== Processing the InfraTec IR8300 data ==

The data are in ascii files with a .asc extension. They have 3 sections, the first 2 containing metadata and the 3rd containing the image data.

The procedure to process the data is:
* convert asc to tif files for easier managing / tagging using {{{convert_infratec_to_tif.sh}}}
* tag the images with the navigation information using {{{get_nav_for_infratec_image.py}}}

After this the images are ready to be geocorrected with APL using the photo geocorrection methodology, using {{{photo_geocorrection.py}}}

There is a helper script {{{infratec_quick_run.sh}}} and container {{{process_infratec}}} that can be used to make this a little easier, but loses some functionality of the individual scripts.

== Instructions for container method ==

Build the container if it doesn't already exist (it does!) by using the {{{make_docker.sh}}} script and the {{{-I}}} flag.

Then run it with the following inputs:
{{{
COMPULSORY
-i <dir> = input directory of asc files from Infratec instrument
-o <dir> = output directory where tifs and geocorrected imagery goes
-s <file> = SBET file of navigation to tag to images
-c <file> = the config file for the geocorrection

OPTIONAL
-d = delete the intermediate geocorrection files
-r = rotate the Infratec imagery by 180 degrees
-T = input data are TIFF format files
}}}

It's important that the container can see the drives where the data are - if not in the user home space or /tmp then you must bind the drives to the container. This is easiest done by exporting the global variable SINGULARITY_BIND, e.g. to add the arsf data you may mount like this:

{{{export SINGULARITY_BIND="/users/rsg/arsf/arsf_data/:/users/rsg/arsf/arsf_data/}}}

The command then can be run:

{{{
process_infratec.sif -i  /users/rsg/arsf/arsf_data/2022/flight_data/uk/GB22_02-2022_230_Alconbury/thermal/infratec/ImageIR_8300/220818_151730_line14 \
    -o /tmp/quickouts/ \
    -s /users/rsg/arsf/arsf_data/2022/flight_data/uk/GB22_02-2022_230_Alconbury/posatt/applanix/proc/sbet_2022230.out  \
    -c photo_geocorrection.cfg \
    -d \
    -r
}}}

== Geocorrection config file ==

For doing the photograph geocorrection you need to create a config file based from the template at:
{{{internal-code/tools/frame_camera_tools/photo_geocorrection.cfg}}}

A very basic example will look like this:

{{{
[DEFAULT]

[navdata]
run: false

[aplcorr]
run: true
exe: aplcorr
vvfile: /users/rsg/arsf/calibration/2022/infratec/infratec_vv.bil
boresight: 0 0 0
dem: /users/rsg/arsf/arsf_data/2022/flight_data/uk/GB22_02-2022_230_Alconbury/processing/hyperspectral/dem/GB22_02-2022_230-SRTM.dem

[apltran]
run: true
exe: apltran
projstr: EPSG:32630

[aplmap]
run: true
exe: aplmap
}}}

== Creation of view vectors  or running scripts independently ==

If view vectors don't exist for the camera then you can create some using the {{{make_camera_vv.py}}} script. You will need a camera parameter config file such as the examples in {{{internal-code/tools/frame_camera_tools/parameter_files}}}.

To run this within the singularity container you can open a shell in it:

{{{singularity shell /tmp/process_infratec.sif}}} and then run e.g., {{{make_camera_vv.py --help}}}

or in docker it'll be something like:

{{{docker exec -it <mycontainer> bash}}}


== Running scripts individually ==

This may be necessary if you don't want to use the basic automated workflow, for instance to work from files other than in the ascii format.

Go into a shell (see above) and then run the scripts individually. These are:

{{{/usr/bin/convert_infratec_to_tif.sh}}}      convert the ASCII to TIFFs and copy the metadata into XMP tags

{{{/usr/bin/get_nav_for_infratec_image.py}}}     Tag the TIFFs with the Navigation information

{{{/usr/bin/make_camera_vv.py}}}      Generate view vector file for the camera based on given parameters                     

{{{/usr/bin/photo_geocorrection.py}}}  Geocorrect the TIFF images

You can also run the APL tools within this container.