How can I do the process …
This is my code.
I’m working in Juno.
using ImageContrastAdjustment, Images, ImageFiltering, TestImages, ColorTypes, HistogramThresholding
const ICA = ImageContrastAdjustment
function example()
img = testimage(“circulo”)
# Grayscale image.
imgg = Gray.(img)
# Linear contrast stretching.
imggₗ = ICA.adjust_histogram(ICA.LinearStretching(), imgg)
# Histogram Equalization.
imggₑ = ICA.adjust_histogram(ICA.Equalization(), imgg)
#=
admiminsion and subtracion
=#
R₁ = float.(imggₗ) + float.(imggₑ)
R₂ = R₁ - float.(imggₑ)
# imggₗ + imggₗ + imggₑ.
R₃ = R₁ + R₂
# Applying a minimum filter.
window_size = (-1:1, -1:1)
imgmin = mapwindow(minimum, R₃, window_size)
# divide el intervalo del valor más pequeño al más grande en imgmin en 256 bin
edges, counts = ICA.build_histogram(imgmin, 256, minval = minimum(imgmin), maxval = maximum(imgmin))
#=
La matriz `count` almacena en el índice 0 las frecuencias que estaban por debajo de
primer borde del cubo. Ya que estamos buscando un umbral sobre el intervalo
divididos por `edges` necesitamos descartar la primera bandeja en` count`
de modo que las dimensiones de `edge` y` count` coincidan.
=#
t = find_threshold(Otsu(), counts[1:end], edges)
# Aplica el umbral a imgmin y checo el resultado.
img₀₁ = zeros(Gray{Bool}, axes(imgmin))
for i in CartesianIndices(img)
img₀₁[i] = imgmin[i] < t ? 0 : 1
end
display(img₀₁)
end
example()
as a result I get this
I need the white part to turn black and the black part to turn white.
this is my original image…
Possibly exchange the 0 and the 1 in
for i in CartesianIndices(img)
img₀₁[i] = imgmin[i] < t ? 0 : 1
end
Or just use
for i in CartesianIndices(img)
img₀₁[i] = imgmin[i] < t
end
How about imgmin .< t or .!(imgmin .< t) alt. imgmin .≮ t to invert
1 Like
How could you define the range in admission and subtraction over an image?
enter (0-255) without going past 255 or reducing me from 0.
my code is something like this …
#=
admiminsion and subtracion
=#
R₁ = float.(imggₗ) + float.(imggₑ)
R₂ = R₁ - float.(imggₑ)
# imggₗ + imggₗ + imggₑ.
R₃ = R₁ + R₂
but there the error is that I’m taking float values …
how could I do it to establish the range (0-255).
help me…!
if … without using float … because with float it is an error to the procedure, and so with float you can not enter whole values to put the range (0-255).
this correct
#=
admiminsion and subtracion
=#
R₁ = clamp(0,255).(imggₗ) + clamp(0,255).(imggₑ)
R₂ = R₁ - clamp(0,255).(imggₑ)
# imggₗ + imggₗ + imggₑ.
R₃ = R₁ + R₂
clamp does not know how work directly on the image pixel type you use.
You would have to overload clamp or translate to a numeric array, clamp that, and translate back to an image array. Note that clamping to 0…255 may mean clamping values 0.0…1.0 or some other range depending upon the representation of your image’s colorant/greyscale.
this is the online help for clamp
help?> clamp
search: clamp clamp!
clamp(x, lo, hi)
Return x if lo <= x <= hi. If x > hi, return hi. If x < lo, return lo. Arguments are promoted to a common
type.
Examples
≡≡≡≡≡≡≡≡≡≡
julia> clamp.([pi, 1.0, big(10.)], 2., 9.)
3-element Array{BigFloat,1}:
3.141592653589793238462643383279502884197169399375105820974944592307816406286198
2.0
9.0
julia> clamp.([11,8,5],10,6) # an example where lo > hi
3-element Array{Int64,1}:
6
6
10
1 Like
and how can I use cleap in my code … using the range (0-255) without overflowing.
