Running Open Drone Map on HiPerGator

Following instructions in containers:

Pull the container

Here, we’ve created a folder (on blue) to store the container image:

module load aptainer
srun -t 6:00:00 apptainer pull /blue/ewhite/your_user_id/odm/odm.sif docker://opendronemap/odm

Setup directories

Setup directories. ODM needs the working folder code to exist, and a subfolder called images:

[your_user_id@login12 odm]$ tree
.
├── code
│   ├── images
│   │   ├── DSC00001.JPG
│   │   └── DSC00002.JPG
└── run.slurm

We’ll create run.slurm next:

Setup SLURM

We need to bind a working directory where the outputs + images will go. You’d think you could just bind to /code, which is what ODM would like, but that doesn’t work because the EntryPoint of the container is /code/run.py. So we need to set our working directory somewhere else. This is analogous to docker -v:

#!/bin/bash
#SBATCH --job-name=odm-node
#SBATCH --nodes=1
#SBATCH --partition=hpg-turin
#SBATCH --cpus-per-task=16
#SBATCH --mem=64GB
#SBATCH --time=12:00:00
#SBATCH --gpus=1
#SBATCH --output=./slurm_logs/%A.out
#SBATCH --error=./slurm_logs/%A.err

printenv | grep -i slurm | sort

srun apptainer run --bind "${PWD}:/project" /blue/ewhite/your_user_id/odm/odm.sif --project-path /project --max-concurrency 16

Save this as run.slurm and make sure you’ve added any other SBATCH arguments like the correct user/group IDs.

Copy data

Copy some drone images and go for a walk while rsync runs:

rsync -avz --progress /data/everglades/Flight_1/DCIM/  hpg:/home/your_user_id/code/odm/code/images

For larger jobs, you should use some scratch storage space on blue. You can delete the images from the working directory afterwards.

Run

Launch and check the job was submitted:

sbatch run.slurm
squeuemine

Check logs

Then tail the log while it runs to confirm it’s doing something sensible:

[your_user_id@login12 odm]$ tail -f ./slurm_logs/20526676_4294967294.out
2025-12-08 21:50:58,774 DEBUG: Found 10000 points in 4.388810873031616s
2025-12-08 21:50:59,008 INFO: Extracting ROOT_DSPSIFT features for image DSC00077.JPG
2025-12-08 21:50:59,219 INFO: Extracting ROOT_DSPSIFT features for image DSC00098.JPG
2025-12-08 21:51:04,036 DEBUG: Found 10000 points in 4.76168966293335s
2025-12-08 21:51:04,185 DEBUG: Found 9406 points in 5.121237516403198s
2025-12-08 21:51:04,482 INFO: Extracting ROOT_DSPSIFT features for image DSC00108.JPG
2025-12-08 21:51:04,600 INFO: Extracting ROOT_DSPSIFT features for image DSC00003.JPG
2025-12-08 21:51:08,902 DEBUG: Found 10000 points in 4.365730285644531s
2025-12-08 21:51:09,338 INFO: Extracting ROOT_DSPSIFT features for image DSC00092.JPG
2025-12-08 21:51:09,627 DEBUG: Found 9079 points in 4.97150993347168s
2025-12-08 21:51:10,010 INFO: Extracting ROOT_DSPSIFT features for image DSC00141.JPG
....

