R/extract_stats.R
In bmjesus/hyperfish: Extracting L*a*b* parameters from hyperspectral data
Defines functions
extract_stats
Documented in
extract_stats
#' @title Calculate several statistics from selected ROI
#' @description This function calculates statistics from six ROI stored in a shapefile. It was designed for Konstantina's workflow and it is unlikely to work in another context. The ROI need to be called D1, D2, V1, V2, eye, gills.
#' @param tif_file path to tif file with the parameters to extract
#' @param roi_file path shapefile with the ROI file
#' @param type_file parameter to setup if the file is LAB or LCH.
#' @return The function returns a list with the statistics and the pixel values for each ROI.
#' @keywords external
#' @export
extract_stats <- function(tif_file,roi_file, type_file ){
fish <- raster::stack(tif_file)
fish_roi <- rgdal::readOGR(roi_file)
if (type_file == "LAB"){
names(fish) <- c("L","a","b")
}
if (type_file == "LCH"){
names(fish) <- c("L","C","H")
}
pixels_roi <- fish[fish_roi]
names(pixels_roi) <- as.character(fish_roi$CLASS_NAME)
if (type_file == "LAB"){
########ROI D1
D1_mean_L <- mean(pixels_roi$`ROI D1`[,"L"])
D1_mean_a <- mean(pixels_roi$`ROI D1`[,"a"])
D1_mean_b <- mean(pixels_roi$`ROI D1`[,"b"])
D1_median_L <- median(pixels_roi$`ROI D1`[,"L"])
D1_median_a <- median(pixels_roi$`ROI D1`[,"a"])
D1_median_b <- median(pixels_roi$`ROI D1`[,"b"])
D1_min_L <- min(pixels_roi$`ROI D1`[,"L"])
D1_min_a <- min(pixels_roi$`ROI D1`[,"a"])
D1_min_b <- min(pixels_roi$`ROI D1`[,"b"])
D1_max_L <- max(pixels_roi$`ROI D1`[,"L"])
D1_max_a <- max(pixels_roi$`ROI D1`[,"a"])
D1_max_b <- max(pixels_roi$`ROI D1`[,"b"])
D1_sd_L <- sd(pixels_roi$`ROI D1`[,"L"])
D1_sd_a <- sd(pixels_roi$`ROI D1`[,"a"])
D1_sd_b <- sd(pixels_roi$`ROI D1`[,"b"])
D1_cv_L <- D1_sd_L/D1_mean_L * 100
D1_cv_a <- D1_sd_a/D1_mean_a * 100
D1_cv_b <- D1_sd_b/D1_mean_b * 100
D1_num_pixels <- length(pixels_roi$`ROI D1`[,"L"])
D1_se_L <- D1_sd_L/sqrt(D1_num_pixels)
D1_se_a <- D1_sd_a/sqrt(D1_num_pixels)
D1_se_b <- D1_sd_b/sqrt(D1_num_pixels)
########ROI D2
D2_mean_L <- mean(pixels_roi$`ROI D2`[,"L"])
D2_mean_a <- mean(pixels_roi$`ROI D2`[,"a"])
D2_mean_b <- mean(pixels_roi$`ROI D2`[,"b"])
D2_median_L <- median(pixels_roi$`ROI D2`[,"L"])
D2_median_a <- median(pixels_roi$`ROI D2`[,"a"])
D2_median_b <- median(pixels_roi$`ROI D2`[,"b"])
D2_min_L <- min(pixels_roi$`ROI D2`[,"L"])
D2_min_a <- min(pixels_roi$`ROI D2`[,"a"])
D2_min_b <- min(pixels_roi$`ROI D2`[,"b"])
D2_max_L <- max(pixels_roi$`ROI D2`[,"L"])
D2_max_a <- max(pixels_roi$`ROI D2`[,"a"])
D2_max_b <- max(pixels_roi$`ROI D2`[,"b"])
D2_sd_L <- sd(pixels_roi$`ROI D2`[,"L"])
D2_sd_a <- sd(pixels_roi$`ROI D2`[,"a"])
D2_sd_b <- sd(pixels_roi$`ROI D2`[,"b"])
D2_cv_L <- D2_sd_L/D2_mean_L * 100
D2_cv_a <- D2_sd_a/D2_mean_a * 100
D2_cv_b <- D2_sd_b/D2_mean_b * 100
D2_num_pixels <- length(pixels_roi$`ROI D2`[,"L"])
D2_se_L <- D2_sd_L/sqrt(D2_num_pixels)
D2_se_a <- D2_sd_a/sqrt(D2_num_pixels)
D2_se_b <- D2_sd_b/sqrt(D2_num_pixels)
#######V1
V1_mean_L <- mean(pixels_roi$`ROI V1`[,"L"])
V1_mean_a <- mean(pixels_roi$`ROI V1`[,"a"])
V1_mean_b <- mean(pixels_roi$`ROI V1`[,"b"])
V1_median_L <- median(pixels_roi$`ROI V1`[,"L"])
V1_median_a <- median(pixels_roi$`ROI V1`[,"a"])
V1_median_b <- median(pixels_roi$`ROI V1`[,"b"])
V1_min_L <- min(pixels_roi$`ROI V1`[,"L"])
V1_min_a <- min(pixels_roi$`ROI V1`[,"a"])
V1_min_b <- min(pixels_roi$`ROI V1`[,"b"])
V1_max_L <- max(pixels_roi$`ROI V1`[,"L"])
V1_max_a <- max(pixels_roi$`ROI V1`[,"a"])
V1_max_b <- max(pixels_roi$`ROI V1`[,"b"])
V1_sd_L <- sd(pixels_roi$`ROI V1`[,"L"])
V1_sd_a <- sd(pixels_roi$`ROI V1`[,"a"])
V1_sd_b <- sd(pixels_roi$`ROI V1`[,"b"])
V1_cv_L <- V1_sd_L/V1_mean_L * 100
V1_cv_a <- V1_sd_a/V1_mean_a * 100
V1_cv_b <- V1_sd_b/V1_mean_b * 100
V1_num_pixels <- length(pixels_roi$`ROI V1`[,"L"])
V1_se_L <- V1_sd_L/sqrt(V1_num_pixels)
V1_se_a <- V1_sd_a/sqrt(V1_num_pixels)
V1_se_b <- V1_sd_b/sqrt(V1_num_pixels)
######V2
V2_mean_L <- mean(pixels_roi$`ROI V2`[,"L"])
V2_mean_a <- mean(pixels_roi$`ROI V2`[,"a"])
V2_mean_b <- mean(pixels_roi$`ROI V2`[,"b"])
V2_median_L <- median(pixels_roi$`ROI V2`[,"L"])
V2_median_a <- median(pixels_roi$`ROI V2`[,"a"])
V2_median_b <- median(pixels_roi$`ROI V2`[,"b"])
V2_min_L <- min(pixels_roi$`ROI V2`[,"L"])
V2_min_a <- min(pixels_roi$`ROI V2`[,"a"])
V2_min_b <- min(pixels_roi$`ROI V2`[,"b"])
V2_max_L <- max(pixels_roi$`ROI V2`[,"L"])
V2_max_a <- max(pixels_roi$`ROI V2`[,"a"])
V2_max_b <- max(pixels_roi$`ROI V2`[,"b"])
V2_sd_L <- sd(pixels_roi$`ROI V2`[,"L"])
V2_sd_a <- sd(pixels_roi$`ROI V2`[,"a"])
V2_sd_b <- sd(pixels_roi$`ROI V2`[,"b"])
V2_cv_L <- V2_sd_L/V2_mean_L * 100
V2_cv_a <- V2_sd_a/V2_mean_a * 100
V2_cv_b <- V2_sd_b/V2_mean_b * 100
V2_num_pixels <- length(pixels_roi$`ROI V2`[,"L"])
V2_se_L <- V2_sd_L/sqrt(V2_num_pixels)
V2_se_a <- V2_sd_a/sqrt(V2_num_pixels)
V2_se_b <- V2_sd_b/sqrt(V2_num_pixels)
#####eye
eye_mean_L <- mean(pixels_roi$`ROI eye`[,"L"])
eye_mean_a <- mean(pixels_roi$`ROI eye`[,"a"])
eye_mean_b <- mean(pixels_roi$`ROI eye`[,"b"])
eye_median_L <- median(pixels_roi$`ROI eye`[,"L"])
eye_median_a <- median(pixels_roi$`ROI eye`[,"a"])
eye_median_b <- median(pixels_roi$`ROI eye`[,"b"])
eye_min_L <- min(pixels_roi$`ROI eye`[,"L"])
eye_min_a <- min(pixels_roi$`ROI eye`[,"a"])
