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main.R
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main.R
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# SETUP VARIABLES
inputFile <- 'inputs/obama.jpg'
kernelSizes <- c(3, 5, 7) # NEEDS to be uneven!
options(scipen = 999)
# PROGRAM START
library('EBImage')
# Load own scripts
source('gaussianNoise.R')
source('saltPepperNoise.R')
source('meanFilter.R')
source('medianFilter.R')
source('gaussianFilter.R')
source('meanSquareError.R')
# Load input image
loadImage <- function(path) {
img <- readImage(path)
img <- channel(img, 'gray')
imgData <- as.array(img)
imgData
}
# Util file saving function
saveImageFile <- function(imgData, folderPath, fileName) {
fileName <- paste(fileName, '.jpg', sep = '')
writeImage(outImg, file.path(folderPath, fileName))
}
imgData <- loadImage(inputFile)
# GAUSSIAN NOISE
gaussianNoiseVals <- seq(from = 0, to = 0.5, by = 0.05)
for (noiseVal in gaussianNoiseVals) {
outImg <- gaussianNoise(imgData, noiseVal)
saveImageFile(outImg,
'outputs/obama/gaussian-noise',
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
# SALT-AND-PEPPER NOISE
saltPepperNoiseVals <- seq(from = 0, to = 0.3, by = 0.05)
for (noiseVal in saltPepperNoiseVals) {
outImg <- saltPepperNoise(imgData, noiseVal)
saveImageFile(outImg,
'outputs/obama/salt-and-pepper-noise',
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
MSETable <- data.frame(matrix(0, ncol = 0, nrow = length(gaussianNoiseVals)))
row.names(MSETable) <- format(gaussianNoiseVals, nsmall = 2)
for (kernelSize in kernelSizes) {
# MEAN FILTER
for (noiseVal in gaussianNoiseVals) {
filePath <- paste('outputs/obama/gaussian-noise/noise-',
format(noiseVal, nsmall = 2), '.jpg', sep = '')
imgData <- loadImage(filePath)
outImg <- meanFilter(imgData, kernelSize)
saveImageFile(outImg,
paste('outputs/obama/mean-filter/gaussian-noise/kernel-size=', kernelSize, sep = ''),
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
for (noiseVal in saltPepperNoiseVals) {
filePath <- paste('outputs/obama/salt-and-pepper-noise/noise-',
format(noiseVal, nsmall = 2), '.jpg', sep = '')
imgData <- loadImage(filePath)
outImg <- meanFilter(imgData, kernelSize)
saveImageFile(outImg,
paste('outputs/obama/mean-filter/salt-and-pepper-noise/kernel-size=', kernelSize, sep = ''),
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
# MEDIAN FILTER
for (noiseVal in gaussianNoiseVals) {
filePath <- paste('outputs/obama/gaussian-noise/noise-',
format(noiseVal, nsmall = 2), '.jpg', sep = '')
imgData <- loadImage(filePath)
outImg <- medianFilter(imgData, kernelSize)
saveImageFile(outImg,
paste('outputs/obama/median-filter/gaussian-noise/kernel-size=', kernelSize, sep = ''),
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
for (noiseVal in saltPepperNoiseVals) {
filePath <- paste('outputs/obama/salt-and-pepper-noise/noise-',
format(noiseVal, nsmall = 2), '.jpg', sep = '')
imgData <- loadImage(filePath)
outImg <- medianFilter(imgData, kernelSize)
saveImageFile(outImg,
paste('outputs/obama/median-filter/salt-and-pepper-noise/kernel-size=', kernelSize, sep = ''),
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
# GAUSSIAN FILTER
for (noiseVal in gaussianNoiseVals) {
filePath <- paste('outputs/obama/gaussian-noise/noise-',
format(noiseVal, nsmall = 2), '.jpg', sep = '')
imgData <- loadImage(filePath)
outImg <- gaussianFilter(imgData, kernelSize)
saveImageFile(outImg,
paste('outputs/obama/gaussian-filter/gaussian-noise/kernel-size=', kernelSize, sep = ''),
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
for (noiseVal in saltPepperNoiseVals) {
filePath <- paste('outputs/obama/salt-and-pepper-noise/noise-',
format(noiseVal, nsmall = 2), '.jpg', sep = '')
imgData <- loadImage(filePath)
outImg <- gaussianFilter(imgData, kernelSize)
saveImageFile(outImg,
paste('outputs/obama/gaussian-filter/salt-and-pepper-noise/kernel-size=', kernelSize, sep = ''),
paste('noise-', format(noiseVal, nsmall = 2), sep = ''))
}
# MEAN SQUARE ERROR (MSE) ANALYSIS OF FILTERED GAUSSIAN NOISE
meanColumnName <- paste('meanFilter-', kernelSize, 'x', kernelSize, sep = '')
MSETable[meanColumnName] = NA
medianColumnName <- paste('medianFilter-', kernelSize, 'x', kernelSize, sep = '')
MSETable[medianColumnName] = NA
gaussianColumnName <- paste('gaussianFilter-', kernelSize, 'x', kernelSize, sep = '')
MSETable[gaussianColumnName] = NA
for (noiseVal in gaussianNoiseVals) {
originalImageData <- loadImage(inputFile)
# Mean filter
filteredImageData <- loadImage(paste('outputs/obama/mean-filter/gaussian-noise/kernel-size=',
kernelSize, '/', 'noise-', format(noiseVal, nsmall = 2),
'.jpg', sep = ''))
MSETable[format(noiseVal, nsmall = 2), meanColumnName] <- meanSquareError(originalImageData, filteredImageData)
# Median filter
filteredImageData <- loadImage(paste('outputs/obama/median-filter/gaussian-noise/kernel-size=',
kernelSize, '/', 'noise-', format(noiseVal, nsmall = 2),
'.jpg', sep = ''))
MSETable[format(noiseVal, nsmall = 2), medianColumnName] <- meanSquareError(originalImageData, filteredImageData)
# Gaussian filter
filteredImageData <- loadImage(paste('outputs/obama/gaussian-filter/gaussian-noise/kernel-size=',
kernelSize, '/', 'noise-', format(noiseVal, nsmall = 2),
'.jpg', sep = ''))
MSETable[format(noiseVal, nsmall = 2), gaussianColumnName] <- meanSquareError(originalImageData, filteredImageData)
}
}