From 6858c2d744181ab8c421ebd22016154632fd62e7 Mon Sep 17 00:00:00 2001
From: dschnei5 <dschnei5@une.edu.au>
Date: Fri, 22 Feb 2019 12:32:33 +1100
Subject: [PATCH] Upload New File
---
ThermalImageScript_1.1.3.R | 480 +++++++++++++++++++++++++++++++++++++
1 file changed, 480 insertions(+)
create mode 100644 ThermalImageScript_1.1.3.R
diff --git a/ThermalImageScript_1.1.3.R b/ThermalImageScript_1.1.3.R
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+################Thermal Image Analysis######################
+
+#1.0 - Does it all with the user measured temps and humidity values and crop coordinates file
+#1.1 - For images that have correct endian - Resizes small images as well
+# - Resaves .png files from flir export when binary image is .tiff
+#1.1.1 - Attempts to do the same as imageJ - auto thresholding for ROI - along with full ROI
+#1.1.2 - Modified for Surantha's tree canopies - FLIR ONE Camera
+# - Corrects incorrect makers notes
+#1.1.3 - Writes the images to ascii file converted to temp
+
+
+####Load libraries####
+
+library(exifr)
+library(EBImage)
+library(plyr)
+library(Thermimage)
+library(autothresholdr)
+library(raster)
+
+#####Add function for later####
+
+as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}
+
+
+####Set wd and get a list of images####
+
+setwd(choose.dir(caption = "Select a folder full of raw and original FLIR images", default = "C:/R_2/2017-07-12_SuranthaThermal/Images_01"));
+wd <- getwd();
+wd;
+
+files.jpg <- list.files(getwd(), pattern = ".jpg", ignore.case = TRUE, recursive = FALSE);
+
+####Use exifTool to extract the raw thermal image####
+
+#exiftool.call(args = c("-rawthermalimage", "-b", "-w png"), fnames = files.jpg, wait = TRUE);
+
+####Get a list of raw thermal images####
+
+files.png <- list.files(getwd(), pattern = ".png", ignore.case = TRUE, recursive = FALSE);
+#file.remove(files.png);
+
+####Get a list of the image masks####
+
+#files.tiff <- list.files(getwd(), pattern = ".tiff", ignore.case = TRUE, recursive = FALSE);
+
+##Read in user temps and humidity
+
+#files.csv <- list.files(getwd(), pattern = ".csv", ignore.case = TRUE, recursive = FALSE);
+
+#Uhumtemps <- read.csv(files.csv[1], header = TRUE);
+
+
+####Get the exif info from all original jpegs####
+
+Exif.info.db <- data.frame();
+
+
+Exif.info.db <- exifr(paste0(wd,"/",files.jpg));
+
+
+
+####Place user relative humidity and temperature into Exif info####
+
+#Exif.info.db$RelativeHumidity <- Uhumtemps$Humidity
+#Exif.info.db$AtmosphericTemperature <- Uhumtemps$Temp
+
+#Exif.info.db$FocusDistance[8:55] <- 1.8 #Fix focus distance error in Hadriana's dataset
+
+####Set Object Distance into Exif Info (metres)#### Not used in this script (using focal distance data)
+
+#Exif.info.db$ObjectDistance <- as.numeric(readline("What is the distance between the camera and target object in metres? (1.5) "));
+
