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Lib_PerformPCA.R
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Lib_PerformPCA.R
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# ==============================================================================
# biodivMapR
# Lib_PerformPCA.R
# ==============================================================================
# PROGRAMMERS:
# Jean-Baptiste FERET <jb.feret@teledetection.fr>
# Florian de Boissieu <fdeboiss@gmail.com>
# Copyright 2020/06 Jean-Baptiste FERET
# ==============================================================================
# This Library is used to perform PCA on raster prior to diversity mapping
# ==============================================================================
#' Performs PCA for all images and create PCA file with either all or a selection of PCs
#'
#' @param Input_Image_File character. Path of the image to be processed
#' @param Input_Mask_File character. Path of the mask corresponding to the image
#' @param Output_Dir character. Path for output directory
#' @param Continuum_Removal boolean. Set to TRUE if continuum removal should be applied
#' @param TypePCA character. Type of PCA: choose either "PCA" or "SPCA"
#' @param NbPCs_To_Keep numeric. number of components to ke saved in the PCA file. default = 30 if set to FALSE (or nb PC if <30)
#' @param FilterPCA boolean. Set to TRUE if 2nd filtering based on PCA is required
#' @param Excluded_WL numeric. Water Vapor Absorption domains (in nanometers, min and max WL). Can also be used to exclude spectific domains. dims = N x 2 (N = number of domains to be eliminated)
#' @param nb_partitions numeric. Number of repetitions to estimate diversity from the raster (averaging repetitions).
#' @param nbCPU numeric. Number fo CPUs in use.
#' @param MaxRAM numeric. Maximum size of chunk in GB to limit RAM allocation when reading image file.
#'
#' @return list of paths corresponding to resulting PCA files
#' @export
perform_PCA <- function(Input_Image_File, Input_Mask_File, Output_Dir,
Continuum_Removal = TRUE, TypePCA = "SPCA",
NbPCs_To_Keep = 30, FilterPCA = FALSE, Excluded_WL = FALSE,
nb_partitions = 20, nbCPU = 1, MaxRAM = 0.25) {
# check if format of raster data is as expected
check_data(Input_Image_File)
if (!Input_Mask_File==FALSE){
check_data(Input_Mask_File,Mask = TRUE)
}
# define the path corresponding to image, mask and output directory
ImNames <- list()
ImNames$Input_Image <- Input_Image_File
ImNames$Mask_list <- Input_Mask_File
Output_Dir_Full <- define_output_directory(Output_Dir, Input_Image_File, TypePCA)
# Identify water vapor absorption bands in image and possibly other spectral domains to discard
SpectralFilter <- exclude_spectral_domains(Input_Image_File, Excluded_WL = Excluded_WL)
# Extract valid data subset and check validity
print("Extract pixels from the images to perform PCA on a subset")
# define number of pixels to be extracted from the image for each iteration
Pix_Per_Partition <- define_pixels_per_iter(ImNames, nb_partitions = nb_partitions)
nb_Pix_To_Sample <- nb_partitions * Pix_Per_Partition
ImPathHDR <- get_HDR_name(Input_Image_File)
HDR <- read_ENVI_header(ImPathHDR)
# extract a random selection of pixels from image
if (TypePCA=='MNF'){
FilterPCA <- FALSE
kernel <- matrix(0, 3, 3)
kernel[c(5, 6, 8)]=c(1, -1/2, -1/2)
Subset <- get_random_subset_from_image(ImPath = Input_Image_File,
