Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

Overview

News!

  • Aug 2020: v0.4.0 version of AlphaPose is released! Stronger tracking! Include whole body(face,hand,foot) keypoints! Colab now available.
  • Dec 2019: v0.3.0 version of AlphaPose is released! Smaller model, higher accuracy!
  • Apr 2019: MXNet version of AlphaPose is released! It runs at 23 fps on COCO validation set.
  • Feb 2019: CrowdPose is integrated into AlphaPose Now!
  • Dec 2018: General version of PoseFlow is released! 3X Faster and support pose tracking results visualization!
  • Sep 2018: v0.2.0 version of AlphaPose is released! It runs at 20 fps on COCO validation set (4.6 people per image on average) and achieves 71 mAP!

AlphaPose

AlphaPose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (75 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset. To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.

AlphaPose supports both Linux and Windows!


COCO 17 keypoints

Halpe 26 keypoints + tracking

Halpe 136 keypoints + tracking

Results

Pose Estimation

Results on COCO test-dev 2015:

Method AP @0.5:0.95 AP @0.5 AP @0.75 AP medium AP large
OpenPose (CMU-Pose) 61.8 84.9 67.5 57.1 68.2
Detectron (Mask R-CNN) 67.0 88.0 73.1 62.2 75.6
AlphaPose 73.3 89.2 79.1 69.0 78.6

Results on MPII full test set:

Method Head Shoulder Elbow Wrist Hip Knee Ankle Ave
OpenPose (CMU-Pose) 91.2 87.6 77.7 66.8 75.4 68.9 61.7 75.6
Newell & Deng 92.1 89.3 78.9 69.8 76.2 71.6 64.7 77.5
AlphaPose 91.3 90.5 84.0 76.4 80.3 79.9 72.4 82.1

More results and models are available in the docs/MODEL_ZOO.md.

Pose Tracking

Please read trackers/README.md for details.

CrowdPose

Please read docs/CrowdPose.md for details.

Installation

Please check out docs/INSTALL.md

Model Zoo

Please check out docs/MODEL_ZOO.md

Quick Start

  • Colab: We provide a colab example for your quick start.

  • Inference: Inference demo

./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional

For high level API, please refer to ./scripts/demo_api.py

  • Training: Train from scratch
./scripts/train.sh ${CONFIG} ${EXP_ID}
  • Validation: Validate your model on MSCOCO val2017
./scripts/validate.sh ${CONFIG} ${CHECKPOINT}

Examples:

Demo using FastPose model.

./scripts/inference.sh configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml pretrained_models/fast_res50_256x192.pth ${VIDEO_NAME}
#or
python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --indir examples/demo/

Train FastPose on mscoco dataset.

./scripts/train.sh ./configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml exp_fastpose

More detailed inference options and examples, please refer to GETTING_STARTED.md

Common issue & FAQ

Check out faq.md for faq. If it can not solve your problems or if you find any bugs, don't hesitate to comment on GitHub or make a pull request!

Contributors

AlphaPose is based on RMPE(ICCV'17), authored by Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai and Cewu Lu, Cewu Lu is the corresponding author. Currently, it is maintained by Jiefeng Li*, Hao-shu Fang*, Yuliang Xiu and Chao Xu.

The main contributors are listed in doc/contributors.md.

TODO

  • Multi-GPU/CPU inference
  • 3D pose
  • add tracking flag
  • PyTorch C++ version
  • Add MPII and AIC data
  • dense support
  • small box easy filter
  • Crowdpose support
  • Speed up PoseFlow
  • Add stronger/light detectors and the mobile pose
  • High level API

We would really appreciate if you can offer any help and be the contributor of AlphaPose.

Citation

Please cite these papers in your publications if it helps your research:

@inproceedings{fang2017rmpe,
  title={{RMPE}: Regional Multi-person Pose Estimation},
  author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},
  booktitle={ICCV},
  year={2017}
}

@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

@inproceedings{xiu2018poseflow,
  author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
  title = {{Pose Flow}: Efficient Online Pose Tracking},
  booktitle={BMVC},
  year = {2018}
}

License

AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please drop an e-mail at mvig.alphapose[at]gmail[dot]com and cc lucewu[[at]sjtu[dot]edu[dot]cn. We will send the detail agreement to you.

