Accessory Tools¶

Several accessory tools are provided with PlantCV. These tools are installed in your system or environment path or are available by downloading/cloning the GitHub repository.

Training machine learning models¶

plantcv-train.py is a command-line tool for training machine learning classifiers or other models in PlantCV. More detail is provided in the Machine Learning Tutorial but command/input details are provided below:

usage: plantcv-train.py [-h] {naive_bayes,naive_bayes_multiclass}

Subcommands:
    naive_bayes
        usage: plantcv-train.py naive_bayes [-h] -i IMGDIR -b MASKDIR -o OUTFILE [-p]

        optional arguments:
            -h, --help                       Show this help message and exit
            -i IMGDIR, --imgdir IMGDIR       Input directory containing images.
            -b MASKDIR, --maskdir MASKDIR    Input directory containing black/white masks.
            -o OUTFILE, --outfile OUTFILE    Trained classifier output filename.
            -p, --plots                      Make output plots.

    naive_bayes_multiclass
        usage: plantcv-train.py naive_bayes_multiclass [-h] -f FILE -o OUTFILE [-p]

        optional arguments:
            -h, --help                       Show this help message and exit
            -f FILE, --file FILE             Input file containing a table of pixel RGB values sampled for each input 
                                             class.
            -o OUTFILE, --outfile OUTFILE    Trained classifier output filename.
            -p, --plots                      Make output plots.

PlantCV utilities¶

plantcv-utils.py is a command-line tool for running various utilities.

Source Code: Here

Convert output JSON data files to CSV tables¶

plantcv-utils.py json2csv is a command-line tool for converting the output JSON files from plantcv-workflow.py to CSV-formatted tables for downstream analysis in R, MVApp, or other programs.

usage: plantcv-utils.py json2csv [-h] -j JSON -c CSV

optional arguments:
  -h, --help            Show this help message and exit
  -j JSON, --json JSON  Input PlantCV JSON filename.
  -c CSV, --csv CSV     Output CSV filename prefix.

The input JSON file is generally created by running plantcv-workflow.py, although there are some advanced scenarios where it is created by running plantcv.parallel.process_results on a directory of JSON files. This hierarchical data structure is convenient for flexible data processing but not for downstream analysis. The tool creates two output CSV files named with the user-provided prefix. prefix-single-value-traits.csv is a table of observations with single values (e.g. area, convex hull area, etc.). The format of this table is one row per image. prefix-multi-value-traits.csv is a table of observations with multiple values (e.g. frequency distribution of hue or other color channel/properties). The format of this table is one row per value/label.

Source Code: Here

Collect a random sample of images for testing workflows¶

plantcv-utils.py sample_images is a command-line tool for gathering a random set of images for testing workflows before running over a full image set.

usage: plantcv-utils.py sample_images [-h] -s SOURCE_DIRECTORY -o OUTPUT_DIRECTORY -n NUMBER

optional arguments:
  -h, --help                                      Show this help message and exit
  -s SOURCE_DIRECTORY, --source SOURCE_DIRECTORY  Input image directory.
  -o OUTPUT_DIRECTORY, --outdir OUTPUT_DIRECTORY  Output directory.
  -n NUMBER, --number NUMBER                      Number of images to randomly select. (default=100)

Testing a workflow on small test set (that ideally spans time and/or treatments) can speed up workflow optimization and test it on other images in the dataset to determine how robust the workflow will be. The random image sampler can help identify 'problem images' before running a workflow in parallel over a large set of images. This tool can handle LemnaTec structured output in addition to a flat file directory. Currently supported image types include .png, .jpg, .jpeg, .tif, .tiff, and .gif. An output directory will be created if it does not already exist. The number of random images requested must be less than or equal to the number of images in the source directory.

Source Code: Here

Parallel workflow processing¶

plantcv-workflow.py is a command-line tool for parallel processing of user-defined PlantCV workflows. It is used to process metadata and execute custom workflows on each image in a dataset. More detail is provided in the Workflow Parallelization Tutorial.

Source Code: Here