ODM Documentation

1. Project Overview & Quickstart (OpenDroneMap/ODM) Copy Chapter

File: README.md Copy Chapter

An open source command line toolkit for processing aerial drone imagery. ODM turns simple 2D images into:

• Classified Point Clouds
• 3D Textured Models
• Georeferenced Orthorectified Imagery
• Georeferenced Digital Elevation Models

The application is available for Windows, Mac and Linux and it works from the command line, making it ideal for power users, scripts and for integration with other software.

If you would rather not type commands in a shell and are looking for a friendly user interface, check out OpenDroneMap Desktop.

Quickstart Copy Chapter

The easiest way to run ODM is via docker. To install docker, see docs.docker.com. Once you have docker installed and working, you can get ODM by running from a Command Prompt / Terminal:

docker pull opendronemap/odm

Run ODM by placing some images (JPEGs, TIFFs or DNGs) in a folder named “images” (for example C:\Users\youruser\datasets\project\images or /home/youruser/datasets/project/images) and simply run from a Command Prompt / Terminal:

# Windows
docker run -ti --rm -v c:/Users/youruser/datasets:/datasets opendronemap/odm --project-path /datasets project

# Mac/Linux
docker run -ti --rm -v /home/youruser/datasets:/datasets opendronemap/odm --project-path /datasets project

You can pass additional parameters by appending them to the command:

docker run -ti --rm -v /datasets:/datasets opendronemap/odm --project-path /datasets project [--additional --parameters --here]

For example, to generate a DSM (--dsm) and increase the orthophoto resolution (--orthophoto-resolution 2) :

docker run -ti --rm -v /datasets:/datasets opendronemap/odm --project-path /datasets project --dsm --orthophoto-resolution 2

Viewing Results Copy Chapter

When the process finishes, the results will be organized as follows:

|-- images/
    |-- img-1234.jpg
    |-- ...
|-- opensfm/
    |-- see mapillary/opensfm repository for more info
|-- odm_meshing/
    |-- odm_mesh.ply                    # A 3D mesh
|-- odm_texturing/
    |-- odm_textured_model.obj          # Textured mesh
    |-- odm_textured_model_geo.obj      # Georeferenced textured mesh
|-- odm_georeferencing/
    |-- odm_georeferenced_model.laz     # LAZ format point cloud
|-- odm_orthophoto/
    |-- odm_orthophoto.tif              # Orthophoto GeoTiff

You can use the following free and open source software to open the files generated in ODM:

• .tif (GeoTIFF): QGIS
• .laz (Compressed LAS): CloudCompare
• .obj (Wavefront OBJ), .ply (Stanford Triangle Format): MeshLab

Note: Opening the .tif files generated by ODM in programs such as Photoshop or GIMP might not work (they are GeoTIFFs, not plain TIFFs). Use QGIS instead.

API Copy Chapter

ODM can be made accessible from a network via NodeODM.

Documentation Copy Chapter

See http://docs.opendronemap.org for tutorials and more guides.

Forum Copy Chapter

We have a vibrant community forum. You can search it for issues you might be having with ODM and you can post questions there. We encourage users of ODM to participate in the forum and to engage with fellow drone mapping users.

Windows Setup Copy Chapter

ODM can be installed natively on Windows. Just download the latest setup from the releases page. After opening the ODM Console you can process datasets by typing:

run C:\Users\youruser\datasets\project  [--additional --parameters --here]

GPU Acceleration Copy Chapter

ODM has support for doing SIFT feature extraction on a GPU, which is about 2x faster than the CPU on a typical consumer laptop. To use this feature, you need to use the opendronemap/odm:gpu docker image instead of opendronemap/odm and you need to pass the --gpus all flag:

docker run -ti --rm -v c:/Users/youruser/datasets:/datasets --gpus all opendronemap/odm:gpu --project-path /datasets project --feature-type sift

When you run ODM, if the GPU is recognized, in the first few lines of output you should see:

[INFO]    Writing exif overrides
[INFO]    Maximum photo dimensions: 4000px
[INFO]    Found GPU device: Intel(R) OpenCL HD Graphics
[INFO]    Using GPU for extracting SIFT features

The SIFT GPU implementation is CUDA-based, so should work with most NVIDIA graphics cards of the GTX 9xx Generation or newer.

