liyang
ae28d55f81
Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER, documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight manifest/downloader. Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the 20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed (third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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import imageio
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import numpy as np
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import torch
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import nvdiffrast.torch as dr
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def tensor(*args, **kwargs):
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return torch.tensor(*args, device='cuda', **kwargs)
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glctx = dr.RasterizeCudaContext()
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pos = tensor([[[-0.8, -0.8, 0, 1], [0.8, -0.8, 0, 1], [-0.8, 0.8, 0, 1]]], dtype=torch.float32)
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col = tensor([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]], dtype=torch.float32)
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tri = tensor([[0, 1, 2]], dtype=torch.int32)
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rast, _ = dr.rasterize(glctx, pos, tri, resolution=[256, 256])
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out, _ = dr.interpolate(col, rast, tri)
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img = out.cpu().numpy()[0, ::-1, :, :] # Flip vertically.
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img = np.clip(np.rint(img * 255), 0, 255).astype(np.uint8) # Quantize to np.uint8
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print("Saving to 'tri.png'.")
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imageio.imsave('tri.png', img)
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