There are two methods for running with docker. One pulls a pre-built image from the docker hub. This is the most reliable. You can also build your own image. In either case, the run command is the same, what you will change is the name of the image. For the docker hub image, use opendronemap/opendronemap. For an image you built yourself, use that image name (in our case, my_odm_image).:

docker run -it --rm \
    -v $(pwd)/images:/code/images \
    -v $(pwd)/odm_texturing:/code/odm_texturing \
    -v $(pwd)/odm_orthophoto:/code/odm_orthophoto \

-v is used to connect folders in the docker container to local folders. See Dataset Structure for reference on the project layout.

If you want to get all intermediate outputs, run the following command::

docker run -it --rm \
    -v $(pwd)/images:/code/images \
    -v $(pwd)/odm_meshing:/code/odm_meshing \
    -v $(pwd)/odm_orthophoto:/code/odm_orthophoto \
    -v $(pwd)/odm_georeferencing:/code/odm_georeferencing \
    -v $(pwd)/odm_texturing:/code/odm_texturing \
    -v $(pwd)/opensfm:/code/opensfm \
    -v $(pwd)/pmvs:/code/pmvs \

To pass in custom parameters to the script, simply pass it as arguments to the docker run command. For example::

docker run -it --rm \
    -v $(pwd)/images:/code/images \
    -v $(pwd)/odm_orthophoto:/code/odm_orthophoto \
    -v $(pwd)/odm_texturing:/code/odm_texturing \
    opendronemap/opendronemap --resize-to 1800 --force-ccd 6.16

If you want to pass in custom parameters using the settings.yaml file, you can pass it as a -v volume binding::

docker run -it --rm \
    -v $(pwd)/images:/code/images \
    -v $(pwd)/odm_orthophoto:/code/odm_orthophoto \
    -v $(pwd)/odm_texturing:/code/odm_texturing \
    -v $(pwd)/settings.yaml:/code/settings.yaml \

For more information about Docker, check out their docs.


First thing you need to do is set the project path. Edit the settings.yaml file to add your projects folder:

# This line is really important to set up properly
project_path: '' # Example: '/home/user/ODMProjects'

# The rest of the settings will default to the values set unless you uncomment and change them
#resize_to: 2400

You must change project_path: '' to add an absolute path to somewhere on your machine. Whenever you run a new project, it will be saved here.

To use OpenDroneMap run the following command:

python --images </path/to/images> [arguments] <project-name>

Then sit back, grab a coffee and wait. You only have to specify --images </path/to/images> on the first run.