If you have an NVIDIA card, you can test that docker is recognizing the GPU by running:

docker run --rm --gpus all nvidia/cuda:10.0-base nvidia-smi

If you see an output that looks like this:

Fri Jul 24 18:51:55 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82       Driver Version: 440.82       CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

You're in good shape!

See https://github.com/NVIDIA/nvidia-docker and https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker for information on docker/NVIDIA setup.

Video Support Copy Chapter

Starting from version 3.0.4, ODM can automatically extract images from video files (.mp4, .mov, .lrv, .ts). Just place one or more video files into the images folder and run the program as usual. Subtitles files (.srt) with GPS information are also supported. Place .srt files in the images folder, making sure that the filenames match. For example, my_video.mp4 ==> my_video.srt (case-sensitive).

Developers Copy Chapter

Help improve our software! We welcome contributions from everyone, whether to add new features, improve speed, fix existing bugs or add support for more cameras. Check our code of conduct, the contributing guidelines and how decisions are made.

Installation and first run Copy Section

Dependencies

Install pixi, (or use the devcontainer):

MSVC and the Windows SDK come from a host install of Visual Studio 2022 or Build Tools (conda-forge Windows native notes):

• Desktop development with C++
• Windows 10/11 SDK

Build and test

pixi install --locked
pixi run build
pixi run test
pixi run smoke

Process a dataset

Process a dataset (from the repo root, with a pixi shell or pixi run):

pixi run odm -- --project-path ~/datasets mydataset
# or, after: eval "$(pixi shell-hook)"
./run.py --project-path ~/datasets mydataset

GPU builds use the gpu environment: pixi install -e gpu && pixi run -e gpu build.

Advanced Copy Chapter

Build Docker images from source Copy Section

CPU image (matches CI):

pixi run docker-build

GPU image:

pixi run docker-build -- --gpu

These stamp the image with the OCI labels (revision, version, created) and tag
it opendronemap/odm:latest (:gpu) on master, or opendronemap/odm:
off master. Pass a custom tag or extra docker build flags after --, e.g.
pixi run docker-build -- -t my_odm_image --no-cache.

If you have questions, join the developer's chat at https://community.opendronemap.org/c/developers-chat/21

1. Try to keep commits clean and simple
2. Submit a pull request with detailed changes and test results
3. Have fun!

Credits Copy Chapter

ODM makes use of several libraries and other awesome open source projects to perform its tasks. Among them we'd like to highlight:

• OpenSfM
• OpenMVS
• PDAL
• Entwine
• MVS Texturing
• GRASS GIS
• GDAL
• PoissonRecon

Citation Copy Chapter

OpenDroneMap Authors ODM - A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. OpenDroneMap/ODM GitHub Page 2020; https://github.com/OpenDroneMap/ODM

Trademark Copy Chapter

See Trademark Guidelines

File: contrib/visveg/readme.md Copy Chapter

Visible Vegetation Indexes

This script produces a Vegetation Index raster from a RGB orthophoto (odm_orthophoto.tif in your project)

Requirements Copy Chapter

• rasterio (pip install rasterio)
• numpy python package (included in ODM build)

Usage Copy Chapter

vegind.py <orthophoto.tif> index

positional arguments:
  <orthophoto.tif>  The RGB orthophoto. Must be a GeoTiff.
  index     Index identifier. Allowed values: ngrdi, tgi, vari

Output will be generated with index suffix in the same directory as input.

