C++ Prediction Library Installation

Note that C++ prediction library requires NVIDIA GPU acceleration. HyperPose is developed and frequently tested on Linux platforms (i.e., Ubuntu 18.04). Hence, we recommend you to build HyperPose on Linux.

Build From Source



Packages of other versions might also work but not tested.

TensorRT Tips

For Linux users, you are 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).

CUDA-CuDNN-TensorRT Compatibility

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

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 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 --build .
  • Step 3: Execute your codes!

Go to Quick Start to test your installation.