MaskPath = Input_Mask_File, nb_partitions = nb_partitions,
Pix_Per_Partition = Pix_Per_Partition, kernel = kernel, MaxRAM = MaxRAM)
} else {
Subset <- get_random_subset_from_image(ImPath = Input_Image_File,
MaskPath = Input_Mask_File, nb_partitions = nb_partitions,
Pix_Per_Partition = Pix_Per_Partition, kernel = NULL, MaxRAM = MaxRAM)
}
# if needed, apply continuum removal
if (Continuum_Removal == TRUE) {
Subset$DataSubset <- apply_continuum_removal(Spectral_Data = Subset$DataSubset,
Spectral = SpectralFilter,
nbCPU = nbCPU)
} else {
if (!length(SpectralFilter$WaterVapor) == 0) {
Subset$DataSubset <- Subset$DataSubset[, -SpectralFilter$WaterVapor]
}
}
# if number of pixels available inferior number initial sample size
if (Subset$nbPix2Sample < nb_Pix_To_Sample) {
nb_Pix_To_Sample <- Subset$nbPix2Sample
nb_partitions <- ceiling(nb_Pix_To_Sample / Pix_Per_Partition)
Pix_Per_Partition <- floor(nb_Pix_To_Sample / nb_partitions)
nb_Pix_To_Sample <- nb_partitions * Pix_Per_Partition
}
DataSubset <- Subset$DataSubset
# clean reflectance data from inf and constant values
CleanData <- rm_invariant_bands(DataSubset, SpectralFilter)
DataSubset <- CleanData$DataMatrix
SpectralFilter <- CleanData$Spectral
# Compute PCA #1 on DataSubset
print(paste('perform',TypePCA,'on the subset image'))
if (TypePCA == "PCA" | TypePCA == "SPCA") {
PCA_model <- pca(DataSubset, TypePCA)
# } else if (TypePCA == "NLPCA") {
# print("performing NL-PCA with autoencoder")
# print("Make sure you properly installed and defined python environment if using this functionality")
# PCA_model <- nlpca(DataSubset)
} else if(TypePCA=="MNF"){
PCA_model <- mnf(DataSubset, Subset$coordPix)
}
# if PCA based filtering:
if (FilterPCA == TRUE) {
# Perform PCA-based pixels filtering
# the shade mask helps discarding most unwanted pixels: Shade, clouds, soil,
# water...). However some unwanted pixels remain. Here we assume that
# such pixels which do not correspond to vegetation will take extreme values
# after PCA transformation.
# In order to exclude these pixels, we compute mean and SD for the 3 first
# components and exclude all pixels showing values ouside "mean+-3SD" range
print("Exclude extreme PCA values")
if (dim(PCA_model$x)[2] > 5) {
PCsel <- 1:5
} else {
PCsel <- 1:dim(PCA_model$x)[2]
}
Shade_Update <- file.path(Output_Dir_Full, "ShadeMask_Update_PCA")
Input_Mask_File <- filter_PCA(Input_Image_File, HDR, Input_Mask_File, Shade_Update,
Spectral = SpectralFilter,Continuum_Removal, PCA_model,
PCsel, TypePCA,nbCPU = nbCPU, MaxRAM = MaxRAM)
## Compute PCA 2 based on the updated shade mask ##
# extract a random selection of pixels from image
Subset <- get_random_subset_from_image(ImPath = Input_Image_File, MaskPath = Input_Mask_File,
nb_partitions = nb_partitions, Pix_Per_Partition = Pix_Per_Partition,
kernel = NULL, MaxRAM = MaxRAM)
# if needed, apply continuum removal
if (Continuum_Removal == TRUE) {
Subset$DataSubset <- apply_continuum_removal(Subset$DataSubset, SpectralFilter, nbCPU = nbCPU)
} else {
if (!length(SpectralFilter$WaterVapor) == 0) {
Subset$DataSubset <- Subset$DataSubset[, -SpectralFilter$WaterVapor]
}
}
# if number of pixels available inferior number initial sample size
if (Subset$nbPix2Sample < nb_Pix_To_Sample) {
nb_Pix_To_Sample <- Subset$nbPix2Sample
nb_partitions <- ceiling(nb_Pix_To_Sample / Pix_Per_Partition)
Pix_Per_Partition <- floor(nb_Pix_To_Sample / nb_partitions)