Examples Copy Chapter

python vegind.py /path/to/odm_orthophoto.tif tgi

Orthophoto photo of Koniaków grass field and forest in QGIS:
The Triangular Greenness Index output in QGIS (with a spectral pseudocolor):
Visible Atmospheric Resistant Index:
Normalized green-red difference index:

Bibliography Copy Chapter

1. Hunt, E. Raymond, et al. "A Visible Band Index for Remote Sensing Leaf Chlorophyll Content At the Canopy Scale." ITC journal 21(2013): 103-112. doi: 10.1016/j.jag.2012.07.020
   (https://doi.org/10.1016/j.jag.2012.07.020)

File: contrib/time-sift/README.md Copy Chapter

Plugin Time-SIFT

This script does Time-SIFT processing with ODM. Time-SIFT is a method for multi-temporal analysis without the need to co-registrate the data.

D. Feurer, F. Vinatier, Joining multi-epoch archival aerial images in a single SfM block allows 3-D change detection with almost exclusively image information, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146, 2018, Pages 495-506, ISSN 0924-2716, doi: 10.1016/j.isprsjprs.2018.10.016
(https://doi.org/10.1016/j.isprsjprs.2018.10.016)

Requirements Copy Chapter

• ODM ! :-)
• subprocess
• json
• os
• shutil
• pathlib
• sys
• argparse
• textwrap

Usage Copy Chapter

Provided example Copy Section

Download or clone this repo to get example data.

Then execute

python Timesift_odm.py datasets --end-with odm_filterpoints

It should make the Time-SIFT processing on the downloaded example data, stopping after the filtered dense clouds step.

In the destination dir, you should obtain new directories, 0_before and 1_after at the same level as the time-sift-block directory. These new directories contain all the results natively co-registered.

You can then use CloudCompare to compute distance between the datasets/0_before/odm_filterpoints/point_cloud.ply and the datasets/1_after/odm_filterpoints/point_cloud.ply and obtain this image showing the difference between the two 3D surfaces. Here, two soil samples were excavated as can be seen on the image below.

Your own data Copy Section

In your dataset directory (usually datasets, but you can have chosen another name) you have to prepare a Time-SIFT project directory (default name : time-sift-block, can be tuned via a parameter) that contains :

• images/ : a subdirectory with all images of all epochs. This directory name is fixed as it is the one expected by ODM
• images_epochs.txt : a file that has the same format as the file used for the split and merge ODM function. This file name can be tuned via a parameter.

The images_epochs.txt file has two columns, the first column contains image names and the second contains the epoch name as follows

DSC_0368.JPG 0_before
DSC_0369.JPG 0_before
DSC_0370.JPG 0_before
DSC_0389.JPG 1_after
DSC_0390.JPG 1_after
DSC_0391.JPG 1_after

Your directory, before running the script, should look like this :

$PWD/datasets/
└── time-sift-block/
    ├── images/
    └── images_epochs.txt

At the end of the script you obtain a directory by epoch (at the same level as the Time-SIFT project directory). Each directory is processed with images of each epoch and all results are natively co-registered due to the initial sfm step done with all images.

$PWD/datasets/
├── 0_before/
├── 1_after/
└── time-sift-block/

File: contrib/resize/README.md Copy Chapter

Resize

Resize a dataset (and optional GCP file).

Resizes images, keeps Exif data. The EXIF width and height attributes will be updated accordingly also. ODM GCP files are scaled also.

Usage:

pip install -r requirements.txt
python3 resize.py -i images/ -o resized/ 25%
python3 resize.py -i images/1.JPG -o resized.JPG 25%
python3 resize.py -i gcp_list.txt -o resized_gcp_list.txt

Originally forked from https://github.com/pierotofy/exifimageresize

File: contrib/pc2dem/README.md Copy Chapter

Point Cloud To DEM

Convert point clouds (LAS, LAZ, PLY, and any other format compatible with PDAL) to GeoTIFF elevation models.

This tool includes methods to perform efficient and scalable gapfill interpolation and is the same method used by ODM's processing pipeline. It is offered here as a standalone module for processing individual point clouds.

