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for (int y = 0; y < height; y++) {
    // line pointer
    float * ptr_disp = (float *) (disp.getPtr<uchar>(sl::MEM::CPU) + y * disp.getStepBytes(sl::MEM::CPU));
    uchar4* ptr_image = (uchar4*) (img.getPtr<uchar>(sl::MEM::CPU) + y * img.getStepBytes(sl::MEM::CPU));

    for (int x = 0; x < width; x++) {
        float px_disp = ptr_disp[x];
        uchar4 px = ptr_image[x];
    }
}

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Notes about using Unity-Technologies/PeopleSansPeople in COCO format for kp detection

DATA Generation

Setup

The data cannot be download like a reference dataset, it needs to be generated. On Linux here's a gist of the command used (more details https://github.com/Unity-Technologies/PeopleSansPeople/tree/main/peoplesanspeople_binaries#running-the-linux-binary):

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# wget https://motchallenge.net/data/MOT17Det.zip
# unzip MOT17Det.zip
# rm -rf MOT17Det/test/

import configparser
import os 
import shutil
import numpy as np

def get_metadata_from_seqinfo(seqinfo_path):

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# from https://community.tp-link.com/en/home/forum/topic/208022
# tested on Ubuntu 20.04 with a TP-Link AC1300

git clone https://github.com/cilynx/rtl88x2bu.git
cd rtl88x2bu
VER=$(sed -n 's/\PACKAGE_VERSION="\(.*\)"/\1/p' dkms.conf)
sudo rsync -rvhP ./ /usr/src/rtl88x2bu-${VER}
sudo dkms add -m rtl88x2bu -v ${VER}
sudo dkms build -m rtl88x2bu -v ${VER}
sudo dkms install -m rtl88x2bu -v ${VER}

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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