Include suggestions given by the CRAN team on the first submission

TiagoOlivoto · May 13, 2021 · b3fb181 · b3fb181
1 parent a94fc94
commit b3fb181
Show file tree

Hide file tree

Showing 20 changed files with 208 additions and 139 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -7,11 +7,12 @@ Authors@R:
            role = c("aut", "cre"),
            email = "[email protected]",
            comment = c(ORCID = "0000-0002-0241-9636"))
-Description: Provides tools for image manipulation and plant image
-    analysis that will help you to segment images, create binary images,
-    measure leaf area, quantify disease severity, count the number of
-    disease lesions, and obtain statistics of image objects such as
-    grains, pods, pollen, leaves and more.
+Description: Provides tools for image manipulation that will help you to
+    quantify plant leaf area, disease severity, number of disease lesions,
+    and obtain statistics of image objects such as grains, pods, pollen,
+    leaves, and more. Tools for segment images and create binary images
+    using the method of automatic threshold selection method proposed by
+    Otsu (1979) <doi:10.1109/tsmc.1979.4310076> are also provided.
 License: GPL (>= 3)
 Depends: 
     R (>= 4.0.0)

diff --git a/NAMESPACE b/NAMESPACE
@@ -20,7 +20,7 @@ export(image_horizontal)
 export(image_hreflect)
 export(image_import)
 export(image_index)
-export(image_pallete)
+export(image_palette)
 export(image_pliman)
 export(image_resize)
 export(image_rotate)
@@ -52,6 +52,7 @@ importFrom(parallel,makeCluster)
 importFrom(parallel,parLapply)
 importFrom(parallel,stopCluster)
 importFrom(stats,aggregate)
+importFrom(stats,as.formula)
 importFrom(stats,binomial)
 importFrom(stats,glm)
 importFrom(stats,kmeans)

diff --git a/R/color_palette.R b/R/color_palette.R
diff --git a/R/count_lesions.R b/R/count_lesions.R
@@ -85,7 +85,12 @@
 #'   processing, when `save_image = TRUE`, the processed image will be also
 #'   saved in such a directory.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
-#' @return A data frame with the results for each image.
+#' @return A list with the following objects:
+#'  * `results` A data frame with the results (area, perimeter, radius) for
+#'  object.
+#'  * `statistics` A data frame with the summary statistics for the image.
+#'  * `count` (If `img_pattern` is used), summarizing the count number for each
+#'  image.
 #' @export
 #' @md
 #' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
@@ -96,6 +101,7 @@
 #' healthy <- image_import(image_pliman("sev_healthy.jpg"))
 #' lesions <- image_import(image_pliman("sev_sympt.jpg"))
 #' image_combine(img, healthy, lesions, ncol = 3)
+#' a <-
 #' count_lesions(img = img,
 #'               img_healthy = healthy,
 #'               img_lesion = lesions,
@@ -224,18 +230,6 @@ count_lesions <- function(img,
                  nmask <- watershed(distmap(plant_symp),
                                     tolerance = tol,
                                     ext = ext))
-          # shape <-
-          #   cbind(data.frame(computeFeatures.shape(nmask)),
-          #         data.frame(computeFeatures.moment(nmask))[,1:2]
-          #   )
-          # ifelse(!is.null(lower_size),
-          #        shape <- shape[shape$s.area > lower_size, ],
-          #        shape <- shape[shape$s.area > mean(shape$s.area) * 0.1, ])
-          # if(!is.null(upper_size)){
-          #   shape <- shape[shape$s.area < upper_size, ]
-          # }
-          # shape$id <- 1:nrow(shape)
-          # shape <- shape[, c(9, 7, 8, 1, 2:6)]
           if(show_original == TRUE & show_segmentation == FALSE){
             im2 <- img
             im2@.Data[,,1][!ID] <- col_lesions[1]
@@ -292,14 +286,12 @@ count_lesions <- function(img,
                   sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,],
                   fundo$df_in[sample(1:nrow(fundo$df_in)),][1:nrows,]) %>%
             transform(Y = ifelse(CODE == "img_background", 0, 1))
-          # fundo_resto$CODE <- NULL
           modelo1 <-
             glm(Y ~ R + G + B, family = binomial("logit"), data = fundo_resto) %>%
             suppressWarnings()
           pred1 <- predict(modelo1, newdata = original$df_in, type="response") %>% round(0)
           plant_background <- matrix(pred1, ncol = ncol(original$R))
           plant_background <- image_correct(plant_background, perc = 0.009)
-          # image_show(plant_background)
           plant_background[plant_background == 1] <- 2
           sadio_sintoma <-
             rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
@@ -318,7 +310,6 @@ count_lesions <- function(img,
           leaf_sympts <- matrix(pred3, ncol = ncol(original$R))
           leaf_sympts <- 1 - image_correct(leaf_sympts, perc = 0.009)
           plant_background[leaf_sympts == 1] <- 3
-          # mpred1 <- bwlabel(leaf_sympts == 0)
           parms <- read.csv(file=system.file("parameters.csv", package = "pliman", mustWork = TRUE),
                             header = T, sep = ";")
           parms2 <- parms[parms$object_size == lesion_size,]
@@ -333,18 +324,6 @@ count_lesions <- function(img,
                  nmask <- watershed(distmap(leaf_sympts),
                                     tolerance = tol,
                                     ext = ext))
-          # shape <-
-          #   cbind(data.frame(computeFeatures.shape(nmask)),
-          #         data.frame(computeFeatures.moment(nmask))[,1:2]
-          #   )
-          # ifelse(!is.null(lower_size),
-          #        shape <- shape[shape$s.area > lower_size, ],
-          #        shape <- shape[shape$s.area > mean(shape$s.area) * 0.1, ])
-          # if(!is.null(upper_size)){
-          #   shape <- shape[shape$s.area < upper_size, ]
-          # }
-          # shape$id <- 1:nrow(shape)
-          # shape <- shape[, c(9, 7, 8, 1, 2:6)]
           if(show_original == TRUE & show_segmentation == TRUE){
             im2 <- colorLabels(nmask)
             if(backg){
@@ -413,9 +392,6 @@ count_lesions <- function(img,
             eval(parse(text=x))}))
         ext <- ifelse(is.null(extension),  parms2[rowid, 3], extension)
         tol <- ifelse(is.null(tolerance), parms2[rowid, 4], tolerance)
-        # print(res)
-        # print(ext)
-        # print(tol)
         nmask <- watershed(distmap(img2),
                            tolerance = tol,
                            ext = ext)
@@ -464,8 +440,6 @@ count_lesions <- function(img,
         }
       }
 