nb_Pix_To_Sample <- nb_partitions * Pix_Per_Partition
}
DataSubset <- Subset$DataSubset
# # # assume that 1st data cleaning is enough...
## Uncommented June 5, 2019
# clean reflectance data from inf and constant values
CleanData <- rm_invariant_bands(DataSubset, SpectralFilter)
DataSubset <- CleanData$DataMatrix
SpectralFilter <- CleanData$Spectral
print(paste('perform',TypePCA,'#2 on the subset image'))
if (TypePCA == "PCA" | TypePCA == "SPCA") {
PCA_model <- pca(DataSubset, TypePCA)
# } else if (TypePCA == "NLPCA") {
# print("performing NL-PCA with autoencoder")
# PCA_model <- nlpca(DataSubset)
}
}
# Number of PCs computed and written in the PCA file: 30 if hyperspectral
Nb_PCs <- dim(PCA_model$x)[2]
if (Nb_PCs > NbPCs_To_Keep){
Nb_PCs <- NbPCs_To_Keep
}
PCA_model$Nb_PCs <- Nb_PCs
# PCA_model$x <- NULL
# CREATE PCA FILE CONTAINING ONLY SELECTED PCs
Output_Dir_PCA <- define_output_subdir(Output_Dir, Input_Image_File, TypePCA, "PCA")
PCA_Files <- file.path(Output_Dir_PCA, paste("OutputPCA_", Nb_PCs, "_PCs", sep = ""))
write_PCA_raster(Input_Image_File = Input_Image_File,
Input_Mask_File = Input_Mask_File,
PCA_Path = PCA_Files, PCA_model = PCA_model,
Spectral = SpectralFilter,
Nb_PCs = Nb_PCs, Continuum_Removal = Continuum_Removal,
TypePCA = TypePCA, nbCPU = nbCPU, MaxRAM = MaxRAM)
# save workspace for this stage
WS_Save <- file.path(Output_Dir_PCA, "PCA_Info.RData")
my_list <- list("PCA_Files" = PCA_Files, "Pix_Per_Partition" = Pix_Per_Partition,
"nb_partitions" = nb_partitions, "MaskPath" = Input_Mask_File,
"PCA_model" = PCA_model, "SpectralFilter" = SpectralFilter,
"TypePCA" = TypePCA)
MaskPath <- Input_Mask_File
save(PCA_Files,Pix_Per_Partition, nb_partitions, MaskPath, PCA_model,
SpectralFilter, TypePCA, file = WS_Save)
return(my_list)
}
#' perform filtering based on extreme values PCA identified through PCA
#'
#' @param Input_Image_File character. Path of the image to be processed
#' @param HDR character. Path of the header file corresponding to the image to be processed
#' @param Input_Mask_File character. Path of the mask raster corresponding to the image (keeps pixels = 1)
#' @param Shade_Update character. Path of the updated mask raster corresponding to the image (keeps pixels = 1)
#' @param Spectral list. spectral information from data
#' @param Continuum_Removal boolean. set TRUE if continuum removal should be applied
#' @param PCA_model dataframe. general parameters of the PCA
#' @param PCsel numeric. PCs used to filter out extreme values
#' @param TypePCA character. Set to PCA, SPCA or MNF
#' @param nbCPU numeric. number of CPUs to be used in parallel
#' @param MaxRAM numeric. indicator of RAM to be used to read image file
#
#' @return Shade_Update = updated shade mask
#' @importFrom stats sd
#' @importFrom matlab ones
#' @export
filter_PCA <- function(Input_Image_File, HDR, Input_Mask_File, Shade_Update,
Spectral, Continuum_Removal, PCA_model, PCsel, TypePCA,
nbCPU = 1, MaxRAM = 0.25) {
# 1- get extreme values falling outside of mean +- 3SD for PCsel first components
# compute mean and sd of the 5 first components of the sampled data
MeanSub <- colMeans(PCA_model$x)
SDSub <- apply(PCA_model$x, 2, sd)
MinPC <- MeanSub - 3.0 * SDSub
MaxPC <- MeanSub + 3.0 * SDSub
# 2- update shade mask based on PCA values
# 2.1- create hdr and binary files corresponding to updated mask
HDR_Shade <- HDR
HDR_Shade$description <- "Mask produced from PCA outlier filtering"
HDR_Shade$bands <- 1
HDR_Shade$`data type` <- 1
HDR_Shade$`band names` <- "{Mask_PCA}"
HDR_Shade$wavelength <- NULL
HDR_Shade$fwhm <- NULL
HDR_Shade$resolution <- NULL
HDR_Shade$bandwidth <- NULL
HDR_Shade$purpose <- NULL
HDR_Shade$`default stretch` <- '0 1 linear'
HDR_Shade$`default bands` <- NULL
HDR_Shade$`data gain values` <- NULL
HDR_Shade$`byte order` <- get_byte_order()
headerFpath <- paste(Shade_Update, ".hdr", sep = "")
write_ENVI_header(HDR_Shade, headerFpath)
# create updated shade mask
fidShade_Update <- file(
