, 1)), "debug/img0.jpg")
def test_compare_with_pointclouds_renderer(self,
batch_size=11,
volume_size=(30, 30, 30),
image_size=(200, 250)):
"""
Generate a volume and its corresponding point cloud and check whether
PointsRenderer returns the same images as the corresponding VolumeRenderer.
"""
# generate NDC camera extrinsics and intrinsics
cameras = init_cameras(batch_size, image_size=image_size, ndc=True)
# init the boundary volume
for shape in ("sphere", "cube"):
if not DEBUG and shape == "cube":
# do not run numeric checks for the cube as the
# differences in rendering equations make the renders incomparable
continue
# get rand offset of the volume
volume_translation = torch.randn(batch_size, 3) * 0.1
# volume_translation[2] = 0.1
volumes = init_boundary_volume(
volume_size=volume_size,
batch_size=batch_size,
shape=shape,
volume_translation=volume_translation,
)[0]
# convert the volumes to a pointcloud
points = []
points_features = []
for densities_one, features_one, grid_one in zip(
volumes.densities(),
volumes.features(),
volumes.get_coord_grid(world_coordinates=True),
):
opaque = densities_one.view(-1) > 1e-4
points.append(grid_one.view(-1, 3)[opaque])
points_features.append(features_one.reshape(3, -1).t()[opaque])
pointclouds = Pointclouds(points, features=points_features)
# init the grid raysampler with the ndc grid
coord_range = 1.0
half_pix_size = coord_range / max(*image_size)
raysampler = NDCMultinomialRaysampler(
image_width=image_size[1],
image_height=image_size[0],
n_pts_per_ray=256,
min_depth=0.1,
max_depth=2.0,
)
# get the EA raymarcher
raymarcher = EmissionAbsorptionRaymarcher()
# jitter the camera intrinsics a bit for each render
cameras_randomized = cameras.clone()
cameras_randomized.principal_point = (
torch.randn_like(cameras.principal_point) * 0.3)
cameras_randomized.focal_length = (
cameras.focal_length +
torch.randn_like(cameras.focal_length) * 0.2)
# get the volumetric render
images = VolumeRenderer(raysampler=raysampler,
raymarcher=raymarcher,
sample_mode="bilinear")(
cameras=cameras_randomized,
volumes=volumes)[0][..., :3]
# instantiate the points renderer
point_radius = 6 * half_pix_size
points_renderer = PointsRenderer(
rasterizer=PointsRasterizer(
cameras=cameras_randomized,
raster_settings=PointsRasterizationSettings(
image_size=image_size,
radius=point_radius,
points_per_pixel=10),
),
compositor=AlphaCompositor(),
)
# get the point render
images_pts = points_renderer(pointclouds)
if shape == "sphere":
diff = (images - images_pts).abs().mean(dim=-1)
mu_diff = diff.mean(dim=(1, 2))
std_diff = diff.std(dim=(1, 2))
self.assertClose(mu_diff, torch.zeros_like(mu_diff), atol=3e-2)
self.assertClose(std_diff,
torch.zeros_like(std_diff),
atol=6e-2)
if DEBUG:
outdir = tempfile.gettempdir() + "/test_volume_vs_pts_renderer"
os.makedirs(outdir, exist_ok=True)
frames = []
for (image, image_pts) in zip(images, images_pts):
diff_image = (((image - image_pts) * 0.5 + 0.5).mean(
dim=2, keepdim=True).repeat(1, 1, 3))
image_pil = Image.fromarray(
(torch.cat((image, image_pts, diff_image),
dim=1).detach().cpu().numpy() *
255.0).astype(np.uint8))
frames.append(image_pil)
# export gif
outfile = os.path.join(outdir,
f"volume_vs_pts_render_{shape}.gif")
frames[0].save(
outfile,
save_all=True,
append_images=frames[1:],
duration=batch_size // 15,
loop=0,
)
print(f"exported {outfile}")
# export concatenated frames
outfile_cat = os.path.join(
outdir, f"volume_vs_pts_render_{shape}.png")
Image.fromarray(
np.concatenate([np.array(f) for f in frames],
axis=0)).save(outfile_cat)
print(f"exported {outfile_cat}")
# pespective projection: x=fX/Z assuming px=py=0, normalization of Z
verts[:, :,
1] = verts[:, :, 1].clone() * proj_cam[:, :1] / verts[:, :,
2].clone()
verts[:, :,
0] = verts[:, :, 0].clone() * proj_cam[:, :1] / verts[:, :,
2].clone()
verts[:, :,
2] = ((verts[:, :, 2] - verts[:, :, 2].min()) /
(verts[:, :, 2].max() - verts[:, :, 2].min()) - 0.5).detach()
verts[:, :, 2] += 10
features = torch.ones_like(verts)
point_cloud = Pointclouds(points=verts[:, :, :3],
features=torch.Tensor(
mesh.visual.vertex_colors[None]).cuda())
cameras = OrthographicCameras(device=device)
raster_settings = PointsRasterizationSettings(image_size=img_size,
radius=0.005,
points_per_pixel=10)
renderer = PointsRenderer(
rasterizer=PointsRasterizer(cameras=cameras,
raster_settings=raster_settings),
compositor=AlphaCompositor(background_color=(33, 33, 33)))
img_pred = renderer(point_cloud)
frames.append(img_pred[0, :, :, :3].cpu())
#cv2.imwrite('%s/points%04d.png'%(args.outdir,i), np.asarray(img_pred[0,:,:,:3].cpu())[:,:,::-1])
imageio.mimsave('./output-depth.gif', frames, duration=5. / len(frames))
for epoch in range(opt.epoch, opt.n_epochs):
for i, batch in enumerate(train_loader):
# Model inputs
real_RGB = batch[0].float() # real_RGB
real_RGB = real_RGB.to(device)
point = batch[1].float()
point = point.to(device)
geom = batch[2]
geom = geom.to(device)
