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README.md

BlazeHand

Input

(Image from https://pixabay.com/photos/stop-no-photo-no-photographing-hand-565609/)

Detector

  • ailia input shape: (1, 256, 256, 3) RGB channel order
  • Pixel value range: [0, 1]

Landmark

  • ailia input shape: (batch_size, 256, 256, 3) BGR channel order
  • Pixel value range: [0, 1]

Output

Detector

  • ailia Predict API output:
    • Classification confidences
      • Shape: (1, 2944, 1)
    • Bounding boxes and keypoints
      • Shape: (1, 2944, 18)
  • With helper functions, filtered detections with keypoints can be obtained.

Estimator

  • ailia Predict API output:
    • hand_flag: confidence score [0, 1] of hand presence
      • Shape: (batch_size, 1, 1, 1)
    • handedness: classification score [0, 1] of right handedness
      • Shape: (batch_size, 1, 1, 1)
      • Estimated probability of the right handedness (and the opposite handedness has an estimated probability of 1 - score).
      • Handedness is determined assuming the input image is mirrored, i.e., taken with a front-facing/selfie camera with images flipped horizontally. If it is not the case, please swap the handedness output in the application.
    • landmarks: 21 hand landmarks with (x, y, z) coordinates
      • Shape: (batch_size, 63) [21 3D points]
      • x and y are normalized to [0.0, 1.0] by the image width and height respectively. z represents the landmark depth with the depth at the wrist being the origin, and the smaller the value the closer the landmark is to the camera. The magnitude of z uses roughly the same scale as x.
  • With helper functions, image coordinates of hand landmarks can be obtained.

Usage

Automatically downloads the tflite file on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 blazehand.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 blazehand.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 blazehand.py --video VIDEO_PATH --savepath SAVE_VIDEO_PATH

By adding the --hands option, you can decide the maximum number of tracked hands. By default, it allows tracking up to 2 hands.

$ python3 blazehand.py --hands 3

Reference

Framework

TensorFlow 2.4.1

Netron