description = Shade_Update, open = "wb", blocking = TRUE,
encoding = getOption("encoding"), raw = FALSE
)
close(fidShade_Update)
# 2.2- read image file sequentially
Image_Format <- ENVI_type2bytes(HDR)
Shade_Format <- ENVI_type2bytes(HDR_Shade)
lenTot <- as.double(HDR$samples) * as.double(HDR$lines) * as.double(HDR$bands)
ImSizeGb <- (lenTot * Image_Format$Bytes) / (1024^3)
# maximum image size read at once. If image larger, then reads in multiple pieces
LimitSizeGb <- MaxRAM
if (ImSizeGb < LimitSizeGb) {
Lines_Per_Read <- HDR$lines
nbPieces <- 1
} else {
# nb of lines corresponding to LimitSizeGb
OneLine <- as.double(HDR$samples) * as.double(HDR$bands) * Image_Format$Bytes
Lines_Per_Read <- floor(LimitSizeGb * (1024^3) / OneLine)
# number of pieces to split the image into
nbPieces <- ceiling(HDR$lines / Lines_Per_Read)
}
# prepare for sequential processing: SeqRead_Image informs about byte location to read
SeqRead_Image <- where_to_read(HDR, nbPieces)
HDR_Shade <- read_ENVI_header(headerFpath)
SeqRead_Shade <- where_to_read(HDR_Shade, nbPieces)
Image_Format <- ENVI_type2bytes(HDR)
print("Perform PCA on image subsets and filter data")
# for each piece of image
for (i in 1:nbPieces) {
print(paste("PCA Piece #", i, "/", nbPieces))
# read image and mask data
nbLines <- SeqRead_Image$Lines_Per_Chunk[i]
ImgFormat <- "2D"
Image_Chunk <- read_image_subset(ImPath = Input_Image_File, HDR = HDR,
Line_Start = SeqRead_Image$Line_Start[i],Lines_To_Read = nbLines,
ImgFormat = ImgFormat)
ImgFormat <- "Shade"
if ((!Input_Mask_File == FALSE) & (!Input_Mask_File == "")) {
Shade_Chunk <- read_image_subset(ImPath = Input_Mask_File, HDR = HDR_Shade,
Line_Start = SeqRead_Image$Line_Start[i],Lines_To_Read = nbLines,
ImgFormat = ImgFormat)
} else {
Shade_Chunk <- ones(nbLines * HDR$samples, 1)
}
keepShade <- which(Shade_Chunk == 1)
Image_Chunk <- Image_Chunk[keepShade,]
# apply Continuum removal if needed
if (Continuum_Removal) {
Image_Chunk <- apply_continuum_removal(Image_Chunk, Spectral, nbCPU = nbCPU)
} else {
if (!length(Spectral$WaterVapor) == 0) {
Image_Chunk <- Image_Chunk[, -Spectral$WaterVapor]
}
}
# remove constant bands if needed
if (!length(Spectral$BandsNoVar) == 0) {
Image_Chunk <- Image_Chunk[, -Spectral$BandsNoVar]
}
# Apply PCA
if (TypePCA == "PCA" | TypePCA == "SPCA" | TypePCA == "MNF") {
Image_Chunk <- scale(Image_Chunk, PCA_model$center, PCA_model$scale) %*% PCA_model$rotation[, PCsel]
}
# get PCA of the group of line and rearrange the data to write it correctly in the output file
linetmp <- matrix(NA, ncol = ncol(Image_Chunk), nrow = (HDR$samples * nbLines))
if (length(keepShade) > 0) {
linetmp[keepShade, ] <- Image_Chunk
}
# find pixels which show extreme PC values
ElimList <- list()
for (pc in PCsel) {
el0 <- matrix(which(linetmp[, pc] < MinPC[pc] | linetmp[, pc] > MaxPC[pc]), ncol = 1)
if (length(el0) > 0) {
ElimList <- c(ElimList, el0)
}
}
elim <- unique(do.call("rbind", ElimList))
if (length(elim) > 0) {
Shade_Chunk[elim] <- 0
}
# files to write in
fidOUT <- file(
description = Shade_Update, open = "r+b", blocking = TRUE,
encoding = getOption("encoding"), raw = FALSE
)
if (!SeqRead_Shade$ReadByte_Start[i] == 1) {
seek(fidOUT, where = SeqRead_Shade$ReadByte_Start[i] - 1, origin = "start", rw = "write")
}
Shade_Chunk <- array(Shade_Chunk, c(nbLines, HDR_Shade$samples, 1))
Shade_Chunk <- aperm(Shade_Chunk, c(2, 3, 1))
writeBin(c(as.integer(Shade_Chunk)), fidOUT, size = 1, endian = .Platform$endian, useBytes = FALSE)
close(fidOUT)
}
gc()
return(Shade_Update)
}
#' writes an ENVI image corresponding to PCA
#'
#' @param Input_Image_File path for the raster on which PCA is applied
#' @param Input_Mask_File path for the corresponding mask
#' @param PCA_Path path for resulting PCA
#' @param PCA_model PCA model description
#' @param Spectral spectral information to be used in the image
#' @param Nb_PCs number of components kept in the resulting PCA raster
#' @param Continuum_Removal boolean. If TRUE continuum removal is performed.
