Splitting Large Datasets
Starting with ODM version
0.6.0 you can split up very large datasets into manageable chunks (called submodels), running the pipeline on each chunk, and then producing merged DEMs, orthophotos and point clouds. The process is referred to as “split-merge”.
Why might you use the split-merge pipeline? If you have a very large number of images in your dataset, split-merge will help make the processing more manageable on a large machine (it will require less memory). If you have many machines all connected to the same network you can also process the submodels in parallel, thus allowing for horizontal scaling and processing thousands of images more quickly.
Split-merge works in WebODM out of the box as long as the processing nodes support split-merge, by enabling the
--split option when creating a new task.
Image calibration is recommended (but not required) for large datasets because error propagation due to image distortion could cause a bowl effect on the models. Calibration instructions can be found at Calibrate Images.
Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.
Splitting a dataset into more manageable submodels and sequentially processing all submodels on the same machine is easy! Just use
--split-overlap to decide the the average number of images per submodels and the overlap (in meters) between submodels respectively
docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 400 --split-overlap 100
If you already know how you want to split the dataset, you can provide that information and it will be used instead of the clustering algorithm.
The grouping can be provided by adding a file named image_groups.txt in the main dataset folder. The file should have one line per image. Each line should have two words: first the name of the image and second the name of the group it belongs to. For example:
01.jpg A 02.jpg A 03.jpg B 04.jpg B 05.jpg C
will create 3 submodels. Make sure to pass
--split-overlap 0 if you manually provide a
Getting Started with Distributed Split-Merge
The first step is start ClusterODM
docker run -ti -p 3001:3000 -p 8080:8080 opendronemap/clusterodm
Then on each machine you want to use for processing, launch a NodeODM instance via
docker run -ti -p 3000:3000 opendronemap/nodeodm
Connect via telnet to ClusterODM and add the IP addresses/port of the machines running NodeODM
$ telnet <cluster-odm-ip> 8080 Connected to <cluster-odm-ip>. Escape character is '^]'. [...] # node add <node-odm-ip-1> 3000 # node add <node-odm-ip-2> 3000 [...] # node list 1) <node-odm-ip-1>:3000 [online] [0/2] <version 1.5.1> 2) <node-odm-ip-2>:3000 [online] [0/2] <version 1.5.1>
Make sure you are running version
1.5.1 or higher of the NodeODM API.
At this point, simply use the
--sm-cluster option to enable distributed split-merge
docker run -ti --rm -v /my/project:/datasets/code opendronemap/odm --project-path /datasets --split 800 --split-overlap 120 --sm-cluster http://<cluster-odm-ip>:3001
Understanding the Cluster
When connected via telnet, it is possible to interrogate what is happening on the cluster. For example, we can use the command HELP to find out available commands
# HELP NODE ADD <hostname> <port> [token] - Add new node NODE DEL <node number> - Remove a node NODE INFO <node number> - View node info NODE LIST - List nodes NODE LOCK <node number> - Stop forwarding tasks to this node NODE UNLOCK <node number> - Resume forwarding tasks to this node NODE UPDATE - Update all nodes info NODE BEST <number of images> - Show best node for the number of images ROUTE INFO <taskId> - Find route information for task ROUTE LIST [node number] - List routes TASK LIST [node number] - List tasks TASK INFO <taskId> - View task info TASK OUTPUT <taskId> [lines] - View task output TASK CANCEL <taskId> - Cancel task TASK REMOVE <taskId> - Remove task ASR VIEWCMD <number of images> - View command used to create a machine !! - Repeat last command
If, for example, the NodeODM instance wasn’t active when ClusterODM started, we might list nodes and see something as follows
# NODE LIST 1) localhost:3000 [offline] [0/2] <version 1.5.3> [L]
To address this, we can start up our local node (if not already started), and then perform a
# NODE UPDATE OK # NODE LIST 1) localhost:3000 [online] [0/2] <version 1.5.3> [L]
Accessing the Logs
While a process is running, it is also possible to list the tasks, and view the task output
# TASK LIST # TASK OUTPUT <taskId> [lines]
ClusterODM also includes the option to autoscale on multiple platforms, including, to date, Amazon and Digital Ocean. This allows users to reduce costs associated with always-on instances as well as being able to scale processing based on demand.
To setup autoscaling you must:
Have a functioning version of NodeJS installed and then install ClusterODM
git clone https://github.com/OpenDroneMap/ClusterODM cd ClusterODM npm install
Make sure docker-machine is installed.
Setup a S3-compatible bucket for storing results.
You can then launch ClusterODM with
node index.js --asr configuration.json
You should see something similar to following messages in the console
info: ASR: DigitalOceanAsrProvider info: Can write to S3 info: Found docker-machine executable
You should always have at least one static NodeODM node attached to ClusterODM, even if you plan to use the autoscaler for all processing. If you setup auto scaling, you can’t have zero nodes and rely 100% on the autoscaler. You need to attach a NodeODM node to act as the “reference node” otherwise ClusterODM will not know how to handle certain requests (for the forwarding the UI, for validating options prior to spinning up an instance, etc.). For this purpose, you should add a “dummy” NodeODM node and lock it
telnet localhost 8080 > NODE ADD localhost 3001 > NODE LOCK 1 > NODE LIST 1) localhost:3001 [online] [0/2] <version 1.5.1> [L]
This way all tasks will be automatically forwarded to the autoscaler.
The 3D textured meshes are currently not being merged as part of the workflow (only point clouds, DEMs and orthophotos are).
GCPs are fully supported, however, there needs to be at least 3 GCP points on each submodel for the georeferencing to take place. If a submodel has fewer than 3 GCPs, a combination of the remaining GCPs + EXIF data will be used instead (which is going to be less accurate). We recommend using the
image_groups.txt file to accurately control the submodel split when using GCPs.
Huge props to Pau and the folks at Mapillary for their amazing contributions to OpenDroneMap through their OpenSfM code, which is a key component of the split-merge pipeline. We look forward to further pushing the limits of OpenDroneMap and seeing how big a dataset we can process.