filt3d: Filter Image (Matrix) with Mask via Convolution
In imagefx: Extract Features from Images
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Apply a filter mask to smooth, sharpen, shift, or otherwise modify an image (matrix)
Usage
1
filt3d(x, mask)
Arguments
x
Image (matrix) to be filtered
mask
Filter mask (matrix) with same dimensions as x
Details
x and mask are convolved in the frequency domain via multiplication and returned to the spatial domain using the fast Fourier transform.
Value
Filtered matrix with same dimensions as x.
Author(s)
Alex J.C. Witsil
See Also
Examples
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##########
## EG 1 ##
##########
## example of a low pass filter
## generate test data
data <- matrix(0,nrow=256,ncol=256)
box.width = 100
box.height = 100
box.mid=c(nrow(data)/2,ncol(data)/2)
## define where the box indices are
box.row.inds <- (box.mid[1]-box.width/2):(box.mid[1]+box.width/2)
box.col.inds <- (box.mid[2]-box.height/2):(box.mid[2]+box.height/2)
## create the box in the data matrix
data[box.row.inds,box.col.inds] = 1
## define the sigma in the low pass Gaussian filter
sig=5
## create a low pass Gaussian filter
gaus <- build.gaus(nrow(data),ncol(data),sig)
## filter the data matrix with the Gaussian filter mask
data.lp <- filt3d(data,gaus)
## PLOTTING EG1 ##
dev.new()
close.screen(all.screens=TRUE)
split.screen(c(1,3))
## set up some grid lines
grid.xs <- 1:nrow(data)
grid.ys <- 1:ncol(data)
screen(1)
image(grid.xs,grid.ys,data,col=gray.colors(200),useRaster=TRUE,main="Data")
screen(2)
image(grid.xs,grid.ys,gaus,col=gray.colors(200),useRaster=TRUE,main="Low Pass Gaussian Mask")
screen(3)
image(grid.xs,grid.ys,data.lp,col=gray.colors(200),useRaster=TRUE,main='Filtered Data')
## close screens
close.screen(all.screens=TRUE)
##########
## EG 2 ##
##########
## example of a high pass filter
## generate test data
data <- matrix(0,nrow=256,ncol=256)
box.width = 100
box.height = 100
box.mid=c(nrow(data)/2,ncol(data)/2)
## define where the box indices are
box.row.inds <- (box.mid[1]-box.width/2):(box.mid[1]+box.width/2)
box.col.inds <- (box.mid[2]-box.height/2):(box.mid[2]+box.height/2)
## create the box in the data matrix
data[box.row.inds,box.col.inds] = 1
## find the middle of the data matrix
mid <- c(nrow(data)/2,ncol(data)/2)
## create a 5-point Laplacian high pass filter
lap <- matrix(0,nrow=nrow(data),ncol=ncol(data))
lap[(mid[1]-1):(mid[1]+1),mid[2]] = c(1,-4,1)
lap[mid[1],(mid[2]-1):(mid[2]+1)] = c(1,-4,1)
## perform high pass filter on the data using the Laplacian mask
data.hp <- filt3d(data,lap)
## PLOTTING EG2 ##
## set up some grid lines
grid.xs <- 1:nrow(data)
grid.ys <- 1:ncol(data)
dev.new()
close.screen(all.screens=TRUE)
split.screen(c(1,3))
screen(1)
image(grid.xs,grid.ys,data,col=gray.colors(200),useRaster=TRUE,main="Data")
screen(2)
image(grid.xs,grid.ys,lap,col=gray.colors(200),useRaster=TRUE,main="High Pass Laplacian Mask")
screen(3)
image(grid.xs,grid.ys,data.hp,col=gray.colors(200),useRaster=TRUE,main='Filtered Data')
## close screens
close.screen(all.screens=TRUE)
##########
## EG 3 ##
##########
## example of a shift transform filter
## generate test data
data <- matrix(0,nrow=256,ncol=256)
box.width = 100
box.height = 100
box.mid=c(nrow(data)/2,ncol(data)/2)
## define where the box indices are
box.row.inds <- (box.mid[1]-box.width/2):(box.mid[1]+box.width/2)
box.col.inds <- (box.mid[2]-box.height/2):(box.mid[2]+box.height/2)
## create the box in the data matrix
data[box.row.inds,box.col.inds] = 1
## create a delta function at some (x,y) location
delta.x = 80
delta.y = 180
delta <- matrix(0,nrow=nrow(data),ncol=ncol(data))
delta[delta.x,delta.y] = 1
## perform the shift transform filter
data.shift <- filt3d(data,delta)
## PLOTTING EG3 ##
## set up some grid lines
grid.xs <- 1:nrow(data)
grid.ys <- 1:ncol(data)
dev.new()
close.screen(all.screens=TRUE)
split.screen(c(1,3))
screen(1)
image(grid.xs,grid.ys,data,col=gray.colors(200),useRaster=TRUE,main="Data")
screen(2)
image(grid.xs,grid.ys,delta,col=gray.colors(200),useRaster=TRUE,main="Shift Delta Mask")
screen(3)
image(grid.xs,grid.ys,data.shift,col=gray.colors(200),useRaster=TRUE,main='Filtered Data')
## close screens
close.screen(all.screens=TRUE)
imagefx documentation built on Feb. 14, 2020, 1:07 a.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Apply a filter mask to smooth, sharpen, shift, or otherwise modify an image (matrix)
Usage
1 | filt3d(x, mask)
|
Arguments
x |
Image (matrix) to be filtered |
mask |
Filter mask (matrix) with same dimensions as |
Details
x and mask are convolved in the frequency domain via multiplication and returned to the spatial domain using the fast Fourier transform.