eye_min_b <- min(pixels_roi$`ROI eye`[,"b"])
eye_max_L <- max(pixels_roi$`ROI eye`[,"L"])
eye_max_a <- max(pixels_roi$`ROI eye`[,"a"])
eye_max_b <- max(pixels_roi$`ROI eye`[,"b"])
eye_sd_L <- sd(pixels_roi$`ROI eye`[,"L"])
eye_sd_a <- sd(pixels_roi$`ROI eye`[,"a"])
eye_sd_b <- sd(pixels_roi$`ROI eye`[,"b"])
eye_cv_L <- eye_sd_L/eye_mean_L * 100
eye_cv_a <- eye_sd_a/eye_mean_a * 100
eye_cv_b <- eye_sd_b/eye_mean_b * 100
eye_num_pixels <- length(pixels_roi$`ROI eye`[,"L"])
eye_se_L <- eye_sd_L/sqrt(eye_num_pixels)
eye_se_a <- eye_sd_a/sqrt(eye_num_pixels)
eye_se_b <- eye_sd_b/sqrt(eye_num_pixels)
###gill
gill_mean_L <- mean(pixels_roi$`ROI gill`[,"L"])
gill_mean_a <- mean(pixels_roi$`ROI gill`[,"a"])
gill_mean_b <- mean(pixels_roi$`ROI gill`[,"b"])
gill_median_L <- median(pixels_roi$`ROI gill`[,"L"])
gill_median_a <- median(pixels_roi$`ROI gill`[,"a"])
gill_median_b <- median(pixels_roi$`ROI gill`[,"b"])
gill_min_L <- min(pixels_roi$`ROI gill`[,"L"])
gill_min_a <- min(pixels_roi$`ROI gill`[,"a"])
gill_min_b <- min(pixels_roi$`ROI gill`[,"b"])
gill_max_L <- max(pixels_roi$`ROI gill`[,"L"])
gill_max_a <- max(pixels_roi$`ROI gill`[,"a"])
gill_max_b <- max(pixels_roi$`ROI gill`[,"b"])
gill_sd_L <- sd(pixels_roi$`ROI gill`[,"L"])
gill_sd_a <- sd(pixels_roi$`ROI gill`[,"a"])
gill_sd_b <- sd(pixels_roi$`ROI gill`[,"b"])
gill_cv_L <- gill_sd_L/gill_mean_L * 100
gill_cv_a <- gill_sd_a/gill_mean_a * 100
gill_cv_b <- gill_sd_b/gill_mean_b * 100
gill_num_pixels <- length(pixels_roi$`ROI gill`[,"L"])
gill_se_L <- gill_sd_L/sqrt(gill_num_pixels)
gill_se_a <- gill_sd_a/sqrt(gill_num_pixels)
gill_se_b <- gill_sd_b/sqrt(gill_num_pixels)
}
if (type_file == "LCH"){
########ROI D1
D1_mean_L <- mean(pixels_roi$`ROI D1`[,"L"])
D1_mean_C <- mean(pixels_roi$`ROI D1`[,"C"])
D1_mean_H <- mean(pixels_roi$`ROI D1`[,"H"])
D1_median_L <- median(pixels_roi$`ROI D1`[,"L"])
D1_median_C <- median(pixels_roi$`ROI D1`[,"C"])
D1_median_H <- median(pixels_roi$`ROI D1`[,"H"])
D1_min_L <- min(pixels_roi$`ROI D1`[,"L"])
D1_min_C <- min(pixels_roi$`ROI D1`[,"C"])
D1_min_H <- min(pixels_roi$`ROI D1`[,"H"])
D1_max_L <- max(pixels_roi$`ROI D1`[,"L"])
D1_max_C <- max(pixels_roi$`ROI D1`[,"C"])
D1_max_H <- max(pixels_roi$`ROI D1`[,"H"])
D1_sd_L <- sd(pixels_roi$`ROI D1`[,"L"])
D1_sd_C <- sd(pixels_roi$`ROI D1`[,"C"])
D1_sd_H <- sd(pixels_roi$`ROI D1`[,"H"])
D1_cv_L <- D1_sd_L/D1_mean_L * 100
D1_cv_C <- D1_sd_C/D1_mean_C * 100
D1_cv_H <- D1_sd_H/D1_mean_H * 100
D1_num_pixels <- length(pixels_roi$`ROI D1`[,"L"])
D1_se_L <- D1_sd_L/sqrt(D1_num_pixels)
D1_se_C <- D1_sd_C/sqrt(D1_num_pixels)
D1_se_H <- D1_sd_H/sqrt(D1_num_pixels)
########ROI D2
D2_mean_L <- mean(pixels_roi$`ROI D2`[,"L"])
D2_mean_C <- mean(pixels_roi$`ROI D2`[,"C"])
D2_mean_H <- mean(pixels_roi$`ROI D2`[,"H"])
D2_median_L <- median(pixels_roi$`ROI D2`[,"L"])
D2_median_C <- median(pixels_roi$`ROI D2`[,"C"])
D2_median_H <- median(pixels_roi$`ROI D2`[,"H"])
D2_min_L <- min(pixels_roi$`ROI D2`[,"L"])
D2_min_C <- min(pixels_roi$`ROI D2`[,"C"])
D2_min_H <- min(pixels_roi$`ROI D2`[,"H"])
D2_max_L <- max(pixels_roi$`ROI D2`[,"L"])
D2_max_C <- max(pixels_roi$`ROI D2`[,"C"])
D2_max_H <- max(pixels_roi$`ROI D2`[,"H"])
D2_sd_L <- sd(pixels_roi$`ROI D2`[,"L"])
D2_sd_C <- sd(pixels_roi$`ROI D2`[,"C"])
D2_sd_H <- sd(pixels_roi$`ROI D2`[,"H"])
D2_cv_L <- D2_sd_L/D2_mean_L * 100
D2_cv_C <- D2_sd_C/D2_mean_C * 100
D2_cv_H <- D2_sd_H/D2_mean_H * 100
D2_num_pixels <- length(pixels_roi$`ROI D2`[,"L"])
D2_se_L <- D2_sd_L/sqrt(D2_num_pixels)
D2_se_C <- D2_sd_C/sqrt(D2_num_pixels)
D2_se_H <- D2_sd_H/sqrt(D2_num_pixels)
########ROI V1
V1_mean_L <- mean(pixels_roi$`ROI V1`[,"L"])
V1_mean_C <- mean(pixels_roi$`ROI V1`[,"C"])
V1_mean_H <- mean(pixels_roi$`ROI V1`[,"H"])
V1_median_L <- median(pixels_roi$`ROI V1`[,"L"])
V1_median_C <- median(pixels_roi$`ROI V1`[,"C"])
V1_median_H <- median(pixels_roi$`ROI V1`[,"H"])
V1_min_L <- min(pixels_roi$`ROI V1`[,"L"])
V1_min_C <- min(pixels_roi$`ROI V1`[,"C"])
V1_min_H <- min(pixels_roi$`ROI V1`[,"H"])
V1_max_L <- max(pixels_roi$`ROI V1`[,"L"])
V1_max_C <- max(pixels_roi$`ROI V1`[,"C"])
V1_max_H <- max(pixels_roi$`ROI V1`[,"H"])
V1_sd_L <- sd(pixels_roi$`ROI V1`[,"L"])
V1_sd_C <- sd(pixels_roi$`ROI V1`[,"C"])
V1_sd_H <- sd(pixels_roi$`ROI V1`[,"H"])
V1_cv_L <- V1_sd_L/V1_mean_L * 100
V1_cv_C <- V1_sd_C/V1_mean_C * 100
V1_cv_H <- V1_sd_H/V1_mean_H * 100
V1_num_pixels <- length(pixels_roi$`ROI V1`[,"L"])
V1_se_L <- V1_sd_L/sqrt(V1_num_pixels)
V1_se_C <- V1_sd_C/sqrt(V1_num_pixels)
V1_se_H <- V1_sd_H/sqrt(V1_num_pixels)
########ROI V2
V2_mean_L <- mean(pixels_roi$`ROI V2`[,"L"])
V2_mean_C <- mean(pixels_roi$`ROI V2`[,"C"])
V2_mean_H <- mean(pixels_roi$`ROI V2`[,"H"])
V2_median_L <- median(pixels_roi$`ROI V2`[,"L"])
V2_median_C <- median(pixels_roi$`ROI V2`[,"C"])
V2_median_H <- median(pixels_roi$`ROI V2`[,"H"])
V2_min_L <- min(pixels_roi$`ROI V2`[,"L"])
V2_min_C <- min(pixels_roi$`ROI V2`[,"C"])
V2_min_H <- min(pixels_roi$`ROI V2`[,"H"])
V2_max_L <- max(pixels_roi$`ROI V2`[,"L"])
V2_max_C <- max(pixels_roi$`ROI V2`[,"C"])
V2_max_H <- max(pixels_roi$`ROI V2`[,"H"])
V2_sd_L <- sd(pixels_roi$`ROI V2`[,"L"])
V2_sd_C <- sd(pixels_roi$`ROI V2`[,"C"])
V2_sd_H <- sd(pixels_roi$`ROI V2`[,"H"])
V2_cv_L <- V2_sd_L/V2_mean_L * 100
V2_cv_C <- V2_sd_C/V2_mean_C * 100