Additional Arguments


-h, --help            show this help message and exit
--images <path>, -i <path>
                      Path to input images
--project-path <path>
                      Path to the project folder
--resize-to <integer>
                      resizes images by the largest side for opensfm. Set to
                      -1 to disable. Default: 2048
--start-with <string>, -s <string>
                      Can be one of: dataset | opensfm | slam | cmvs | pmvs
                      | odm_meshing | odm_25dmeshing | mvs_texturing |
                      odm_georeferencing | odm_dem | odm_orthophoto
--end-with <string>, -e <string>
                      Can be one of:dataset | opensfm | slam | cmvs | pmvs |
                      odm_meshing | odm_25dmeshing | mvs_texturing |
                      odm_georeferencing | odm_dem | odm_orthophoto
--rerun <string>, -r <string>
                      Can be one of:dataset | opensfm | slam | cmvs | pmvs |
                      odm_meshing | odm_25dmeshing | mvs_texturing |
                      odm_georeferencing | odm_dem | odm_orthophoto
--rerun-all           force rerun of all tasks
--rerun-from <string>
                      Can be one of:dataset | opensfm | slam | cmvs | pmvs |
                      odm_meshing | odm_25dmeshing | mvs_texturing |
                      odm_georeferencing | odm_dem | odm_orthophoto
--video <string>      Path to the video file to process
--slam-config <string>
                      Path to config file for orb-slam
--force-focal <positive float>
                      Override the focal length information for the images
--proj <PROJ4 string>
                      Projection used to transform the model into geographic
--force-ccd <positive float>
                      Override the ccd width information for the images
--min-num-features <integer>
                      Minimum number of features to extract per image. More
                      features leads to better results but slower execution.
                      Default: 8000
--matcher-neighbors <integer>
                      Number of nearest images to pre-match based on GPS
                      exif data. Set to 0 to skip pre-matching. Neighbors
                      works together with Distance parameter, set both to 0
                      to not use pre-matching. OpenSFM uses both parameters
                      at the same time, Bundler uses only one which has
                      value, prefering the Neighbors parameter. Default: 8
--matcher-distance <integer>
                      Distance threshold in meters to find pre-matching
                      images based on GPS exif data. Set both matcher-
                      neighbors and this to 0 to skip pre-matching. Default:
                      Turn off camera parameter optimization during bundler
--opensfm-processes <positive integer>
                      The maximum number of processes to use in dense
                      reconstruction. Default: <num cpus>
--opensfm-depthmap-resolution <positive float>
                      Resolution of the depthmaps. Higher values take longer
                      to compute but produce denser point clouds. Default:
--opensfm-depthmap-min-consistent-views <integer: 2 <= x <= 9>
                      Minimum number of views that should reconstruct a
                      point for it to be valid. Use lower values if your
                      images have less overlap. Lower values result in
                      denser point clouds but with more noise. Default: 3
--opensfm-depthmap-method <string>
                      Raw depthmap computation algorithm. PATCH_MATCH and
                      PATCH_MATCH_SAMPLE are faster, but might miss some
                      valid points. BRUTE_FORCE takes longer but produces
                      denser reconstructions. Default: PATCH_MATCH
--opensfm-depthmap-min-patch-sd <positive float>
                      When using PATCH_MATCH or PATCH_MATCH_SAMPLE, controls
                      the standard deviation threshold to include patches.
                      Patches with lower standard deviation are ignored.
                      Default: 1
                      Run local bundle adjustment for every image added to
                      the reconstruction and a global adjustment every 100
                      images. Speeds up reconstruction for very large
--use-25dmesh         Use a 2.5D mesh to compute the orthophoto. This option
                      tends to provide better results for planar surfaces.
--use-pmvs            Use pmvs to compute point cloud alternatively
--cmvs-maxImages <integer>
                      The maximum number of images per cluster. Default: 500
--pmvs-level <positive integer>
                      The level in the image pyramid that is used for the
                      computation. see
             for more
                      pmvs documentation. Default: 1
--pmvs-csize <positive integer>
                      Cell size controls the density of
                      reconstructionsDefault: 2
--pmvs-threshold <float: -1.0 <= x <= 1.0>
                      A patch reconstruction is accepted as a success and
                      kept if its associated photometric consistency measure
                      is above this threshold. Default: 0.7
--pmvs-wsize <positive integer>
                      pmvs samples wsize x wsize pixel colors from each
                      image to compute photometric consistency score. For
                      example, when wsize=7, 7x7=49 pixel colors are sampled
                      in each image. Increasing the value leads to more
                      stable reconstructions, but the program becomes
                      slower. Default: 7
--pmvs-min-images <positive integer>
                      Each 3D point must be visible in at least minImageNum
                      images for being reconstructed. 3 is suggested in
                      general. Default: 3
--pmvs-num-cores <positive integer>
                      The maximum number of cores to use in dense
                      reconstruction. Default: 16
--mesh-size <positive integer>
                      The maximum vertex count of the output mesh Default:
--mesh-octree-depth <positive integer>
                      Oct-tree depth used in the mesh reconstruction,
                      increase to get more vertices, recommended values are
                      8-12. Default: 9
--mesh-samples <float >= 1.0>
                      Number of points per octree node, recommended and
                      default value: 1.0
--mesh-solver-divide <positive integer>
                      Oct-tree depth at which the Laplacian equation is
                      solved in the surface reconstruction step. Increasing
                      this value increases computation times slightly but
                      helps reduce memory usage. Default: 9
--mesh-neighbors <positive integer>
                      Number of neighbors to select when estimating the
                      surface model used to compute the mesh and for
