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Merge pull request #41 from take2rohit/main
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Making GLAMR actually work and solving all errors!
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@Khrylx
Khrylx authored Nov 21, 2023
2 parents cc752f1 + 739fcfc commit 17cfe74
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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -53,7 +53,8 @@ GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
pip install -r requirements.txt
```
5. Download [SMPL](https://smpl.is.tue.mpg.de/) models & joint regressors and place them in the `data` folder. You can obtain the model following [SPEC](https://github.com/mkocabas/SPEC)'s instructions [here](https://github.com/mkocabas/SPEC/blob/master/scripts/prepare_data.sh).
6. Download `pretrained_w_cam.pth` from [Google Drive](https://drive.google.com/uc?id=16Y_MGUynFeEzV8GVtKTE5AtkHSi3xsF9)
### Pretrained Models
* You can download [third-party](https://github.com/YanglanOu) pretrained models from [Google Drive](https://drive.google.com/file/d/1_3h0DExyHkPH9cv1O8Y42YPI3c08G-2b/view?usp=sharing) or [BaiduYun](https://pan.baidu.com/s/1nvSzfuffB5yBaZ3GRBDC5w?pwd=pj3z).
* Once the `glamr_models.zip` file is downloaded, unzipping it will create the `results` folder:
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228 changes: 228 additions & 0 deletions environment.yml
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name: hybrik
channels:
- pytorch3d
- pytorch
- bottler
- iopath
- anaconda
- conda-forge
- nvidia
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=5.1=1_gnu
- blas=1.0=mkl
- brotli-python=1.0.9=py39h6a678d5_7
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.19.1=h5eee18b_0
- ca-certificates=2023.08.22=h06a4308_0
- certifi=2023.7.22=py39h06a4308_0
- cffi=1.15.1=py39h5eee18b_3
- charset-normalizer=2.0.4=pyhd3eb1b0_0
- cryptography=41.0.3=py39hdda0065_0
- cuda=11.6.1=0
- cuda-cccl=11.6.55=hf6102b2_0
- cuda-command-line-tools=11.6.2=0
- cuda-compiler=11.6.2=0
- cuda-cudart=11.6.55=he381448_0
- cuda-cudart-dev=11.6.55=h42ad0f4_0
- cuda-cuobjdump=11.6.124=h2eeebcb_0
- cuda-cupti=11.6.124=h86345e5_0
- cuda-cuxxfilt=11.6.124=hecbf4f6_0
- cuda-driver-dev=11.6.55=0
- cuda-gdb=12.3.52=0
- cuda-libraries=11.6.1=0
- cuda-libraries-dev=11.6.1=0
- cuda-memcheck=11.8.86=0
- cuda-nsight=12.3.52=0
- cuda-nsight-compute=12.3.0=0
- cuda-nvcc=11.6.124=hbba6d2d_0
- cuda-nvdisasm=12.3.52=0
- cuda-nvml-dev=11.6.55=haa9ef22_0
- cuda-nvprof=12.3.52=0
- cuda-nvprune=11.6.124=he22ec0a_0
- cuda-nvrtc=11.6.124=h020bade_0
- cuda-nvrtc-dev=11.6.124=h249d397_0
- cuda-nvtx=11.6.124=h0630a44_0
- cuda-nvvp=12.3.52=0
- cuda-runtime=11.6.1=0
- cuda-samples=11.6.101=h8efea70_0
- cuda-sanitizer-api=12.3.52=0
- cuda-toolkit=11.6.1=0
- cuda-tools=11.6.1=0
- cuda-visual-tools=11.6.1=0
- curl=7.88.1=hdbd6064_2
- expat=2.5.0=h6a678d5_0
- ffmpeg=4.2.2=h20bf706_0
- freetype=2.12.1=h4a9f257_0
- fvcore=0.1.5.post20221221=pyhd8ed1ab_0
- gdbm=1.18=hd4cb3f1_4
- gds-tools=1.8.0.34=0
- gettext=0.21.0=h39681ba_1
- giflib=5.2.1=h5eee18b_3
- git=2.40.1=pl5340h36fbf9e_1
- gmp=6.2.1=h295c915_3
- gnutls=3.6.15=he1e5248_0
- icu=73.1=h6a678d5_0
- idna=3.4=py39h06a4308_0
- intel-openmp=2023.1.0=hdb19cb5_46305
- iopath=0.1.9=py39
- jpeg=9e=h5eee18b_1
- krb5=1.20.1=h143b758_1