Usage Copy Chapter

docker run -ti --rm -v /home/youruser/folder_with_point_cloud:/input opendronemap/odm python3 /code/contrib/pc2dem/pc2dem.py /input/point_cloud.las [flags]

The result (dsm.tif or dtm.tif) will be stored in the same folder as the input point cloud. See additional flags you can pass at the end of the command above:

usage: pc2dem.py [-h] [--type {dsm,dtm}] [--resolution RESOLUTION]
                 [--gapfill-steps GAPFILL_STEPS]
                 point_cloud

Generate DEMs from point clouds using ODM's algorithm.

positional arguments:
  point_cloud           Path to point cloud file (.las, .laz,
                        .ply)

optional arguments:
  -h, --help            show this help message and exit
  --type {dsm,dtm}      Type of DEM. Default: dsm
  --resolution RESOLUTION
                        Resolution in m/px. Default: 0.05
  --gapfill-steps GAPFILL_STEPS
                        Number of steps used to fill areas with
                        gaps. Set to 0 to disable gap filling.
                        Starting with a radius equal to the output
                        resolution, N different DEMs are generated
                        with progressively bigger radius using the
                        inverse distance weighted (IDW) algorithm
                        and merged together. Remaining gaps are
                        then merged using nearest neighbor
                        interpolation. Default: 3

File: contrib/orthorectify/README.md Copy Chapter

Orthorectification Tool

This tool is capable of orthorectifying individual images (or all images) from an existing ODM reconstruction.

Usage Copy Chapter

After running a reconstruction using ODM:

docker run -ti --rm -v /home/youruser/datasets:/datasets opendronemap/odm --project-path /datasets project

You can run the orthorectification module by running:

docker run -ti --rm -v /home/youruser/datasets:/datasets opendronemap/odm bash /code/contrib/orthorectify/run.sh /datasets/project

This will start the orthorectification process for all images in the dataset. See additional flags you can pass at the end of the command above:

usage: orthorectify.py [-h] [--dem DEM] [--no-alpha NO_ALPHA]
                       [--interpolation {nearest,bilinear}]
                       [--outdir OUTDIR] [--image-list IMAGE_LIST]
                       [--images IMAGES] [--threads THREADS]
                       [--skip-visibility-test SKIP_VISIBILITY_TEST]
                       dataset

Orthorectification Tool

positional arguments:
  dataset               Path to ODM dataset

optional arguments:
  -h, --help            show this help message and exit
  --dem DEM             Absolute path to DEM to use to
                        orthorectify images. Default:
                        odm_dem/dsm.tif
  --no-alpha NO_ALPHA   Don't output an alpha channel
  --interpolation {nearest,bilinear}
                        Type of interpolation to use to sample
                        pixel values.Default: bilinear
  --outdir OUTDIR       Output directory where to store results.
                        Default: orthorectified
  --image-list IMAGE_LIST
                        Path to file that contains the list of
                        image filenames to orthorectify. By
                        default all images in a dataset are
                        processed. Default: img_list.txt
  --images IMAGES       Comma-separated list of filenames to
                        rectify. Use as an alternative to --image-
                        list. Default: process all images.
  --skip-visibility-test SKIP_VISIBILITY_TEST
                        Skip visibility testing (faster but leaves
                        artifacts due to relief displacement)

Roadmap Copy Chapter

Help us improve this module! We could add:

• GPU support for faster processing
• Merging of multiple orthorectified images (blending, filtering, seam leveling)
• Faster visibility test
• Different methods for orthorectification (direct)

File: contrib/ndvi/README.md Copy Chapter

NDVI

This script produces a NDVI raster from a CIR orthophoto (odm_orthophoto.tif in your project)

Requirements Copy Chapter

• python_gdal package from apt
• numpy python package (included in ODM build)

Usage Copy Chapter

ndvi.py [-h] [--overwrite] <orthophoto.tif> N N <outfile.tif>

positional arguments:
  <orthophoto.tif>  The CIR orthophoto. Must be a GeoTiff.
  N                 NIR band number
  N                 Vis band number
  <outfile.tif>     The output file. Also must be in GeoTiff format

optional arguments:
  -h, --help        show this help message and exit
  --overwrite, -o   Will overwrite output file if it exists.

