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#' Performs plant disease measurements by leaf
#'
#' Computes the percentage of symptomatic leaf area using color palettes or RGB
#' indexes `by` each `l`eaf of an image. This allows, for example, processing
#' replicates of the same treatment and obtaining the results for each
#' replication with a single image. To do that, leaf samples are first splitten
#' with [object_split()] and then, [measure_disease()] is applied to the list of
#' leaves.
#'
#' @inheritParams object_split
#' @inheritParams measure_disease
#' @param dir_original,dir_processed The directory containing the original and
#' processed images. Defaults to `NULL`. In this case, the function will
#' search for the image `img` in the current working directory. After
#' processing, when `save_image = TRUE`, the processed image will be also
#' saved in such a directory. It can be either a full path, e.g.,
#' `"C:/Desktop/imgs"`, or a subfolder within the current working directory,
#' e.g., `"/imgs"`.
#' @param index A character value specifying the target mode for
#' conversion to binary to segment the leaves from background. Defaults to "B"
#' (blue). See [image_index()] for more details. Personalized indexes can be
#' informed as, e.g., `index = "R*G/B`.
#' @param lower_size To prevent dust from affecting object segmentation, objects
#' with lesser than `10%` of the mean of all objects are removed. . One can
#' set a known area or use `lower_limit = 0` to select all objects (not
#' advised).
#' @param ... Additional arguments passed on to [measure_disease()].
#' @return
#' * A list with the following objects:
#' - `severity` A data frame with the percentage of healthy and symptomatic
#' areas for each leaf in the image(s).
#' - `shape`,`statistics` If `show_features = TRUE` is used, returns the shape
#' (area, perimeter, etc.) for each lesion and a summary statistic of the
#' results.
#' @export
#'
#' @examples
#'library(pliman)
#'img <- image_pliman("mult_leaves.jpg", plot = TRUE)
#'sev <-
#' measure_disease_byl(img = img,
#' index_lb = "B",
#' index_dh = "NGRDI",
#' workers = 2)
#' sev$severity
#'
#'
measure_disease_byl <- function(img,
index = "B",
index_lb = "B",
index_dh = "NGRDI",
lower_size = NULL,
watershed = TRUE,
invert = FALSE,
fill_hull = FALSE,
filter = 3,
threshold = "Otsu",
extension = NULL,
tolerance = NULL,
object_size = "large",
img_healthy = NULL,
img_symptoms = NULL,
plot = TRUE,
save_image = FALSE,
dir_original = NULL,
dir_processed = NULL,
pattern = NULL,
parallel = FALSE,
workers = NULL,
show_features = FALSE,
verbose = TRUE,
...){
if(is.null(dir_original)){
diretorio_original <- paste("./", sep = "")
} else{
diretorio_original <-
ifelse(grepl("[/\\]", dir_original),
dir_original,
paste0("./", dir_original))
}
if(is.null(dir_processed)){
diretorio_processada <- paste("./", sep = "")
} else{
diretorio_processada <-
ifelse(grepl("[/\\]", dir_processed),
dir_processed,
paste0("./", dir_processed))
}
if(is.character(img_healthy)){
all_files <- sapply(list.files(diretorio_original), file_name)
imag <- list.files(diretorio_original, pattern = img_healthy)
check_names_dir(img_healthy, all_files, "")
name_h <- file_name(imag)
extens <- file_extension(imag)
img_healthy <- image_import(paste(diretorio_original, "/", name_h, ".", extens, sep = ""))
}
if(is.character(img_symptoms)){
all_files <- sapply(list.files(diretorio_original), file_name)
imag <- list.files(diretorio_original, pattern = img_symptoms)
check_names_dir(img_symptoms, all_files, "")
name_h <- file_name(imag)
extens <- file_extension(imag)
img_symptoms <- image_import(paste(diretorio_original, "/", name_h, ".", extens, sep = ""))
}
back <- EBImage::Image(rep(1, 100*300),dim=c(100,300,3), colormode = 'Color')
help_byl <- function(img,
img_healthy,
img_symptoms,
back,
index_dh,
index_lb){
if(is.character(img)){
all_files <- sapply(list.files(diretorio_original), file_name)
check_names_dir(img, all_files, diretorio_original)
imag <- list.files(diretorio_original, pattern = paste0("^",img, "\\."))
name_ori <- file_name(imag)
extens_ori <- file_extension(imag)
img <- image_import(paste(name_ori, ".", extens_ori, sep = ""), path = diretorio_original)
} else{
name_ori <- match.call()[[2]]
extens_ori <- "jpg"
}
splits <- object_split(img,
index = index,
watershed = watershed,
invert = invert,
fill_hull = fill_hull,
filter = filter,
threshold = threshold,
extension = extension,
tolerance = tolerance,
object_size = object_size,
remove_bg = TRUE,
plot = FALSE,
verbose = FALSE)
results <- list()
if(is.null(img_healthy)){
results <-
lapply(seq_along(splits),
function(i){
measure_disease(splits[[i]],
index_dh = index_dh,
index_lb = index_lb,
plot = plot,
save_image = save_image,
show_features = show_features,
dir_processed = diretorio_processada,
prefix = paste0(name_ori, "_", i),
name = "",
filter = filter,
threshold = threshold,
...)
