Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.

Last update: Oct 03, 2022

Overview

human-pose-estimation-3d-python-cpp

RealSenseD435 (RGB) 480x640 + CPU Corei9 45 FPS (Depth is not used)

1. Run

1-1. RealSenseD435 (RGB) 480x640 + CPU Corei9 45 FPS (Depth is not used)

$ xhost +local: && \
docker run -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--device /dev/video1:/dev/video1:mwr \
--device /dev/video2:/dev/video2:mwr \
--device /dev/video3:/dev/video3:mwr \
--device /dev/video4:/dev/video4:mwr \
--device /dev/video5:/dev/video5:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
ghcr.io/pinto0309/openvino2tensorflow:latest

$ python3 human_pose_estimation_3d_demo.py \
--model models/openvino/FP16/human-pose-estimation-3d-0001_bgr_480x640.xml \
--device CPU \
--input 4

1-2. RealSenseD435 (RGB) 480x640 + iGPU (OpenCL)

$ xhost +local: && \
docker run -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--device /dev/video1:/dev/video1:mwr \
--device /dev/video2:/dev/video2:mwr \
--device /dev/video3:/dev/video3:mwr \
--device /dev/video4:/dev/video4:mwr \
--device /dev/video5:/dev/video5:mwr \
--net=host \
-e LIBVA_DRIVER_NAME=iHD \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
ghcr.io/pinto0309/openvino2tensorflow:latest

$ python3 human_pose_estimation_3d_demo.py \
--model models/openvino/FP16/human-pose-estimation-3d-0001_bgr_480x640.xml \
--device GPU \
--input 4

1-3. General USB Camera 480x640 + CPU

$ xhost +local: && \
docker run -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
ghcr.io/pinto0309/openvino2tensorflow:latest

$ python3 human_pose_estimation_3d_demo.py \
--model models/openvino/FP16/human-pose-estimation-3d-0001_bgr_480x640.xml \
--device CPU \
--input 0

2. Build

$ PYTHON_PREFIX=$(python3 -c "import sys; print(sys.prefix)") \
&& PYTHON_VERSION=$(python3 -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')") \
&& PYTHON_INCLUDE_DIRS=${PYTHON_PREFIX}/include/python${PYTHON_VERSION}

$ NUMPY_INCLUDE_DIR=$(python3 -c "import numpy; print(numpy.get_include())")

$ mkdir -p pose_extractor/build && cd pose_extractor/build

$ cmake \
-DPYTHON_INCLUDE_DIRS=${PYTHON_INCLUDE_DIRS} \
-DNUMPY_INCLUDE_DIR=${NUMPY_INCLUDE_DIR} ..

$ make && cp pose_extractor.so ../.. && cd ../..

Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.

Related tags

Overview

human-pose-estimation-3d-python-cpp

1. Run

1-1. RealSenseD435 (RGB) 480x640 + CPU Corei9 45 FPS (Depth is not used)

1-2. RealSenseD435 (RGB) 480x640 + iGPU (OpenCL)

1-3. General USB Camera 480x640 + CPU

2. Build

3. Reference

Owner

Katsuya Hyodo

👐OpenHands : Making Sign Language Recognition Accessible (WiP 🚧👷‍♂️🏗)

Image Deblurring using Generative Adversarial Networks

Background-Click Supervision for Temporal Action Localization

Steer OpenAI's Jukebox with Music Taggers

PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

SGoLAM - Simultaneous Goal Localization and Mapping

RTSeg: Real-time Semantic Segmentation Comparative Study

[3DV 2021] Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation

商品推荐系统

[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation

Semi-supervised semantic segmentation needs strong, varied perturbations

PyElastica is the Python implementation of Elastica, an open-source software for the simulation of assemblies of slender, one-dimensional structures using Cosserat Rod theory.

disentanglement_lib is an open-source library for research on learning disentangled representations.

Create images and texts with the First Order Generative Adversarial Networks

Zsseg.baseline - Zero-Shot Semantic Segmentation

[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

Code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization,

Adaptive Denoising Training (ADT) for Recommendation.

This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task