Also
you can just write
stretch = ICA.adjust_histogram(ICA.LinearStretching(), imgg)
adjust = ICA.adjust_histogram(ICA.Equalization(), imgg)
r3 = 2*stretch + adjust
This works.
using Images, ImageView, ImageContrastAdjustment, HistogramThresholding, TestImages
const ICA = ImageContrastAdjustment
img = testimage("lake_color") # change this to the image file you are using
# Grayscale image.
imgg = Gray.(img)
# Linear contrast stretching.
imggₗ = ICA.adjust_histogram(ICA.LinearStretching(), imgg)
# Histogram Equalization.
imggₑ = ICA.adjust_histogram(ICA.Equalization(), imgg)
#=
admiminsion and subtracion
=#
R₃ = imggₗ + imggₗ + imggₑ
# Applying a minimum filter
window_size = (-1:1, -1:1)
imgmin = mapwindow(minimum, R₃, window_size)
# divide el intervalo del valor más pequeño al más grande en imgmin en 256 bin
edges, counts = ICA.build_histogram(imgmin, 256,
minval = minimum(imgmin), maxval = maximum(imgmin))
#=
La matriz `count` almacena en el índice 0 las frecuencias que estaban por debajo de
primer borde del cubo. Ya que estamos buscando un umbral sobre el intervalo
divididos por `edges` necesitamos descartar la primera bandeja en` count`
de modo que las dimensiones de `edge` y` count` coincidan.
=#
t = find_threshold(Otsu(), counts[1:end], edges)
# Aplica el umbral a imgmin y checo el resultado.
img₀₁ = zeros(Gray{Bool}, axes(imgmin))
for i in CartesianIndices(img)
img₀₁[i] = imgmin[i] < t
end
imshow(img₀₁)
you can put it in a function
using Images, ImageView, ImageContrastAdjustment, HistogramThresholding, TestImages
const ICA = ImageContrastAdjustment
function example()
img = testimage("lake_color") # change this to the image file you are using
# Grayscale image.
imgg = Gray.(img)
# Linear contrast stretching.
imggₗ = ICA.adjust_histogram(ICA.LinearStretching(), imgg)
# Histogram Equalization.
imggₑ = ICA.adjust_histogram(ICA.Equalization(), imgg)
#=
admiminsion and subtracion
=#
R₃ = imggₗ + imggₗ + imggₑ
# Applying a minimum filter
window_size = (-1:1, -1:1)
imgmin = mapwindow(minimum, R₃, window_size)
# divide el intervalo del valor más pequeño al más grande en imgmin en 256 bin
edges, counts = ICA.build_histogram(imgmin, 256,
minval = minimum(imgmin), maxval = maximum(imgmin))
#=
La matriz `count` almacena en el índice 0 las frecuencias que estaban por debajo de
primer borde del cubo. Ya que estamos buscando un umbral sobre el intervalo
divididos por `edges` necesitamos descartar la primera bandeja en` count`
de modo que las dimensiones de `edge` y` count` coincidan.
=#
t = find_threshold(Otsu(), counts[1:end], edges)
# Aplica el umbral a imgmin y checo el resultado.
img₀₁ = zeros(Gray{Bool}, axes(imgmin))
for i in CartesianIndices(img)
img₀₁[i] = imgmin[i] < t
end
imshow(img₀₁)
end
example()
Sorry friend, a question …
(Maybe it sounds silly)
and where are you establishing the range between (0-255) of the process?
Why do you think that the Gray pixel values can be outside of that range?
julia> typeof(R₃)
Array{Gray{Normed{UInt8,8}},2}
The UInt8 there means that, internally, pixel values can range from typemin(UInt8) to typemax(UInt8)
julia> Int(typemin(UInt8)), Int(typemax(UInt8))
(0, 255)
The Normed means the values are normalized floats in the range [0.0, 1.0].
Maybe you have some unusual image format or you use some other Grayer or the the image may have been tampered. You can force the matter this way
function clampgray(im::AbstractArray)
gray = Gray.(im)
T = typeof(gray)
gray = T(map(x->clamp(x, 0.0, 1.0), gray))
return gray
end
# and replace
# imgg = Gray.(img)
# with
imgg = clampgray(img)
if you are really concerned … or just want to see 255 (UInt(255) == 0xff):
clampgraypixel(pixel) =
N0f8(clamp(x.val.i, 0x00, 0xff) / 0xff)
function clampgray(im::AbstractArray)
gray = Gray.(im)
T = typeof(gray)
gray = T(map(pixel->clampgraypixel(pixel), imgg)))
return gray
end
