R/auto_id.R

In soundClass: Sound Classification Using Convolutional Neural Networks

#' @title Automatic classification of sound events in recordings
#' @description Run automatic classification of sound events on a set of
#' recordings using a fitted model.
#' @param model_path Character. Path to the fitted model.
#' @param update_progress Progress bar only to be used inside shiny.
#' @param metadata The object created with the function train_metadata()
#' containing the parameters used to fit the model, or the path to the saved
#' RDATA file.
#' @param file_path Character. Path to the folder containing recordings to be
#' classified by the fitted model.
#' @param out_file Character. Name of the output file to save the results.
#' Will be used to name the csv file and the sqlite database.
#' @param out_dir Character. Path to the folder where the output results will
#' be stored. Will be created if it doesn't exist already.
#' @param save_png Logical. Should a spectrogram of the classified recordings
#' with the identified event(s) and respective classification(s) be saved
#' as png file?
#' @param win_size Integer. Window size in ms to split recordings in chunks
#' for classification. One peak per chunk is obtained and classified.
#' @param plot2console Logical. Should a spectrogram of the classified
#' recordings with the identified event(s) and respective classification(s)
#' be plotted in the console while the analysis is running?
#' @param remove_noise Logical. TRUE indicates that the model was fitted
#' with a non-relevant class which will be deleted from the final output.
#' @param recursive Logical. FALSE indicates that the recordings are in
#' a single folder and TRUE indicates that there are recordings
#' inside subfolders.
#' @param tx Only used in recorders specifically intended for
#' bat recordings. Can take the values "auto" or any numeric value. If the
#' recording is not time expanded tx must be set to 1 (the default). If it's
#' time expanded the numeric value corresponding to the time expansion should
#' be indicated or "auto" should be selected. If tx = "auto" the function
#' assumes that sampling rates < 50kHz corresponds to
#' tx = 10 and > 50kHz to tx = 1.
#' @usage auto_id(model_path, update_progress = NA, metadata,
#' file_path, out_file, out_dir, save_png = TRUE, win_size = 50,
#' plot2console = FALSE, remove_noise = TRUE, recursive = FALSE, tx = 1)
#' @return Nothing.
#' @details Runs a classification task on the recordings of a specified folder
#' and saves the results of the analysis.
#' @export
#' @author Bruno Silva

auto_id <- function(model_path,
                    update_progress = NA,
                    metadata,
                    file_path,
                    out_file,
                    out_dir,
                    save_png = TRUE,
                    win_size = 50,
                    plot2console = FALSE,
                    remove_noise = TRUE,
                    recursive = FALSE,
                    tx = 1) {
  
  if (!dir.exists(out_dir)) dir.create(out_dir)
  
  if (is.character(metadata)) {
    env <- load2env(as.character(metadata))
    metadata <- env[[names(env)[1]]]
    rm(env)
  }
  
  spec_size <- metadata$parameters$spec_size
  window_length <- metadata$parameters$window_length
  dynamic_range <- metadata$parameters$dynamic_range
  frequency_resolution <- metadata$parameters$frequency_resolution
  freq_range <- c(
    metadata$parameters$freq_range_low,
    metadata$parameters$freq_range_high
  )
  
  time_step_size <- (1 - as.numeric(metadata$parameters$overlap)) * as.numeric(metadata$parameters$window_length)
  class_labels <- as.character(metadata$classes$name)
  
  file_name <- list.files(file_path, recursive = recursive, pattern = "wav|WAV")
  if (recursive == TRUE) file_name <- paste0("/", file_name)
  
  size <- length(file_name)
  
  model <- keras::load_model_hdf5(model_path)

  for (i in seq(file_name)) {

    try({
      morc <- import_audio(
        path = paste0(file_path, file_name[i]),
        butt = FALSE,
        low = freq_range[1], high = freq_range[2],
        tx = tx
      )

      if (recursive == TRUE) morc$file_name_full_path <- file_name[i]
      sound_peaks <- peaks(morc, win_size = win_size)

      calls <- peaks2spec(recording = morc,
                          sound_peaks = sound_peaks,
                          spec_size = spec_size,
                          window_length = window_length,
                          frequency_resolution = frequency_resolution,
                          time_step_size = time_step_size,
                          dynamic_range = dynamic_range,
                          freq_range = freq_range
      )

      out <- classify_calls(calls, sound_peaks, model = model)

      out_tidy <- tidy_output(out,
                              class_labels = class_labels, fs = morc$fs, tx = morc$tx,
                              remove_noise = remove_noise, min_dist = win_size
      )

      png_file <- ifelse(save_png == T,
                         paste0(
                           out_dir,
                           morc$file_name, ".png"
                         ),
                         NA
      )
      
      out_peaks <- save_output(
        output = out_tidy,
        recording = morc,
        out_file = paste0(out_dir, out_file),
        png_file = png_file,
        plot2console = plot2console,
        recursive = recursive,
        metadata = metadata
      )
    })
    
    if (is.function(update_progress)) {
      text <- paste0(i, " of ", length(file_name))
      update_progress(detail = text)
    }
    
    print(paste0("recording ", i, " of ", size))
  }
}