human_part_segmentation

Human-Part-Segmentation

Input

(Image from https://github.com/PeikeLi/Self-Correction-Human-Parsing/blob/master/demo/demo.jpg)

Shape : (1, 3, 473, 473)

Output

• parsing shape : (1, 20, 119, 119)
• fusion shape : (1, 20, 119, 119)
• edge shape : (1, 2, 119, 119)

Category

CATEGORY = (
    'Background', 'Hat', 'Hair', 'Glove', 'Sunglasses', 'Upper-clothes', 'Dress', 'Coat',
    'Socks', 'Pants', 'Jumpsuits', 'Scarf', 'Skirt', 'Face', 'Left-arm', 'Right-arm',
    'Left-leg', 'Right-leg', 'Left-shoe', 'Right-shoe'
)

usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 human_part_segmentation.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 human_part_segmentation.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 human_part_segmentation.py --video VIDEO_PATH

Reference

• Self Correction for Human Parsing
• Human-Part-Segmentation

Framework

Pytorch

Model Format

ONNX opset=11

Netron

resnet-lip.onnx.prototxt