Merge pull request #3 from EPFL-LAPD/diffusion

Diffusion

Project.toml

@@ -1,7 +1,7 @@
 name = "SwissVAMyKnife"
 uuid = "fafdf525-54c7-48ec-ae29-528bd4cccfab"
 authors = ["roflmaostc <[email protected]>"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

examples/6_diffusion_TVAM.jl

@@ -0,0 +1,252 @@
+### A Pluto.jl notebook ###
+# v0.19.40
+
+using Markdown
+using InteractiveUtils
+
+# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
+macro bind(def, element)
+ quote
+ local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
+ local el = $(esc(element))
+ global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
+ el
+ end
+end
+
+# ╔═╡ c982861a-5a10-4a82-8be8-fa5418d729da
+begin
+ using Pkg
+ Pkg.activate(".")
+ Pkg.instantiate()
+ using Revise
+end
+
+# ╔═╡ 23a890a3-ae1b-4566-b050-677604a0c5e0
+# this is our main package
+using SwissVAMyKnife
+
+# ╔═╡ ce68dbde-20c0-4bb6-bbca-9914986d9f63
+using ImageShow, ImageIO, PlutoUI, IndexFunArrays, Optim, RadonKA, FileIO, Colors, Plots
+
+# ╔═╡ b49b0a49-1317-4820-a1a3-51f3bc4cdef3
+using CUDA
+
+# ╔═╡ c4dfa825-b276-4b8c-811e-3ad3d6a9b129
+using NDTools
+
+# ╔═╡ 8d4ea5a5-3f98-4481-ac16-3fda7ccec3bb
+md"""# Supplemental simulations
+
+Note, in the publication we optimize a volume of 550x550x550 voxels which only runs on a large GPU (NVIDIA A100 with 80GB). Hence, here a smaller version which produces similar but not identical results because of a different discretization of the boat.
+
+```bibtex
+@article{Wechsler:24,
+author = {Felix Wechsler and Carlo Gigli and Jorge Madrid-Wolff and Christophe Moser},
+journal = {Opt. Express},
+keywords = {3D printing; Computed tomography; Liquid crystal displays; Material properties; Ray tracing; Refractive index},
+number = {8},
+pages = {14705--14712},
+publisher = {Optica Publishing Group},
+title = {Wave optical model for tomographic volumetric additive manufacturing},
+volume = {32},
+month = {Apr},
+year = {2024},
+url = {https://opg.optica.org/oe/abstract.cfm?URI=oe-32-8-14705},
+doi = {10.1364/OE.521322},
+}
+```
+"""
+
+# ╔═╡ e4d6ebe2-6f2d-43ff-9b6b-109808935aff
+md"# 0. Load packages
+On the first run, Julia is going to install some packages automatically. So start this notebook and give it some minutes (5-10min) to install all packages. 
+No worries, any future runs will be much faster to start!
+"
+
+# ╔═╡ 1bac4230-2837-4146-a49b-248097fd0cf3
+Pkg.resolve()
+
+# ╔═╡ 015d4d1b-8921-41c6-9bc3-a4f43f3586aa
+TableOfContents()
+
+# ╔═╡ be1d52e3-c2a2-4cd0-8d10-ab088473bec3
+use_CUDA = Ref(true && CUDA.functional())
+
+# ╔═╡ a3077f1e-403b-421b-bf14-ab2307c2369b
+md" ## CUDA
+CUDA accelerates the pattern generation easily by 5-20 times!
+Otherwise most of the code will be multithreaded on your CPU but we strongly recommended the usage of CUDA for large scale 3D pattern generation.
+
+Your CUDA is functional: **$(use_CUDA[])**
+"
+
+# ╔═╡ d4395dd0-c784-45db-b80b-516840127f17
+var"@mytime" = use_CUDA[] ? CUDA.var"@time" : var"@time"
+
