NE-Smart-Tracker/tracker

NE3-Tracker

This document contains detailed instructions for installing the necessary dependencies for PyTracking. The instrustions have been tested on an Ubuntu 18.04 system.

Requirements

• Conda installation with Python 3.7. If not already installed, install from https://www.anaconda.com/distribution/.
• Nvidia GPU.

Step-by-step instructions

Create and activate a conda environment

conda create --name pytracking python=3.7
conda activate pytracking

Install PyTorch

Install PyTorch with cuda10.

conda install pytorch torchvision cudatoolkit=10.0 -c pytorch

Note:

• It is possible to use any PyTorch supported version of CUDA (not necessarily v10).
• For more details about PyTorch installation, see https://pytorch.org/get-started/previous-versions/.

Install matplotlib, pandas, tqdm, opencv, scikit-image, visdom, tikzplotlib, gdown, and tensorboad

conda install matplotlib pandas tqdm
pip install opencv-python visdom tb-nightly scikit-image tikzplotlib gdown

Install the coco and lvis toolkits

conda install cython
pip install pycocotools
pip install lvis

Install ninja-build for Precise ROI pooling

To compile the Precise ROI pooling module (https://github.com/vacancy/PreciseRoIPooling), you may additionally have to install ninja-build.

sudo apt-get install ninja-build

In case of issues, we refer to https://github.com/vacancy/PreciseRoIPooling.

Install jpeg4py

In order to use jpeg4py for loading the images instead of OpenCV's imread(), install jpeg4py in the following way,

sudo apt-get install libturbojpeg
pip install jpeg4py 

Note: The first step (sudo apt-get install libturbojpeg) can be optionally ignored, in which case OpenCV's imread() will be used to read the images. However the second step is a must.

In case of issues, we refer to https://github.com/ajkxyz/jpeg4py.

Setup the environment

Create the default environment setting files.

# Environment settings for pytracking. Saved at pytracking/evaluation/local.py
python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()"

# Environment settings for ltr. Saved at ltr/admin/local.py
python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"

You can modify these files to set the paths to datasets, results paths etc.