+#Exif.info.db$ObjectDistance <- as.numeric(1.0) #Not using because found that focal distance is better
+
+
+
+####Create the output data frame####
+
+#output.df1 <- data.frame();
+
+#output.df2 <- data.frame();
+
+#output.df3 <- data.frame();
+
+#output.df4 <- data.frame();
+
+#output.df5 <- data.frame();
+
+#output.df6 <- data.frame();
+
+#output.df7 <- data.frame();
+
+#output.df8 <- data.frame();
+
+#output.df9 <- data.frame();
+
+
+
+
+####Do it all with a loop####
+
+for (j in seq_along(files.png)){
+
+ #j = 47;
+ print(paste("Image",j));
+
+ ####Use imagemagick to resave .png file (used if binary image is a .tiff)
+
+ cd <- paste0("cd ",wd);
+ shell(cd,translate = TRUE, wait = TRUE);
+ cmd <- paste0("magick convert ", files.png[j]," ", files.png[j])
+ shell(cmd,translate = FALSE, wait = TRUE);
+
+
+ ####Get the correct endian raw image####
+ #cd <- paste0("cd ",wd);
+ #shell(cd,translate = TRUE, wait = TRUE);
+ #cmd <- paste0("exiftool -b -RawThermalImage -F ", files.jpg[j], " | magick convert - gray:- |magick convert -depth 16 -endian msb -size ", Exif.info.db$RawThermalImageWidth[j], "x", Exif.info.db$RawThermalImageHeight[j], " gray:- ",files.png[j])
+ #cmd
+ #shell(cmd,translate = FALSE, wait = TRUE);
+
+
+ ##Use imageMagick to do the wavelet denoise - if necessary##
+
+ #cd <- paste0("cd ",wd);
+ #cmd <- paste0("magick convert ",files.png[j]," -wavelet-denoise 50% ", files.png[j]);
+ #shell(cd,translate = TRUE, wait = TRUE);
+ #shell(cmd,translate = FALSE, wait = TRUE);
+
+ ##read in image##
+
+ #im <- readImage(paste0(wd,"/",files.png[j]));
+
+ #print(dim(im));
+
+ #im <- EBImage::resize(im,640,480, filter = "bilinear");
+
+ #print(dim(im));
+
+ #EBImage::display(im);
+ #im.thresh <- thresh(im,w=5,h=5,offset = 0);
+ #EBImage::display(im.thresh)
+ #im.thresh.i <- ifelse(im.thresh == 1,0,1)
+ #display(im.thresh.i)
+ #im.thresh.i <- opening(im.thresh.i,makeBrush(5,shape = "disc"))
+ #im.thresh <- opening(im.thresh,makeBrush(5,shape = "disc"))
+ #oi.i <- bwlabel(im.thresh.i)
+ #display(normalize(oi))
+ #cols.i <- c('black', sample(rainbow(max(oi.i))))
+ #oirainbow.i <- Image(cols[1+oi.i], dim=dim(oi.i))
+ #display(oirainbow.i)
+ #oi <- bwlabel(im.thresh)
+ #display(normalize(oi))
+ #cols <- c('black', sample(rainbow(max(oi))))
+ #oirainbow <- Image(cols[1+oi], dim=dim(oi))
+ #display(oirainbow)
+
+
+ ##scale image##
+
+ #max(im)
+ #im.ran <- Exif.info.db[j,73];
+ #im.med <- Exif.info.db[j,72];
+ #im.min <- Exif.info.db[j,72]-0.5*Exif.info.db[j,73];
+ #im.max <- Exif.info.db[j,72]+0.5*Exif.info.db[j,73];
+ #im <- im * im.ran + im.min;
+ #im <- im * 2^16;
+
+ ##Threshold Image##
+
+ #im.thresh <- as.integer(im)
+
+ #im.thresh <- auto_thresh(im.thresh,"Otsu");
+
+ #im.masked <- readImage(paste0(wd,"/",files.tiff[j]))
+
+ #mask <- ifelse(im >= im.thresh, 1,0);
+
+ #im.masked <- as.array(im.masked);
+
+ #im.masked.tree <- ifelse(im.masked == 1,im,NA);
+
+ #im.masked.sky <- ifelse(im.masked == 0, im,NA);
