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Extract Taxa Images from MATLAB Classified Sample

Source: R/ifcb_extract_classified_images.R
ifcb_extract_classified_images.Rd

This function reads a MATLAB classified sample file (.mat) generated by the start_classify_batch_user_training function from the ifcb-analysis repository (Sosik and Olson 2007), extracts specified taxa images from the corresponding ROI files, and saves each image in a specified directory.

Usage

ifcb_extract_classified_images(
  sample,
  classified_folder,
  roi_folder,
  out_folder,
  taxa = "All",
  threshold = "opt",
  overwrite = FALSE,
  scale_bar_um = NULL,
  scale_micron_factor = 1/3.4,
  scale_bar_position = "bottomright",
  scale_bar_color = "black",
  old_adc = FALSE,
  gamma = 1,
  use_python = FALSE,
  verbose = TRUE
)

Arguments

sample

A character string specifying the sample name.

classified_folder

A character string specifying the directory containing the classified files.

roi_folder

A character string specifying the directory containing the ROI files.

out_folder

A character string specifying the directory to save the extracted images.

taxa

A character string specifying the taxa to extract. Default is "All".

threshold

A character string specifying the threshold to use ("none", "opt", "adhoc"). Default is "opt".

overwrite

A logical value indicating whether to overwrite existing PNG files. Default is FALSE.

scale_bar_um

An optional numeric value specifying the length of the scale bar in micrometers. If NULL, no scale bar is added.

scale_micron_factor

A numeric value defining the conversion factor from micrometers to pixels. Defaults to 1/3.4.

scale_bar_position

A character string specifying the position of the scale bar in the image. Options are "topright", "topleft", "bottomright", or "bottomleft". Defaults to "bottomright".

scale_bar_color

A character string specifying the scale bar color. Options are "black" or "white". Defaults to "black".

old_adc

A logical value indicating whether the adc file is of the old format (samples from IFCB1-6, labeled "IFCBxxx_YYYY_DDD_HHMMSS"). Default is FALSE.

gamma

A numeric value for gamma correction applied to the image. Default is 1 (no correction). Values <1 increase contrast in dark regions, while values >1 decrease contrast.

use_python

Logical. If TRUE, attempts to read the .mat file using a Python-based method. Default is FALSE.

verbose

A logical value indicating whether to print progress messages. Default is TRUE.

Value

No return value, called for side effects. Extracts and saves taxa images to a directory.

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See also

ifcb_extract_pngs ifcb_extract_annotated_images https://github.com/hsosik/ifcb-analysis

Examples

if (FALSE) { # \dontrun{
# Define the parameters
sample <- "D20230311T092911_IFCB135"
classified_folder <- "path/to/classified_folder"
roi_folder <- "path/to/roi_folder"
out_folder <- "path/to/outputdir"
taxa <- "All"  # or specify a particular taxa
threshold <- "opt"  # or specify another threshold

# Extract taxa images from the classified sample
ifcb_extract_classified_images(sample, classified_folder, roi_folder, out_folder, taxa, threshold)
} # }