#' @title Smooth a tile
#' @name .smooth_tile
#' @keywords internal
#' @noRd
#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br}
#'
#' @param tile. Subset of a data cube containing one tile
#' @param band Band to be processed
#' @param block Individual block that will be processed
#' @param overlap Overlap between tiles (if required)
#' @param smooth_fn Smoothing function
#' @param output_dir Directory where image will be save
#' @param version Version of result
#' @return Smoothed tile-band combination
.smooth_tile <- function(tile,
band,
block,
overlap,
smooth_fn,
output_dir,
version) {
# Output file
out_file <- .file_derived_name(
tile = tile, band = band, version = version,
output_dir = output_dir
)
# Resume feature
if (file.exists(out_file)) {
.check_recovery(tile[["tile"]])
probs_tile <- .tile_derived_from_file(
file = out_file,
band = band,
base_tile = tile,
labels = .tile_labels(tile),
derived_class = "probs_cube",
update_bbox = FALSE
)
return(probs_tile)
}
# Create chunks as jobs
chunks <- .tile_chunks_create(tile = tile, overlap = overlap, block = block)
# Process jobs in parallel
block_files <- .jobs_map_parallel_chr(chunks, function(chunk) {
# Job block
block <- .block(chunk)
# Block file name
block_file <- .file_block_name(
pattern = .file_pattern(out_file),
block = block,
output_dir = output_dir
)
# Resume processing in case of failure
if (.raster_is_valid(block_file)) {
return(block_file)
}
# Read and preprocess values
values <- .tile_read_block(
tile = tile, band = .tile_bands(tile), block = block
)
# Apply the probability function to values
values <- smooth_fn(values = values, block = block)
# Prepare probability to be saved
band_conf <- .conf_derived_band(
derived_class = "probs_cube", band = band
)
offset <- .offset(band_conf)
if (.has(offset) && offset != 0) {
values <- values - offset
}
scale <- .scale(band_conf)
if (.has(scale) && scale != 1) {
values <- values / scale
}
# Job crop block
crop_block <- .block(.chunks_no_overlap(chunk))
# Prepare and save results as raster
.raster_write_block(
files = block_file, block = block, bbox = .bbox(chunk),
values = values, data_type = .data_type(band_conf),
missing_value = .miss_value(band_conf),
crop_block = crop_block
)
# Free memory
gc()
# Return block file
block_file
})
# Merge blocks into a new probs_cube tile
probs_tile <- .tile_derived_merge_blocks(
file = out_file,
band = band,
labels = .tile_labels(tile),
base_tile = tile,
block_files = block_files,
derived_class = "probs_cube",
multicores = .jobs_multicores(),
update_bbox = FALSE
)
# Return probs tile
probs_tile
}
#---- Bayesian smoothing ----
#' @title Smooth probability cubes with spatial predictors
#' @noRd
#' @param cube Probability data cube.
#' @param block Individual block that will be processed
#' @param window_size Size of the neighborhood.
#' @param neigh_fraction Fraction of neighbors with high probabilities
#' to be used in Bayesian inference.
#' @param smoothness Estimated variance of logit of class probabilities
#' (Bayesian smoothing parameter). It can be either
#' a vector or a scalar.
#' @param multicores Number of cores to run the smoothing function
#' @param memsize Maximum overall memory (in GB) to run the
#' smoothing.
#' @param output_dir Output directory for image files
#' @param version Version of resulting image
#' (in the case of multiple tests)
#'
.smooth <- function(cube,
block,
window_size,
neigh_fraction,
smoothness,
multicores,
memsize,
output_dir,
version) {
# Smooth parameters checked in smooth function creation
# Create smooth function
smooth_fn <- .smooth_fn_bayes(
window_size = window_size,
neigh_fraction = neigh_fraction,
smoothness = smoothness
)
# Overlapping pixels
overlap <- ceiling(window_size / 2) - 1
# Smoothing
# Process each tile sequentially
.cube_foreach_tile(cube, function(tile) {
# Smooth the data
.smooth_tile(
tile = tile,
band = "bayes",
block = block,
overlap = overlap,
smooth_fn = smooth_fn,
output_dir = output_dir,
version = version
)
})
}
#' @title Define smoothing function
#' @noRd
#' @param window_size Size of the neighborhood.
#' @param neigh_fraction Fraction of neighbors with high probabilities
#' to be used in Bayesian inference.
#' @param smoothness Estimated variance of logit of class probabilities
#' (Bayesian smoothing parameter). It can be either
#' a vector or a scalar.
#' @return Function to be applied to smoothen data
.smooth_fn_bayes <- function(window_size,
neigh_fraction,
smoothness) {
# Check window size
.check_int_parameter(window_size, min = 5, is_odd = TRUE)
# Check neigh_fraction
.check_num_parameter(neigh_fraction, exclusive_min = 0, max = 1)
# Define smooth function
smooth_fn <- function(values, block) {
# Check values length
input_pixels <- nrow(values)
# Compute logit
values <- log(values / (rowSums(values) - values))
# Process Bayesian
values <- bayes_smoother_fraction(
logits = values,
nrows = .nrows(block),
ncols = .ncols(block),
window_size = window_size,
smoothness = smoothness,
neigh_fraction = neigh_fraction
)
# Compute inverse logit
values <- exp(values) / (exp(values) + 1)
# Are the results consistent with the data input?
.check_processed_values(values, input_pixels)
# Return values
values
}
# Return a closure
smooth_fn
}
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