iRfcb (version 0.8.0)

ifcb_annotate_samples: Create Manual Classification MAT Files from PNG Subfolders

Description

This function creates manual classification .mat files compatible with the code in the ifcb-analysis MATLAB repository (Sosik and Olson 2007) by mapping ROIs to class IDs based on user-provided PNG images (organized into subfolders named after classes) and a class2use MAT file.

Usage

ifcb_annotate_samples(
  png_folder,
  adc_folder,
  class2use_file,
  output_folder,
  sample_names = NULL,
  unclassified_id = 1,
  remove_trailing_numbers = TRUE,
  do_compression = TRUE
)

Value

Invisibly returns TRUE on successful completion.

Arguments

png_folder

Directory containing PNG images organized into subfolders named after classes. Each PNG file represents a single ROI extracted from an IFCB sample and must follow the standard IFCB naming convention (for example, "D20220712T210855_IFCB134_00042.png"), which is used to map the image to the corresponding ROI index in the ADC file.

adc_folder

Directory containing ADC files for the samples.

class2use_file

Path to a class2use MAT file. This file should contain the vector of classes used for matching PNG annotations to class IDs.

output_folder

Directory where the resulting MAT files will be written. If the folder does not exist, it will be created automatically.

sample_names

Optional character vector of IFCB sample names (e.g., "D20220712T210855_IFCB134"). If NULL (default), all samples detected from the PNG filenames in png_folder will be processed. Each sample must have a corresponding ADC file in adc_folder.

unclassified_id

An integer specifying the class ID to use for unclassified regions of interest (ROIs) when creating new manual .mat files. Default is 1.

remove_trailing_numbers

Logical. If TRUE (default), trailing numeric suffixes are removed from PNG subfolder names before matching them to entries in class2use (for example, "Skeletonema_036" becomes "Skeletonema"). This is useful when class folders include numeric identifiers that are not part of the class names in class2use.

do_compression

A logical value indicating whether to compress the .mat file. Default is TRUE.

Details

Python must be installed to use this function. The required python packages can be installed in a virtual environment using ifcb_py_install().

Each sample should have ADC files in adc_folder and corresponding PNG images stored in subfolders under png_folder, where each subfolder is named after a class (e.g., Skeletonema, Dinophysis_acuminata, unclassified). The function automatically maps PNG filenames to ROI indices, assigns class IDs based on class2use, and writes the resulting MAT file in output_folder.

• The function reads all PNG images in subfolders of png_folder, extracts class names from folder names, and converts PNG filenames to ROI indices using ifcb_convert_filenames().

• Class IDs are assigned using match() against class2use. If any classes cannot be matched, a warning lists the unmatched classes and shows the ifcb_get_mat_variable() command to inspect available classes.

• The function writes one MAT file per sample using ifcb_create_manual_file().

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_py_install ifcb_create_class2use https://github.com/hsosik/ifcb-analysis

Examples

if (FALSE) {
# Example: Annotate a single IFCB sample
sample_names <- "D20220712T210855_IFCB134"
png_folder <- "data/annotated_png_images/"
adc_folder <- "data/raw"
class2use_file <- "data/manual/class2use.mat"
output_folder <- "data/manual/"

# Create manual MAT file for this sample
ifcb_annotate_samples(
  png_folder = png_folder,
  adc_folder = adc_folder,
  class2use_file = class2use_file,
  output_folder = output_folder,
  sample_names = sample_names
)
}