e2pose

E2Pose

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/)

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 e2pose.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 e2pose.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 e2pose.py --video VIDEO_PATH

By adding the --model_type option, you can specify model type which is selected from "resnet50", "resnet101", "resnet152", "mobilenet_320", "mobilenet_448". (default is resnet101)

$ python3 e2pose.py --model_type resnet101

Reference

• E2Pose

Framework

Tensorflow

Model Format

ONNX opset=11

Netron

COCO_ResNet50_320x320.onnx.prototxt
COCO_ResNet101_512x512.onnx.prototxt
COCO_ResNet152_448x448.onnx.prototxt
COCO_MobileNetV2_320x320.onnx.prototxt
COCO_MobileNetV2_448x512.onnx.prototxt