# Initialize a camera.
R, T = look_at_view_transform(1, 0, 0) #真上
cameras = FoVOrthographicCameras(device=device, R=R, T=T, znear=0)
raster_settings = PointsRasterizationSettings(
image_size=512,
radius = 0.01,
points_per_pixel = 10
)
# Create a points renderer by compositing points using an alpha compositor (nearer points
# are weighted more heavily). See [1] for an explanation.
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
renderer = PointsRenderer(
rasterizer=rasterizer,
compositor=AlphaCompositor()
)
verts = Pointclouds(points=[point[0,:,:]])
real_A = rasterizer(verts)
real_A = real_A[1]
# print(real_A.size())
def test_ndc_grid_sample_rendering(self):
"""
Use PyTorch3D point renderer to render a colored point cloud, then
sample the image at the locations of the point projections with
`ndc_grid_sample`. Finally, assert that the sampled colors are equal to the
original point cloud colors.
Note that, in order to ensure correctness, we use a nearest-neighbor
assignment point renderer (i.e. no soft splatting).
"""
# generate a bunch of 3D points on a regular grid lying in the z-plane
n_grid_pts = 10
grid_scale = 0.9
z_plane = 2.0
image_size = [128, 128]
point_radius = 0.015
n_pts = n_grid_pts * n_grid_pts
pts = torch.stack(
meshgrid_ij([torch.linspace(-grid_scale, grid_scale, n_grid_pts)] *
2, ),
dim=-1,
)
pts = torch.cat([pts, z_plane * torch.ones_like(pts[..., :1])], dim=-1)
pts = pts.reshape(1, n_pts, 3)
# color the points randomly
pts_colors = torch.rand(1, n_pts, 3)
# make trivial rendering cameras
cameras = PerspectiveCameras(
R=eyes(dim=3, N=1),
device=pts.device,
T=torch.zeros(1, 3, dtype=torch.float32, device=pts.device),
)
# render the point cloud
pcl = Pointclouds(points=pts, features=pts_colors)
renderer = NearestNeighborPointsRenderer(
rasterizer=PointsRasterizer(
cameras=cameras,
raster_settings=PointsRasterizationSettings(
image_size=image_size,
radius=point_radius,
points_per_pixel=1,
),
),
compositor=AlphaCompositor(),
)
im_render = renderer(pcl)
# sample the render at projected pts
pts_proj = cameras.transform_points(pcl.points_padded())[..., :2]
pts_colors_sampled = ndc_grid_sample(
im_render,
pts_proj,
mode="nearest",
align_corners=False,
).permute(0, 2, 1)
# assert that the samples are the same as original points
self.assertClose(pts_colors, pts_colors_sampled, atol=1e-4)
def render_point_cloud_pytorch3d(
camera,
point_cloud,
render_size: Tuple[int, int],
point_radius: float = 0.03,
topk: int = 10,
eps: float = 1e-2,
bg_color=None,
bin_size: Optional[int] = None,
**kwargs
):
# feature dimension
featdim = point_cloud.features_packed().shape[-1]
# move to the camera coordinates; using identity cameras in the renderer
point_cloud = _transform_points(camera, point_cloud, eps, **kwargs)
camera_trivial = camera.clone()
camera_trivial.R[:] = torch.eye(3)
camera_trivial.T *= 0.0
bin_size = (
bin_size
if bin_size is not None
else (64 if int(max(render_size)) > 1024 else None)
)
rasterizer = PointsRasterizer(
cameras=camera_trivial,
raster_settings=PointsRasterizationSettings(
image_size=render_size,
radius=point_radius,
points_per_pixel=topk,
bin_size=bin_size,
),
)
fragments = rasterizer(point_cloud, **kwargs)
# Construct weights based on the distance of a point to the true point.
# However, this could be done differently: e.g. predicted as opposed
# to a function of the weights.
r = rasterizer.raster_settings.radius
# set up the blending weights
dists2 = fragments.dists
weights = 1 - dists2 / (r * r)
ok = cast(torch.BoolTensor, (fragments.idx >= 0)).float()
weights = weights * ok
fragments_prm = fragments.idx.long().permute(0, 3, 1, 2)
weights_prm = weights.permute(0, 3, 1, 2)
images = AlphaCompositor()(
fragments_prm,
weights_prm,
point_cloud.features_packed().permute(1, 0),
background_color=bg_color if bg_color is not None else [0.0] * featdim,
**kwargs,
)
# get the depths ...
# weighted_fs[b,c,i,j] = sum_k cum_alpha_k * features[c,pointsidx[b,k,i,j]]
# cum_alpha_k = alphas[b,k,i,j] * prod_l=0..k-1 (1 - alphas[b,l,i,j])
cumprod = torch.cumprod(1 - weights, dim=-1)
cumprod = torch.cat((torch.ones_like(cumprod[..., :1]), cumprod[..., :-1]), dim=-1)
depths = (weights * cumprod * fragments.zbuf).sum(dim=-1)
# add the rendering mask
render_mask = -torch.prod(1.0 - weights, dim=-1) + 1.0
# cat depths and render mask
rendered_blob = torch.cat((images, depths[:, None], render_mask[:, None]), dim=1)
# reshape back
rendered_blob = Fu.interpolate(
rendered_blob,
# pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int,
# ...]`.
size=tuple(render_size),
mode="bilinear",
)
data_rendered, depth_rendered, render_mask = rendered_blob.split(
[rendered_blob.shape[1] - 2, 1, 1],
dim=1,
)
return data_rendered, render_mask, depth_rendered
