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.

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.

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 but command/input details are provided below:

usage: plantcv-workflow.py [-h] -d DIR [-a ADAPTOR] -p WORKFLOW -j JSON
                           [-i OUTDIR] [-T CPU] [-c] [-D DATES] [-t TYPE]
                           [-l DELIMITER] [-f META] [-M MATCH] [-C COPROCESS]
                           [-w] [-o OTHER_ARGS]

Parallel imaging processing with PlantCV.

optional arguments:
  -h, --help            
                        Show this help message and exit
  -d DIR, --dir DIR     
                        Input directory containing images or snapshots.
                        (default: None)
  -a ADAPTOR, --adaptor ADAPTOR
                        Image metadata reader adaptor. PhenoFront metadata is
                        stored in a CSV file and the image file name. For the
                        filename option, all metadata is stored in the image
                        file name. Current adaptors: phenofront, filename
                        (default: phenofront)
  -p WORKFLOW, --workflow WORKFLOW
                        Workflow script file. (default: None)
  -j JSON, --json JSON  
                        Output database file name. (default: None)
  -i OUTDIR, --outdir OUTDIR
                        Output directory for images. Not required by all
                        workflows. (default: .)
  -T CPU, --cpu CPU     
                        Number of CPU to use. (default: 1)
  -c, --create          
                        Will overwrite an existing database. Warning: activating
                        this option will delete an existing database!
                        (default: False)
  -D DATES, --dates DATES
                        Date range. Format: YYYY-MM-DD-hh-mm-ss_YYYY-MM-DD-hh-
                        mm-ss. If the second date is excluded then the current
                        date is assumed. (default: None)
  -t TYPE, --type TYPE  
                        Image format type (extension). (default: png)
  -l DELIMITER, --delimiter DELIMITER
                        Image file name metadata delimiter character.
                        (default: _)
  -f META, --meta META  
                        Image file name metadata format. List valid metadata
                        fields separated by the delimiter (-l/--delimiter).
                        Valid metadata fields are: camera, imgtype, zoom,
                        exposure, gain, frame, lifter, timestamp, id,
                        plantbarcode, treatment, cartag, measurementlabel,
                        other (default: imgtype_camera_frame_zoom_id)
  -M MATCH, --match MATCH
                        Restrict analysis to images with metadata matching
                        input criteria. Input a metadata:value comma-separated
                        list. This is an exact match search. E.g.
                        imgtype:VIS,camera:SV,zoom:z500 (default: None)
  -C COPROCESS, --coprocess COPROCESS
                        Coprocess the specified imgtype with the imgtype
                        specified in --match (e.g. coprocess NIR images with
                        VIS). (default: None)
  -w, --writeimg        
                        Include analysis images in output. (default: False)
  -o OTHER_ARGS, --other_args OTHER_ARGS
                        Other arguments to pass to the workflow script.
                        (default: None)