#' @param TypePCA PCA, SPCA, NLPCA
#' @param nbCPU number of CPUs to process data
#' @param MaxRAM max RAM when initial image is read (in Gb)
#'
#' @return None
#' @export
write_PCA_raster <- function(Input_Image_File, Input_Mask_File, PCA_Path, PCA_model,
Spectral, Nb_PCs, Continuum_Removal, TypePCA, nbCPU = 1, MaxRAM = 0.25) {
if (is.character(Input_Mask_File) && (Input_Mask_File != "")) {
ShadeHDR <- get_HDR_name(Input_Mask_File)
HDR_Shade <- read_ENVI_header(ShadeHDR)
} else {
HDR_Shade <- FALSE
}
# 1- create hdr and binary files corresponding to PCA file
ImPathHDR <- get_HDR_name(Input_Image_File)
HDR <- read_ENVI_header(ImPathHDR)
HDR_PCA <- prepare_HDR_PCA(HDR, Nb_PCs, PCA_Path)
# apply PCA to the image
Image_Format <- ENVI_type2bytes(HDR)
PCA_Format <- ENVI_type2bytes(HDR_PCA)
if (typeof(HDR_Shade) == 'list') {
Shade_Format <- ENVI_type2bytes(HDR_Shade)
} else if (typeof(HDR_Shade) == 'logical'){
Shade_Format <- FALSE
}
# prepare for sequential processing: SeqRead_Image informs about byte location to read
nbPieces <- split_image(HDR, LimitSizeGb = MaxRAM)
SeqRead_Image <- where_to_read(HDR, nbPieces)
# if (typeof(Shade_Format) == 'list') SeqRead_Shade <- where_to_read(HDR_Shade, nbPieces)
SeqRead_PCA <- where_to_read(HDR_PCA, nbPieces)
# for each piece of image
print(paste('Apply PCA model to the full raster:',nbPieces,'chunks distributed on',nbCPU,'CPU'))
for (i in 1:nbPieces) {
message(paste('Computing PCA for image subset #',i,' / ',nbPieces))
# read image and mask data
nbLines <- SeqRead_Image$Lines_Per_Chunk[i]
ImgFormat <- "2D"
Image_Chunk <- read_image_subset(ImPath = Input_Image_File, HDR = HDR,
Line_Start = SeqRead_Image$Line_Start[i],Lines_To_Read = nbLines,
ImgFormat = ImgFormat)
if (typeof(HDR_Shade) == 'list') {
ImgFormat <- "Shade"
Shade_Chunk <- read_image_subset(ImPath = Input_Mask_File, HDR = HDR_Shade,
Line_Start = SeqRead_Image$Line_Start[i],Lines_To_Read = nbLines,
ImgFormat = ImgFormat)
keepShade <- which(Shade_Chunk == 1)
Image_Chunk <- Image_Chunk[keepShade, ]
} else {
# update 2024/04/24
keepShade <- matrix(seq_len(nrow(Image_Chunk)),ncol = 1)
# keepShade <- matrix(1,ncol = 1,nrow = nrow(Image_Chunk))
}
# apply Continuum removal if needed
if (Continuum_Removal) {
Image_Chunk <- apply_continuum_removal(Image_Chunk, Spectral, nbCPU = nbCPU)
## added June 5, 2019
if (length(Spectral$BandsNoVar) > 0) {
Image_Chunk <- Image_Chunk[, -Spectral$BandsNoVar]
}
} else {
# Eliminate water vapor
Image_Chunk <- Image_Chunk[, Spectral$Bands2Keep]
## added June 5, 2019
if (length(Spectral$BandsNoVar) > 0) {
Image_Chunk <- Image_Chunk[, -Spectral$BandsNoVar]
}
}
# Apply PCA
if (TypePCA == "PCA" | TypePCA == "SPCA" | TypePCA == "MNF") {
Image_Chunk <- scale(Image_Chunk, PCA_model$center, PCA_model$scale) %*% PCA_model$rotation[, 1:Nb_PCs]
}
# get PCA of the group of line and rearrange the data to write it correctly in the output file
PCA_Chunk <- matrix(NA, ncol = Nb_PCs, nrow = (HDR$samples * nbLines))
if (length(keepShade) > 0) {
PCA_Chunk[keepShade, ] <- Image_Chunk
}
# files to write in
fidOUT <- file(
description = PCA_Path, open = "r+b", blocking = TRUE,
