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

Overview

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

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

ezgif com-gif-maker (16)

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 ../..

3. Reference

  1. https://github.com/openvinotoolkit/open_model_zoo/tree/2021.4.1/demos/human_pose_estimation_3d_demo/python
  2. https://docs.openvino.ai/2021.4/omz_models_model_human_pose_estimation_3d_0001.html
  3. https://github.com/PINTO0309/PINTO_model_zoo/tree/main/029_human-pose-estimation-3d-0001
Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
Stochastic gradient descent with model building

Stochastic Model Building (SMB) This repository includes a new fast and robust stochastic optimization algorithm for training deep learning models. Th

S. Ilker Birbil 22 Jan 19, 2022
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

An-Introduction-to-Statistical-Learning This repository contains the exercises and its solution contained in the book An Introduction to Statistical L

2.1k Jan 02, 2023
CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary.

CUP-DNN CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary. The model was trained on the expre

1 Oct 27, 2021
CVPRW 2021: How to calibrate your event camera

E2Calib: How to Calibrate Your Event Camera This repository contains code that implements video reconstruction from event data for calibration as desc

Robotics and Perception Group 104 Nov 16, 2022
Official implementation of the MM'21 paper Constrained Graphic Layout Generation via Latent Optimization

[MM'21] Constrained Graphic Layout Generation via Latent Optimization This repository provides the official code for the paper "Constrained Graphic La

Kotaro Kikuchi 73 Dec 27, 2022
A hybrid SOTA solution of LiDAR panoptic segmentation with C++ implementations of point cloud clustering algorithms. ICCV21, Workshop on Traditional Computer Vision in the Age of Deep Learning

ICCVW21-TradiCV-Survey-of-LiDAR-Cluster Motivation In contrast to popular end-to-end deep learning LiDAR panoptic segmentation solutions, we propose a

YimingZhao 103 Nov 22, 2022
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers This is the official PyTorch implementation and models for UP-DETR paper: @a

dddzg 430 Dec 23, 2022
DiAne is a smart fuzzer for IoT devices

Diane Diane is a fuzzer for IoT devices. Diane works by identifying fuzzing triggers in the IoT companion apps to produce valid yet under-constrained

seclab 28 Jan 04, 2023
A variational Bayesian method for similarity learning in non-rigid image registration (CVPR 2022)

A variational Bayesian method for similarity learning in non-rigid image registration We provide the source code and the trained models used in the re

daniel grzech 14 Nov 21, 2022
An imperfect information game is a type of game with asymmetric information

DecisionHoldem An imperfect information game is a type of game with asymmetric information. Compared with perfect information game, imperfect informat

Decision AI 25 Dec 23, 2022
Semantic segmentation task for ADE20k & cityscapse dataset, based on several models.

semantic-segmentation-tensorflow This is a Tensorflow implementation of semantic segmentation models on MIT ADE20K scene parsing dataset and Cityscape

HsuanKung Yang 83 Oct 13, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation

JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation This the repository for this paper. Find extensions of this w

Zhuoyuan Mao 14 Oct 26, 2022
Learned model to estimate number of distinct values (NDV) of a population using a small sample.

Learned NDV estimator Learned model to estimate number of distinct values (NDV) of a population using a small sample. The model approximates the maxim

2 Nov 21, 2022
Joint Channel and Weight Pruning for Model Acceleration on Mobile Devices

Joint Channel and Weight Pruning for Model Acceleration on Mobile Devices Abstract For practical deep neural network design on mobile devices, it is e

11 Dec 30, 2022
Repo for 2021 SDD assessment task 2, by Felix, Anna, and James.

SoftwareTask2 Repo for 2021 SDD assessment task 2, by Felix, Anna, and James. File/folder structure: helloworld.py - demonstrates various map backgrou

3 Dec 13, 2022
Contains code for the paper "Vision Transformers are Robust Learners".

Vision Transformers are Robust Learners This repository contains the code for the paper Vision Transformers are Robust Learners by Sayak Paul* and Pin

Sayak Paul 103 Jan 05, 2023
FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware.

FIRM-AFL FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware. FIRM-AFL addresses two fundamental problems in IoT fuzzing. First, it

356 Dec 23, 2022
Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

LMFD-PAD Note This is the official repository of the paper: LMFD-PAD: Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechani

28 Dec 02, 2022
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations The code of: Weakly Supervised Learning of Instance Segmentation with I

Jiwoon Ahn 472 Dec 29, 2022