- lame=3.100=h7b6447c_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.38=h1181459_1
- lerc=3.0=h295c915_0
- libcublas=11.9.2.110=h5e84587_0
- libcublas-dev=11.9.2.110=h5c901ab_0
- libcufft=10.7.1.112=hf425ae0_0
- libcufft-dev=10.7.1.112=ha5ce4c0_0
- libcufile=1.8.0.34=0
- libcufile-dev=1.8.0.34=0
- libcurand=10.3.4.52=0
- libcurand-dev=10.3.4.52=0
- libcurl=7.88.1=h251f7ec_2
- libcusolver=11.3.4.124=h33c3c4e_0
- libcusparse=11.7.2.124=h7538f96_0
- libcusparse-dev=11.7.2.124=hbbe9722_0
- libdeflate=1.17=h5eee18b_1
- libedit=3.1.20221030=h5eee18b_0
- libev=4.33=h7f8727e_1
- libffi=3.4.4=h6a678d5_0
- libgcc-ng=11.2.0=h1234567_1
- libgomp=11.2.0=h1234567_1
- libidn2=2.3.4=h5eee18b_0
- libnghttp2=1.57.0=h2d74bed_0
- libnpp=11.6.3.124=hd2722f0_0
- libnpp-dev=11.6.3.124=h3c42840_0
- libnvjpeg=11.6.2.124=hd473ad6_0
- libnvjpeg-dev=11.6.2.124=hb5906b9_0
- libopus=1.3.1=h7b6447c_0
- libpng=1.6.39=h5eee18b_0
- libssh2=1.10.0=hdbd6064_2
- libstdcxx-ng=11.2.0=h1234567_1
- libtasn1=4.19.0=h5eee18b_0
- libtiff=4.5.1=h6a678d5_0
- libunistring=0.9.10=h27cfd23_0
- libvpx=1.7.0=h439df22_0
- libwebp=1.3.2=h11a3e52_0
- libwebp-base=1.3.2=h5eee18b_0
- libxml2=2.10.4=hf1b16e4_1
- lz4-c=1.9.4=h6a678d5_0
- mkl=2023.1.0=h213fc3f_46343
- mkl-service=2.4.0=py39h5eee18b_1
- mkl_fft=1.3.8=py39h5eee18b_0
- mkl_random=1.2.4=py39hdb19cb5_0
- ncurses=6.4=h6a678d5_0
- nettle=3.7.3=hbbd107a_1
- nsight-compute=2023.3.0.12=0
- nvidiacub=1.10.0=0
- openh264=2.1.1=h4ff587b_0
- openjpeg=2.4.0=h3ad879b_0
- openssl=3.0.12=h7f8727e_0
- pcre2=10.42=hebb0a14_0
- perl=5.34.0=h5eee18b_2
- pip=23.3=py39h06a4308_0
- portalocker=2.3.0=py39h06a4308_1
- pycparser=2.21=pyhd3eb1b0_0
- pyopenssl=23.2.0=py39h06a4308_0
- pysocks=1.7.1=py39h06a4308_0
- python=3.9.18=h955ad1f_0
- python_abi=3.9=2_cp39
- pytorch=1.13.0=py3.9_cuda11.6_cudnn8.3.2_0
- pytorch-cuda=11.6=h867d48c_1
- pytorch-mutex=1.0=cuda
- pytorch3d=0.7.5=py39_cu116_pyt1130
- readline=8.2=h5eee18b_0
- requests=2.31.0=py39h06a4308_0
- setuptools=68.0.0=py39h06a4308_0
- sqlite=3.41.2=h5eee18b_0
- tabulate=0.8.10=py39h06a4308_0
- tbb=2021.8.0=hdb19cb5_0
- termcolor=2.1.0=py39h06a4308_0
- tk=8.6.12=h1ccaba5_0
- torchvision=0.14.0=py39_cu116
- tqdm=4.65.0=py39hb070fc8_0
- typing_extensions=4.7.1=py39h06a4308_0
- urllib3=1.26.18=py39h06a4308_0
- wheel=0.41.2=py39h06a4308_0
- x264=1!157.20191217=h7b6447c_0
- xz=5.4.2=h5eee18b_0
- yacs=0.1.6=pyhd3eb1b0_1
- yaml=0.2.5=h7b6447c_0
- zlib=1.2.13=h5eee18b_0
- zstd=1.5.5=hc292b87_0
- pip:
- aiohttp==3.8.6
- aiosignal==1.3.1
- appdirs==1.4.4
- async-timeout==4.0.3
- attrs==23.1.0
- autograd==1.6.2
- beautifulsoup4==4.12.2
- contourpy==1.2.0
- cycler==0.12.1
- cython==3.0.5
- dill==0.3.7
- ffmpeg-python==0.2.0
- fonttools==4.44.0
- frozenlist==1.4.0
- fsspec==2023.10.0
- future==0.18.3
- h5py==3.1.0
- imageio==2.32.0
- importlib-metadata==6.8.0
- importlib-resources==6.1.1
- imutils==0.5.4
- joblib==1.3.2
- kiwisolver==1.4.5
- lazy-loader==0.3
- lightning-utilities==0.9.0
- llvmlite==0.41.1
- lmdb==1.4.1
- markupsafe==2.1.3
- matplotlib==3.8.1
- meshio==4.4.6
- multidict==6.0.4
- multiprocess==0.70.15
- networkx==3.2.1
- numba==0.58.1
- numpy==1.23.1
- opencv-python==4.8.1.78
- opendr==0.77
- packaging==23.2
- pandas==2.1.2
- pathos==0.3.1
- pillow==10.1.0
- pox==0.3.3
- ppft==1.7.6.7
- progress==1.6
- protobuf==3.19.6
- pydeprecate==0.3.0
- pynvml==11.5.0
- pyparsing==3.1.1
- python-dateutil==2.8.2