encoding = getOption("encoding"), raw = FALSE
)
if (!SeqRead_PCA$ReadByte_Start[i] == 1) {
nbSkip <- (SeqRead_PCA$ReadByte_Start[i] - 1) * PCA_Format$Bytes
seek(fidOUT, where = nbSkip, origin = "start", rw = "write")
}
PCA_Chunk <- array(PCA_Chunk, c(nbLines, HDR_PCA$samples, HDR_PCA$bands))
PCA_Chunk <- aperm(PCA_Chunk, c(2, 3, 1))
writeBin(c(PCA_Chunk), fidOUT, size = PCA_Format$Bytes, endian = .Platform$endian, useBytes = FALSE)
close(fidOUT)
}
list <- ls()
rm(list = list)
gc()
return(invisible())
}
#' Function to perform PCA on a matrix
#'
#' @param X matrix to apply PCA on
#' @param type PCA (no rescale) or SPCA (rescale)
#'
#' @return list of PCA parameters (PCs from X, mean, eigenvectors and values)
#' @importFrom stats prcomp
#' @export
pca <- function(X, type) {
p <- ncol(X)
if (type == "SPCA") {
modPCA <- stats::prcomp(X, scale = TRUE)
} else if (type == "PCA") {
modPCA <- stats::prcomp(X, scale = FALSE)
}
return(modPCA)
}
#' defines the number of pixels per iteration based on a trade-off between image size and sample size per iteration
#'
#' @param ImNames Path and name of the images to be processed
#' @param nb_partitions number of iterations peformed to average diversity indices
#'
#' @return Pix_Per_Partition number of pixels per iteration
#' @export
define_pixels_per_iter <- function(ImNames, nb_partitions) {
Input_Image_File <- ImNames$Input_Image
Input_Mask_File <- ImNames$Mask_list
# define dimensions of the image
ImPathHDR <- get_HDR_name(Input_Image_File)
HDR <- read_ENVI_header(ImPathHDR)
Image_Format <- ENVI_type2bytes(HDR)
ipix <- as.double(HDR$lines)
jpix <- as.double(HDR$samples)
nbPixels <- ipix * jpix
# if shade mask, update number of pixels
if (is.character(Input_Mask_File) && (Input_Mask_File != "")) {
# read shade mask
fid <- file(
description = Input_Mask_File, open = "rb", blocking = TRUE,
encoding = getOption("encoding"), raw = FALSE
)
ShadeMask <- readBin(fid, integer(), n = nbPixels, size = 1)
close(fid)
ShadeMask <- aperm(array(ShadeMask, dim = c(jpix, ipix)))
# number of sunlit pixels
nbPixels_Sunlit <- length(which(ShadeMask == 1))
} else {
nbPixels_Sunlit <- nbPixels
}
# adjust the number of pixels per iteration
# trade-off between number of pixels and total pixel size
# maximum number of pixels to be used
Max_Pixel_Per_Iter <- min(c(nbPixels_Sunlit, 5000000))/nb_partitions
Max_Pixel_Per_Iter_Size <- nb_partitions * (Max_Pixel_Per_Iter * as.double(HDR$bands) * Image_Format$Bytes) / (1024^3)
# maximum amount of data (Gb) to be used
Max_Size_Per_Iter <- 0.3
# amount of data available after first mask
ImDataGb <- (nbPixels_Sunlit * as.double(HDR$bands) * Image_Format$Bytes) / (1024^3)
# if Max_Pixel_Per_Iter correspond to reasonable size (<Max_Size_Per_Iter)
if (Max_Pixel_Per_Iter_Size < Max_Size_Per_Iter) {
# if enough data
if (ImDataGb >= Max_Pixel_Per_Iter_Size) {
Pix_Per_Partition <- Max_Pixel_Per_Iter
} else if (ImDataGb < Max_Pixel_Per_Iter_Size) {
# define number of pixels corresponding to ImDataGb
Pix_Per_Partition <- floor(nbPixels_Sunlit / nb_partitions)
}