                      statistical outlier removal. Higher values lead to
                      smoother meshes but take longer to process. Applies to
                      2.5D mesh only. Default: 24
--mesh-resolution <positive float>
                      Size of the interpolated surface model used for
                      deriving the 2.5D mesh, expressed in pixels per meter.
                      Higher values work better for complex or urban
                      terrains. Lower values work better on flat areas.
                      Resolution has no effect on the number of vertices,
                      but high values can severely impact runtime speed and
                      memory usage. When set to zero, the program
                      automatically attempts to find a good value based on
                      the point cloud extent and target vertex count.
                      Applies to 2.5D mesh only. Default: 0
--fast-orthophoto     Skips dense reconstruction and 3D model generation. It
                      generates an orthophoto directly from the sparse
                      reconstruction. If you just need an orthophoto and do
                      not need a full 3D model, turn on this option.
--crop <positive float>
                      Automatically crop image outputs by creating a smooth
                      buffer around the dataset boundaries, shrinked by N
                      meters. Use 0 to disable cropping. Default: 3
--pc-classify <string>
                      Classify the .LAS point cloud output using either a
                      Simple Morphological Filter or a Progressive
                      Morphological Filter. If --dtm is set this parameter
                      defaults to smrf. You can control the behavior of both
                      smrf and pmf by tweaking the --dem-* parameters.
                      Default: none
--texturing-data-term <string>
                      Data term: [area, gmi]. Default: gmi
--texturing-outlier-removal-type <string>
                      Type of photometric outlier removal method: [none,
                      gauss_damping, gauss_clamping]. Default:
                      Skip geometric visibility test. Default: False
                      Skip global seam leveling. Useful for IR data.Default:
                      Skip local seam blending. Default: False
                      Skip filling of holes in the mesh. Default: False
                      Keep faces in the mesh that are not seen in any
                      camera. Default: False
--texturing-tone-mapping <string>
                      Turn on gamma tone mapping or none for no tone
                      mapping. Choices are 'gamma' or 'none'. Default: none
--gcp <path string>   path to the file containing the ground control points
                      used for georeferencing. Default: None. The file needs
                      to be on the following line format: easting northing
                      height pixelrow pixelcol imagename
--use-exif            Use this tag if you have a gcp_list.txt but want to
                      use the exif geotags instead
--dtm                 Use this tag to build a DTM (Digital Terrain Model,
                      ground only) using a progressive morphological filter.
                      Check the --dem* parameters for fine tuning.
--dsm                 Use this tag to build a DSM (Digital Surface Model,
                      ground + objects) using a progressive morphological
                      filter. Check the --dem* parameters for fine tuning.
--dem-gapfill-steps <positive integer>
                      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=4
--dem-resolution <float>
                      Length of raster cell edges in meters. Default: 0.1
--dem-maxangle <positive float>
                      Points that are more than maxangle degrees off-nadir
                      are discarded. Default: 20
--dem-maxsd <positive float>
                      Points that deviate more than maxsd standard
                      deviations from the local mean are discarded. Default:
--dem-initial-distance <positive float>
                      Used to classify ground vs non-ground points. Set this
                      value to account for Z noise in meters. If you have an
                      uncertainty of around 15 cm, set this value large
                      enough to not exclude these points. Too small of a
                      value will exclude valid ground points, while too
                      large of a value will misclassify non-ground points
                      for ground ones. Default: 0.15
--dem-approximate     Use this tag use the approximate progressive
                      morphological filter, which computes DEMs faster but
                      is not as accurate.
--dem-decimation <positive integer>
                      Decimate the points before generating the DEM. 1 is no
                      decimation (full quality). 100 decimates ~99% of the
                      points. Useful for speeding up generation. Default=1
--dem-terrain-type <string>
                      One of: FlatNonForest, FlatForest, ComplexNonForest,
                      ComplexForest. Specifies the type of terrain. This
                      mainly helps reduce processing time. FlatNonForest:
                      Relatively flat region with little to no vegetation
                      FlatForest: Relatively flat region that is forested
                      ComplexNonForest: Varied terrain with little to no
                      vegetation ComplexForest: Varied terrain that is
                      forested Default=ComplexForest
--orthophoto-resolution <float > 0.0>
                      Orthophoto ground resolution in pixels/meterDefault:
--orthophoto-target-srs <EPSG:XXXX>
                      Target spatial reference for orthophoto creation. Not
                      implemented yet. Default: None
                      Set this parameter if you want a stripped geoTIFF.
                      Default: False
--orthophoto-compression <string>
                      Set the compression to use. Note that this could break
                      gdal_translate if you don't know what you are doing.
                      Options: JPEG, LZW, PACKBITS, DEFLATE, LZMA, NONE.
                      Default: DEFLATE
--orthophoto-bigtiff {YES,NO,IF_NEEDED,IF_SAFER}
                      Control whether the created orthophoto is a BigTIFF or
                      classic TIFF. BigTIFF is a variant for files larger
                      than 4GiB of data. Options are YES, NO, IF_NEEDED,
                      IF_SAFER. See GDAL specs:
             for more info.
                      Default: IF_SAFER
--build-overviews     Build orthophoto overviews using gdaladdo.
--zip-results         compress the results using gunzip
--verbose, -v         Print additional messages to the console Default:
--time                Generates a benchmark file with runtime info Default:
--version             Displays version number and exits.