-
-
       shape <-
         cbind(data.frame(computeFeatures.shape(nmask)),
               data.frame(computeFeatures.moment(nmask))[,1:2]
@@ -478,7 +452,6 @@ count_lesions <- function(img,
       }
       shape$id <- 1:nrow(shape)
       shape <- shape[, c(9, 7, 8, 1, 2:6)]
-
       show_mark <- !is.null(marker) && show_segmentation == TRUE | show_segmentation == FALSE
       marker <- ifelse(is.null(marker), "point", marker)
       marker_col <- ifelse(is.null(marker_col), "white", marker_col)
@@ -553,8 +526,12 @@ count_lesions <- function(img,
                                  NA)) %>%
         transform(statistics = c("n", "min", "mean", "max", "sd", "sum", "prop"))
       stats <- stats[c(3, 1, 2)]
+      shape <- shape[,c(1:6, 8:9, 7)]
+      colnames(shape) <- c("id", "x", "y", "area", "perimeter", "radius_mean",
+                           "radius_min", "radius_max", "radius_sd")
       results <- list(results = shape,
                       statistics = stats)
+      class(results) <- "plm_count"
       if(verbose == TRUE){
         print(results$statistics, row.names = FALSE)
       }

diff --git a/R/count_objects.R b/R/count_objects.R
@@ -95,7 +95,12 @@
 #'   processing, when `save_image = TRUE`, the processed image will be also
 #'   saved in such a directory.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
-#' @return A data frame with the results for each image.
+#' @return A list with the following objects:
+#'  * `results` A data frame with the results (area, perimeter, radius) for
+#'  object.
+#'  * `statistics` A data frame with the summary statistics for the image.
+#'  * `count` (If `img_pattern` is used), summarizing the count number for each
+#'  image.
 #' @export
 #' @import EBImage
 #' @md
@@ -245,7 +250,6 @@ count_objects <- function(img,
                            ext = ext)
         ID <- which(img2 == 1)
         ID2 <- which(img2 == 0)
-        feat <- computeFeatures.moment(nmask)
       }
       backg <- !is.null(col_background)
       col_background <- col2rgb(ifelse(is.null(col_background), "white", col_background))
@@ -309,7 +313,7 @@ count_objects <- function(img,
       shape$id <- 1:nrow(shape)
       shape <- shape[, c(9, 7, 8, 1, 2:6)]
       show_mark <- !is.null(marker) && show_segmentation == TRUE | show_segmentation == FALSE
-      marker <- ifelse(is.null(marker), "point", marker)
+      marker <- ifelse(is.null(marker), "text", marker)
       marker_col <- ifelse(is.null(marker_col), "white", marker_col)
       marker_size <- ifelse(is.null(marker_size), 0.9, marker_size)
       if(show_image == TRUE){
@@ -346,16 +350,16 @@ count_objects <- function(img,
         if(marker == "text"){
           marker_size <- ifelse(is.null(marker_size), 0.75, marker_size)
           image_show(im2)
-          text(feat[,1],
-               feat[,2],
-               seq(1:nrow(feat)),
+          text(shape[,2],
+               shape[,3],
+               shape[,1],
                col = marker_col,
                cex = marker_size)
         } else{
           marker_size <- ifelse(is.null(marker_size), 0.75, marker_size)
           image_show(im2)
-          points(feat[,1],
-                 feat[,2],
+          text(shape[,2],
+               shape[,3],
                  col = marker_col,
                  pch = 16,
                  cex = marker_size)

