Package 'fvfmR'

Path to the example images

Description

fvfmR ships with two example .pim images from a Walz ImagingPAM. Use them to try out run_fvfm or to check your installation.

Usage

fvfm_example(file = NULL)

Arguments

file	Name of an example file. NULL (default) returns the folder containing all example images.

Details

example1.pim

A full 5 x 9 tray. The default settings detect all 45 ROIs and the correct grid, so it can be analysed straight away.

example2.pim

A 6 x 10 tray with three empty cells (column 10, rows 4 to 6), which needs some adjustment before analysis:

• Fill the three empty cells with dummy ROIs (double-click) so that every ROI is assigned to the correct row and column.

• Rotating the image slightly helps align the grid.

• One leaf is split into two ROIs with the default settings; increasing Watershed segmentation size slightly (to about 36) merges them.

Remove any extra ROIs outside the leaves by double-clicking them.

Value

Path to the example folder, or to file within it.

Examples

fvfm_example()
list.files(fvfm_example())
fvfm_example("example1.pim")
## Not run: 
results <- run_fvfm(fvfm_example())

## End(Not run)

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Set up the Python environment for the fvfmR package

Description

Creates a dedicated virtual environment called fvfm-env and installs the fvfmPy Python package and its dependencies into it. Run this once after installing the package, or again to update fvfmPy. The environment is created in reticulate's virtualenv folder (see virtualenv_root) and needs about 1.5 GB of disk space.

Usage

fvfm_setup(python = NULL, source = NULL, ask = interactive())

Arguments

python	Path to a Python 3.10+ executable used to create the virtual environment. NULL (default) lets reticulate find Python on your system.
source	Optional path to a local fvfmPy source (.tar.gz) or wheel (.whl) file. NULL (default) installs the latest release from PyPI.
ask	Logical. Ask for confirmation before creating or modifying the Python environment? Default: TRUE in interactive sessions.

Details

By default the latest release of fvfmPy is installed from PyPI. Use source to install from a downloaded .tar.gz or .whl file instead. Running fvfm_setup() again updates fvfmPy to the latest release.

If you already have a Python environment with fvfmPy installed, you can skip this step and point the package at it with options(fvfmR.python = "/path/to/venv/bin/python").

Value

Invisible path to the environment's Python executable, or NULL if setup was cancelled.

Examples

## Not run: 
fvfm_setup()
fvfm_setup(source = "~/Downloads/fvfmpy-1.0.0.tar.gz")

## End(Not run)

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Run the fvfmPy leaf disc analysis GUI

Description

Opens the fvfmPy window, either on a folder of .pim, .tif or .tiff images from a Walz ImagingPAM or with no folder open (choose one with the Open folder button). Use the window to check the detected leaf discs, adjust rotation, cropping, rows/columns and segmentation settings, and press Analyze (or Enter) to log each image. See the fvfmPy User's Guide for details.

Usage

run_fvfm(directory = NULL, output = NULL, overwrite = FALSE, python = NULL)

Arguments

directory	Optional path to the folder containing the images. NULL (default) opens the window without a folder.
output	Optional path to a CSV file to write the results to.
overwrite	Logical. Overwrite output if it already exists? Default: FALSE.
python	Optional path to a Python executable with fvfmPy installed. See fvfm_setup for how the default is chosen.

Details

R waits while the window is open. When you close it, every observation logged during the session is returned as a data frame, so there is no need to use the Save results button (it still works if you want an extra copy).

The GUI runs as a separate Python process, so a problem in the GUI cannot crash your R session.

Value

Invisibly, a data frame with one row per leaf disc and columns filename, row, col, leaf_number, centroid_x, centroid_y, mean_Fo, mean_Fm and FvFm; or NULL if no images were analysed.

Examples

## Not run: 
results <- run_fvfm()   # choose a folder in the window
results <- run_fvfm("~/Dropbox/Experiment1/images")
run_fvfm("~/Dropbox/Experiment1/images", output = "results.csv")

## End(Not run)

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