Value
Filtered matrix with same dimensions as x.
Author(s)
Alex J.C. Witsil
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | ##########
## EG 1 ##
##########
## example of a low pass filter
## generate test data
data <- matrix(0,nrow=256,ncol=256)
box.width = 100
box.height = 100
box.mid=c(nrow(data)/2,ncol(data)/2)
## define where the box indices are
box.row.inds <- (box.mid[1]-box.width/2):(box.mid[1]+box.width/2)
box.col.inds <- (box.mid[2]-box.height/2):(box.mid[2]+box.height/2)
## create the box in the data matrix
data[box.row.inds,box.col.inds] = 1
## define the sigma in the low pass Gaussian filter
sig=5
## create a low pass Gaussian filter
gaus <- build.gaus(nrow(data),ncol(data),sig)
## filter the data matrix with the Gaussian filter mask
data.lp <- filt3d(data,gaus)
## PLOTTING EG1 ##
dev.new()
close.screen(all.screens=TRUE)
split.screen(c(1,3))
## set up some grid lines
grid.xs <- 1:nrow(data)
grid.ys <- 1:ncol(data)
screen(1)
image(grid.xs,grid.ys,data,col=gray.colors(200),useRaster=TRUE,main="Data")
screen(2)
image(grid.xs,grid.ys,gaus,col=gray.colors(200),useRaster=TRUE,main="Low Pass Gaussian Mask")
screen(3)
image(grid.xs,grid.ys,data.lp,col=gray.colors(200),useRaster=TRUE,main='Filtered Data')
## close screens
close.screen(all.screens=TRUE)
##########
## EG 2 ##
##########
## example of a high pass filter
## generate test data
data <- matrix(0,nrow=256,ncol=256)
box.width = 100
box.height = 100
box.mid=c(nrow(data)/2,ncol(data)/2)
## define where the box indices are
box.row.inds <- (box.mid[1]-box.width/2):(box.mid[1]+box.width/2)
box.col.inds <- (box.mid[2]-box.height/2):(box.mid[2]+box.height/2)
## create the box in the data matrix
data[box.row.inds,box.col.inds] = 1
## find the middle of the data matrix
mid <- c(nrow(data)/2,ncol(data)/2)
## create a 5-point Laplacian high pass filter
lap <- matrix(0,nrow=nrow(data),ncol=ncol(data))
lap[(mid[1]-1):(mid[1]+1),mid[2]] = c(1,-4,1)
lap[mid[1],(mid[2]-1):(mid[2]+1)] = c(1,-4,1)
## perform high pass filter on the data using the Laplacian mask
data.hp <- filt3d(data,lap)
## PLOTTING EG2 ##
## set up some grid lines
grid.xs <- 1:nrow(data)
grid.ys <- 1:ncol(data)
dev.new()
close.screen(all.screens=TRUE)
split.screen(c(1,3))
screen(1)
image(grid.xs,grid.ys,data,col=gray.colors(200),useRaster=TRUE,main="Data")
screen(2)
image(grid.xs,grid.ys,lap,col=gray.colors(200),useRaster=TRUE,main="High Pass Laplacian Mask")
screen(3)
image(grid.xs,grid.ys,data.hp,col=gray.colors(200),useRaster=TRUE,main='Filtered Data')
## close screens
close.screen(all.screens=TRUE)
##########
## EG 3 ##
##########
## example of a shift transform filter
## generate test data
data <- matrix(0,nrow=256,ncol=256)
box.width = 100
box.height = 100
box.mid=c(nrow(data)/2,ncol(data)/2)
## define where the box indices are
box.row.inds <- (box.mid[1]-box.width/2):(box.mid[1]+box.width/2)
box.col.inds <- (box.mid[2]-box.height/2):(box.mid[2]+box.height/2)
## create the box in the data matrix
data[box.row.inds,box.col.inds] = 1
## create a delta function at some (x,y) location
delta.x = 80
delta.y = 180
delta <- matrix(0,nrow=nrow(data),ncol=ncol(data))
delta[delta.x,delta.y] = 1
## perform the shift transform filter
data.shift <- filt3d(data,delta)
## PLOTTING EG3 ##
## set up some grid lines
grid.xs <- 1:nrow(data)
grid.ys <- 1:ncol(data)
dev.new()
close.screen(all.screens=TRUE)
split.screen(c(1,3))
screen(1)
image(grid.xs,grid.ys,data,col=gray.colors(200),useRaster=TRUE,main="Data")
screen(2)
image(grid.xs,grid.ys,delta,col=gray.colors(200),useRaster=TRUE,main="Shift Delta Mask")
screen(3)
image(grid.xs,grid.ys,data.shift,col=gray.colors(200),useRaster=TRUE,main='Filtered Data')
## close screens
close.screen(all.screens=TRUE)
|
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