hopenet

Hopenet

Input

(Image from https://pixabay.com/photos/person-human-male-face-man-view-829966/)

Face Detector: BlazeFace

• ailia input shape: (1, 3, 128, 128) RGB channel order
• Pixel value range: [-1, 1]

Head Pose Estimator: Hopenet / Hopenet Lite

• ailia input shape: (batch_size, 3, 224, 224) RGB channel order
• Pixel value range: [0, 1] before normalization
• Preprocessing: normalization using ImageNet statistics

Output

Face Detector: BlazeFace

• ailia Predict API output:
  • Bounding boxes and keypoints
    • Shape: (1, 896, 16)
  • Classification confidences
    • Shape: (1, 896, 1)
• With helper functions, filtered detections with keypoints can be obtained.

Head Pose Estimator: Hopenet / Hopenet Lite

• ailia Predict API output:
  • yaw: scores for yaw angle
    • Shape: (batch_size, 66)
  • pitch: scores for pitch angle
    • Shape: (batch_size, 66)
  • roll: scores for roll angle
    • Shape: (batch_size, 66)
• With helper functions, yaw, pitch and roll in radians can be obtained.

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 hopenet.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 hopenet.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 hopenet.py --video VIDEO_PATH --savepath SAVE_VIDEO_PATH

By adding the --lite option, a lite version of Hopenet is used.

$ python3 blazehand.py --lite

Reference

• deep-head-pose
• deep-head-pose-lite

Framework

PyTorch 1.7.1

Model Format

ONNX opset = 10

Netron

• hopenet_robust_alpha1.onnx.prototxt
• hopenet_lite.onnx.prototxt