indexnet

Indexnet

input

input image

input trimap

(from https://github.com/open-mmlab/mmediting/tree/master/tests/data/merged and https://github.com/open-mmlab/mmediting/tree/master/tests/data/trimap)

Ailia input shape: (1, 4, 576, 800) input range: (0,1) input color order : RGBA(torch)

output

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 indexnet.py

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

$ python3 indexnet.py --input IMAGE_PATH --trimap TRIMAP_PATH --savepath SAVE_IMAGE_PATH

If you do not have a trimap image for your input image, you can use the -a u2net option, while not setting the --trimap option. It will automatically use the U^2-Net model to compute a trimap of your input image.

$ python3 indexnet.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH -a u2net

You can use onnxRuntime for inference with -n option.

$ python3 indexnet.py -n

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. The trimap is generated by U^2-Net.

$ python3 indexnet.py --video VIDEO_PATH

Reference

Indices Matter: Learning to Index for Deep Image Matting

Framework

Pytorch 1.3.0

Model Format

ONNX opset = 11

Netron

indexnet.onnx.prototxt