+
+ #hist(im.masked.tree)
+ #hist(im.masked.sky)
+
+ ##Determine the number of ROIs for that image;
+
+ #ROI.n <- (dim(Uhumtemps[j,-c(1,2,3,4)])[2] - sum(is.na(Uhumtemps[j,-c(1,2,3,4)]))) / 4;
+ #ROI.n <- 1
+
+
+ ##Extract 1 Regions of Interest - This is where you need to customise for your images##
+
+ #crop.ims <- c();
+ #averages <- c();
+ #maximums <- c();
+ #minimums <- c();
+
+ #for (l in 1:ROI.n){
+
+ # print(paste("Entire Image ",l));
+
+ # nam <- ifelse(l <= 9, paste0("im0",l),paste0("im",l));
+ # #assign(nam, im[Uhumtemps[j,(l-1)*4+5]:Uhumtemps[j,(l-1)*4+7],Uhumtemps[j,(l-1)*4+6]:Uhumtemps[j,(l-1)*4+8]]);
+ # assign(nam, im);
+ #a <- ifelse(dim(get(nam))[1] > dim(get(nam))[2],dim(get(nam))[1],as.numeric(dim(get(nam))[2] - dim(get(nam))[1]));
+ #b <- ifelse(dim(get(nam))[2] < dim(get(nam))[1],as.numeric(dim(get(nam))[1] - dim(get(nam))[2]),dim(get(nam))[2]);
+ #a <- ifelse(a == 0, dim(get(nam))[1], a);
+ #b <- ifelse(b == 0, dim(get(nam))[2], b);
+ #assign(nam,(get(nam)[-c(b),-c(a)]));
+ # d <- round(dim(get(nam))[2] * dim(get(nam))[1] * 0.001, digits = 0);
+ # crop.ims <- c(crop.ims,nam);
+ # averages <- c(averages, mean(get(nam)));
+ # max0.001 <- data.frame(Breaks = hist(get(nam))[[1]], Counts = c(0,hist(get(nam))[[2]]));
+ # max0.001 <- max0.001[with(max0.001, order(-max0.001[1])),];
+ # max0.001[,2] <- ifelse(max0.001[,2] <= d, NA, max0.001[,2]);
+ # max0.001 <- max0.001[complete.cases(max0.001),];
+ # maximums <- c(maximums,max(max0.001[,1]));
+
+ # min0.001 <- data.frame(Breaks = hist(get(nam))[[1]], Counts = c(0,hist(get(nam))[[2]]));
+ # min0.001 <- min0.001[with(min0.001, order(min0.001[1])),];
+ # min0.001[,2] <- ifelse(min0.001[,2] <= d, NA, min0.001[,2]);
+ # min0.001 <- min0.001[complete.cases(min0.001),];
+ # minimums <- c(minimums,min(min0.001[,1]));
+
+ #}; #END l loop
+
+ #rm(l, a, b, d);
+
+
+ ##########################################################################################################
+ ###Tree Only###
+
+ #crop.ims.tree <- c();
+ #averages.tree <- c();
+ #maximums.tree <- c();
+ #minimums.tree <- c();
+
+# for (l in 1:ROI.n){
+
+# print(paste("Tree Crop ",l));
+
+# nam.tree <- ifelse(l <= 9, paste0("im0",l,"_tree"),paste0("im",l,"_tree"));
+# assign(nam.tree, im.masked.tree);
+# d.tree <- round(dim(get(nam.tree))[2] * dim(get(nam.tree))[1] * 0.001, digits = 0);
+# crop.ims.tree <- c(crop.ims.tree,nam.tree);
+# averages.tree <- c(averages.tree, mean(get(nam.tree),na.rm = TRUE));
+# max0.001.tree <- data.frame(Breaks = hist(get(nam.tree))[[1]], Counts = c(0,hist(get(nam.tree))[[2]]));
+# max0.001.tree <- max0.001.tree[with(max0.001.tree, order(-max0.001.tree[1])),];
+# max0.001.tree[,2] <- ifelse(max0.001.tree[,2] <= d.tree, NA, max0.001.tree[,2]);
+# max0.001.tree <- max0.001.tree[complete.cases(max0.001.tree),];
+# maximums.tree <- c(maximums.tree,max(max0.001.tree[,1]));
+
