Developers

Command Line Interface

To use TreeEyed functionality in CLI mode you need to create a python environment using:

conda env create -f environment.yml
conda activate tree_eyed

To use GPU processing, make sure cuda and cudnn DLLs are installed in the system or install the following dependencies:

conda activate tree_eyed
conda install conda-forge::cudnn
conda install conda-forge::libcufft
conda install conda-forge::cuda-cudart

Additionally, you need to have the models in a local folder and a configuration file, for example a minimal configuration file example_config.json is:

{
         "model": "HighResCanopyHeight",
         "model_dir": "path/to/models",
         "output_path": "path/to/output/folder",
         "prefix": "output_name",
         "input_raster_path": "path/to/input/raster",
         "task": "inference",
         "raster_outputs": ["grayscale"],
         "vector_outputs": []
}

To execute the inference call tree_eyed_app.py, for example:

python src/tree_eyed/tree_eyed_app.py --config example_config.json

Docker Usage

You can also use TreeEyed as a docker container.

First build the docker image:

docker build -t treeeyed-image .

Run the container interactively:

docker run --gpus all -v <path-to-local-workspace-folder>:/app/data -v <path-to-local-models-folder>:/app/models -it treeeyed-image

Execute inference:

python src/tree_eyed/tree_eyed_app.py --config /app/data/example_config.json