- python-graphviz==0.20.1
- pytorch-lightning==0.8.3
- pytube==15.0.0
- pytz==2023.3.post1
- pyvista==0.36.0
- pyyaml==5.4.1
- scikit-image==0.22.0
- scikit-learn==1.3.2
- scipy==1.11.3
- scooby==0.9.2
- seaborn==0.13.0
- six==1.16.0
- soupsieve==2.5
- tb-nightly==2.16.0a20231108
- threadpoolctl==3.2.0
- tifffile==2023.9.26
- torchmetrics==1.2.0
- transforms3d==0.3.1
- tzdata==2023.3
- vtk==9.1.0
- werkzeug==3.0.1
- wslink==1.12.4
- yarl==1.9.2
- zipp==3.17.0
10 changes: 9 additions & 1 deletion global_recon/models/global_recon_model.py
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Expand Up @@ -2,6 +2,7 @@
import os
import torch
import numpy as np
from scipy.spatial.transform import Rotation as R
from scipy.interpolate import interp1d
from lib.models.smpl import SMPL, SMPL_MODEL_DIR
from lib.utils.geometry import perspective_projection
Expand Down Expand Up @@ -99,7 +100,14 @@ def init_data(self, in_dict):
new_dict['invis_frames'] = invis_frames = visible == 0
new_dict['frame2ind'] = {f: i for i, f in enumerate(new_dict['frames'])}
new_dict['scale'] = None
smpl_pose_wroot = quaternion_to_angle_axis(torch.tensor(pose_dict[f'smpl_pose_quat_wroot'], device=self.device)).cpu().numpy()

## Credits: https://github.com/NVlabs/GLAMR/issues/34#issuecomment-1671664667
hybrik_poses_rot = pose_dict['smpl_pose_quat_wroot']
batch_size = hybrik_poses_rot.shape[0]
smpl_pose_wroot = R.as_rotvec(R.from_matrix(hybrik_poses_rot.reshape((-1, 3, 3)))).reshape((batch_size, -1, 3))
smpl_pose_wroot = smpl_pose_wroot.astype(np.float32)
# smpl_pose_wroot = quaternion_to_angle_axis(torch.tensor(pose_dict[f'smpl_pose_quat_wroot'], device=self.device)).cpu().numpy()
# new_dict['smpl_pose'] = smpl_pose_wroot[:, 1:].reshape(-1, 69)
new_dict['smpl_pose'] = smpl_pose_wroot[:, 1:].reshape(-1, 69)
if idx in in_dict['gt']:
new_dict['smpl_pose_gt'] = in_dict['gt'][idx]['pose'][:, 3:]
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5 changes: 3 additions & 2 deletions global_recon/run_demo.py
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Expand Up @@ -110,10 +110,11 @@
visualizer = GReconVisualizer(out_dict, coord='world', verbose=False, show_camera=False,
render_cam_pos=render_specs.get('cam_pos', None), render_cam_focus=render_specs.get('cam_focus', None))
visualizer.save_animation_as_video(video_world, window_size=render_specs.get('wsize', (int(1.5 * img_h), img_h)), cleanup=True, crf=5)

print(f'Saved results at: {video_world}')
log.info(f'saving cam animation for {seq_name}')
visualizer = GReconVisualizer(out_dict, coord='cam_in_world', verbose=False, background_img_dir=frame_dir)
visualizer.save_animation_as_video(video_cam, window_size=(img_w, img_h), cleanup=True)

log.info(f'saving side-by-side animation for {seq_name}')
hstack_video_arr([pose_est_video, video_cam, video_world], video_sbs, text_arr=[pose_est_model_name, 'GLAMR (Cam)', 'GLAMR (World)'], text_color='blue', text_size=img_h // 16, verbose=False)
hstack_video_arr([pose_est_video, video_cam, video_world], video_sbs, verbose=False)
print(f'Saved results at: {video_sbs}')
2 changes: 1 addition & 1 deletion requirements.txt
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Expand Up @@ -15,7 +15,7 @@ tqdm
scikit-image
scikit-learn
scipy
sklearn
scikit-learn
chumpy
pytorch-lightning==1.3.5
pyvista==0.31.3
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