[INFO]    No more stages to run
[INFO]    MMMMMMMMMMMNNNMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMNNNMMMMMMMMMMM
[INFO]    MMMMMMdo:..---../sNMMMMMMMMMMMMMMMMMMMMMMMMMMNs/..---..:odMMMMMM
[INFO]    MMMMy-.odNMMMMMNy/`/mMMMMMMMMMMMMMMMMMMMMMMm/`/hNMMMMMNdo.-yMMMM
[INFO]    MMN/`sMMMMMMMMMNNMm/`yMMMMMMMMMMMMMMMMMMMMy`/mMNNMMMMMMMMNs`/MMM
[INFO]    MM/ hMMMMMMMMNs.+MMM/ dMMMMMMMMMMMMMMMMMMh +MMM+.sNMMMMMMMMh +MM
[INFO]    MN /MMMMMMNo/./mMMMMN :MMMMMMMMMMMMMMMMMM: NMMMMm/./oNMMMMMM: NM
[INFO]    Mm +MMMMMN+ `/MMMMMMM`-MMMMMMMMMMMMMMMMMM-`MMMMMMM:` oNMMMMM+ mM
[INFO]    MM..NMMNs./mNMMMMMMMy sMMMMMMMMMMMMMMMMMMo hMMMMMMMNm/.sNMMN`-MM
[INFO]    MMd`:mMNomMMMMMMMMMy`:MMMMMMMNmmmmNMMMMMMN:`hMMMMMMMMMdoNMm-`dMM
[INFO]    MMMm:.omMMMMMMMMNh/  sdmmho/.`..`-``-/sddh+  /hNMMMMMMMMdo.:mMMM
[INFO]    MMMMMd+--/osss+:-:/`  ```:- .ym+ hmo``:-`   `+:-:ossso/-:+dMMMMM
[INFO]    MMMMMMMNmhysosydmNMo   /ds`/NMM+ hMMd..dh.  sMNmdysosyhmNMMMMMMM
[INFO]    MMMMMMMMMMMMMMMMMMMs .:-:``hmmN+ yNmds -:.:`-NMMMMMMMMMMMMMMMMMM
[INFO]    MMMMMMMMMMMMMMMMMMN.-mNm- //:::. -:://: +mMd`-NMMMMMMMMMMMMMMMMM
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[INFO]    MMMMMMMMMMMMMMMMMMMy /o:- `oyhd/ shys+ `-:s-`hMMMMMMMMMMMMMMMMMM
[INFO]    MMMMMMMMNmdhhhdmNMMM`  +d+ sMMM+ hMMN:`hh-  sMMNmdhhhdmNMMMMMMMM
[INFO]    MMMMMms:::/++//::+ho    .+- /dM+ hNh- +/`   -h+:://++/::/smMMMMM
[INFO]    MMMN+./hmMMMMMMNds-  ./oso:.``:. :-``.:os+-  -sdNMMMMMMmy:.oNMMM
[INFO]    MMm-.hMNhNMMMMMMMMNo`/MMMMMNdhyyyyhhdNMMMM+`oNMMMMMMMMNhNMh.-mMM
[INFO]    MM:`mMMN/-sNNMMMMMMMo yMMMMMMMMMMMMMMMMMMy sMMMMMMMNNs-/NMMm`:MM
[INFO]    Mm /MMMMMd/.-oMMMMMMN :MMMMMMMMMMMMMMMMMM-`MMMMMMMo-./dMMMMM/ NM
[INFO]    Mm /MMMMMMm:-`sNMMMMN :MMMMMMMMMMMMMMMMMM-`MMMMMNs`-/NMMMMMM/ NM
[INFO]    MM:`mMMMMMMMMd/-sMMMo yMMMMMMMMMMMMMMMMMMy sMMMs-/dMMMMMMMMd`:MM
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[INFO]    MMMMMMMMMMMMMh/-...-+dMMMm......:+hMMMMs../MMMMMo..sMMMMMMMMMMMM
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[INFO]    ODM app finished - Mon Dec 08 22:13:26  2025
.100 - done.
(ASCII art is fun)

The output will be in odm_orthophoto/odm_orthophoto.tif

Useful flags and other run options

Store high-precision geotags from PPK in geo.txt in the project folder and ODM will pick them up automatically (see here)