# if size too important, adjust number of pixels to match Max_Size_Per_Iter
} else if (Max_Pixel_Per_Iter_Size >= Max_Size_Per_Iter) {
Pix_Per_Partition <- floor((Max_Size_Per_Iter * (1024^3) / (as.double(HDR$bands) * Image_Format$Bytes)) / nb_partitions)
}
return(Pix_Per_Partition)
}
#' Check if principal components are properly selected as expected by the method
#'
#' @param Input_Image_File character. Path of the image to be processed
#' @param Output_Dir character. Path for output directory
#' @param PCA_Files character. Path of the PCA image
#' @param TypePCA character. Type of PCA: choose either "PCA" or "SPCA"
#' @param File_Open Boolean. Set to TRUE for file to open automatically
#'
#' @return Sel_PC
#' @importFrom utils file.edit
#' @importFrom tools file_path_sans_ext
#' @export
select_PCA_components <- function(Input_Image_File, Output_Dir, PCA_Files,
TypePCA = "SPCA", File_Open = FALSE) {
message("")
message("*********************************************************")
message("Please check following PCA file:")
print(PCA_Files)
message("*********************************************************")
Image_Name <- file_path_sans_ext(basename(Input_Image_File))
Output_Dir_Full <- file.path(Output_Dir, Image_Name, TypePCA)
Sel_PC <- file.path(Output_Dir_Full, "PCA","Selected_Components.txt")
message("list the principal components that will be used to estimate biodiversity in the file")
message("")
print(Sel_PC)
message("")
message("Then press ENTER")
if (!file.exists(Sel_PC)) {
file.create(Sel_PC)
}
if (File_Open == TRUE) {
# if (Sys.info()["sysname"]=='Windows'){
# file.edit(Sel_PC, title=basename(Sel_PC),editor = "internal")
# } else if (Sys.info()["sysname"]=='Linux'){
# file.edit(Sel_PC, title=basename(Sel_PC))
# } else {
# file.edit(Sel_PC, title=basename(Sel_PC))
# }
utils::file.edit(Sel_PC, title=basename(Sel_PC))
}
message("*********************************************************")
message("")
readline(prompt = "")
return(Sel_PC)
}
#' this function performs rescaling and
#' either defines min and max from each feature in a data set,
#' or applies the transformation based on a previously defined min and max
#'
#' @param x numeric. data matrix
#' @param mode character. 'define' or 'apply'
#' @param MinX numeric. if 'apply'
#' @param MaxX numeric. if 'apply'
#'
#' @return rescaled data, min and max values
#' @export
minmax <- function(x, mode = "define", MinX = FALSE, MaxX = FALSE) {
## Function to
if (mode == "define") {
MinX <- min(x)
MaxX <- max(x)
(x - MinX) / (MaxX - MinX)
} else if (mode == "apply") {
(x - MinX) / (MaxX - MinX)
}
my_list <- list("data" = x, "MinX" = MinX, "MaxX" = MaxX)
return(my_list)
}
#' this function
#'
#' @param X numeric. data matrix
#' @param coordPix numeric.
#'
#' @return rescaled data, min and max values
#' @export
# If coordPix is not NULL, X and coordPix are exepected to have the same order,
# i.e. coordPix[1, ] corresponds to X[1, ], coordPix[2, ] corresponds to X[2, ], ...
noise <- function(X, coordPix=NULL){
if(is.null(coordPix)){
# if(matlab::ndims(X)!=3)
if(length(dim(X))!=3)
stop('X is expected to be a 3D array: y,x,band for row,col,depth.')