Comments
  • About Training SPPE

    About Training SPPE

    你好作者,我这几天正在尝试在coco数据集上训练SPPE,第一阶段按照你们的步骤python train.py --dataset coco --expID exp1 --nClasses 17 --LR 1e-4 没有加入dpg训练,50个epoch之后acc大概只有55%左右,我看您在另一个issue中的解答是 等acc达到90以后再加入dpg操作,所以我尝试着在第一阶段的基础上python train.py --dataset coco --expID exp1 --nClasses 17 --LR 1e-5 --loadmodel '第一阶段的模型model_49.pkl' 将lr调为1e-5 然后load第一阶段的模型继续训练想进一步提高acc,但好像20个epoch之后acc也只有56% 提升并不明显。 想请问一下在这种情况下,我是继续衰减 lr且不加入dpg训练,还是直接加入dpg进行训练呢?望作者还能够指点一下,十分感谢!

    opened by tangchen2 27
  • Python3.6 can't pickle cv2.VideoCapture objects

    Python3.6 can't pickle cv2.VideoCapture objects

    Running on python3.6 got this error:

      File "video_demo.py", line 41, in <module>
        data_loader = VideoLoader(videofile, batchSize=args.detbatch).start()
      File "/~/AlphaPose/dataloader.py", line 214, in start
        p.start()
      File "/usr/lib/python3.6/multiprocessing/process.py", line 105, in start
        self._popen = self._Popen(self)
      File "/usr/lib/python3.6/multiprocessing/context.py", line 223, in _Popen
        return _default_context.get_context().Process._Popen(process_obj)
      File "/usr/lib/python3.6/multiprocessing/context.py", line 291, in _Popen
        return Popen(process_obj)
      File "/usr/lib/python3.6/multiprocessing/popen_forkserver.py", line 35, in __init__
        super().__init__(process_obj)
      File "/usr/lib/python3.6/multiprocessing/popen_fork.py", line 19, in __init__
        self._launch(process_obj)
      File "/usr/lib/python3.6/multiprocessing/popen_forkserver.py", line 47, in _launch
        reduction.dump(process_obj, buf)
      File "/usr/lib/python3.6/multiprocessing/reduction.py", line 60, in dump
        ForkingPickler(file, protocol).dump(obj)
    TypeError: can't pickle cv2.VideoCapture objects
    
    
    opened by jinfagang 25
  • PyTorch Version: `torch_shm_manager` error when running with multiprocessing

    PyTorch Version: `torch_shm_manager` error when running with multiprocessing

    Running code doesnt work. I get the following error:

    (venv) juggernaut@xmen9:/hdd/AlphaPose$ python demo.py --indir examples/demo/
    Loading YOLO model..
    torch_shm_manager: error while loading shared libraries: libcudart.so.10.0: cannot open shared object file: No such file or directory
    Traceback (most recent call last):
      File "/usr/lib/python3.6/multiprocessing/queues.py", line 234, in _feed
        obj = _ForkingPickler.dumps(obj)
      File "/usr/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
        cls(buf, protocol).dump(obj)
      File "/hdd/kps_pipeline/venv/lib/python3.6/site-packages/torch/multiprocessing/reductions.py", line 314, in reduce_storage
        metadata = storage._share_filename_()
    RuntimeError: error executing torch_shm_manager at "/hdd/kps_pipeline/venv/lib/python3.6/site-packages/torch/bin/torch_shm_manager" at /pytorch/torch/lib/libshm/core.cpp:99
    torch_shm_manager: error while loading shared libraries: libcudart.so.10.0: cannot open shared object file: No such file or directory
    Traceback (most recent call last):
      File "/usr/lib/python3.6/multiprocessing/queues.py", line 234, in _feed
        obj = _ForkingPickler.dumps(obj)
      File "/usr/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
        cls(buf, protocol).dump(obj)
      File "/hdd/kps_pipeline/venv/lib/python3.6/site-packages/torch/multiprocessing/reductions.py", line 314, in reduce_storage
        metadata = storage._share_filename_()
    RuntimeError: error executing torch_shm_manager at "/hdd/kps_pipeline/venv/lib/python3.6/site-packages/torch/bin/torch_shm_manager" at /pytorch/torch/lib/libshm/core.cpp:99
    torch_shm_manager: error while loading shared libraries: libcudart.so.10.0: cannot open shared object file: No such file or directory
    torch_shm_manager: error while loading shared libraries: libcudart.so.10.0: cannot open shared object file: No such file or directory
    Traceback (most recent call last):
      File "demo.py", line 50, in <module>
        det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
      File "/hdd/AlphaPose/dataloader.py", line 309, in start
        p.start()
      File "/usr/lib/python3.6/multiprocessing/process.py", line 105, in start
        self._popen = self._Popen(self)
      File "/usr/lib/python3.6/multiprocessing/context.py", line 223, in _Popen
        return _default_context.get_context().Process._Popen(process_obj)
      File "/usr/lib/python3.6/multiprocessing/context.py", line 291, in _Popen
        return Popen(process_obj)
      File "/usr/lib/python3.6/multiprocessing/popen_forkserver.py", line 35, in __init__
        super().__init__(process_obj)
      File "/usr/lib/python3.6/multiprocessing/popen_fork.py", line 19, in __init__
        self._launch(process_obj)
      File "/usr/lib/python3.6/multiprocessing/popen_forkserver.py", line 47, in _launch
        reduction.dump(process_obj, buf)
      File "/usr/lib/python3.6/multiprocessing/reduction.py", line 60, in dump
        ForkingPickler(file, protocol).dump(obj)
      File "/hdd/kps_pipeline/venv/lib/python3.6/site-packages/torch/multiprocessing/reductions.py", line 314, in reduce_storage
        metadata = storage._share_filename_()
    RuntimeError: error executing torch_shm_manager at "/hdd/kps_pipeline/venv/lib/python3.6/site-packages/torch/bin/torch_shm_manager" at /pytorch/torch/lib/libshm/core.cpp:99
    

