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Summary: Update all FB license strings to the new format. Reviewed By: patricklabatut Differential Revision: D33403538 fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
90 lines
3.0 KiB
Python
Executable File
90 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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This example demonstrates the most trivial use of the pulsar PyTorch3D
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interface for sphere renderering. It renders and saves an image with
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10 random spheres.
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Output: basic-pt3d.png.
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"""
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import logging
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from os import path
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import imageio
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import torch
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from pytorch3d.renderer import (
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PerspectiveCameras,
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PointsRasterizationSettings,
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PointsRasterizer,
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PulsarPointsRenderer,
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)
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from pytorch3d.structures import Pointclouds
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LOGGER = logging.getLogger(__name__)
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def cli():
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"""
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Basic example for the pulsar sphere renderer using the PyTorch3D interface.
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Writes to `basic-pt3d.png`.
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"""
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LOGGER.info("Rendering on GPU...")
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torch.manual_seed(1)
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n_points = 10
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width = 1_000
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height = 1_000
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device = torch.device("cuda")
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# Generate sample data.
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vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 10.0
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vert_pos[:, 2] += 25.0
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vert_pos[:, :2] -= 5.0
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vert_col = torch.rand(n_points, 3, dtype=torch.float32, device=device)
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pcl = Pointclouds(points=vert_pos[None, ...], features=vert_col[None, ...])
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# Alternatively, you can also use the look_at_view_transform to get R and T:
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# R, T = look_at_view_transform(
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# dist=30.0, elev=0.0, azim=180.0, at=((0.0, 0.0, 30.0),), up=((0, 1, 0),),
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# )
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cameras = PerspectiveCameras(
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# The focal length must be double the size for PyTorch3D because of the NDC
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# coordinates spanning a range of two - and they must be normalized by the
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# sensor width (see the pulsar example). This means we need here
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# 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar.
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focal_length=(5.0 * 2.0 / 2.0,),
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R=torch.eye(3, dtype=torch.float32, device=device)[None, ...],
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T=torch.zeros((1, 3), dtype=torch.float32, device=device),
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image_size=((height, width),),
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device=device,
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)
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vert_rad = torch.rand(n_points, dtype=torch.float32, device=device)
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raster_settings = PointsRasterizationSettings(
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image_size=(height, width),
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radius=vert_rad,
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)
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rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
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renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device)
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# Render.
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image = renderer(
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pcl,
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gamma=(1.0e-1,), # Renderer blending parameter gamma, in [1., 1e-5].
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znear=(1.0,),
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zfar=(45.0,),
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radius_world=True,
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bg_col=torch.ones((3,), dtype=torch.float32, device=device),
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)[0]
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LOGGER.info("Writing image to `%s`.", path.abspath("basic-pt3d.png"))
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imageio.imsave(
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"basic-pt3d.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy()
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)
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LOGGER.info("Done.")
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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cli()
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