3dmppe_posenet

PoseNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image"

input

(from https://github.com/mks0601/3DMPPE_POSENET_RELEASE/tree/master/demo)

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,

$ python 3dmppe_posenet.py
(ex on CPU)  $ python 3dmppe_posenet.py -e 0
(ex on BLAS) $ python 3dmppe_posenet.py -e 1
(ex on GPU)  $ python 3dmppe_posenet.py -e 2

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 3dmppe_posenet.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH
$ python3 3dmppe_posenet.py -i IMAGE_PATH -s SAVE_IMAGE_PATH

By adding the --video option, you can input the video.

$ python3 3dmppe_posenet.py --video VIDEO_PATH --savepath SAVE_VIDEO_PATH
$ python3 3dmppe_posenet.py -v VIDEO_PATH -s SAVE_VIDEO_PATH
(ex) $ python3 3dmppe_posenet.py --video input.mp4 --savepath output.mp4

Reference

Mask R-CNN : real-time neural network for object instance segmentation PoseNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image"

Framework

Pytorch

Model Format

ONNX opset = 10

Netron

mask_rcnn_R_50_FPN_1x.onnx.prototxt rootnet_snapshot_18.opt.onnx.prototxt posenet_snapshot_24.opt.onnx.prototxt