pose_resnet

Simple Baselines for Human Pose Estimation and Tracking

Input

(Image from https://pixabay.com/ja/photos/%E5%A5%B3%E3%81%AE%E5%AD%90-%E7%BE%8E%E3%81%97%E3%81%84-%E8%8B%A5%E3%81%84-%E3%83%9B%E3%83%AF%E3%82%A4%E3%83%88-5204299/)

Ailia input shape: (1, 3, 256, 192)
Range: [-2.0, 2.0]

Output

Usage

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

For the sample image,

$ python3 pose_resnet.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 pose_resnet.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 pose_resnet.py --video VIDEO_PATH

Two versions of the model are provided: full integer quantization (8-bit) and full precision floating point (32-bit). By default, the full integer quantization is used but the user can select the other version by passing the --float flag.

$ python3 midas.py --float

Reference

Simple Baselines for Human Pose Estimation and Tracking

Framework

tensorflow 2.12.0

Netron

pose_resnet_50_256x192_float32.tflite

pose_resnet_50_256x192_int8.tflite