crimac/bottomdetection

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By crimac

Updated 4 months ago

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Data Science
Machine Learning & AI

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CRIMAC-classifiers-bottom

This repository contains code for bottom detection in the CRIMAC project.

The bottom detection works on zarr files created by CRIMAC-preprocessing.

Running using Docker

Two directories must be mounted:

  1. /in_dir - Input directory containing zarr data.
  2. /out_dir - Output directory where the annotation file will be written.

Options as environment variables:

  1. INPUT_NAME - Name of the zarr file in in_dir.
  2. OUTPUT_NAME - Name of the annotation file in out_dir. The output format is given by the file name suffix.
    • Pandas DataFrame:
      • .csv
      • .html
      • .parquet
    • Xarray Dataset:
      • .nc
      • .zarr
  3. ALGORITHM - Optional. The bottom detection algorithm to use:
    • constant - A very fast algorithm for testing and debugging.
    • simple - A very simple threshold based algorithm.
  4. Algorithm parameters. Optional.
    • PARAMETER_minimum_range [m] - The minimum range of the detected bottom.
    • PARAMETER_offset [m] - Additional offset to the bottom after backstepping.
    • PARAMETER_threshold_log_sv [dB] - The minimum Sv value for detecting bottom.

Example:

docker run -it --name bottomdetection \
  -v /home/user/data:/in_dir \
  -v /home/user/output:/out_dir  \
  --env INPUT_NAME=dataset.zarr \
  --env OUTPUT_NAME=bottom.parquet \
  --env ALGORITHM=simple \
  --env PARAMETER_offset=0.5 \
  crimac/bottomdetection

Docker Pull Command

docker pull crimac/bottomdetection