})
} else{
results <-
lapply(seq_along(splits),
function(i){
measure_disease(splits[[i]],
img_healthy = img_healthy,
img_symptoms = img_symptoms,
img_background = back,
plot = plot,
show_features = show_features,
save_image = save_image,
dir_processed = diretorio_processada,
prefix = paste0(name_ori, "_", i),
name = "",
filter = filter,
threshold = threshold,
...)
})
}
names(results) <- paste0(name_ori, "-", 1:length(results))
severity <-
do.call(rbind,
lapply(seq_along(results), function(i){
transform(results[[i]][["severity"]],
img = names(results[i]))[, c(3, 1:2)]
})
) |>
separate_col(img, into = c("img", "leaf"), sep = "-")
if(isTRUE(show_features)){
stats <-
do.call(rbind,
lapply(seq_along(results), function(i){
if(is.null(results[[i]][["statistics"]])){
res <- data.frame(stat = "NA", value = 0)
} else{
res <- results[[i]][["statistics"]]
}
transform(res, img = names(results[i]))[,c(3, 1, 2)]
})
) |>
separate_col(img, into = c("img", "leaf"), sep = "-")
shape <-
do.call(rbind,
lapply(seq_along(results), function(i){
if(is.null(results[[i]][["shape"]])){
names <- names_measures()
res <- data.frame(matrix(nrow = 1, ncol = length(names)))
res[1, ] <- 0
colnames(res) <- names
} else{
res <- results[[i]][["shape"]]
}
transform(res, img = names(results[i]))
})
) |>
separate_col(img, into = c("img", "leaf"), sep = "-")
} else{
shape <- NULL
stats <- NULL
}
invisible(
structure(
list(severity = severity,
stats = stats,
shape = shape),
class = "plm_disease_byl")
)
}
if(missing(pattern)){
results <- help_byl(img, img_healthy, img_symptoms, back, index_dh, index_lb)
} else{
if(pattern %in% c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9")){
pattern <- "^[0-9].*$"
}
plants <- list.files(pattern = pattern, diretorio_original)
extensions <- as.character(sapply(plants, file_extension))
names_plant <- as.character(sapply(plants, file_name))
if(length(grep(pattern, names_plant)) == 0){
stop(paste("'", pattern, "' pattern not found in '",
paste(getwd(), sub(".", "", diretorio_original), sep = ""), "'", sep = ""),
call. = FALSE)
}
if(!all(extensions %in% c("png", "jpeg", "jpg", "tiff", "PNG", "JPEG", "JPG", "TIFF"))){
stop("Allowed extensions are .png, .jpeg, .jpg, .tiff")
}
if(parallel == TRUE){
nworkers <- ifelse(is.null(workers), trunc(detectCores()*.5), workers)
clust <- makeCluster(nworkers)
clusterExport(clust,
varlist = c("names_plant", "help_byl", "img_healthy", "img_symptoms", "back"),
envir=environment())
on.exit(stopCluster(clust))
if(verbose == TRUE){
message("Processing ", length(names_plant), " images in multiple sessions (",nworkers, "). Please, wait.")
}
results <-
parLapply(clust, names_plant,
function(x){
help_byl(img = x, img_healthy, img_symptoms, back, index_dh, index_lb)
})
} else{
results <- list()
pb <- progress(max = length(plants), style = 4)
for (i in 1:length(plants)) {
if(verbose == TRUE){
run_progress(pb, actual = i,
text = paste("Processing image", names_plant[i]))
}
results[[i]] <- help_byl(img = names_plant[i], img_healthy, img_symptoms, back, index_dh, index_lb)
}
}
names(results) <- names_plant
if(isTRUE(show_features)){
stats <- do.call(rbind, lapply(results, function(x){x[["stats"]]}))
rownames(stats) <- NULL
shape <- do.call(rbind, lapply(results, function(x){x[["shape"]]}))
rownames(shape) <- NULL
} else{
shape <- NULL
stats <- NULL
}
severity <- do.call(rbind, lapply(results, function(x){x[["severity"]]}))
rownames(severity) <- NULL
results <- list(severity = severity,
shape = shape,
stats = stats,
parms = list(
pattern = pattern,
img_healthy = img_healthy,
img_symptoms = img_symptoms,
dir_original = diretorio_original,
dir_processed = diretorio_processada,
save_image = save_image))
}
invisible(structure(
results, class = "plm_disease_byl"
))
}
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