epic_viewer_setup-1.0.2.sh

#!/bin/bash

# If you don't have conda installed, run these in advance.
#wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
#bash Miniconda3-latest-Linux-x86_64.sh


# Run the script using `source epic_viewer_setup.sh`.
if [[ "${BASH_SOURCE[0]}" == "${0}" ]]
then
    echo "Please run script like: source $0"
    exit 1
fi


# IMPORTANT: Replace URLs
# split-N
epic_viewer_URL='https://...'
split_pkl_URL='https://'
split_tar_URL='https://'

# Extract epic_viewer.tar.gz
cd ~
wget "$epic_viewer_URL" -O epic_viewer.tar.gz
tar xvzf epic_viewer.tar.gz
cd epic_viewer-1.0.2

# Install epic_viewer
conda create -n epic_viewer python=3 -y
conda activate epic_viewer
pip install -e .
cd submodules/video_datasets_api
pip install -e .

# Download video and split data
cd ../../data
wget --content-disposition "$split_pkl_URL"
wget "$split_tar_URL" -O split.tar
mkdir videos
tar xvf split.tar -C videos

# Optionally, installing Pillow-SIMD makes video loading so much faster.
# You may see an error if you don't have gcc installed.
conda uninstall -y --force pillow pil jpeg libtiff libjpeg-turbo
pip   uninstall -y         pillow pil jpeg libtiff libjpeg-turbo
conda install -yc conda-forge libjpeg-turbo
CFLAGS="${CFLAGS} -mavx2" pip install --upgrade --no-cache-dir --force-reinstall --no-binary :all: --compile pillow-simd
conda install -y jpeg libtiff

# Run the program
cd ../tools
./epic_multilabel.py

# The program will ask you for video path and split.pkl path. It's in ../data/videos and ../data/split-N.pkl.