I met this error when run Weiner deconv example code.

[bemoregt]

Hi, @jfsantos @staticfloat @simonster @rob-luke @giordano

I met this error when run Weiner deconv example code.

ERROR: LoadError: MethodError: no method matching data(::Array{Gray{Normed{UInt8,8}},2})
Closest candidates are:
  data(::AxisArray) at /Users/elx/.julia/packages/ImageAxes/l0FE8/src/ImageAxes.jl:166
  data(::ImageMeta) at /Users/elx/.julia/packages/ImageMetadata/W26Yo/src/ImageMetadata.jl:202

What's wrong to me ?

Thanks.

[giordano]

Could you please provide a Minimal, Complete, and Verifiable example to reproduce the error? There is not much information in the error message you provided

[giordano]

Ok, now I see: the example is horribly out of date. Please give me a few days to find out how to reproduce it again, there have quite a lot of changes in the packages used in the example

[giordano]

Ok, it took me a lot less:

using Images, TestImages, Deconvolution, ImageView, Random, FFTW

Random.seed!(42)

# Open the test image
img = Float64.(testimage("cameraman"))
# Create the blurring kernel in frequency domain
x = hcat(ntuple(x -> collect((1:512) .- 257), 512)...)
k = 0.001
blurring_ft = @. exp(-k*(x ^ 2 + x ^ 2)^(5//6))
# Create additive noise
noise = rand(Float64, size(img))
# Fourier transform of the blurred image, with additive noise
blurred_img_ft = fftshift(blurring_ft) .* fft(img) + fft(noise)
# Get the blurred image from its Fourier transform
blurred_img = real(ifft(blurred_img_ft))
# Get the blurring kernel in the space domain
blurring = ifft(fftshift(blurring_ft))
# Polish the image with Deconvolution deconvolution
polished = wiener(blurred_img, img, noise, blurring)

# Wiener deconvolution works also when you don't have the real image and noise,
# that is the most common and useful case.  This happens because the Wiener
# filter only cares about the power spectrum of the signal and the noise, so you
# don't need to have the exact signal and noise but something with a similar
# power spectrum.
img2 = Float64.(testimage("livingroom")) # Load another image
noise2 = rand(Float64, size(img)) # Create another additive noise
# Polish the image with Deconvolution deconvolution
polished2 = wiener(blurred_img, img2, noise2, blurring)

out = ((vcat(hcat(img, ones(512, 20), blurred_img),
             ones(20, 1024+20),
             hcat(polished, ones(512, 20), polished2))))
save("wiener-cameraman.jpg", Images.clamp01nan.(out))

Now I get this:

Which is the same output shown in the documentation:

[bemoregt]

Hi, @giordano

Ok, I'll try that one. and

Report again ~

Thanks a lot !!