Ground Control Points

If you supply a GCP file called gcp_list.txt then ODM will automatically detect it. If it has another name you can specify using --gcp <path>. If you have a gcp file and want to do georeferencing with exif instead, then you can specify --use-exif.

This post has some information about placing Ground Control Targets before a flight, but if you already have images, you can find your own points in the images post facto. It’s important that you find high-contrast objects that are found in at least 3 photos, and that you find a minimum of 5 objects.

For example, in this image, I would use the sharp corners of the diamond-shaped bioswales in the parking lot:


add file for .. image:: _static/tol_sm.jpg

You should also place/find the GCPs evenly around your survey area.

The gcp_list.txt file must then be created in the base of your project folder:

The format of the GCP file is simple. The header line is a description of the coordinate system, which must be written as a is a good resource for finding that information. proj4 string. Please note that currently angular coordinates (like lat/lon) do not work. Subsequent lines are the X, Y & Z coordinate in your coordinate system, your associated pixel and line number in the image, and the image name itself:

coordinate system description
x1 y1 z1 pixelx1 pixely1 imagename1
x2 y2 z2 pixelx2 pixely2 imagename2
x3 y3 z3 pixelx3 pixely3 imagename3

e.g. for the Langley dataset:

544256.7 5320919.9 5 3044 2622 IMG_0525.jpg
544157.7 5320899.2 5 4193 1552 IMG_0585.jpg
544033.4 5320876.0 5 1606 2763 IMG_0690.jpg

Given the recommendations above, your file should have a minimum of 15 lines after the header (5 points with 3 images to each point).

Video Reconstruction (Experimental)

Note: This is an experimental feature

It is possible to build a reconstruction using a video file instead of still images. The technique for reconstructing the camera trajectory from a video is called Simultaneous Localization And Mapping (SLAM). OpenDroneMap uses the opensource ORB_SLAM2 library for this task.

We will explain here how to use it. We will need to build the SLAM module, calibrate the camera and finally run the reconstruction from a video.

