/demo_bodypix_node

Demo BodyPix 2.0 on Node.js

Primary LanguagePython

BodyPix on Node.js

Segmented by BodyPix 2.0 and visualized by Python.

Usage

Directory tree

├── demo                # `root_dir` 
│   ├── mask.mp4   # step 4 output
│   ├── demo.mp4        # origin input
│   ├── jpgs            # step 1 output
│   ├── jsons           # stpe 2 output
│   └── masked_jpgs     # step 3 output

Requirements

  • ffmpeg
  • Python3
  • opencv Python
  • node.js

Running: 5 steps

  • step 0 Make a directory as root_dir, and put a .mp4 file to your root_dir. The .mp4 file name must be the same as your root_dir, e.g. a demo.mp4 in demo directory.

run local BodyPix models file server:

cd models && python3 -m http.server

steps 1-4

  • step 1
python3 1_mp4_to_jpgs.py `root_dir`
  • step 2
node main.js `root_dir`
  • step 3
python3 utils/visualize_masked_image.py `root_dir`
  • step 4
python3 4_jpgs_to_mp4.py `root_dir`

Or All in on steps 1-4

python3 all_in_one.py `root_dir`

logs on CPU:

python3 all_in_one.py /demo_bodypix/demo1
run: python3 1_mp4_to_jpgs.py /demo_bodypix/demo1
Elapsed time: 0.88
run: node src/main.js /demo_bodypix/demo1
Elapsed time: 106.41
run: python3 utils/visualize_masked_image.py /demo_bodypix/demo1
Elapsed time: 123.97
run: python3 4_jpgs_to_mp4.py /demo_bodypix/demo1
Elapsed time: 1.19

TODO

  • Fix Out of memory when called in loop.
async function loadImage(path) {
    const file = await fs.promises.readFile(path);
    const image = await tfjs.node.decodeImage(file, 3);
    return image;
}

坑