@alezanai/torquator

@alezanai/torquator

Command line interface for Alezan.ai Torquator SaaS

Installation

Torquator render command is using opencv4nodejs. To avoid re-compiling the code each time, you can do export OPENCV_BUILD_ROOT=~/opencv so it will reuse the existing openc installation.

npm install -g @alezanai/torquator

Full installation

the render command requires the opencv package to be used. It can be installed using

torquator-install-opencv

Simple usage

First you need to Assume an AWS Role that give you access to torquator SaaS.

Then you can run

export AWS_PROFILE=<your profile>
torquator replay <video filename> <gps-filename> --race-name <race name>

<race name>: should be like 2022-10-05_R1C1_A (A=Attelé)

Commands

torquator torquator replay

Usage: torquator replay [options] <video-filename> <gps-filename>

Arguments:
  video-filename             The .mxf/.footage video filename
  gps-filename               The .jsonl video filename

Options:
  --race-name <raceName>     The race name (ex 2020-12-08_R4C3_A)
  --output [filename]        The .jsonl shower output filename
  --env [string]             The environment (default: "prd")
  --client-id [string]       The client id (default: "equidia")
  --user-id [string]         The user id (default: "YYYY-MM-DD_torquator_cli")
  --region [string]          aws region (default: "eu-west-1")
  --aws-account-id [string]  Service provider account id
  -f, --force                If set, reupload the material with the same name without throwing error
  --reuse                    If set, reuse existing cloud file without re-uploading, --force and --reuse cannot be use together
  -h, --help                 display help for command

torquator torquator render

Usage: torquator render [options] <video-filename> <output-video-filename> <shower-filename>

Arguments:
  video-filename                                   The .mxf/.footage video filename
  output-video-filename                            The .mxf/.footage output video filename
  shower-filename                                  jsonl showers input filename

Options:
  --start [num]                                    Number of second to offset at the begining of the file (default: 0)
  --duration [num]                                 Maximum number of second of the output
  --start-date [date]                              ISO Date String to force the start of the video timestamp
  --display-frame-info                             will print frame info
  --display-interpolation                          will print interpolate box in different color
  --template <box|sky-shower|arrow|arrow-trackid>  design of the output result (default: "arrow")
  --nums <nums>                                    numbers to display (default: "all")
  --pitch-conf-path <path>                         json pitch conf to draw
  -h, --help                                       display help for command

torquator torquator info

Usage: torquator info [options] <video-filename>

Arguments:
  video-filename  The .mxf/.footage video filename

Options:
  -h, --help      display help for command

torquator torquator slice

Usage: torquator slice [options] <video-filename> <output-filename>

Arguments:
  video-filename    The .mxf/.footage video filename
  output-filename   The .mxf/.footage output video filename

Options:
  --start <num>     Number of second to offset at the begining of the file
  --duration <num>  Maximum number of second of the output
  -h, --help        display help for command

debug

To get more debugging information just do

export LOG_LEVEL=debug

on Windows

set LOG_LEVEL=debug

Docker Usage

You can also use the docker image

docker run --tty -v $(pwd):$(pwd) -w $(pwd)\
        alezanai/torquator \
        render \
        <input-filename> \
        <output-video-filename> \
        <shower-filename>

Connect to AWS from docker

With AWS_PROFILE

We suggest to use the AWS_PROFILE environment variable with $HOME/.aws/credentials and $HOME/.aws/config

docker run --tty -v $(pwd):$(pwd) -w $(pwd) \
        -v $HOME/.aws/credentials:/root/.aws/credentials:ro \
        -v $HOME/.aws/config:/root/.aws/config:ro \
        -e AWS_PROFILE \
        alezanai/torquator \
        render \
        <input-filename> \
        <output-video-filename> \
        <shower-filename>

Directly with AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY

You can also use AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY

docker run --tty -v $(pwd):$(pwd) -w $(pwd) \
        -e AWS_ACCESS_KEY_ID \
        -e AWS_SECRET_ACCESS_KEY \
        alezanai/torquator \
        render \
        <input-filename> \
        <output-video-filename> \
        <shower-filename>

Known issues

### Cannot read property 'VideoCapture' of undefined

This might occurs because Opencv is not installed, you should try npm run opencv

Debug

export LOG_LEVEL=debug