"""Convert WISPR PPK coords to OpenDroneMap"""

import argparse
import csv
from pathlib import Path

parser = argparse.ArgumentParser(description="Convert WISPR CSV to ODM geo.txt")
parser.add_argument("root_dir", help="Root directory to search for exif_image_list.csv")
parser.add_argument("output_txt", help="Output geo.txt file for ODM")
args = parser.parse_args()

csv_files = list(Path(args.root_dir).rglob("exif_image_list.csv"))
if len(csv_files) == 0:
    raise SystemExit("Error: No exif_image_list.csv found")
if len(csv_files) > 1:
    raise SystemExit(f"Error: Found multiple exif_image_list.csv files:\n" + "\n".join(str(f) for f in csv_files))

with open(csv_files[0]) as infile, open(args.output_txt, "w") as outfile:
    reader = csv.DictReader(infile)
    outfile.write("EPSG:4326\n")

    for row in reader:
        # ODM format: filename lon lat alt yaw pitch roll [h_acc v_acc]
        outfile.write(
            f"{row['image_name']} {row['longitude']} {row['latitude']} "
            f"{row['altitude']} {row['gimbal_yaw']} {row['gimbal_pitch']} "
            f"{row['gimbal_roll']} {row['x_accuracy']} {row['z_accuracy']}\n"
        )

print(f"Created {args.output_txt}")

Set --max-concurrency to the number of CPUs assigned to the job
--optimize-disk-space to clean up intermediate files at the cost of needing to re-run the job if it crashes (no resume)
--orthophoto-resolution 2 ortho resolution in centimeters. The lower this parameter, the larger the output ortho will be and the slower the processing. The default is 5 which runs fairly quickly and is a good sanity check.
Use GCPs

Array processing.

Here is an example job file that can run on an array of surveys. The list of input folders is provided in folder_list.txt (newline delineated).

#!/bin/bash
#SBATCH --job-name=odm-array
#SBATCH --nodes=1
#SBATCH --partition=hpg-turin
#SBATCH --cpus-per-task=16
#SBATCH --mem=64GB
#SBATCH --time=12:00:00
#SBATCH --gpus=1
#SBATCH --array=1-N%4
#SBATCH --output=./slurm_logs/%A_%a.out
#SBATCH --error=./slurm_logs/%A_%a.err

# Configuration
INPUT_FILE="folder_list.txt"  # Text file with one absolute path per line
WORKING_DIR= # Where you want to create the output folders
ODM_SIF=# Path to the SIF file

# Get the source folder for this array task
SOURCE_FOLDER=$(sed -n "${SLURM_ARRAY_TASK_ID}p" "$INPUT_FILE")

# Get the basename
BASENAME=$(basename "$SOURCE_FOLDER")

# Create target directory structure
TARGET_DIR="${WORKING_DIR}/${BASENAME}"

# Print environment for debugging
printenv | grep -i slurm | sort

mkdir -p "${TARGET_DIR}/code/images"

# Copy JPG files
echo "Copying images from ${SOURCE_FOLDER} to ${TARGET_DIR}/code/images"
rsync -av --include='*.JPG' --include='*.jpg' --exclude='*' "${SOURCE_FOLDER}/" "${TARGET_DIR}/code/images/" || \
echo "Warning: No JPG files found in ${SOURCE_FOLDER}"

# Perform PPK geotagging
python3 wispr_to_odm_ppk.py ${SOURCE_FOLDER} ${TARGET_DIR}/geo.txt || \
echo "Failed to find a PPK coordinate file. Processing will use EXIF GPS data only."

module load cuda

# Run ODM with the target directory as project path
echo "Running ODM on ${TARGET_DIR}"
srun apptainer run --nv --bind "${TARGET_DIR}:/project" \
    "$ODM_SIF" \
    --project-path /project \
    --max-concurrency 16 \
    --orthophoto-resolution 2 \
    --optimize-disk-space

# Clean up the images in the target folder
echo "Removing image folder ${TARGET_DIR}/code/images"
rm -rf "${TARGET_DIR}/code/images"

echo "Setting permissions on target foler"
bash group-permissions-update.sh ${TARGET_DIR}

echo "Completed processing ${BASENAME}"

The param #SBATCH --array=1-N%4 starts an array job with a maximum of 4 concurrent tasks.
Note module load cuda to pass GPU to job

Submission script:

#!/bin/bash

INPUT_FILE="folder_list.txt"
N=$(wc -l < "$INPUT_FILE")

sbatch --array=1-$N job_array.slurm

Last updated on 2026-02-09

Accessing the T-drive 0001-01-01 →