CLI Batch Processing#

Process an entire directory of plate images using the PhenoTypic command-line interface.

This is the condensed recipe for the default full mode. For what the other three modes (measure, recompile, process) produce and which flags each one accepts, see CLI Execution Modes.

Basic Usage#

python -m phenotypic --mode full --pipeline pipeline.json --input /path/to/plates/ --output /path/to/output/

Required path options:

  1. --pipeline pipeline.json — Pipeline configuration (created with pipeline.to_json())

  2. --input /path/to/plates/ — Folder containing plate images

  3. --output /path/to/output/ — Where results are saved

Grid Plates#

--image-type already defaults to GridImage; pass --nrows / --ncols to override the pipeline’s grid preset (which itself falls back to 8 × 12).

python -m phenotypic --mode full --pipeline pipeline.json --input /plates/ --output /output/ \
    --image-type GridImage --nrows 8 --ncols 12

Parallelism#

python -m phenotypic --mode full --pipeline pipeline.json --input /plates/ --output /output/ --njobs 4

Omit --njobs to use all available CPU cores.

Resume After Interruption#

python -m phenotypic --mode full --pipeline pipeline.json --input /plates/ --output /output/ --resume

Add --retry-failures (which requires --resume) to also re-process images that previously failed, instead of skipping them.

Testing#

# Dry run: validate pipeline and list images without processing
python -m phenotypic --mode full --pipeline pipeline.json --input /plates/ --output /output/ --dry-run

# Process 5 random images per dataset as a test
python -m phenotypic --mode full --pipeline pipeline.json --input /plates/ --output /output/ \
    --sample 5 --random-seed 42

--sample draws N images from each dataset (each first-level subdirectory of --input). Pass --random-seed to draw the same subset every time.