V2_cv_H <- V2_sd_H/V2_mean_H * 100
V2_num_pixels <- length(pixels_roi$`ROI V2`[,"L"])
V2_se_L <- V2_sd_L/sqrt(V2_num_pixels)
V2_se_C <- V2_sd_C/sqrt(V2_num_pixels)
V2_se_H <- V2_sd_H/sqrt(V2_num_pixels)
########ROI eye
eye_mean_L <- mean(pixels_roi$`ROI eye`[,"L"])
eye_mean_C <- mean(pixels_roi$`ROI eye`[,"C"])
eye_mean_H <- mean(pixels_roi$`ROI eye`[,"H"])
eye_median_L <- median(pixels_roi$`ROI eye`[,"L"])
eye_median_C <- median(pixels_roi$`ROI eye`[,"C"])
eye_median_H <- median(pixels_roi$`ROI eye`[,"H"])
eye_min_L <- min(pixels_roi$`ROI eye`[,"L"])
eye_min_C <- min(pixels_roi$`ROI eye`[,"C"])
eye_min_H <- min(pixels_roi$`ROI eye`[,"H"])
eye_max_L <- max(pixels_roi$`ROI eye`[,"L"])
eye_max_C <- max(pixels_roi$`ROI eye`[,"C"])
eye_max_H <- max(pixels_roi$`ROI eye`[,"H"])
eye_sd_L <- sd(pixels_roi$`ROI eye`[,"L"])
eye_sd_C <- sd(pixels_roi$`ROI eye`[,"C"])
eye_sd_H <- sd(pixels_roi$`ROI eye`[,"H"])
eye_cv_L <- eye_sd_L/eye_mean_L * 100
eye_cv_C <- eye_sd_C/eye_mean_C * 100
eye_cv_H <- eye_sd_H/eye_mean_H * 100
eye_num_pixels <- length(pixels_roi$`ROI eye`[,"L"])
eye_se_L <- eye_sd_L/sqrt(eye_num_pixels)
eye_se_C <- eye_sd_C/sqrt(eye_num_pixels)
eye_se_H <- eye_sd_H/sqrt(eye_num_pixels)
########ROI gill
gill_mean_L <- mean(pixels_roi$`ROI gill`[,"L"])
gill_mean_C <- mean(pixels_roi$`ROI gill`[,"C"])
gill_mean_H <- mean(pixels_roi$`ROI gill`[,"H"])
gill_median_L <- median(pixels_roi$`ROI gill`[,"L"])
gill_median_C <- median(pixels_roi$`ROI gill`[,"C"])
gill_median_H <- median(pixels_roi$`ROI gill`[,"H"])
gill_min_L <- min(pixels_roi$`ROI gill`[,"L"])
gill_min_C <- min(pixels_roi$`ROI gill`[,"C"])
gill_min_H <- min(pixels_roi$`ROI gill`[,"H"])
gill_max_L <- max(pixels_roi$`ROI gill`[,"L"])
gill_max_C <- max(pixels_roi$`ROI gill`[,"C"])
gill_max_H <- max(pixels_roi$`ROI gill`[,"H"])
gill_sd_L <- sd(pixels_roi$`ROI gill`[,"L"])
gill_sd_C <- sd(pixels_roi$`ROI gill`[,"C"])
gill_sd_H <- sd(pixels_roi$`ROI gill`[,"H"])
gill_cv_L <- gill_sd_L/gill_mean_L * 100
gill_cv_C <- gill_sd_C/gill_mean_C * 100
gill_cv_H <- gill_sd_H/gill_mean_H * 100
gill_num_pixels <- length(pixels_roi$`ROI gill`[,"L"])
gill_se_L <- gill_sd_L/sqrt(gill_num_pixels)
gill_se_C <- gill_sd_C/sqrt(gill_num_pixels)
gill_se_H <- gill_sd_H/sqrt(gill_num_pixels)
}
#constructing the outputs
if (type_file == "LAB"){
#L dataframe
L <- data.frame(matrix(nrow=6,ncol=9))
names(L) <- c("roi","mean","median","min","max","sd","cv","num_pixels","se")
L$roi <- c("D1","D2","V1","V2","eye","gill")
L$mean <- c(D1_mean_L, D2_mean_L, V1_mean_L,V2_mean_L,eye_mean_L,gill_mean_L)
L$median <- c(D1_median_L, D2_median_L, V1_median_L,V2_median_L,eye_median_L,gill_median_L)
L$min <- c(D1_min_L, D2_min_L, V1_min_L,V2_min_L,eye_min_L,gill_min_L)
L$max <- c(D1_max_L, D2_max_L, V1_max_L,V2_max_L,eye_max_L,gill_max_L)
L$sd <- c(D1_sd_L, D2_sd_L, V1_sd_L,V2_sd_L,eye_sd_L,gill_sd_L)
L$cv <- c(D1_cv_L, D2_cv_L, V1_cv_L,V2_cv_L,eye_cv_L,gill_cv_L)
L$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
L$se <- c(D1_se_L, D2_se_L, V1_se_L,V2_se_L,eye_se_L,gill_se_L)
#a* dataframe
a <- data.frame(matrix(nrow=6,ncol=7))
names(a) <- c("roi","mean","median","min","max","sd","cv")
a$roi <- c("D1","D2","V1","V2","eye","gill")
a$mean <- c(D1_mean_a, D2_mean_a, V1_mean_a,V2_mean_a,eye_mean_a,gill_mean_a)
a$median <- c(D1_median_a, D2_median_a, V1_median_a,V2_median_a,eye_median_a,gill_median_a)
a$min <- c(D1_min_a, D2_min_a, V1_min_a,V2_min_a,eye_min_a,gill_min_a)
a$max <- c(D1_max_a, D2_max_a, V1_max_a,V2_max_a,eye_max_a,gill_max_a)
a$sd <- c(D1_sd_a, D2_sd_a, V1_sd_a,V2_sd_a,eye_sd_a,gill_sd_a)
a$cv <- c(D1_cv_a, D2_cv_a, V1_cv_a,V2_cv_a,eye_cv_a,gill_cv_a)
a$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
a$se <- c(D1_se_a, D2_se_a, V1_se_a,V2_se_a,eye_se_a,gill_se_a)
#b* dataframe
b <- data.frame(matrix(nrow=6,ncol=7))
names(b) <- c("roi","mean","median","min","max","sd","cv")
b$roi <- c("D1","D2","V1","V2","eye","gill")
b$mean <- c(D1_mean_b, D2_mean_b, V1_mean_b,V2_mean_b,eye_mean_b,gill_mean_b)
b$median <- c(D1_median_b, D2_median_b, V1_median_b,V2_median_b,eye_median_b,gill_median_b)
b$min <- c(D1_min_b, D2_min_b, V1_min_b,V2_min_b,eye_min_b,gill_min_b)
b$max <- c(D1_max_b, D2_max_b, V1_max_b,V2_max_b,eye_max_b,gill_max_b)
b$sd <- c(D1_sd_b, D2_sd_b, V1_sd_b,V2_sd_b,eye_sd_b,gill_sd_b)
b$cv <- c(D1_cv_b, D2_cv_b, V1_cv_b,V2_cv_b,eye_cv_b,gill_cv_b)
b$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
b$se <- c(D1_se_b, D2_se_b, V1_se_b,V2_se_b,eye_se_b,gill_se_b)
output <- list(L,a,b)
names(output) <- c("Lightness","a","b")
}
if (type_file == "LCH"){
#Lightness dataframe
L <- data.frame(matrix(nrow=6,ncol=9))
names(L) <- c("roi","mean","median","min","max","sd","cv","num_pixels","se")
L$roi <- c("D1","D2","V1","V2","eye","gill")
L$mean <- c(D1_mean_L, D2_mean_L, V1_mean_L,V2_mean_L,eye_mean_L,gill_mean_L)
L$median <- c(D1_median_L, D2_median_L, V1_median_L,V2_median_L,eye_median_L,gill_median_L)
L$min <- c(D1_min_L, D2_min_L, V1_min_L,V2_min_L,eye_min_L,gill_min_L)
L$max <- c(D1_max_L, D2_max_L, V1_max_L,V2_max_L,eye_max_L,gill_max_L)
L$sd <- c(D1_sd_L, D2_sd_L, V1_sd_L,V2_sd_L,eye_sd_L,gill_sd_L)
L$cv <- c(D1_cv_L, D2_cv_L, V1_cv_L,V2_cv_L,eye_cv_L,gill_cv_L)
L$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
L$se <- c(D1_se_L, D2_se_L, V1_se_L,V2_se_L,eye_se_L,gill_se_L)
