computational-imaging / threedeconv.jl Goto Github PK
View Code? Open in Web Editor NEWA convex 3D deconvolution algorithm for low photon count fluorescence imaging
License: Other
A convex 3D deconvolution algorithm for low photon count fluorescence imaging
License: Other
When I hit the key ] in the Julia REPL to ender the Pkg REPL-mode, and run
pkg> add https://github.com/computational-imaging/ThreeDeconv.jl.git
There is an error:
ERROR: could not find project file in package at https://github.com/computational-imaging/ThreeDeconv.jl.git
I am not familiar with Julia language, does this affect use?
@JuliaRegistrator register()
When I run ThreeDeconv.deconvolve
in the first time, it work well.
But at second time, error occur:
CUFFTError(code 2, cuFFT failed to allocate GPU or CPU memory)
Then I run same command against, no error!!! I am confused why GC.gc() don't release GPU memory.
I run Julia 1.2 in Fedora 30 with a NVIDIA 2080Ti.
What should I do?
Please update so this works on current Julia 1.6. Failure:
Hit the key ] in the Julia REPL to ender the Pkg REPL-mode, and run
pkg> add https://github.com/computational-imaging/ThreeDeconv.jl.git
resulted in error ... for package [CUDA]
Screen shot attached.
First, Thanks for this awesome package! I am really enjoy it using for widefield images. But i have a little question. Is this package suitable to deconvolute confocal images in Z-stack mode? If the answer is "yes", i should to have psf image generated with depth same as my image depth, and deconvolute each image in "for" cycle?
Thanks for the package! Excited to try it out, but calling deconvolve
causes the following error (using the example code).
Julia 1.5.3 and up-to-date packages with CUDA.
MethodError: no method matching zero(::Type{CuArray{Float32,N} where N})
Closest candidates are:
zero(!Matched::Type{Pkg.Resolve.FieldValue}) at /home/.../Src/julia/julia-1.5.3/usr/share/julia/stdlib/v1.5/Pkg/src/Resolve/fieldvalues.jl:38
zero(!Matched::Type{Dates.Time}) at /home/.../Src/julia/julia-1.5.3/usr/share/julia/stdlib/v1.5/Dates/src/types.jl:406
zero(!Matched::Type{Dates.DateTime}) at /home/.../Src/julia/julia-1.5.3/usr/share/julia/stdlib/v1.5/Dates/src/types.jl:404
...
Stacktrace:
[1] zeros(::Type{CuArray{Float32,N} where N}, ::Tuple{Int64,Int64,Int64}) at ./array.jl:526
[2] ThreeDeconv.ADMMstate(::Tuple{Int64,Int64,Int64}, ::Array{Tuple,1}) at /home/.../.julia/packages/ThreeDeconv/vtLlk/src/deconvolution/admm.jl:78
[3] initialize_optimizer(::ThreeDeconv.ADMM, ::ThreeDeconv.StackedLinearOperator, ::ThreeDeconv.var"#58#65"{CuArray{Complex{Float32},3},ThreeDeconv.LinearOperator,ThreeDeconv.LinearOperator,CuArray{Complex{Float32},3},CuArray{Complex{Float32},3},CuArray{Complex{Float32},3},CuArray{Complex{Float32},3},CuArray{Complex{Float32},3},CuArray{Complex{Float32},3}}, ::ThreeDeconv.var"#60#67"{Float32,Float32,Float32}) at /home/.../.julia/packages/ThreeDeconv/vtLlk/src/deconvolution/admm.jl:103
[4] setup_optimizer(::ThreeDeconv.ADMM, ::Array{Float32,3}, ::Array{Float32,3}, ::Float32, ::Float32, ::Float32) at /home/.../.julia/packages/ThreeDeconv/vtLlk/src/deconvolution/setup_admm.jl:128
[5] deconvolve(::Array{Float32,3}, ::Array{Float32,3}, ::Float64, ::Float64, ::Float64, ::ThreeDeconv.ADMM; options::ThreeDeconv.DeconvolutionOptions) at /home/.../.julia/packages/ThreeDeconv/vtLlk/src/deconvolution/deconvolve.jl:88
[6] top-level scope at In[25]:4
[7] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091
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