C++ Prediction Library Installation

Docker Environment Installation

To ease the installation, you can use HyperPose library in our docker image where the environment is pre-installed.

Official Docker Image

NVidia docker support is required to execute our docker image.

The official image is on DockerHub.

# Pull the latest image.
docker pull tensorlayer/hyperpose

# Dive into the image. (Connect local camera and imshow window)
xhost +; docker run --gpus all -it -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --device=/dev/video0:/dev/video0 --entrypoint /bin/bash tensorlayer/hyperpose
# For users that cannot access a camera or X11 server. You may also use:
# docker run --rm --gpus all -it --entrypoint /bin/bash tensorlayer/hyperpose

Note that the entry point is the hyperpose-cli binary in the build directory.

Build docker from source

# Enter the repository folder.
USER_DEF_NAME=my_hyperpose
docker build -t $(USER_DEF_NAME) .
docker run --gpus all $(USER_DEF_NAME)

Build From Source

Prerequisites

  • C++ 17 Compiler. (g++7, clang++4.0, MSVC19.0 or newer)

  • CMake 3.5+

  • Third-Party

Other versions of the packages may also work but not tested.

About TensorRT installation

  • For Linux users, you highly recommended to install it in a system-wide setting. You can install TensorRT7 via the debian distributions or NVIDIA network repo(CUDA and CuDNN dependency will be automatically installed).

  • Different TensorRT version requires specific CUDA and CuDNN version. For specific CUDA and CuDNN requirements of TensorRT7, please refer to this.

  • Also, for Ubuntu 18.04 users, this 3rd party blog may help you.

Build On Ubuntu 18.04

# >>> Install OpenCV3+ and other dependencies. 
sudo apt -y install cmake libopencv-dev libgflags-dev
# !Note that the APT version OpenCV3.2 on Ubuntu18.04 has some trouble on Cameras Newer version is suggested.
# You are highly recommended to install OpenCV 4+ from scratch also for better performance.

# >>> Install dependencies to run the scripts in `${REPO}/scripts`
sudo apt install python3-dev python3-pip 

# >>> Install CUDA/CuDNN/TensorRT: https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-debian

# >>> Build HyperPose
git clone https://github.com/tensorlayer/hyperpose.git
cd hyperpose
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release && cmake --build .

Build with User Codes

You can directly write codes and execute it under the hyperpose repository.

  • Step 1: Write your own codes in hyperpose/examples/user_codes with suffix .cpp.

  • Step 2:

mkdir -p build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_USER_CODES=ON   # BUILD_USER_CODES is by default on
cmake --build .

  • Step 3: Execute your codes!

Just go to Quick Start to test your installation.