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

Framework for joint representation learning, evaluation through multimodal registration and comparison with image translation based approaches

An interpreter for RASP as described in the ICML 2021 paper "Thinking Like Transformers"

Can we do Customers Segmentation using PHP and Unsupervized Machine Learning ? Yes we can ! 🤡

Library for time-series-forecasting-as-a-service.

Code to compute permutation and drop-column importances in Python scikit-learn models

Dense Unsupervised Learning for Video Segmentation (NeurIPS*2021)

Code accompanying "Learning What To Do by Simulating the Past", ICLR 2021.

Code for the paper "Asymptotics of ℓ2 Regularized Network Embeddings"

Converting CPT to bert form for use

Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021

This repo contains code to reproduce all experiments in Equivariant Neural Rendering

Python port of R's Comprehensive Dynamic Time Warp algorithm package

Files for a tutorial to train SegNet for road scenes using the CamVid dataset

AntiFuzz: Impeding Fuzzing Audits of Binary Executables

Intrusion Test Tool with Python

A-ESRGAN aims to provide better super-resolution images by using multi-scale attention U-net discriminators.

[ICCV-2021] An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation

Source code for From Stars to Subgraphs

Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

Learning Domain Invariant Representations in Goal-conditioned Block MDPs