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# This gitlab runner config provide full gpu support for a docker runner (including hw decoding etc)
# Requires gitlab runner >= 13.9 for 'gpus=all', can be 'runtime = "nvidia"' alternatively for older versions (not tested with NVIDIA_DRIVER_CAPABILITIES)

# Some other options may not be required such as 'privileged' but I needed it (for USB device access)
concurrent = 1
check_interval = 0

[[runners]]
  name = "my_nvidia_runner"
  url = <gitlab_url>

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alias make='make -j$(nproc)'
alias m='cmake ..; make -j$(nproc)'
alias c='mkdir -p build; cd build; cmake ..; make -j$(nproc)'
alias b='mkdir -p build; cd build; cmake .. -G Ninja; ninja'
alias magicmerge='git merge -s recursive -Xignore-space-at-eol'
alias gsa='git submodule add '
alias dock='docker run -it --privileged --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all -v /:/host --network=host -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix '

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# This script will setup USB rules to open the ZED cameras without root access
# This can also be useful to access the cameras from a docker container without root (this script needs to be run on the host)
# NB: Running the ZED SDK installer will already setup those

# Print the commands
set -x
# Download the lightest installer
wget -q https://download.stereolabs.com/zedsdk/3.5/jp44/jetsons -O zed_installer.run
# Extracting only the file we're interested in
bash ./zed_installer.run --tar -x './99-slabs.rules'  > /dev/null 2>&1

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# escape=`

# References:
# https://docs.microsoft.com/en-us/visualstudio/install/build-tools-container?view=vs-2019 
# https://github.com/microsoft/vs-dockerfiles/blob/main/native-desktop/Dockerfile
FROM mcr.microsoft.com/dotnet/framework/sdk:4.8-windowsservercore-ltsc2019

# Restore the default Windows shell for correct batch processing.
SHELL ["cmd", "/S", "/C"]

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# Goal is to convert this dataset into a mmaction2 friendly one (VideoDataset)
# https://github.com/open-mmlab/mmaction2/blob/master/docs/tutorials/3_new_dataset.md
import os
import random

output="ucf_crimes_custom"
output_ext="_list.txt"
ratio_train=0.8

output_list=[]

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dest="/foo/bar"
curr="/foo/bar2/"

mkdir "${dest}"
dirs=($(find . -type d))
for dir in "${dirs[@]}"; do
echo "========================="
  echo $dir
  cd "${curr}${dir}"
  pwd

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"""
Based on https://raw.githubusercontent.com/jkjung-avt/yolov4_crowdhuman/master/data/gen_txts.py

Inputs:
    * nothing
    * or folder with CrowdHuman_train01.zip, CrowdHuman_train02.zip, CrowdHuman_train03.zip, CrowdHuman_val.zip, annotation_train.odgt, annotation_val.odgt

python crowdhuman_to_yolo.py --dataset_path foo/bar/

Outputs:

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#docker run --gpus=all -it -v /:/host/ nvcr.io/nvidia/pytorch:22.02-py3
host_folder="/host/tmp/yolov5_results.txt"
rm ${host_folder}

git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -qr requirements.txt
rm requirements.txt
pip uninstall opencv-python
pip install opencv-python-headless==4.4.0.40 ujson

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import cv2
import numpy as np

disp_u8c4 = cv2.imread("IROS_ORStereo_4K/FoV60/CoalMine/disparity/000002_disp_lu1.png", cv2.IMREAD_UNCHANGED)
disp_gt_f32 = disp_u8c4.view(dtype=np.float32)

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ZED_SDK_VERSION="3.5"
URL_base="https://download.stereolabs.com/zedsdk/${ZED_SDK_VERSION}/"
URL_BUCKET="https://stereolabs.sfo2.digitaloceanspaces.com/zedsdk"
#URL_BUCKET="https://stereolabs.sfo2.cdn.digitaloceanspaces.com/zedsdk"

check_HTTP_err_code() {
    http_err=$(curl -I -s -o /dev/null -w "%{http_code}" $1)
    if [ "$http_err" != "200" ] && [ "$http_err" != "302" ]; then
        echo "ERROR: $http_err -> $1"
    fi