    Although, when i add flag --sp it works fine.

    Python 3.6
    CUDA 9.0
    CUDNN 7
    torch 1.2.0    
    torchfile 0.1.0    
    torchvision 0.4.0 
    
    opened by alex-razor 24
  • Is there any plan to implement PoseFlow on pytorch branch?

    Is there any plan to implement PoseFlow on pytorch branch?

    Hello,

    I'm very impressed with your project AlphaPose!

    I really would like to use AlphaPose to track pose in my project, but it seems PoseFlow is not implemented on pytorch branch now. Are you planning to implement PoseFlow on pytorch branch?

    If not, is it possible to run PoseFlow and get tracked pose with following steps?

    • Get alpha-pose-results-{...}.json by executing video_demo.py from pytorch branch
    • Extract images from each frame of video for the deepmatching
    • Execute deepmatching and get result file ({...}.txt)
    • Edit PoseFlow/tracker.py to enable it to read images that were extracted from my video and deepmatching result
    • Execute PoseFlow/tracker.py and get result
    opened by yusukefs 23
  • cannot import name 'deform_conv_cuda'

    cannot import name 'deform_conv_cuda'

    Hi,i'm working on Windows 10,python 3.6.7 and get error as below when runing .scripts\demo_inference.py with nessary parameters.

    Traceback (most recent call last):
      File "D:/WorkProgram/PoseCheck/code/AlphaPose/scripts/demo_inference.py", line 13, in <module>
        from alphapose.models import builder
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\__init__.py", line 1, in <module>
        from .datasets import *  # noqa: F401,F403
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\datasets\__init__.py", line 1, in <module>
        from .coco_det import Mscoco_det
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\datasets\coco_det.py", line 17, in <module>
        from alphapose.models.builder import DATASET
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\models\__init__.py", line 1, in <module>
        from .fastpose import FastPose
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\models\fastpose.py", line 10, in <module>
        from .layers.SE_Resnet import SEResnet
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\models\layers\SE_Resnet.py", line 9, in <module>
        from .dcn import DeformConv, ModulatedDeformConv
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\models\layers\dcn\__init__.py", line 1, in <module>
        from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv,
      File "D:\WorkProgram\PoseCheck\code\AlphaPose\alphapose\models\layers\dcn\deform_conv.py", line 9, in <module>
        from . import deform_conv_cuda
    ImportError: cannot import name 'deform_conv_cuda'
    
    opened by shartoo 18
  • Pose Flow NMS

    Pose Flow NMS

    Hi,according to your paper, the method of Pose Flow NMS is different from the conventional NMS method using frame-by-frame Pose-NMS. However, the code in your github means that the NMS process depends on frame-by-frame Pose-NMS. The code about the Pose flow building puzzled me also, could you help me .

