hrnet_segmentation

HRNet segmentation

input

(from https://www.cityscapes-dataset.com/)

Ailia input shape: (1, 3, 512, 1024)
Range:[0, 1]

output

• Normal output

• Smoothed 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 hrnet_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 hrnet_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 hrnet_segmentation.py --video VIDEO_PATH

We have three pretrained-model:

• HRNetV2-W48
• HRNetV2-W18-Small-v1
• HRNetV2-W18-Small-v2 (default)
  You can specify the architecture you want following --arch / -a option.

python3 hrnet_segmentation.py --arch HRNetV2-W48

If you want the segmentated image to be smooth, use --smooth option.
By applying resize method interpolaation=cv2.INTER_LINEAR, the visualisation will be more smooth.

python3 hrnet_segmentation.py -a HRNetV2-W48 --smooth

Reference

High-resolution networks (HRNets) for Semantic Segmentation

Framework

PyTorch 0.4.1

Model Format

ONNX opset = 10

Netron

HRNetV2-W18-Small-v1.onnx.prototxt

HRNetV2-W18-Small-v2.onnx.prototxt