Argument order matters! NIR first, then VIS

Examples: Copy Chapter

Use the Seneca dataset for a good working CIR. The band order for that set is NIR-G-B, so you will want to use bands 1 and 2 for this script. After running ODM, the command goes as follows:

python ndvi.py /path/to/odm_orthophoto.tif 1 2 /path/to/ndvi.tif

The output in QGIS (with a spectral pseudocolor):

File: contrib/mergepreview/README.md Copy Chapter

Merge Preview

Quickly projects drone images on a map by using georeferencing, camera angles and a global DTM. The images are then merged using ODM's split-merge algorithms.

Quality is obviously not good, works only for nadir-only images and requires the images to have gimbal/camera angle information (not all drones provide this information).

Usage:

# Install DDB (required for geoprojection)

curl -fsSL https://get.dronedb.app -o get-ddb.sh
sh get-ddb.sh

# Run

python3 mergepreview.py -i images/*.JPG --size 25%

Example Copy Chapter

Sheffield Park images processed with this script.

Disclaimer Copy Chapter

This script is highly experimental. We welcome contributions to improve it.

File: contrib/mask-duplicator/README.md Copy Chapter

Semi-automated Masking for 360 images

For usage with 360 images and Open Drone Map (ODM) to mask out the tripod/user/camera mount. 360 models in ODM can be made from 360 images, but unless you mask out the camera mount there will be repeated artifacts along the camera path. ODM supports image masking but requires a mask for each image. Since the 360 camera is generally on a fixed mount (bike helmet, moving tripod, etc), you can make one mask and then duplicate this for all images, but this is tedious to do by hand.

This snippet takes the file path of a single image mask and duplicates it for all images in the dataset. After creating the masks, process the original images and the masks together in ODM you'll get a clean model with the camera mount artifacts eliminated.

Before using this code snippet, open one of your 360 images in an image editor and mask out the helmet or tripod, etc at the bottom of your image. Save this image as a png and then use it as the mask image that will be duplicated for all images in the dataset.

See https://docs.opendronemap.org/masks/ for more details on mask creation.

File: contrib/grass/README.md Copy Chapter

GRASS scripts

odm_grass

Generates contour and textured relief maps.

Requirements Copy Chapter

• Compile and install GRASS 7 version or higher, https://grasswiki.osgeo.org/wiki/Compile_and_Install
• Environment variables:
  • PYTHONHOME set to the location of Python
  • PYTHONPATH set to the location of GRASS Python libs
  • PATH includes GRASS bin and lib directories
  • GISBASE set to the location of GRASS

Usage Copy Chapter

python odm_grass.py

Output is /odm_georeferencing/odm_contour.shp and /odm_orthophoto/odm_relief.tif.

2. Official Technical Reference & Guides (OpenDroneMap/ODM-docs) Copy Chapter

OpenDroneMap Docs

Contribute to OpenDroneMap's documentation!!! Anyone is welcome to share their knowledge and improve our documentation. 🎉 And it's pretty simple too!

"But I don't know if I can contribute"

Even if you think your contribution might not be valuable, it might be for other users! If you discovered something remotely useful to you, it probably is very useful to others! :)

Tips, tricks, hacks, datasets, lessons learned, best practices, every bit helps. We want to know! Share it.

How To Make Your First Contribution

If you don't have a GitHub account, register first. It's free and GitHub is awesome !!!

Once you have an account there are two ways to contribute. One is quick for small changes, the second takes a bit longer to setup but makes writing long parts of documentation much quicker.

The Quick Method

Browse to https://github.com/OpenDroneMap/docs/tree/publish/source. Each row in the table corresponds to a page in the documentation. Tap on the file name, in the second (2nd) column from left. Press the "Edit" icon.

Make your changes, then scroll down and tap the "Propose Changes" button.

When the next page is displayed, tap the "Create pull request" button. Wait for your changes to be approved! That's it!