+# min0.001.tree <- data.frame(Breaks = hist(get(nam.tree))[[1]], Counts = c(0,hist(get(nam.tree))[[2]]));
+ # min0.001.tree <- min0.001.tree[with(min0.001.tree, order(min0.001.tree[1])),];
+ # min0.001.tree[,2] <- ifelse(min0.001.tree[,2] <= d.tree, NA, min0.001.tree[,2]);
+ # min0.001.tree <- min0.001.tree[complete.cases(min0.001.tree),];
+ # minimums.tree <- c(minimums.tree,min(min0.001.tree[,1]));
+ #
+ # }; #END l loop
+ #
+ # rm(l, a.f, b.f, d.f);
+ #
+ # ##########################################################################################################
+ # ###Dag###
+ #
+ # crop.ims.sky <- c();
+ # averages.sky <- c();
+ # maximums.sky <- c();
+ # minimums.sky <- c();
+ #
+ # for (l in 1:ROI.n){
+ #
+ # print(paste("Sky Crop ",l));
+ #
+ # nam.sky <- ifelse(l <= 9, paste0("im0",l,"_tree"),paste0("im",l,"_tree"));
+ # assign(nam.sky, im.masked.sky);
+ # d.sky <- round(dim(get(nam.sky))[2] * dim(get(nam.sky))[1] * 0.001, digits = 0);
+ # crop.ims.sky <- c(crop.ims.sky,nam.sky);
+ # averages.sky <- c(averages.sky, mean(get(nam.sky),na.rm = TRUE));
+ # max0.001.sky <- data.frame(Breaks = hist(get(nam.sky))[[1]], Counts = c(0,hist(get(nam.sky))[[2]]));
+ # max0.001.sky <- max0.001.sky[with(max0.001.sky, order(-max0.001.sky[1])),];
+ # max0.001.sky[,2] <- ifelse(max0.001.sky[,2] <= d.sky, NA, max0.001.sky[,2]);
+ # max0.001.sky <- max0.001.sky[complete.cases(max0.001.sky),];
+ # maximums.sky <- c(maximums.sky,max(max0.001.sky[,1]));
+ #
+ # min0.001.sky <- data.frame(Breaks = hist(get(nam.sky))[[1]], Counts = c(0,hist(get(nam.sky))[[2]]));
+ # min0.001.sky <- min0.001.sky[with(min0.001.sky, order(min0.001.sky[1])),];
+ # min0.001.sky[,2] <- ifelse(min0.001.sky[,2] <= d.sky, NA, min0.001.sky[,2]);
+ # min0.001.sky <- min0.001.sky[complete.cases(min0.001.sky),];
+ # minimums.sky <- c(minimums.sky,min(min0.001.sky[,1]));
+ #
+ # }; #END l loop
+ #
+ # rm(l, a.d, b.d, d.d);
+ #
+ #
+ #
+ # ##Get Temperatures - check your exif info to ensure column indexes are correct##
+ #
+ #
+ # output.df1 <- rbind.fill(output.df1,as.data.frame(t(c(Exif.info.db[j,3],raw2temp(averages,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j])))));
+ #
+ #
+ #
+ # output.df2 <- rbind.fill(output.df2,as.data.frame(t(raw2temp(maximums,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+ #
+ # output.df3 <- rbind.fill(output.df3,as.data.frame(t(raw2temp(minimums,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+ #
+ #
+ # output.df4 <- rbind.fill(output.df4,as.data.frame(t(raw2temp(averages.tree,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+ #
+ #
+ # output.df5 <- rbind.fill(output.df5,as.data.frame(t(raw2temp(maximums.tree,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+ #
+ # output.df6 <- rbind.fill(output.df6,as.data.frame(t(raw2temp(minimums.tree,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+ #
+ # output.df7 <- rbind.fill(output.df7,as.data.frame(t(raw2temp(averages.sky,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+ #
