PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

Related tags

Deep Learning computer-vision deep-learning python3 pytorch human-pose-estimation cvpr-2017
Overview

PyTorch Realtime Multi-Person Pose Estimation

This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-Person_Pose_Estimation.

Model

Thanks to @last-one, we can just download converted model weight file from Google Drive or Baidu, password: s7qh.

Citation

Please cite the paper in your publications if it helps your research:

@inproceedings{cao2017realtime,
  author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  booktitle = {CVPR},
  title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  year = {2017}
 }
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Comments
  • Finding the body parts

    Finding the body parts

    Hi,

    Thanks for the nice work. Is there any way to extract the different body parts? I mean given an output coordinate of the algorithm, find the corresponding body part.

    opened by alrzmshy 1
  • Misalignment error

    Misalignment error

    Hi, you resize(bilinear upsample) the output heatmap and paf to original image size, however, the outputs comes from padded images. Since you have changedthe height-width ratio, would it cause misalignment error?

    for i, scale in enumerate(multiplier):
        img_test = cv2.resize(img_raw, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
        img_test_pad, pad = pad_right_down_corner(img_test, param_stride, param_stride)
        img_test_pad = np.transpose(np.float32(img_test_pad[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5

        feed = Variable(torch.from_numpy(img_test_pad)).cuda()
        output1, output2 = model(feed)

        print(output1.size())
        print(output2.size())

        heatmap = nn.UpsamplingBilinear2d((img_raw.shape[0], img_raw.shape[1])).cuda()(output2)

        paf = nn.UpsamplingBilinear2d((img_raw.shape[0], img_raw.shape[1])).cuda()(output1)
    opened by lck1201 0
Owner
Dave Fang
Dave Fang
GitHub
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