diff --git a/R/symptomatic_area.R b/R/symptomatic_area.R
@@ -44,7 +44,8 @@
 #'   processing, when `save_image = TRUE`, the processed image will be also
 #'   saved in such a directory.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
-#' @return A data frame with the results for each image.
+#' @return A data frame with the results (healthy and symptomatic area) for each
+#'   image.
 #' @export
 #' @md
 #' @importFrom stats binomial glm predict kmeans sd aggregate

diff --git a/R/utils_file.R b/R/utils_file.R
@@ -29,9 +29,23 @@
 #' @param remove_original Remove original files after manipulation? defaults to
 #'   `FALSE`. If `TRUE` the files in `pattern` will be removed.
 #' @param verbose If `FALSE`, the code is run silently.
+#' @return
+#' * `file_extension()`,  `file_name()`, and `file_dir()` return a character
+#' string.
+#' * `manipulate_files()` No return value. If `verbose == TRUE`, a message is
+#' printed indicating which operation succeeded (or not) for each of the files
+#' attempted.
 #' @export
 #' @examples
 #' \donttest{
+#' library(pliman)
+#' # get file name, directory and extension
+#' file <- "E:/my_folder/my_subfolder/image1.png"
+#' file_dir(file)
+#' file_name(file)
+#' file_extension(file)
+#'
+#' # manipulate files
 #' dir <- tempdir()
 #' list.files(dir)
 #' file.create(paste0(dir, "/test.txt"))

diff --git a/R/utils_imagem.R b/R/utils_imagem.R
@@ -535,9 +535,9 @@ image_binary <- function(image,
   }
 }
 
-#' Image indices
+#' Image indexes
 #'
-#' Builds image indices using Red, Green, Blue, Red-Edge, and NIR bands.
+#' Builds image indexes using Red, Green, Blue, Red-Edge, and NIR bands.
 #' @details
 #' The following indexes are available in pliman.
 #'
@@ -992,14 +992,17 @@ image_to_mat <- function(image,
 #' @param workers A positive numeric scalar or a function specifying the maximum
 #'   number of parallel processes that can be active at the same time.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
+#' @return A list with `npal` color palettes of class `Image`.
 #' @export
 #' @examples
+#' \donttest{
 #' library(pliman)
 #'img <- image_import(image_pliman("sev_leaf.jpg"))
-#'pal <- image_pallete(img, 2)
+#'pal <- image_palette(img, 2)
 #'image_show(pal[[1]])
 #'image_show(pal[[2]])
-image_pallete <- function(image,
+#'}
+image_palette <- function(image,
                           npal,
                           nstart = 25,
                           parallel = FALSE,
@@ -1017,9 +1020,9 @@ image_pallete <- function(image,
       if(verbose == TRUE){
         message("Image processing using multiple sessions (",nworkers, "). Please wait.")
       }
-      parLapply(clust, image, image_pallete, npal, nstart)
+      parLapply(clust, image, image_palette, npal, nstart)
     } else{
-      lapply(image, image_pallete, npal, nstart)
+      lapply(image, image_palette, npal, nstart)
     }
   } else{
   df <- image_to_mat(image)$df_in
@@ -1032,7 +1035,7 @@ image_pallete <- function(image,
     R <- df1[1:dim_mat^2, 1]
     G <- df1[1:dim_mat^2, 2]
     B <- df1[1:dim_mat^2, 3]
-    rgbs[[i]] <- array(c(R, G, B), dim = c(dim_mat, dim_mat, 3))
+    rgbs[[i]] <- as.Image(array(c(R, G, B), dim = c(dim_mat, dim_mat, 3)))
   }
   return(rgbs)
   }
@@ -1048,7 +1051,8 @@ image_pallete <- function(image,
 #' @param dpi The image resolution in dots per inch.
 #' @param px The number of pixels.
 #' @param cm The size in centimeters.
-#' @return A numerical value.
+#' @return A numeric value or a vector of numeric values if the input data is a
+#'   vector
 #' @export
 #' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
 #' @examples