+ #
+ # output.df8 <- rbind.fill(output.df8,as.data.frame(t(raw2temp(maximums.sky,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+ #
+ # output.df9 <- rbind.fill(output.df9,as.data.frame(t(raw2temp(minimums.sky,
+ # E = Exif.info.db$Emissivity[j],
+ # OD = Exif.info.db$FocusDistance[j],
+ # RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ # ATemp = Exif.info.db$AtmosphericTemperature[j],
+ # IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ # IRT = Exif.info.db$IRWindowTransmission[j],
+ # RH = Exif.info.db$RelativeHumidity[j],
+ # PR1 = Exif.info.db$PlanckR1[j],
+ # PB = Exif.info.db$PlanckB[j],
+ # PF = Exif.info.db$PlanckF[j],
+ # PO = Exif.info.db$PlanckO[j],
+ # PR2 = Exif.info.db$PlanckR2[j]))));
+ #
+
+ im.tmp <- raster(paste0(wd,"/",files.png[j]))
+
+ im.tmp <- raw2temp(im.tmp,E = Exif.info.db$Emissivity[j],
+ OD = Exif.info.db$FocusDistance[j],
+ RTemp = Exif.info.db$ReflectedApparentTemperature[j],
+ ATemp = Exif.info.db$AtmosphericTemperature[j],
+ IRWTemp = Exif.info.db$IRWindowTemperature[j],
+ IRT = Exif.info.db$IRWindowTransmission[j],
+ RH = Exif.info.db$RelativeHumidity[j],
+ PR1 = Exif.info.db$PlanckR1[j],
+ PB = Exif.info.db$PlanckB[j],
+ PF = Exif.info.db$PlanckF[j],
+ PO = Exif.info.db$PlanckO[j],
+ PR2 = Exif.info.db$PlanckR2[j])
+
+
+ writeRaster(im.tmp,paste0("out_",files.jpg[j]),format = "ascii")
+ rm(im.tmp)
+
+}; # END j Loop
+
+#rm(j);
+
+
+##Add some column names, write a .csv and cleanup
+
+# output.df10 <- cbind(output.df1,output.df2,output.df3,output.df4,output.df5,output.df6,output.df7,output.df8,output.df9)
+# col.ns <- 1:((dim(output.df10)[2] - 1)/9)
+# colnames(output.df10) <- c("ImageName",ifelse(col.ns <= 9,paste0("All_ROI_Average_0",col.ns), paste0("All_ROI_Average_",col.ns)),ifelse(col.ns <= 9,paste0("All_ROI_Maximum_0",col.ns), paste0("All_ROI_Maximums_",col.ns)),ifelse(col.ns <= 9,paste0("All_ROI_Minimum_0",col.ns), paste0("All_ROI_Minimums_",col.ns)),ifelse(col.ns <= 9,paste0("Tree_ROI_Average_0",col.ns), paste0("Tree_ROI_Average_",col.ns)),ifelse(col.ns <= 9,paste0("Tree_ROI_Maximum_0",col.ns), paste0("Tree_ROI_Maximums_",col.ns)),ifelse(col.ns <= 9,paste0("Tree_ROI_Minimum_0",col.ns), paste0("Tree_ROI_Minimums_",col.ns)), ifelse(col.ns <= 9,paste0("Sky_ROI_Average_0",col.ns), paste0("Sky_ROI_Average_",col.ns)),ifelse(col.ns <= 9,paste0("Sky_ROI_Maximum_0",col.ns), paste0("Sky_ROI_Maximums_",col.ns)),ifelse(col.ns <= 9,paste0("Sky_ROI_Minimum_0",col.ns), paste0("Sky_ROI_Minimums_",col.ns)))
+#
+# #####Stick them all together and export#############
+#
+# write.table(t(output.df10), file.choose(new = TRUE),sep = ",", col.names = FALSE);
+
+rm(list = ls());
+#########################################################################################################################
+
--
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