You may want to read the English Style Guide for github https://github.com/raywenderlich/english-style-guide.

The Pro Method

For quick changes the method above is quick and fast. But if you want to write docs like a pro, in your own text editor of choice, follow these steps.

Depending on your operating system, you should follow these steps to create a local copy of the documentation. This way you can modify and check the results before sending them for review.

You may want to read the English Style Guide for github https://github.com/raywenderlich/english-style-guide.

Step 1. Make a Copy (fork) This Repository Copy Chapter

Press the "Fork" button at the top of this page.

Step 2. Install Git and Python 3 Copy Chapter

As there is an issue with python3.13 (https://github.com/ethereum/s……olidity/issues/15811) with python3.12 at most should be used.

Linux: Copy Section

sudo apt-get install -y git python3 python3-pip

MacOS Copy Section

We recommend to use Brew for installing Python3.

brew install python3

Git comes preinstalled on MacOS.

Windows Copy Section

Download Git from https://git-scm.com/download/win and Python 3 from https://www.python.org/downloads/windows/

Step 3. Make A Local Copy of The Documentation Copy Chapter

Open a Terminal (or a command prompt). In Windows you open a command prompt by pressing the Window Key and typing "Command Prompt" then Enter.

Then run (replacing "yourusername" with your GitHub username):

git clone https://github.com/yourusername/docs

If it's your first time using git, also run:

git config --global user.name "Your Name"
git config --global user.email "[email protected]"

With your information. So that we can credit the contributions to you.

Step 4. Get The Documentation Running Copy Chapter

From the same Terminal (or command prompt) run the following:

cd docs/
python3.12 -m venv .venv

# Linux/Mac
source .venv/bin/activate

# Windows
.venv\scripts\activate

pip install -r requirements.txt

After running source .venv/bin/activate there should be some indication that the Python virtual environment is active (see the (.venv) that appears at the start of terminal prompt in the screengrab below). Note: The next time you can cd into the docs folder and just run source .venv/bin/activate. There should be no need to rerun the pip install and python3 -m venv .venv commands.

Note: If you've installed sphinx on your system, you may run into issues with commands using that version instead of the version inside your virtualenv.

Then if there are no errors, run:

make livehtml

Note that if OpenDroneMap Desktop or another application is running on port 8000 you should temporarily stop it while you edit the documentation.

Step 5. Start Editing Copy Chapter

You can now open a web browser to http://localhost:8000 and see the documentation!

You can use a text editor to edit the .rst files in the "source" folder (for example, Visual Studio Code).

As soon as you make a change to one of the files, your web browser will reload the documentation with the new changes!

Step 6. Publish Your Changes Copy Chapter

Open a Terminal (or command prompt) and type:

cd docs/
git commit -a -m "A description of my changes"
git push origin publish

You will be prompted for your GitHub credentials.

Step 7. Open a Pull Request Copy Chapter

Open https://github.com/OpenDroneMap/docs and you'll see a box asking you to open a pull request. Open a pull request by filling in a description of your changes and you're set! Somebody will review your changes and your contribution will be live on https://docs.opendronemap.org once approved.

Options and Flags

Any additions or edits to the Options and Flags on https://docs.opendronemap.org/arguments/ need to happen to the /source/arguments_edit/*.rst files.

The /source/arguments.rst file is a list of all options and flags currently available in the ODM code. It is automatically created by running scripts/extract_odm_strings.py (you can use make autogenerate to run the script) and should not be manually edited. To change the descriptions within the list, the ODM code must be modified. Any manual changes to this file will be overwritten when it is next autogenerated. The script also autogenerates a file for each argument with any information available in the ODM code. These *.rst files are in /source/arguments/ and should not be manually edited.

The script creates a blank *.rst file for each argument in /source/arguments_edit/. Additional details may be added to these file and will be merged with the information available in the ODM code when the documentation site is published.

Translations

Contributing translations Copy Chapter

Transifex has a getting started guide for translators that will walk you through the steps to setup an account.

