82 lines
2.9 KiB
Markdown
82 lines
2.9 KiB
Markdown
<!--
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* @Date: 2021-04-02 11:53:16
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* @Author: Qing Shuai
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* @LastEditors: Qing Shuai
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* @LastEditTime: 2021-07-22 20:57:16
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* @FilePath: /EasyMocapRelease/doc/quickstart.md
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-->
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# Quick Start
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First install this project following [install](./installation.md)
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## Demo
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We provide an example multiview dataset[[dropbox](https://www.dropbox.com/s/24mb7r921b1g9a7/zju-ls-feng.zip?dl=0)][[BaiduDisk](https://pan.baidu.com/s/1lvAopzYGCic3nauoQXjbPw)(vg1z)], which has 800 frames from 23 synchronized and calibrated cameras. After downloading the dataset, you can run the following example scripts.
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```bash
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data=path/to/data
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# 0. extract the video to images
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python3 scripts/preprocess/extract_video.py ${data} --handface
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# 2.1 example for SMPL reconstruction
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python3 apps/demo/mv1p.py ${data} --out ${data}/output/smpl --vis_det --vis_repro --undis --sub_vis 1 7 13 19 --vis_smpl
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# 2.2 example for SMPL-X reconstruction
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python3 apps/demo/mv1p.py ${data} --out ${data}/output/smplx --vis_det --vis_repro --undis --sub_vis 1 7 13 19 --body bodyhandface --model smplx --gender male --vis_smpl
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# 2.3 example for MANO reconstruction
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# MANO model is required for this part
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python3 apps/demo/mv1p.py ${data} --out ${data}/output/manol --vis_det --vis_repro --undis --sub_vis 1 7 13 19 --body handl --model manol --gender male --vis_smpl
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python3 apps/demo/mv1p.py ${data} --out ${data}/output/manor --vis_det --vis_repro --undis --sub_vis 1 7 13 19 --body handr --model manor --gender male --vis_smpl
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```
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# Demo On Your Dataset
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## 0. Prepare Your Own Dataset
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```bash
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<seq>
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├── intri.yml
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├── extri.yml
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└── videos
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├── 1.mp4
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├── 2.mp4
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├── ...
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├── 8.mp4
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└── 9.mp4
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```
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The input videos are placed in `videos/`.
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Here `intri.yml` and `extri.yml` store the camera intrinsici and extrinsic parameters.
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See [`apps/calibration/Readme`](../apps/calibration/Readme.md) for instruction of camera calibration.
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See [`apps/calibration/camera_parameters`](../apps/calibration/camera_parameters.md) for the format of camera parameters.
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### 1. Run [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
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```bash
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data=path/to/data
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out=path/to/output
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python3 scripts/preprocess/extract_video.py ${data} --openpose <openpose_path> --handface
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```
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- `--openpose`: specify the openpose path
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- `--handface`: detect hands and face keypoints
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### 2. Run the code
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The input flags:
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- `--undis`: use to undistort the images
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- `--start, --end`: control the begin and end number of frames.
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The output flags:
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- `--vis_det`: visualize the detection
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- `--vis_repro`: visualize the reprojection
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- `--sub_vis`: use to specify the views to visualize. If not set, the code will use all views
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- `--vis_smpl`: use to render the SMPL mesh to images.
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- `--write_smpl_full`: use to write the full poses of the SMPL parameters
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### 3. Output
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Please refer to [output.md](../doc/02_output.md) |