    opened by lc4321 16
  • 用重新训练得到的模型进行检测时权重维度报错

    用重新训练得到的模型进行检测时权重维度报错

    您好,大神,根据您之前的指导初步完成了在COCO数据上的训练,当用模型进行检测的时候却出现了权重维度大小不匹配的情况,折腾很久没有解决,求指点。。。谢谢 Loading pose model from ./models/sppe/model_49.pkl Traceback (most recent call last): File "demo.py", line 56, in pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset) File "/home/zhong/AlphaPose/SPPE/src/main_fast_inference.py", line 61, in init model.load_state_dict(torch.load('./models/sppe/model_49.pkl')) File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 719, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for FastPose: size mismatch for conv_out.bias: copying a param of torch.Size([33]) from checkpoint, where the shape is torch.Size([17]) in current model. size mismatch for conv_out.weight: copying a param of torch.Size([33, 128, 3, 3]) from checkpoint, where the shape is torch.Size([17, 128, 3, 3]) in current model.

    opened by ZhongXiaoFang 15
  • Colab with --tracking --pose_track?

    Colab with --tracking --pose_track?

    Hello, thanks for a fantastic project - it was the most accurate & easiest of all posetracking projects that I could get to run! I used the Colab notebook provided https://github.com/tugstugi/dl-colab-notebooks/blob/master/notebooks/AlphaPose.ipynb and it worked pretty much out of the box (I ran into issues with a local GPU though). However, this notebook installs from the pytorch-subbranch of the repository, which is older. I tried to install the master, but failed. In particular, I would like to obtain tracking results about person identity so that I can track multiple persons. However, the options --tracking or --pose_track are not working (it complains that the module 'detector' is missing - that seems to be only present in master). Does anyone have suggestions for how to get the master branch to work on colab? I tried to using the instructions, but didn't succeed (the old pytorch branch had a requirements-file -- the setup/build isn't working as smoothly). Thank you very much R

    opened by rschmaelzle 14
  • 求助!!运行中的一些问题

    求助!!运行中的一些问题

    您好! 我的电脑是windows10, cuda9.2, pytorch0.4.1, python3.6 请问要到哪里查看检测后的图片呢? 我按照您的说明运行程序后,example/res文件夹里有一个.json文件和一个vis文件夹,但是vis文件夹是空的。。。 我运行的代码如下 hp@pc MINGW64 /d/AlphaPose-pytorch/AlphaPose-pytorch
    $ python demo.py --indir examples/demo --outdir examples/res --save_img --sp --p osebatch 10 --vis Loading YOLO model..
    D:\Anaconda3\lib\site-packages\torch\nn\modules\upsampling.py:122: UserWarning: nn.Upsampling is deprecated. Use nn.functional.interpolate instead.
    warnings.warn("nn.Upsampling is deprecated. Use nn.functional.interpolate inst ead.")
    Loading pose model from ./models/sppe/duc_se.pth 100%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒| 2/2 [00:02<00:00, 1.28s/it] ===========================> Finish Model Running. ===========================> Rendering remaining images in the queue... ===========================> If this step takes too long, you can enable the --v is_fast flag to use fast rendering (real-time).

    此外,运行时会出现如上所示的 warnings.warn("nn.Upsampling is deprecated. Use nn.functional.interpolate inst ead.,请问这是因为什么呢?会影响结果吗?怎么能使它不报这个warning呢? 最后,如果要检测视频,视频需要什么格式呢?mp4吗? 特别需要您的回答QAQ感谢~!!

    opened by yyyyyff 14
  • A question about the data used in PoseFlow/deepmatching.py

    A question about the data used in PoseFlow/deepmatching.py

    Hi, could you tell me the detailed name of the data used in PoseFlow/deepmatching.py, I donn't know which the data you used that satisfied the condition of "len(a.split("/")) == 9". When I debug the code, the value of vidnames is empty.

    opened by lc4321 14
  • deform_conv_cuda ImportError

    deform_conv_cuda ImportError

    Is there a requirement for the CUDA version? ImportError happens when I run the demo:

    $ ./scripts/inference.sh configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml pretrained_models/simple_res50_256x192.pth ./data/1.mp4
    ++ CONFIG=configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml
    ++ CKPT=pretrained_models/simple_res50_256x192.pth
    ++ VIDEO=./data/1.mp4
    ++ OUTDIR=./examples/res
    ++ python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/simple_res50_256x192.pth --video ./data/1.mp4 --outdir ./examples/res --detector yolo --save_img --save_video
    Traceback (most recent call last):
      File "scripts/demo_inference.py", line 13, in <module>
        from alphapose.models import builder
      File "/home/tianxin/AlphaPose/alphapose/models/__init__.py", line 1, in <module>
        from .fastpose import FastPose
      File "/home/tianxin/AlphaPose/alphapose/models/fastpose.py", line 10, in <module>
        from .layers.SE_Resnet import SEResnet
      File "/home/tianxin/AlphaPose/alphapose/models/layers/SE_Resnet.py", line 9, in <module>
        from .dcn import DeformConv, ModulatedDeformConv
      File "/home/tianxin/AlphaPose/alphapose/models/layers/dcn/__init__.py", line 1, in <module>
        from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv,
      File "/home/tianxin/AlphaPose/alphapose/models/layers/dcn/deform_conv.py", line 9, in <module>
        from . import deform_conv_cuda
    ImportError: /home/tianxin/AlphaPose/alphapose/models/layers/dcn/deform_conv_cuda.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN2at7getTypeERKNS_6TensorE
    

    Current environment: pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=9.2

    opened by starry-xin 13
  • model train and validation how can i?

    model train and validation how can i?

    how can i solve this error?

    this code error: !scripts/validate.sh --cfg /content/drive/MyDrive/AlphaPose/AlphaPose/configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint /content/drive/MyDrive/AlphaPose/AlphaPose/pretrained_models/fast_res50_256x192.pth

    scripts/validate.sh: Permission denied

    ! scripts /content/drive/MyDrive/AlphaPose/AlphaPose/scripts/train.sh configs /content/drive/MyDrive/AlphaPose/AlphaPose/configs/halpe_26/resnet/256x192_res50_lr1e-3_1x.yaml

    /bin/bash: scripts: command not found

    opened by arkpak 4
  • How to switch COCO mode to MPII mode

    How to switch COCO mode to MPII mode

    I want to generate JSON files with MPII keypoint order, not COCO.

    So I ran the following command. I changed --format coco to --format MPII.

    python scripts/demo_inference.py \
    --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x-simple.yaml \
    --checkpoint pretrained_models/simple_res50_256x192.pth \
    --video example.mp4 \
    --outdir examples/res \
    --detbatch 1 \
    --posebatch 30 \
    --detector tracker \
    --showbox \
    --format MPII \
    --save_video
    

    However, a portion of the output data shows that the number of elements in keypoints is 51, which is keypoints in COCO.

    {
            "image_id": "0.jpg",
            "category_id": 1,
            "keypoints": [
                1470.4735107421875,
                167.3895721435547,
                0.9206457138061523,
                1473.953857421875,
                163.9093017578125,
                0.9169428944587708,
                1466.9932861328125,
                163.9093017578125,
                0.9339861273765564,
                1480.9144287109375,
                165.64944458007812,
                0.8938273787498474,
                1463.512939453125,
                165.64944458007812,
                0.9008600115776062,
                1487.8748779296875,
                184.79098510742188,
                0.9028074145317078,
                1461.7728271484375,
                186.5311279296875,
                0.8846182823181152,
                1482.654541015625,
                209.15293884277344,
                0.8575469255447388,
                1458.2926025390625,
                209.15293884277344,
                0.887753427028656,
                1468.7333984375,
                230.0346221923828,
                0.881260097026825,
                1458.2926025390625,
                230.0346221923828,
                0.8903049826622009,
                1482.654541015625,
                236.9951934814453,
                0.8013991713523865,
                1465.2530517578125,
                236.9951934814453,
                0.8115606904029846,
                1480.9144287109375,
                273.53814697265625,
                0.8705073595046997,
                1465.2530517578125,
                273.53814697265625,
                0.8841249942779541,
                1484.3946533203125,
                308.3409423828125,
                0.8712695240974426,
                1465.2530517578125,
                306.6007995605469,
                0.9167873859405518
            ],
            "score": 3.051330089569092,
            "box": [
                1437.9459228515625,
                145.28985595703125,
                70.275634765625,
                178.19027709960938
            ],
            "idx": 2.0
        },
    

    How can I output the JSON file for the MPII case?

    opened by thoth000 1
  • Fix Colab example Program

    Fix Colab example Program

    The google colab sample program is presented in ReadMe.md, but it does not work properly.