Xdim <- dim(X)
# Shift x/y difference
Y = ((X[2:Xdim[1],2:Xdim[2],]-X[1:(Xdim[1]-1),2:Xdim[2],]) +
(X[2:Xdim[1],2:Xdim[2],]-X[2:Xdim[1],1:(Xdim[2]-1),]))/2
}else{
if(!all(c('Kind', 'id') %in% colnames(coordPix)))
stop("Columns 'Kind' and 'id' are missing in coordPix.")
kernel = matrix(0, 3, 3)
kernel[c(5, 6, 8)]=c(1, -1/2, -1/2)
if(!identical(order(coordPix$id), 1:nrow(coordPix)))
stop("coordPix is not ordered along column 'id'. Order coordPix as well as X before trying again.")
Y=0
for(ik in which(kernel!=0)){
Y = Y + X[coordPix$Kind==ik,]*kernel[ik]
}
}
return(Y)
}
#' Function to perform MNF
#'
#' @param X numeric. matrix to apply MNF on
#' @param coordPix dataframe to compute noise, cf get_random_subset_from_image
#' @param retx boolean.
#'
#' @return results of MNF applied on matrix
#' @importFrom stats cov
#' @export
# used in noise
# TODO: faire 2 fonctions: mnf et mnf.subset, de même pour noise, noise.subset
mnf <- function(X, coordPix=NULL, retx=TRUE){
if(any(is.na(X))){
stop('Pixels with NAs found in X. Remove NA pixels before trying again.')
}
if(length(dim(X))>3)
stop('X has more than 3 dimensions.')
nz <- noise(X, coordPix)
Xdim <- dim(X)
if(is.null(coordPix) && length(dim(X))>2){
X <- matrix(X[1:(Xdim[1]-1), 1:(Xdim[2]-1),], nrow = Xdim[1]*Xdim[2])
nz <- matrix(nz, nrow = Xdim[1]*Xdim[2])
}
Xc = scale(X, center = T, scale = F)
covNoise <- stats::cov(nz)
covXc <- stats::cov(Xc)
eig <- eigen(solve(covNoise)%*%covXc)
colnames(eig$vectors) = paste0('PC', 1:ncol(eig$vectors))
modMNF <- list(sdev = sqrt(eig$values), rotation = eig$vectors,
center = colMeans(X), scale = FALSE)
attr(modMNF, 'class') <- 'prcomp'
# eig_pairs = tofsims:::EigenDecompose(covXc, covNoise, 1, nrow(covNoise))
# vord = order(Re(eig_pairs$eigval), decreasing = T)
# eig_pairs$eigval = Re(eig_pairs$eigval)[vord]
# eig_pairs$eigvec = Re(eig_pairs$eigvec[, vord])
# modMNF = list(rotation=eig_pairs$eigvec,
# sdev=sqrt(eig_pairs$eigval),
# center=colMeans(X),
# scale=FALSE)
if(retx==T)
modMNF$x= array(Xc %*% modMNF$rotation, dim = Xdim)
return(modMNF)
}
# prepares PCA file and header
#
# @param HDR list. header of the image file used as template
# @param Nb_PCs numeric. number of PCs to be written
# @param PCA_Path character. path for PCA file
# @param window_size numeric. window size
#
# @return HDR_PCA
prepare_HDR_PCA <- function(HDR, Nb_PCs, PCA_Path){
HDR_PCA <- HDR
HDR_PCA$bands <- Nb_PCs
HDR_PCA$`data type` <- 4
HDR_PCA$interleave <- 'BIL'
HDR_PCA$`default bands` <- NULL
HDR_PCA$`wavelength units` <- NULL
HDR_PCA$`z plot titles` <- NULL
HDR_PCA$`data gain values` <- NULL
HDR_PCA$`file type` <- NULL
HDR_PCA$`band names` <- paste('PC', 1:Nb_PCs, collapse = ", ")
HDR_PCA$wavelength <- NULL
HDR_PCA$fwhm <- NULL
HDR_PCA$resolution <- NULL
HDR_PCA$bandwidth <- NULL
HDR_PCA$purpose <- NULL
HDR_PCA$`default stretch` <- NULL
HDR_PCA$`byte order` <- get_byte_order()
headerFpath <- paste(PCA_Path, ".hdr", sep = "")
write_ENVI_header(HDR_PCA, headerFpath)
# create updated shade mask
fidPCA <- file(
description = PCA_Path, open = "wb", blocking = TRUE,
encoding = getOption("encoding"), raw = FALSE
)
close(fidPCA)
return(HDR_PCA)
}