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

PyTorch implementation of PNASNet-5 on ImageNet

Tesla Light Show xLights Guide With python

Autotype on websites that have copy-paste disabled like Moodle, HackerEarth contest etc.

Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

Playing around with FastAPI and streamlit to create a YoloV5 object detector

This repository compare a selfie with images from identity documents and response if the selfie match.

Framework that uses artificial intelligence applied to mathematical models to make predictions

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

An Artificial Intelligence trying to drive a car by itself on a user created map

Python scripts form performing stereo depth estimation using the HITNET model in ONNX.

[AAAI22] Reliable Propagation-Correction Modulation for Video Object Segmentation

Source code for deep symbolic optimization.

Code of paper Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification.

Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.

PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

[NeurIPS 2020] Official repository for the project "Listening to Sound of Silence for Speech Denoising"

This is code of book "Learn Deep Learning with PyTorch"

Code for "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds", CVPR 2021

Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021