    Please refer to this isuue #1087 I set up for more details.

    So, I put the properly modified program on ReadMe.md and made a pull request. Please check it out.

    URL from : https://colab.research.google.com/drive/1c7xb_7U61HmeJp55xjXs24hf1GUtHmPs?usp=sharing to : https://colab.research.google.com/drive/1_3Wxi4H3QGVC28snL3rHIoeMAwI2otMR?usp=sharing

    opened by thoth000 1
  • Issues getting alphapose to work

    Issues getting alphapose to work

    Running this command from a conda environment: python setup.py build develop --user

    /home/julian/miniconda3/lib/python3.9/site-packages/torch/cuda/__init__.py:88: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
      return torch._C._cuda_getDeviceCount() > 0
    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
    /home/julian/miniconda3/lib/python3.9/site-packages/setuptools/installer.py:27: SetuptoolsDeprecationWarning: setuptools.installer is deprecated. Requirements should be satisfied by a PEP 517 installer.
      warnings.warn(
    running build
    running build_py
    copying alphapose/version.py -> build/lib.linux-x86_64-3.9/alphapose
    running build_ext
    /home/julian/miniconda3/lib/python3.9/site-packages/torch/utils/cpp_extension.py:387: UserWarning: The detected CUDA version (11.8) has a minor version mismatch with the version that was used to compile PyTorch (11.7). Most likely this shouldn't be a problem.
      warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda))
    /home/julian/miniconda3/lib/python3.9/site-packages/torch/utils/cpp_extension.py:397: UserWarning: There are no g++ version bounds defined for CUDA version 11.8
      warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}')
    building 'detector.nms.soft_nms_cpu' extension
    Emitting ninja build file /home/julian/Documents/Alphapose attempt/AlphaPose/build/temp.linux-x86_64-3.9/build.ninja...
    Compiling objects...
    Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
    ninja: no work to do.
    g++ -pthread -B /home/julian/miniconda3/compiler_compat -shared -Wl,-rpath,/home/julian/miniconda3/lib -Wl,-rpath-link,/home/julian/miniconda3/lib -L/home/julian/miniconda3/lib -L/home/julian/miniconda3/lib -Wl,-rpath,/home/julian/miniconda3/lib -Wl,-rpath-link,/home/julian/miniconda3/lib -L/home/julian/miniconda3/lib "/home/julian/Documents/Alphapose attempt/AlphaPose/build/temp.linux-x86_64-3.9/detector/nms/src/soft_nms_cpu.o" -o build/lib.linux-x86_64-3.9/detector/nms/soft_nms_cpu.cpython-39-x86_64-linux-gnu.so
    /home/julian/miniconda3/compiler_compat/ld: /lib/x86_64-linux-gnu/libm.so.6: unknown type [0x13] section `.relr.dyn'
    /home/julian/miniconda3/compiler_compat/ld: skipping incompatible /lib/x86_64-linux-gnu/libm.so.6 when searching for /lib/x86_64-linux-gnu/libm.so.6
    /home/julian/miniconda3/compiler_compat/ld: cannot find /lib/x86_64-linux-gnu/libm.so.6
    /home/julian/miniconda3/compiler_compat/ld: /lib/x86_64-linux-gnu/libm.so.6: unknown type [0x13] section `.relr.dyn'
    /home/julian/miniconda3/compiler_compat/ld: skipping incompatible /lib/x86_64-linux-gnu/libm.so.6 when searching for /lib/x86_64-linux-gnu/libm.so.6
    /home/julian/miniconda3/compiler_compat/ld: /lib/x86_64-linux-gnu/libmvec.so.1: unknown type [0x13] section `.relr.dyn'
    /home/julian/miniconda3/compiler_compat/ld: skipping incompatible /lib/x86_64-linux-gnu/libmvec.so.1 when searching for /lib/x86_64-linux-gnu/libmvec.so.1
    /home/julian/miniconda3/compiler_compat/ld: cannot find /lib/x86_64-linux-gnu/libmvec.so.1
    /home/julian/miniconda3/compiler_compat/ld: /lib/x86_64-linux-gnu/libmvec.so.1: unknown type [0x13] section `.relr.dyn'
    /home/julian/miniconda3/compiler_compat/ld: skipping incompatible /lib/x86_64-linux-gnu/libmvec.so.1 when searching for /lib/x86_64-linux-gnu/libmvec.so.1
    collect2: error: ld returned 1 exit status
    error: command '/usr/bin/g++' failed with exit code 1
    

    Do you know what could cause this? Thanks in advance

    opened by JulianGodd 1
  • Bump pillow from 5.3.0 to 9.3.0 in /trackers/PoseFlow

    Bump pillow from 5.3.0 to 9.3.0 in /trackers/PoseFlow

    Bumps pillow from 5.3.0 to 9.3.0.

