R/LEEF_2_classify_bemovi_files.R

In LEEF-UZH/LEEF.analysis: Access Functions, Tests and Basic Analysis of the RRD Data from the LEEF Project

#' Classify algae_traits data.frame
#'
#' @param datadir `character` vector containing the root directory for all files
#' @param bemovi_extract_name `character` vector containing the name of the
#'   bemovi config file including path.
#' @param classifier classifier for bemovi 16 including path
#' @param exclude_videos file names of videos to exclude. If \code{NULL}, all will be used.
#'
#' @return `list` containing three objects:
#'       - `mean_density_per_ml`
#'       - `morph_mvt`
#'       - `trajectory_data`
#'
#' @importFrom yaml read_yaml
#' @importFrom utils read.table
#'
#' @export
#'
#' @md
#' @examples
LEEF_2_classify_bemovi_files <- function(
    datadir,
    bemovi_extract_name = NULL,
    classifier = NULL,
    exclude_videos = NULL) {
  p <- yaml::read_yaml(bemovi_extract_name)


  # The classification ------------------------------------------------------

  morph_mvt <- readRDS(file.path(datadir, p$merged.data.folder, p$morph_mvt))
  morph_mvt <- morph_mvt[, grep("_prob|species", names(morph_mvt), invert = TRUE)]

  traj <- readRDS(file.path(datadir, p$merged.data.folder, p$master))

  if (!is.null(exclude_videos)) {
    morph_mvt <- morph_mvt[!(morph_mvt$file %in% exclude_videos), ]
    traj <- traj[!(traj$file %in% exclude_videos), ]

# TODO    
    # vdf <- as.data.frame(
    #   read.table(
    #     file.path(datadir, p$video.description.folder, p$video.description.file),
    #     sep = "\t",
    #     header = TRUE,
    #     stringsAsFactors = FALSE
    #   )
    


  }
  classified <- LEEF.2.measurement.bemovi::classify_LEEF_2(
    bemovi_extract = bemovi_extract_name,
    morph_mvt = morph_mvt,
    trajectory_data = traj,
    classifiers = classifier,
    video_description_file = as.data.frame(
      read.table(
        file.path(datadir, p$video.description.folder, p$video.description.file),
        sep = "\t",
        header = TRUE,
        stringsAsFactors = FALSE
      )
    ),
    composition = utils::read.csv(file.path(datadir, "compositions.csv"))
  )


  # Return the classifications --------------------------------------------------


  return(classified)
}