C <- data.frame(matrix(nrow=6,ncol=7))
names(C) <- c("roi","mean","median","min","max","sd","cv")
C$roi <- c("D1","D2","V1","V2","eye","gill")
C$mean <- c(D1_mean_C, D2_mean_C, V1_mean_C,V2_mean_C,eye_mean_C,gill_mean_C)
C$median <- c(D1_median_C, D2_median_C, V1_median_C,V2_median_C,eye_median_C,gill_median_C)
C$min <- c(D1_min_C, D2_min_C, V1_min_C,V2_min_C,eye_min_C,gill_min_C)
C$max <- c(D1_max_C, D2_max_C, V1_max_C,V2_max_C,eye_max_C,gill_max_C)
C$sd <- c(D1_sd_C, D2_sd_C, V1_sd_C,V2_sd_C,eye_sd_C,gill_sd_C)
C$cv <- c(D1_cv_C, D2_cv_C, V1_cv_C,V2_cv_C,eye_cv_C,gill_cv_C)
C$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
C$se <- c(D1_se_C, D2_se_C, V1_se_C,V2_se_C,eye_se_C,gill_se_C)
H <- data.frame(matrix(nrow=6,ncol=7))
names(H) <- c("roi","mean","median","min","max","sd","cv")
H$roi <- c("D1","D2","V1","V2","eye","gill")
H$mean <- c(D1_mean_H, D2_mean_H, V1_mean_H,V2_mean_H,eye_mean_H,gill_mean_H)
H$median <- c(D1_median_H, D2_median_H, V1_median_H,V2_median_H,eye_median_H,gill_median_H)
H$min <- c(D1_min_H, D2_min_H, V1_min_H,V2_min_H,eye_min_H,gill_min_H)
H$max <- c(D1_max_H, D2_max_H, V1_max_H,V2_max_H,eye_max_H,gill_max_H)
H$sd <- c(D1_sd_H, D2_sd_H, V1_sd_H,V2_sd_H,eye_sd_H,gill_sd_H)
H$cv <- c(D1_cv_H, D2_cv_H, V1_cv_H,V2_cv_H,eye_cv_H,gill_cv_H)
H$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
H$se <- c(D1_se_H, D2_se_H, V1_se_H,V2_se_H,eye_se_H,gill_se_H)
output <- list(L,C,H)
names(output) <- c("Lightness","Chroma","Hue")
}
#export the pixel values for each ROI and each parameter. It could be useful to use in histogram analysis
if (type_file == "LAB"){
pixels_L_D1 <- pixels_roi$`ROI D1`[,"L"]
pixels_a_D1 <- pixels_roi$`ROI D1`[,"a"]
pixels_b_D1 <- pixels_roi$`ROI D1`[,"b"]
pixels_L_D2 <- pixels_roi$`ROI D2`[,"L"]
pixels_a_D2 <- pixels_roi$`ROI D2`[,"a"]
pixels_b_D2 <- pixels_roi$`ROI D2`[,"b"]
pixels_L_V1 <- pixels_roi$`ROI V1`[,"L"]
pixels_a_V1 <- pixels_roi$`ROI V1`[,"a"]
pixels_b_V1 <- pixels_roi$`ROI V1`[,"b"]
pixels_L_V2 <- pixels_roi$`ROI V2`[,"L"]
pixels_a_V2 <- pixels_roi$`ROI V2`[,"a"]
pixels_b_V2 <- pixels_roi$`ROI V2`[,"b"]
pixels_L_eye <- pixels_roi$`ROI eye`[,"L"]
pixels_a_eye <- pixels_roi$`ROI eye`[,"a"]
pixels_b_eye <- pixels_roi$`ROI eye`[,"b"]
pixels_L_gill <- pixels_roi$`ROI gill`[,"L"]
pixels_a_gill <- pixels_roi$`ROI gill`[,"a"]
pixels_b_gill <- pixels_roi$`ROI gill`[,"b"]
D1 <- data.frame(matrix(nrow= D1_num_pixels,ncol=3))
names(D1) <- c("L","a","b")
D1$L <- pixels_L_D1
D1$a <- pixels_a_D1
D1$b <- pixels_b_D1
D2 <- data.frame(matrix(nrow= D2_num_pixels,ncol=3))
names(D2) <- c("L","a","b")
D2$L <- pixels_L_D2
D2$a <- pixels_a_D2
D2$b <- pixels_b_D2
V1 <- data.frame(matrix(nrow= V1_num_pixels,ncol=3))
names(V1) <- c("L","a","b")
V1$L <- pixels_L_V1
V1$a <- pixels_a_V1
V1$b <- pixels_b_V1
V2 <- data.frame(matrix(nrow= V2_num_pixels,ncol=3))
names(V2) <- c("L","a","b")
V2$L <- pixels_L_V2
V2$a <- pixels_a_V2
V2$b <- pixels_b_V2
eye <- data.frame(matrix(nrow= eye_num_pixels,ncol=3))
names(eye) <- c("L","a","b")
eye$L <- pixels_L_eye
eye$a <- pixels_a_eye
eye$b <- pixels_b_eye
gill <- data.frame(matrix(nrow= gill_num_pixels,ncol=3))
names(gill) <- c("L","a","b")
gill$L <- pixels_L_gill
gill$a <- pixels_a_gill
gill$b <- pixels_b_gill
output_pixels <- list(D1,D2,V1,V2,eye,gill)
names(output_pixels) <- c("D1","D2","V1","V2","eye","gill")
}
if (type_file == "LCH"){
pixels_L_D1 <- pixels_roi$`ROI D1`[,"L"]
pixels_C_D1 <- pixels_roi$`ROI D1`[,"C"]
pixels_H_D1 <- pixels_roi$`ROI D1`[,"H"]
pixels_L_D2 <- pixels_roi$`ROI D2`[,"L"]
pixels_C_D2 <- pixels_roi$`ROI D2`[,"C"]
pixels_H_D2 <- pixels_roi$`ROI D2`[,"H"]
pixels_L_V1 <- pixels_roi$`ROI V1`[,"L"]
pixels_C_V1 <- pixels_roi$`ROI V1`[,"C"]
pixels_H_V1 <- pixels_roi$`ROI V1`[,"H"]
pixels_L_V2 <- pixels_roi$`ROI V2`[,"L"]
pixels_C_V2 <- pixels_roi$`ROI V2`[,"C"]
pixels_H_V2 <- pixels_roi$`ROI V2`[,"H"]
pixels_L_eye <- pixels_roi$`ROI eye`[,"L"]
pixels_C_eye <- pixels_roi$`ROI eye`[,"C"]
pixels_H_eye <- pixels_roi$`ROI eye`[,"H"]
pixels_L_gill <- pixels_roi$`ROI gill`[,"L"]
pixels_C_gill <- pixels_roi$`ROI gill`[,"C"]
pixels_H_gill <- pixels_roi$`ROI gill`[,"H"]
D1 <- data.frame(matrix(nrow= D1_num_pixels,ncol=3))
names(D1) <- c("L","C","H")
D1$L <- pixels_L_D1
D1$C <- pixels_C_D1
D1$H <- pixels_H_D1
D2 <- data.frame(matrix(nrow= D2_num_pixels,ncol=3))
names(D2) <- c("L","C","H")
D2$L <- pixels_L_D2
D2$C <- pixels_C_D2
D2$H <- pixels_H_D2
V1 <- data.frame(matrix(nrow= V1_num_pixels,ncol=3))
names(V1) <- c("L","C","H")
V1$L <- pixels_L_V1
V1$C <- pixels_C_V1
V1$H <- pixels_H_V1
V2 <- data.frame(matrix(nrow= V2_num_pixels,ncol=3))
names(V2) <- c("L","C","H")
V2$L <- pixels_L_V2
V2$C <- pixels_C_V2
V2$H <- pixels_H_V2
eye <- data.frame(matrix(nrow= eye_num_pixels,ncol=3))
names(eye) <- c("L","C","H")
eye$L <- pixels_L_eye
eye$C <- pixels_C_eye
eye$H <- pixels_H_eye
gill <- data.frame(matrix(nrow= gill_num_pixels,ncol=3))
names(gill) <- c("L","C","H")
gill$L <- pixels_L_gill
gill$C <- pixels_C_gill
gill$H <- pixels_H_gill
output_pixels <- list(D1,D2,V1,V2,eye,gill)
names(output_pixels) <- c("D1","D2","V1","V2","eye","gill")
}
output<-list(output,output_pixels)
names(output)<- c("parameters","pixels")
return(output)
}
bmjesus/hyperfish documentation built on May 30, 2022, 4:04 p.m.