The ODM documentation Transifex project can be found at: https://app.transifex.com/americanredcross/opendronemap_docs/

The documentation uses a formatting known as reStructuredTest (reST) to add links, formatting, and other styling. reST uses plain text to annotate the documentation and identify to the system how to process the different page items. Unfortunately, the translation management process doesn't hide the reST markup. It is important that you keep the reST markup when translating.

There are special characters used for:

• Italic
• Bold
• Links
• Code (Additionally, code snippets should NOT be translated - only translate the surrounding words.)

Italic Copy Section

An `italic section of text` will have a single backtick on either side.
An *italic section of text* may also have a single asterisk on either side.

Italic text needs to have a space before, then a backtick or grave accent (`) that touches the first character of the italic text, then the italic text, then a backtick or grave accent (`) that touches the last character of the italic text, then a space. The backticks may also be asterisks.

Bold Copy Section

A **bold section of text** will have double asterisks on either side.

Bold text needs to have a space before, then a double asterisk (**) that touches the first character of the bold text, then the bold text, then a double asterisk (**) that touches the last character of the bold text, then a space.

Link Copy Section

Check out the `ODM documentation <https://docs.opendronemap.org/>`_!

Links need to have a space before, then a backtick or grave accent (`), then the text that will become the link on the page (in the above example: "ODM documentation"), then a space, then a less than symbol (<), then the complete URL, then a greater than symbol and underscore (>_).

Code Copy Section

Code snippets need to have a space before, then a double backtick or grave accent (``) that touches the first character of the code text, then the code text, then a double backtick or grave accent (``) that touches the last character of the code text, then a space.

NOTE: Code snippets may be referring to specific software commands and so often should NOT be translated.

Managing the translation process Copy Chapter

This project uses Transifex and the transifex-client tool to help people contribute translations. The transifex-client tool is included in the requirements.txt file and should be installed on your system during setup when you run pip install -r requirements.txt.

You need to configure your account. For this, you need to create an API Token for your user to access this service through the command line. This can be done under your Transifex User’s Settings. Set it up to use the token:

tx init --token $TOKEN --skipsetup

If there are new English .rst source files, you can map them by updating the ./.tx/config file.

After updating the individual argument files with make autogenerate, you can run

make updatepot
make updatelangpo
make pushlang
make pulllang

To add a new language, do it through the Transifex interface and then add a new line to the Makefile deploy command.

The Travis CI build failed... 😞 Copy Chapter

Don't panic! Unfortunately, Transifex doesn't protect the reST notation that Sphinx uses for things like formatting and links. It's possible that during translation, some of the syntax was broken.

Start up your Python virtual environment if it's not already with source venv/bin/activate and then run make allerr. This does the same thing as make deploy but removes the nit-picky mode so that it logs all errors and doesn't stop the build after hitting the first one.

You can also run the build for just one specific language, for example:

sphinx-build -b dirhtml -D language='sw' source "_build/html/sw/"

You should see output in your console such as:

Warning, treated as error:
/path/to/my/project/OpenDroneMap/docs/source/multispectral.rst:25::1:Inline interpreted text or phrase reference start-string without end-string.

Look at the source file and line that is mentioned. In this case the file is source/multispectral.rst and the line is the number after the colon after the filename (25). Looking at the file we see that line 25 is the last line and the "Help edit these docs!" link.

Go to Transifex, go to the resource, and go to the string. The warning/error message should help you understand what went wrong. In this case the link syntax wasn't matched correctly. Fix and save the translation.

Pull the language down (for example, tx pull -l sw) and then try the build again.

Questions? Copy Section

Reach out to https://community.opendronemap.org, we'll help you get up and running with your first contribution if you get stuck!

Repository Notes

Travis-CI docs for Building a Python Project and GitHub Pages Deployment. Personal access token with 'public_repo - Access public repositories' permissions created and used it in travis encrypt GH_TOKEN=my_github_token --add env.matrix as described in the Travis-CI docs.