Building with SLAM support

By default, OpenDroneMap does not build the SLAM module. To build it we need to do the following two steps

Build SLAM dependencies:

sudo apt-get install libglew-dev
cd SuperBuild/build
cd ../..

Build the SLAM module:

cd build
cd ..

Calibrating the camera

The SLAM algorithm requires the camera to be calibrated. It is difficult to extract calibration parameters from the video’s metadata as we do when using still images. Thus, it is required to run a calibration procedure that will compute the calibration from a video of a checkerboard.

We will start by recording the calibration video. Display this chessboard pattern on a large screen, or print it on a large paper and stick it on a flat surface. Now record a video pointing the camera to the chessboard.

While recording move the camera to both sides and up and down always maintaining the entire pattern framed. The goal is to capture the pattern from different points of views.

Now you can run the calibration script as follows:

python modules/odm_slam/src/ --visual PATH_TO_CHESSBOARD_VIDEO.mp4

You will see a window displaying the video and the detected corners. When it finish, it will print the computed calibration parameters. They should look like this (with different values):

# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 1512.91332401
Camera.fy: 1512.04223185 956.585155225 527.321715394

Camera.k1: 0.140581949184
Camera.k2: -0.292250537695
Camera.p1: 0.000188785464717
Camera.p2: 0.000611510377372
Camera.k3: 0.181424769625

Keep this text. We will use it on the next section.

Running OpenDroneMap from a video

We are now ready to run the OpenDroneMap pipeline from a video. For this we need the video and a config file for ORB_SLAM2. Here’s an example config.yaml. Before using it, copy-paste the calibration parameters for your camera that you just computed on the previous section.

Put the video and the config.yaml file on an empty folder. Then run OpenDroneMap using the following command:

python --project-path PROJECT_PATH --video VIDEO.mp4 --slam-config config.yaml --resize-to VIDEO_WIDTH

where PROJECT_PATH is the path to the folder containing the video and config file, VIDEO.mp4 is the name of your video, and VIDEO_WIDTH is the width of the video (for example, 1920 for an HD video).

That command will run the pipeline starting with SLAM and continuing with stereo matching and mesh reconstruction and texturing.

When done, the textured model will be in PROJECT_PATH/odm_texturing/odm_textured_model.obj. The point cloud created by the stereo matching algorithm will be in PROJECT_PATH/pmvs/recon0/models/option-0000.ply

Camera Calibration

It is highly recommended that you calibrate your images to reduce lens distortion. Doing so will increase the likelihood of finding quality matches between photos and reduce processing time. You can do this in Photoshop or ImageMagick. We also have some simple scripts to perform this task: . This suite of scripts will find camera matrix and distortion parameters with a set of checkerboard images, then use those parameters to remove distortion from photos.


You need to install numpy and opencv::

pip install numpy
sudo apt-get install python-opencv exiftool

Usage: Calibrate chessboard

First you will need to take some photos of a black and white chessboard with a white border, like this one.

Then you will run the script to generate the matrix and distortion files.:

python ./sample/chessboard/ 10 7

The first argument is the path to the chessboard. You will also have to input the chessboard dimensions (the number of squares in x and y) Optional arguments::

--out           path      if you want to output the parameters and the image outputs to a specific path. otherwise it gets writting to ./out
--square_size   float     if your chessboard squares are not square, you can change this. default is 1.0

Usage: undistort photos

With the photos and the produced matrix.txt and distortion.txt, run the following::

python --matrix matrix.txt --distortion distortion.txt "/path/to/images/"

Note: Do not forget the quotes in “/path/to/images”

Docker Usage for undistorting images

The script depends on exiftool to copy exif metadata to the new images, so on Windows you may have to use Docker for the undistort step. Put the matrix.txt and distortion.txt in their own directory (eg. sample/config) and do the following::

docker build -t cc_undistort .
docker run -v ~/CameraCalibration/sample/images:/app/images \
           -v ~/CameraCalibration/sample/config:/app/config \