R Package Documentation
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Defines functions extract_stats
Documented in extract_stats
#' @title Calculate several statistics from selected ROI
#' @description This function calculates statistics from six ROI stored in a shapefile. It was designed for Konstantina's workflow and it is unlikely to work in another context. The ROI need to be called D1, D2, V1, V2, eye, gills.
#' @param tif_file path to tif file with the parameters to extract
#' @param roi_file path shapefile with the ROI file
#' @param type_file parameter to setup if the file is LAB or LCH.
#' @return The function returns a list with the statistics and the pixel values for each ROI.
#' @keywords external
#' @export
extract_stats <- function(tif_file,roi_file, type_file ){
fish <- raster::stack(tif_file)
fish_roi <- rgdal::readOGR(roi_file)
if (type_file == "LAB"){
names(fish) <- c("L","a","b")
}
if (type_file == "LCH"){
names(fish) <- c("L","C","H")
}
pixels_roi <- fish[fish_roi]
names(pixels_roi) <- as.character(fish_roi$CLASS_NAME)
if (type_file == "LAB"){
########ROI D1
D1_mean_L <- mean(pixels_roi$`ROI D1`[,"L"])
D1_mean_a <- mean(pixels_roi$`ROI D1`[,"a"])
D1_mean_b <- mean(pixels_roi$`ROI D1`[,"b"])
D1_median_L <- median(pixels_roi$`ROI D1`[,"L"])
D1_median_a <- median(pixels_roi$`ROI D1`[,"a"])
D1_median_b <- median(pixels_roi$`ROI D1`[,"b"])
D1_min_L <- min(pixels_roi$`ROI D1`[,"L"])
D1_min_a <- min(pixels_roi$`ROI D1`[,"a"])
D1_min_b <- min(pixels_roi$`ROI D1`[,"b"])
D1_max_L <- max(pixels_roi$`ROI D1`[,"L"])
D1_max_a <- max(pixels_roi$`ROI D1`[,"a"])
D1_max_b <- max(pixels_roi$`ROI D1`[,"b"])
D1_sd_L <- sd(pixels_roi$`ROI D1`[,"L"])
D1_sd_a <- sd(pixels_roi$`ROI D1`[,"a"])
D1_sd_b <- sd(pixels_roi$`ROI D1`[,"b"])
D1_cv_L <- D1_sd_L/D1_mean_L * 100
D1_cv_a <- D1_sd_a/D1_mean_a * 100
D1_cv_b <- D1_sd_b/D1_mean_b * 100
D1_num_pixels <- length(pixels_roi$`ROI D1`[,"L"])
D1_se_L <- D1_sd_L/sqrt(D1_num_pixels)
D1_se_a <- D1_sd_a/sqrt(D1_num_pixels)
D1_se_b <- D1_sd_b/sqrt(D1_num_pixels)
########ROI D2
D2_mean_L <- mean(pixels_roi$`ROI D2`[,"L"])
D2_mean_a <- mean(pixels_roi$`ROI D2`[,"a"])
D2_mean_b <- mean(pixels_roi$`ROI D2`[,"b"])
D2_median_L <- median(pixels_roi$`ROI D2`[,"L"])
D2_median_a <- median(pixels_roi$`ROI D2`[,"a"])
D2_median_b <- median(pixels_roi$`ROI D2`[,"b"])
D2_min_L <- min(pixels_roi$`ROI D2`[,"L"])
D2_min_a <- min(pixels_roi$`ROI D2`[,"a"])
D2_min_b <- min(pixels_roi$`ROI D2`[,"b"])
D2_max_L <- max(pixels_roi$`ROI D2`[,"L"])
D2_max_a <- max(pixels_roi$`ROI D2`[,"a"])
D2_max_b <- max(pixels_roi$`ROI D2`[,"b"])
D2_sd_L <- sd(pixels_roi$`ROI D2`[,"L"])
D2_sd_a <- sd(pixels_roi$`ROI D2`[,"a"])
D2_sd_b <- sd(pixels_roi$`ROI D2`[,"b"])
D2_cv_L <- D2_sd_L/D2_mean_L * 100
D2_cv_a <- D2_sd_a/D2_mean_a * 100
D2_cv_b <- D2_sd_b/D2_mean_b * 100
D2_num_pixels <- length(pixels_roi$`ROI D2`[,"L"])
D2_se_L <- D2_sd_L/sqrt(D2_num_pixels)
D2_se_a <- D2_sd_a/sqrt(D2_num_pixels)
D2_se_b <- D2_sd_b/sqrt(D2_num_pixels)
#######V1
V1_mean_L <- mean(pixels_roi$`ROI V1`[,"L"])
V1_mean_a <- mean(pixels_roi$`ROI V1`[,"a"])
V1_mean_b <- mean(pixels_roi$`ROI V1`[,"b"])
V1_median_L <- median(pixels_roi$`ROI V1`[,"L"])
V1_median_a <- median(pixels_roi$`ROI V1`[,"a"])
V1_median_b <- median(pixels_roi$`ROI V1`[,"b"])
V1_min_L <- min(pixels_roi$`ROI V1`[,"L"])
V1_min_a <- min(pixels_roi$`ROI V1`[,"a"])
V1_min_b <- min(pixels_roi$`ROI V1`[,"b"])
V1_max_L <- max(pixels_roi$`ROI V1`[,"L"])
V1_max_a <- max(pixels_roi$`ROI V1`[,"a"])
V1_max_b <- max(pixels_roi$`ROI V1`[,"b"])
V1_sd_L <- sd(pixels_roi$`ROI V1`[,"L"])
V1_sd_a <- sd(pixels_roi$`ROI V1`[,"a"])
V1_sd_b <- sd(pixels_roi$`ROI V1`[,"b"])
V1_cv_L <- V1_sd_L/V1_mean_L * 100
V1_cv_a <- V1_sd_a/V1_mean_a * 100
V1_cv_b <- V1_sd_b/V1_mean_b * 100
V1_num_pixels <- length(pixels_roi$`ROI V1`[,"L"])
V1_se_L <- V1_sd_L/sqrt(V1_num_pixels)
V1_se_a <- V1_sd_a/sqrt(V1_num_pixels)
V1_se_b <- V1_sd_b/sqrt(V1_num_pixels)
######V2
V2_mean_L <- mean(pixels_roi$`ROI V2`[,"L"])
V2_mean_a <- mean(pixels_roi$`ROI V2`[,"a"])
V2_mean_b <- mean(pixels_roi$`ROI V2`[,"b"])
V2_median_L <- median(pixels_roi$`ROI V2`[,"L"])
V2_median_a <- median(pixels_roi$`ROI V2`[,"a"])
V2_median_b <- median(pixels_roi$`ROI V2`[,"b"])
V2_min_L <- min(pixels_roi$`ROI V2`[,"L"])
V2_min_a <- min(pixels_roi$`ROI V2`[,"a"])
V2_min_b <- min(pixels_roi$`ROI V2`[,"b"])
V2_max_L <- max(pixels_roi$`ROI V2`[,"L"])
V2_max_a <- max(pixels_roi$`ROI V2`[,"a"])
V2_max_b <- max(pixels_roi$`ROI V2`[,"b"])
V2_sd_L <- sd(pixels_roi$`ROI V2`[,"L"])
V2_sd_a <- sd(pixels_roi$`ROI V2`[,"a"])
V2_sd_b <- sd(pixels_roi$`ROI V2`[,"b"])
V2_cv_L <- V2_sd_L/V2_mean_L * 100
V2_cv_a <- V2_sd_a/V2_mean_a * 100
V2_cv_b <- V2_sd_b/V2_mean_b * 100
V2_num_pixels <- length(pixels_roi$`ROI V2`[,"L"])
V2_se_L <- V2_sd_L/sqrt(V2_num_pixels)
V2_se_a <- V2_sd_a/sqrt(V2_num_pixels)
V2_se_b <- V2_sd_b/sqrt(V2_num_pixels)
#####eye
eye_mean_L <- mean(pixels_roi$`ROI eye`[,"L"])
eye_mean_a <- mean(pixels_roi$`ROI eye`[,"a"])
eye_mean_b <- mean(pixels_roi$`ROI eye`[,"b"])
eye_median_L <- median(pixels_roi$`ROI eye`[,"L"])
eye_median_a <- median(pixels_roi$`ROI eye`[,"a"])
eye_median_b <- median(pixels_roi$`ROI eye`[,"b"])
eye_min_L <- min(pixels_roi$`ROI eye`[,"L"])
eye_min_a <- min(pixels_roi$`ROI eye`[,"a"])