    Release notes

    Sourced from pillow's releases.

    9.3.0

    https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

    Changes

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    9.3.0 (2022-10-29)

    • Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

    • Initialize libtiff buffer when saving #6699 [radarhere]

    • Inline fname2char to fix memory leak #6329 [nulano]

    • Fix memory leaks related to text features #6330 [nulano]

    • Use double quotes for version check on old CPython on Windows #6695 [hugovk]

    • Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

    • Fixed set_variation_by_name offset #6445 [radarhere]

    • Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

    • Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

    • Added ExifTags enums #6630 [radarhere]

    • Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

    • Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

    • Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

    • Added GPS TIFF tag info #6661 [radarhere]

    • Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

    • Do not attempt normalization if mode is already normal #6644 [radarhere]

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
Owner
Machine Vision and Intelligence Group @ SJTU
Machine Vision and Intelligence Group @ SJTU
Full body anonymization - Realistic Full-Body Anonymization with Surface-Guided GANs

Realistic Full-Body Anonymization with Surface-Guided GANs This is the official

Håkon Hukkelås 30 Nov 18, 2022
Demo for Real-time RGBD-based Extended Body Pose Estimation paper

Real-time RGBD-based Extended Body Pose Estimation This repository is a real-time demo for our paper that was published at WACV 2021 conference The ou

Renat Bashirov 118 Dec 26, 2022
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

SimplePose Code and pre-trained models for our paper, “Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation”, a

Jia Li 256 Dec 24, 2022
Code for "Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo"

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo This repository includes the source code for our CVPR 2021 paper on multi-view mult

Jiahao Lin 66 Jan 4, 2023
Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors, CVPR 2021

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors Human POSEitioning System (H

Aymen Mir 66 Dec 21, 2022
Pose Detection and Machine Learning for real-time body posture analysis during exercise to provide audiovisual feedback on improvement of form.

Posture: Pose Tracking and Machine Learning for prescribing corrective suggestions to improve posture and form while exercising. This repository conta

Pratham Mehta 10 Nov 11, 2022
Real-Time-Student-Attendence-System - Real Time Student Attendence System

Real-Time-Student-Attendence-System The Student Attendance Management System Pro

Rounak Das 1 Feb 15, 2022
🕺Full body detection and tracking

Pose-Detection ?? Overview Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign

Abbas Ataei 20 Nov 21, 2022
3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks

3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks Introduction This repository contains the code and models for the follo

null 124 Jan 6, 2023
Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation.

PersonLab This is a Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation. The model predicts heatmaps and vari

OCTI 160 Dec 21, 2022
PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

PyTorch Realtime Multi-Person Pose Estimation This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-P

Dave Fang 157 Nov 12, 2022
Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)

Realtime Multi-Person Pose Estimation By Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. Introduction Code repo for winning 2016 MSCOCO Keypoints Cha

Zhe Cao 4.9k Dec 31, 2022
Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

PoseNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image" Introduction This repo is official Py

Gyeongsik Moon 677 Dec 25, 2022
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

HKBU High Performance Machine Learning Lab 6 Nov 18, 2022
pytorch implementation of openpose including Hand and Body Pose Estimation.

pytorch-openpose pytorch implementation of openpose including Body and Hand Pose Estimation, and the pytorch model is directly converted from openpose

Hzzone 1.4k Jan 7, 2023
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation Official PyTroch implementation of HPRNet. HPRNet: Hierarchical Point Regre

Nermin Samet 53 Dec 4, 2022
PoseViz – Multi-person, multi-camera 3D human pose visualization tool built using Mayavi.

PoseViz – 3D Human Pose Visualizer Multi-person, multi-camera 3D human pose visualization tool built using Mayavi. As used in MeTRAbs visualizations.

István Sárándi 79 Dec 30, 2022
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image [Project Page] [Paper] [Supp. Mat.] Table of Contents License Description Fittin

Vassilis Choutas 1.3k Jan 7, 2023