Save sample annotations

Saves the current annotations for a sample. By default annotations are stored in a local SQLite database (annotations.sqlite in the database folder). Optionally, a MATLAB-compatible .mat file can also be written (requires Python + scipy).

Usage

save_sample_annotations(
  sample_name,
  classifications,
  original_classifications,
  changes_log,
  temp_png_folder,
  output_folder,
  png_output_folder,
  roi_folder,
  class2use_path,
  class2use = NULL,
  annotator = "Unknown",
  adc_folder = NULL,
  save_format = "sqlite",
  db_folder = get_default_db_dir(),
  export_statistics = TRUE
)

Arguments

sample_name

Sample name (e.g., "D20230101T120000_IFCB134")

classifications

Current classifications data frame

original_classifications

Original classifications data frame (for comparison)

changes_log

Changes log data frame from create_empty_changes_log

temp_png_folder

Path to temporary folder with extracted PNG images

output_folder

Output folder path for MAT files and statistics

png_output_folder

PNG output folder path (organized by class)

roi_folder

ROI folder path (for ADC file location, used as fallback)

class2use_path

Path to class2use file

class2use

Character vector of class names. When NULL (default), loaded from class2use_path.

annotator

Annotator name for statistics

adc_folder

Direct path to the ADC folder. When provided, this is used instead of constructing the path via get_sample_paths. This supports non-standard folder structures.

save_format

One of "sqlite" (default), "mat", or "both". Controls which backend(s) are written.

db_folder

Path to the database folder for SQLite storage. Defaults to get_default_db_dir(). Should be a local filesystem path, not a network drive.

export_statistics

Logical. When TRUE (default), validation statistics CSV files are written to a validation_statistics/ subfolder inside output_folder. Set to FALSE to skip this export, e.g. when annotating from scratch.

Value

TRUE on success, FALSE on failure

Examples

if (FALSE) { # \dontrun{
# Save annotations for a sample (default: SQLite)
success <- save_sample_annotations(
  sample_name = "D20230101T120000_IFCB134",
  classifications = current_classifications,
  original_classifications = original_classifications,
  changes_log = changes_log,
  temp_png_folder = "/tmp/png",
  output_folder = "/data/manual",
  png_output_folder = "/data/png_output",
  roi_folder = "/data/raw",
  class2use_path = "/data/class2use.mat",
  annotator = "John Doe"
)
} # }