eye_min_b <- min(pixels_roi$`ROI eye`[,"b"])
eye_max_L <- max(pixels_roi$`ROI eye`[,"L"])
eye_max_a <- max(pixels_roi$`ROI eye`[,"a"])
eye_max_b <- max(pixels_roi$`ROI eye`[,"b"])
eye_sd_L <- sd(pixels_roi$`ROI eye`[,"L"])
eye_sd_a <- sd(pixels_roi$`ROI eye`[,"a"])
eye_sd_b <- sd(pixels_roi$`ROI eye`[,"b"])
eye_cv_L <- eye_sd_L/eye_mean_L * 100
eye_cv_a <- eye_sd_a/eye_mean_a * 100
eye_cv_b <- eye_sd_b/eye_mean_b * 100
eye_num_pixels <- length(pixels_roi$`ROI eye`[,"L"])
eye_se_L <- eye_sd_L/sqrt(eye_num_pixels)
eye_se_a <- eye_sd_a/sqrt(eye_num_pixels)
eye_se_b <- eye_sd_b/sqrt(eye_num_pixels)
###gill
gill_mean_L <- mean(pixels_roi$`ROI gill`[,"L"])
gill_mean_a <- mean(pixels_roi$`ROI gill`[,"a"])
gill_mean_b <- mean(pixels_roi$`ROI gill`[,"b"])
gill_median_L <- median(pixels_roi$`ROI gill`[,"L"])
gill_median_a <- median(pixels_roi$`ROI gill`[,"a"])
gill_median_b <- median(pixels_roi$`ROI gill`[,"b"])
gill_min_L <- min(pixels_roi$`ROI gill`[,"L"])
gill_min_a <- min(pixels_roi$`ROI gill`[,"a"])
gill_min_b <- min(pixels_roi$`ROI gill`[,"b"])
gill_max_L <- max(pixels_roi$`ROI gill`[,"L"])
gill_max_a <- max(pixels_roi$`ROI gill`[,"a"])
gill_max_b <- max(pixels_roi$`ROI gill`[,"b"])
gill_sd_L <- sd(pixels_roi$`ROI gill`[,"L"])
gill_sd_a <- sd(pixels_roi$`ROI gill`[,"a"])
gill_sd_b <- sd(pixels_roi$`ROI gill`[,"b"])
gill_cv_L <- gill_sd_L/gill_mean_L * 100
gill_cv_a <- gill_sd_a/gill_mean_a * 100
gill_cv_b <- gill_sd_b/gill_mean_b * 100
gill_num_pixels <- length(pixels_roi$`ROI gill`[,"L"])
gill_se_L <- gill_sd_L/sqrt(gill_num_pixels)
gill_se_a <- gill_sd_a/sqrt(gill_num_pixels)
gill_se_b <- gill_sd_b/sqrt(gill_num_pixels)
}
if (type_file == "LCH"){
########ROI D1
D1_mean_L <- mean(pixels_roi$`ROI D1`[,"L"])
D1_mean_C <- mean(pixels_roi$`ROI D1`[,"C"])
D1_mean_H <- mean(pixels_roi$`ROI D1`[,"H"])
D1_median_L <- median(pixels_roi$`ROI D1`[,"L"])
D1_median_C <- median(pixels_roi$`ROI D1`[,"C"])
D1_median_H <- median(pixels_roi$`ROI D1`[,"H"])
D1_min_L <- min(pixels_roi$`ROI D1`[,"L"])
D1_min_C <- min(pixels_roi$`ROI D1`[,"C"])
D1_min_H <- min(pixels_roi$`ROI D1`[,"H"])
D1_max_L <- max(pixels_roi$`ROI D1`[,"L"])
D1_max_C <- max(pixels_roi$`ROI D1`[,"C"])
D1_max_H <- max(pixels_roi$`ROI D1`[,"H"])
D1_sd_L <- sd(pixels_roi$`ROI D1`[,"L"])
D1_sd_C <- sd(pixels_roi$`ROI D1`[,"C"])
D1_sd_H <- sd(pixels_roi$`ROI D1`[,"H"])
D1_cv_L <- D1_sd_L/D1_mean_L * 100
D1_cv_C <- D1_sd_C/D1_mean_C * 100
D1_cv_H <- D1_sd_H/D1_mean_H * 100
D1_num_pixels <- length(pixels_roi$`ROI D1`[,"L"])
D1_se_L <- D1_sd_L/sqrt(D1_num_pixels)
D1_se_C <- D1_sd_C/sqrt(D1_num_pixels)
D1_se_H <- D1_sd_H/sqrt(D1_num_pixels)
########ROI D2
D2_mean_L <- mean(pixels_roi$`ROI D2`[,"L"])
D2_mean_C <- mean(pixels_roi$`ROI D2`[,"C"])
D2_mean_H <- mean(pixels_roi$`ROI D2`[,"H"])
D2_median_L <- median(pixels_roi$`ROI D2`[,"L"])
D2_median_C <- median(pixels_roi$`ROI D2`[,"C"])
D2_median_H <- median(pixels_roi$`ROI D2`[,"H"])
D2_min_L <- min(pixels_roi$`ROI D2`[,"L"])
D2_min_C <- min(pixels_roi$`ROI D2`[,"C"])
D2_min_H <- min(pixels_roi$`ROI D2`[,"H"])
D2_max_L <- max(pixels_roi$`ROI D2`[,"L"])
D2_max_C <- max(pixels_roi$`ROI D2`[,"C"])
D2_max_H <- max(pixels_roi$`ROI D2`[,"H"])
D2_sd_L <- sd(pixels_roi$`ROI D2`[,"L"])
D2_sd_C <- sd(pixels_roi$`ROI D2`[,"C"])
D2_sd_H <- sd(pixels_roi$`ROI D2`[,"H"])
D2_cv_L <- D2_sd_L/D2_mean_L * 100
D2_cv_C <- D2_sd_C/D2_mean_C * 100
D2_cv_H <- D2_sd_H/D2_mean_H * 100
D2_num_pixels <- length(pixels_roi$`ROI D2`[,"L"])
D2_se_L <- D2_sd_L/sqrt(D2_num_pixels)
D2_se_C <- D2_sd_C/sqrt(D2_num_pixels)
D2_se_H <- D2_sd_H/sqrt(D2_num_pixels)
########ROI V1
V1_mean_L <- mean(pixels_roi$`ROI V1`[,"L"])
V1_mean_C <- mean(pixels_roi$`ROI V1`[,"C"])
V1_mean_H <- mean(pixels_roi$`ROI V1`[,"H"])
V1_median_L <- median(pixels_roi$`ROI V1`[,"L"])
V1_median_C <- median(pixels_roi$`ROI V1`[,"C"])
V1_median_H <- median(pixels_roi$`ROI V1`[,"H"])
V1_min_L <- min(pixels_roi$`ROI V1`[,"L"])
V1_min_C <- min(pixels_roi$`ROI V1`[,"C"])
V1_min_H <- min(pixels_roi$`ROI V1`[,"H"])
V1_max_L <- max(pixels_roi$`ROI V1`[,"L"])
V1_max_C <- max(pixels_roi$`ROI V1`[,"C"])
V1_max_H <- max(pixels_roi$`ROI V1`[,"H"])
V1_sd_L <- sd(pixels_roi$`ROI V1`[,"L"])
V1_sd_C <- sd(pixels_roi$`ROI V1`[,"C"])
V1_sd_H <- sd(pixels_roi$`ROI V1`[,"H"])
V1_cv_L <- V1_sd_L/V1_mean_L * 100
V1_cv_C <- V1_sd_C/V1_mean_C * 100
V1_cv_H <- V1_sd_H/V1_mean_H * 100
V1_num_pixels <- length(pixels_roi$`ROI V1`[,"L"])
V1_se_L <- V1_sd_L/sqrt(V1_num_pixels)
V1_se_C <- V1_sd_C/sqrt(V1_num_pixels)
V1_se_H <- V1_sd_H/sqrt(V1_num_pixels)
########ROI V2
V2_mean_L <- mean(pixels_roi$`ROI V2`[,"L"])
V2_mean_C <- mean(pixels_roi$`ROI V2`[,"C"])
V2_mean_H <- mean(pixels_roi$`ROI V2`[,"H"])
V2_median_L <- median(pixels_roi$`ROI V2`[,"L"])
V2_median_C <- median(pixels_roi$`ROI V2`[,"C"])
V2_median_H <- median(pixels_roi$`ROI V2`[,"H"])
V2_min_L <- min(pixels_roi$`ROI V2`[,"L"])
V2_min_C <- min(pixels_roi$`ROI V2`[,"C"])
V2_min_H <- min(pixels_roi$`ROI V2`[,"H"])
V2_max_L <- max(pixels_roi$`ROI V2`[,"L"])
V2_max_C <- max(pixels_roi$`ROI V2`[,"C"])
V2_max_H <- max(pixels_roi$`ROI V2`[,"H"])
V2_sd_L <- sd(pixels_roi$`ROI V2`[,"L"])
V2_sd_C <- sd(pixels_roi$`ROI V2`[,"C"])
V2_sd_H <- sd(pixels_roi$`ROI V2`[,"H"])
V2_cv_L <- V2_sd_L/V2_mean_L * 100
V2_cv_C <- V2_sd_C/V2_mean_C * 100
V2_cv_H <- V2_sd_H/V2_mean_H * 100
V2_num_pixels <- length(pixels_roi$`ROI V2`[,"L"])
V2_se_L <- V2_sd_L/sqrt(V2_num_pixels)
V2_se_C <- V2_sd_C/sqrt(V2_num_pixels)
V2_se_H <- V2_sd_H/sqrt(V2_num_pixels)
########ROI eye
eye_mean_L <- mean(pixels_roi$`ROI eye`[,"L"])
eye_mean_C <- mean(pixels_roi$`ROI eye`[,"C"])
eye_mean_H <- mean(pixels_roi$`ROI eye`[,"H"])
eye_median_L <- median(pixels_roi$`ROI eye`[,"L"])
eye_median_C <- median(pixels_roi$`ROI eye`[,"C"])
eye_median_H <- median(pixels_roi$`ROI eye`[,"H"])
eye_min_L <- min(pixels_roi$`ROI eye`[,"L"])
eye_min_C <- min(pixels_roi$`ROI eye`[,"C"])
eye_min_H <- min(pixels_roi$`ROI eye`[,"H"])
eye_max_L <- max(pixels_roi$`ROI eye`[,"L"])
eye_max_C <- max(pixels_roi$`ROI eye`[,"C"])
eye_max_H <- max(pixels_roi$`ROI eye`[,"H"])
eye_sd_L <- sd(pixels_roi$`ROI eye`[,"L"])
eye_sd_C <- sd(pixels_roi$`ROI eye`[,"C"])
eye_sd_H <- sd(pixels_roi$`ROI eye`[,"H"])
eye_cv_L <- eye_sd_L/eye_mean_L * 100
eye_cv_C <- eye_sd_C/eye_mean_C * 100
eye_cv_H <- eye_sd_H/eye_mean_H * 100
eye_num_pixels <- length(pixels_roi$`ROI eye`[,"L"])
eye_se_L <- eye_sd_L/sqrt(eye_num_pixels)
eye_se_C <- eye_sd_C/sqrt(eye_num_pixels)
eye_se_H <- eye_sd_H/sqrt(eye_num_pixels)
########ROI gill
gill_mean_L <- mean(pixels_roi$`ROI gill`[,"L"])
gill_mean_C <- mean(pixels_roi$`ROI gill`[,"C"])
gill_mean_H <- mean(pixels_roi$`ROI gill`[,"H"])
gill_median_L <- median(pixels_roi$`ROI gill`[,"L"])
gill_median_C <- median(pixels_roi$`ROI gill`[,"C"])
gill_median_H <- median(pixels_roi$`ROI gill`[,"H"])
gill_min_L <- min(pixels_roi$`ROI gill`[,"L"])
gill_min_C <- min(pixels_roi$`ROI gill`[,"C"])
gill_min_H <- min(pixels_roi$`ROI gill`[,"H"])
gill_max_L <- max(pixels_roi$`ROI gill`[,"L"])
gill_max_C <- max(pixels_roi$`ROI gill`[,"C"])
gill_max_H <- max(pixels_roi$`ROI gill`[,"H"])
gill_sd_L <- sd(pixels_roi$`ROI gill`[,"L"])
gill_sd_C <- sd(pixels_roi$`ROI gill`[,"C"])
gill_sd_H <- sd(pixels_roi$`ROI gill`[,"H"])
gill_cv_L <- gill_sd_L/gill_mean_L * 100
gill_cv_C <- gill_sd_C/gill_mean_C * 100
gill_cv_H <- gill_sd_H/gill_mean_H * 100
gill_num_pixels <- length(pixels_roi$`ROI gill`[,"L"])
gill_se_L <- gill_sd_L/sqrt(gill_num_pixels)
gill_se_C <- gill_sd_C/sqrt(gill_num_pixels)
gill_se_H <- gill_sd_H/sqrt(gill_num_pixels)
}
#constructing the outputs
if (type_file == "LAB"){
#L dataframe
L <- data.frame(matrix(nrow=6,ncol=9))
names(L) <- c("roi","mean","median","min","max","sd","cv","num_pixels","se")
L$roi <- c("D1","D2","V1","V2","eye","gill")
L$mean <- c(D1_mean_L, D2_mean_L, V1_mean_L,V2_mean_L,eye_mean_L,gill_mean_L)
L$median <- c(D1_median_L, D2_median_L, V1_median_L,V2_median_L,eye_median_L,gill_median_L)
L$min <- c(D1_min_L, D2_min_L, V1_min_L,V2_min_L,eye_min_L,gill_min_L)
L$max <- c(D1_max_L, D2_max_L, V1_max_L,V2_max_L,eye_max_L,gill_max_L)
L$sd <- c(D1_sd_L, D2_sd_L, V1_sd_L,V2_sd_L,eye_sd_L,gill_sd_L)
L$cv <- c(D1_cv_L, D2_cv_L, V1_cv_L,V2_cv_L,eye_cv_L,gill_cv_L)
L$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
L$se <- c(D1_se_L, D2_se_L, V1_se_L,V2_se_L,eye_se_L,gill_se_L)
#a* dataframe
a <- data.frame(matrix(nrow=6,ncol=7))
names(a) <- c("roi","mean","median","min","max","sd","cv")
a$roi <- c("D1","D2","V1","V2","eye","gill")
a$mean <- c(D1_mean_a, D2_mean_a, V1_mean_a,V2_mean_a,eye_mean_a,gill_mean_a)
a$median <- c(D1_median_a, D2_median_a, V1_median_a,V2_median_a,eye_median_a,gill_median_a)
a$min <- c(D1_min_a, D2_min_a, V1_min_a,V2_min_a,eye_min_a,gill_min_a)
a$max <- c(D1_max_a, D2_max_a, V1_max_a,V2_max_a,eye_max_a,gill_max_a)
a$sd <- c(D1_sd_a, D2_sd_a, V1_sd_a,V2_sd_a,eye_sd_a,gill_sd_a)
a$cv <- c(D1_cv_a, D2_cv_a, V1_cv_a,V2_cv_a,eye_cv_a,gill_cv_a)
a$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
a$se <- c(D1_se_a, D2_se_a, V1_se_a,V2_se_a,eye_se_a,gill_se_a)
#b* dataframe
b <- data.frame(matrix(nrow=6,ncol=7))
names(b) <- c("roi","mean","median","min","max","sd","cv")
b$roi <- c("D1","D2","V1","V2","eye","gill")
b$mean <- c(D1_mean_b, D2_mean_b, V1_mean_b,V2_mean_b,eye_mean_b,gill_mean_b)
b$median <- c(D1_median_b, D2_median_b, V1_median_b,V2_median_b,eye_median_b,gill_median_b)
b$min <- c(D1_min_b, D2_min_b, V1_min_b,V2_min_b,eye_min_b,gill_min_b)
b$max <- c(D1_max_b, D2_max_b, V1_max_b,V2_max_b,eye_max_b,gill_max_b)
b$sd <- c(D1_sd_b, D2_sd_b, V1_sd_b,V2_sd_b,eye_sd_b,gill_sd_b)
b$cv <- c(D1_cv_b, D2_cv_b, V1_cv_b,V2_cv_b,eye_cv_b,gill_cv_b)
b$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
b$se <- c(D1_se_b, D2_se_b, V1_se_b,V2_se_b,eye_se_b,gill_se_b)
output <- list(L,a,b)
names(output) <- c("Lightness","a","b")
}
if (type_file == "LCH"){
#Lightness dataframe
L <- data.frame(matrix(nrow=6,ncol=9))
names(L) <- c("roi","mean","median","min","max","sd","cv","num_pixels","se")
L$roi <- c("D1","D2","V1","V2","eye","gill")
L$mean <- c(D1_mean_L, D2_mean_L, V1_mean_L,V2_mean_L,eye_mean_L,gill_mean_L)
L$median <- c(D1_median_L, D2_median_L, V1_median_L,V2_median_L,eye_median_L,gill_median_L)
L$min <- c(D1_min_L, D2_min_L, V1_min_L,V2_min_L,eye_min_L,gill_min_L)
L$max <- c(D1_max_L, D2_max_L, V1_max_L,V2_max_L,eye_max_L,gill_max_L)
L$sd <- c(D1_sd_L, D2_sd_L, V1_sd_L,V2_sd_L,eye_sd_L,gill_sd_L)
L$cv <- c(D1_cv_L, D2_cv_L, V1_cv_L,V2_cv_L,eye_cv_L,gill_cv_L)
L$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
L$se <- c(D1_se_L, D2_se_L, V1_se_L,V2_se_L,eye_se_L,gill_se_L)
C <- data.frame(matrix(nrow=6,ncol=7))
names(C) <- c("roi","mean","median","min","max","sd","cv")
C$roi <- c("D1","D2","V1","V2","eye","gill")
C$mean <- c(D1_mean_C, D2_mean_C, V1_mean_C,V2_mean_C,eye_mean_C,gill_mean_C)
C$median <- c(D1_median_C, D2_median_C, V1_median_C,V2_median_C,eye_median_C,gill_median_C)
C$min <- c(D1_min_C, D2_min_C, V1_min_C,V2_min_C,eye_min_C,gill_min_C)
C$max <- c(D1_max_C, D2_max_C, V1_max_C,V2_max_C,eye_max_C,gill_max_C)
C$sd <- c(D1_sd_C, D2_sd_C, V1_sd_C,V2_sd_C,eye_sd_C,gill_sd_C)
C$cv <- c(D1_cv_C, D2_cv_C, V1_cv_C,V2_cv_C,eye_cv_C,gill_cv_C)
C$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
C$se <- c(D1_se_C, D2_se_C, V1_se_C,V2_se_C,eye_se_C,gill_se_C)
H <- data.frame(matrix(nrow=6,ncol=7))
names(H) <- c("roi","mean","median","min","max","sd","cv")
H$roi <- c("D1","D2","V1","V2","eye","gill")
H$mean <- c(D1_mean_H, D2_mean_H, V1_mean_H,V2_mean_H,eye_mean_H,gill_mean_H)
H$median <- c(D1_median_H, D2_median_H, V1_median_H,V2_median_H,eye_median_H,gill_median_H)
H$min <- c(D1_min_H, D2_min_H, V1_min_H,V2_min_H,eye_min_H,gill_min_H)
H$max <- c(D1_max_H, D2_max_H, V1_max_H,V2_max_H,eye_max_H,gill_max_H)
H$sd <- c(D1_sd_H, D2_sd_H, V1_sd_H,V2_sd_H,eye_sd_H,gill_sd_H)
H$cv <- c(D1_cv_H, D2_cv_H, V1_cv_H,V2_cv_H,eye_cv_H,gill_cv_H)
H$num_pixels <- c(D1_num_pixels, D2_num_pixels, V1_num_pixels,V2_num_pixels,eye_num_pixels,gill_num_pixels)
H$se <- c(D1_se_H, D2_se_H, V1_se_H,V2_se_H,eye_se_H,gill_se_H)
output <- list(L,C,H)
names(output) <- c("Lightness","Chroma","Hue")
}
#export the pixel values for each ROI and each parameter. It could be useful to use in histogram analysis
if (type_file == "LAB"){
pixels_L_D1 <- pixels_roi$`ROI D1`[,"L"]
pixels_a_D1 <- pixels_roi$`ROI D1`[,"a"]
pixels_b_D1 <- pixels_roi$`ROI D1`[,"b"]
pixels_L_D2 <- pixels_roi$`ROI D2`[,"L"]
pixels_a_D2 <- pixels_roi$`ROI D2`[,"a"]
pixels_b_D2 <- pixels_roi$`ROI D2`[,"b"]
pixels_L_V1 <- pixels_roi$`ROI V1`[,"L"]
pixels_a_V1 <- pixels_roi$`ROI V1`[,"a"]
pixels_b_V1 <- pixels_roi$`ROI V1`[,"b"]
pixels_L_V2 <- pixels_roi$`ROI V2`[,"L"]
pixels_a_V2 <- pixels_roi$`ROI V2`[,"a"]
pixels_b_V2 <- pixels_roi$`ROI V2`[,"b"]
pixels_L_eye <- pixels_roi$`ROI eye`[,"L"]
pixels_a_eye <- pixels_roi$`ROI eye`[,"a"]
pixels_b_eye <- pixels_roi$`ROI eye`[,"b"]
pixels_L_gill <- pixels_roi$`ROI gill`[,"L"]
pixels_a_gill <- pixels_roi$`ROI gill`[,"a"]
pixels_b_gill <- pixels_roi$`ROI gill`[,"b"]
D1 <- data.frame(matrix(nrow= D1_num_pixels,ncol=3))
names(D1) <- c("L","a","b")
D1$L <- pixels_L_D1
D1$a <- pixels_a_D1
D1$b <- pixels_b_D1
D2 <- data.frame(matrix(nrow= D2_num_pixels,ncol=3))
names(D2) <- c("L","a","b")
D2$L <- pixels_L_D2
D2$a <- pixels_a_D2
D2$b <- pixels_b_D2
V1 <- data.frame(matrix(nrow= V1_num_pixels,ncol=3))
names(V1) <- c("L","a","b")
V1$L <- pixels_L_V1
V1$a <- pixels_a_V1
V1$b <- pixels_b_V1
V2 <- data.frame(matrix(nrow= V2_num_pixels,ncol=3))
names(V2) <- c("L","a","b")
V2$L <- pixels_L_V2
V2$a <- pixels_a_V2
V2$b <- pixels_b_V2
eye <- data.frame(matrix(nrow= eye_num_pixels,ncol=3))
names(eye) <- c("L","a","b")
eye$L <- pixels_L_eye
eye$a <- pixels_a_eye
eye$b <- pixels_b_eye
gill <- data.frame(matrix(nrow= gill_num_pixels,ncol=3))
names(gill) <- c("L","a","b")
gill$L <- pixels_L_gill
gill$a <- pixels_a_gill
gill$b <- pixels_b_gill
output_pixels <- list(D1,D2,V1,V2,eye,gill)
names(output_pixels) <- c("D1","D2","V1","V2","eye","gill")
}
if (type_file == "LCH"){
pixels_L_D1 <- pixels_roi$`ROI D1`[,"L"]
pixels_C_D1 <- pixels_roi$`ROI D1`[,"C"]
pixels_H_D1 <- pixels_roi$`ROI D1`[,"H"]
pixels_L_D2 <- pixels_roi$`ROI D2`[,"L"]
pixels_C_D2 <- pixels_roi$`ROI D2`[,"C"]
pixels_H_D2 <- pixels_roi$`ROI D2`[,"H"]
pixels_L_V1 <- pixels_roi$`ROI V1`[,"L"]
pixels_C_V1 <- pixels_roi$`ROI V1`[,"C"]
pixels_H_V1 <- pixels_roi$`ROI V1`[,"H"]
pixels_L_V2 <- pixels_roi$`ROI V2`[,"L"]
pixels_C_V2 <- pixels_roi$`ROI V2`[,"C"]
pixels_H_V2 <- pixels_roi$`ROI V2`[,"H"]
pixels_L_eye <- pixels_roi$`ROI eye`[,"L"]
pixels_C_eye <- pixels_roi$`ROI eye`[,"C"]
pixels_H_eye <- pixels_roi$`ROI eye`[,"H"]
pixels_L_gill <- pixels_roi$`ROI gill`[,"L"]
pixels_C_gill <- pixels_roi$`ROI gill`[,"C"]
pixels_H_gill <- pixels_roi$`ROI gill`[,"H"]
D1 <- data.frame(matrix(nrow= D1_num_pixels,ncol=3))
names(D1) <- c("L","C","H")
D1$L <- pixels_L_D1
D1$C <- pixels_C_D1
D1$H <- pixels_H_D1
D2 <- data.frame(matrix(nrow= D2_num_pixels,ncol=3))
names(D2) <- c("L","C","H")
D2$L <- pixels_L_D2
D2$C <- pixels_C_D2
D2$H <- pixels_H_D2
V1 <- data.frame(matrix(nrow= V1_num_pixels,ncol=3))
names(V1) <- c("L","C","H")
V1$L <- pixels_L_V1
V1$C <- pixels_C_V1
V1$H <- pixels_H_V1
V2 <- data.frame(matrix(nrow= V2_num_pixels,ncol=3))
names(V2) <- c("L","C","H")
V2$L <- pixels_L_V2
V2$C <- pixels_C_V2
V2$H <- pixels_H_V2
eye <- data.frame(matrix(nrow= eye_num_pixels,ncol=3))
names(eye) <- c("L","C","H")
eye$L <- pixels_L_eye
eye$C <- pixels_C_eye
eye$H <- pixels_H_eye
gill <- data.frame(matrix(nrow= gill_num_pixels,ncol=3))
names(gill) <- c("L","C","H")
gill$L <- pixels_L_gill
gill$C <- pixels_C_gill
gill$H <- pixels_H_gill
output_pixels <- list(D1,D2,V1,V2,eye,gill)
names(output_pixels) <- c("D1","D2","V1","V2","eye","gill")
}
output<-list(output,output_pixels)
names(output)<- c("parameters","pixels")
return(output)
}
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