+# ╔═╡ 9960dd8c-c57f-4e1b-898e-110b3b5b5363
+togoc(x) = use_CUDA[] ? CuArray(x) : x
+
+# ╔═╡ fc67829c-53ed-4b92-a46d-c1216116e14d
+md"# 1. Load Benchy"
+
+# ╔═╡ 5b85bcc4-6bdf-4f09-969e-5c56cc236f81
+target = load_example_target("3DBenchy_180");
+
+# ╔═╡ 58963b11-5534-40b9-8101-977e6d21a0a4
+md"z slide value $(@bind slice PlutoUI.Slider(axes(target, 3), show_value=true, default=77))"
+
+# ╔═╡ bcd3affd-a880-4ba3-bcd2-52b45de97b1e
+simshow(Array(target[:, :, slice]))
+
+# ╔═╡ b52d634d-37dc-47c7-8f6c-5e5684e066a2
+md"# 2. Specify Optimization Parameters"
+
+# ╔═╡ 542d400f-4f63-45f7-bccf-a9370ee5663a
+loss = LossThreshold(thresholds=(0.90, 0.97))
+
+# ╔═╡ 2a3d1e2e-7603-4ee6-93e6-80faddfafc07
+angles = range(0, 2π, 151)[begin:end-1]
+
+# ╔═╡ 5e6ae68e-5dd7-4e26-84ad-f5ef7fd4b970
+geometry = ParallelRayOptics(angles, nothing)
+
+# ╔═╡ aeadc55a-6601-403a-b9cd-a3c6639cb15b
+diffusion = Diffusion(25f-6, 1f-10, 40f0, 3, 5)
+
+# ╔═╡ 43136316-d95d-47e5-bfbb-45443ae615cd
+optimizer = GradientBased(optimizer=Optim.LBFGS(), options=Optim.Options(iterations=20, store_trace=true))
+
+# ╔═╡ a7d4db4f-94b8-444a-846a-6e2ebfa5f368
+md"# 3. Optimize with Diffusion and without"
+
+# ╔═╡ 00004b83-427a-4704-bf13-c240ce4870fe
+@mytime patterns, printed_intensity, optim_res = optimize_patterns(togoc(target),
+ geometry, optimizer, loss)
+
+# ╔═╡ e5a8c428-1dca-42df-87f7-3c429f29b61a
+optim_res
+
+# ╔═╡ 985bf921-4783-4f0f-99c8-c08cb772e2f8
+@mytime patterns2, printed_intensity2, optim_res2 = optimize_patterns(togoc(target),
+ geometry, diffusion, optimizer, loss)
+
+# ╔═╡ 5d1b390d-175a-47f5-a8e9-ca8212eca646
+optim_res2
+
+# ╔═╡ 0cc85f47-068f-49f9-9701-0fcd9334f178
+md"# 4. Inspect"
+
+# ╔═╡ b48ff258-5163-42b5-9168-0ccab5ab10dc
+md"Choose threshold for image: $(@bind thresh4 PlutoUI.Slider(0:0.01:1, show_value=true, default=0.7))"
+
+# ╔═╡ a5c3f411-42f8-401b-b0b2-380dc9b57bfa
+md"z slice $(@bind slice2 PlutoUI.Slider(axes(target, 3), show_value=true, default=77))
+
+Intensity distribution ----------- after threshold -------------------- target -------------------------------difference
+"
+
+# ╔═╡ 0d665c2b-7e6c-4af4-afeb-cee2db582a02
+[simshow(Array(printed_intensity[:, :, slice2]), set_one=false, cmap=:turbo) simshow(ones((size(target, 1), 5))) simshow(thresh4 .< Array(printed_intensity[:, :, slice2])) simshow(ones((size(target, 1), 5))) simshow(Array(target[:, :, slice2])) simshow(ones((size(target, 1), 5))) simshow(Array(togoc(target)[:, :, slice2] .!= (thresh4 .< (printed_intensity[:, :, slice2]))))]
+
+# ╔═╡ 5f8ae887-5d69-41bc-9601-8894f8557ffd
+[simshow(Array(printed_intensity2[:, :, slice2]), set_one=false, cmap=:turbo) simshow(ones((size(target, 1), 5))) simshow(thresh4 .< Array(printed_intensity2[:, :, slice2])) simshow(ones((size(target, 1), 5))) simshow(Array(target[:, :, slice2])) simshow(ones((size(target, 1), 5))) simshow(Array(togoc(target)[:, :, slice2] .!= (thresh4 .< (printed_intensity2[:, :, slice2]))))]
+
+# ╔═╡ 44b67acf-b31f-4cd1-b675-475a81e8ed85
+plot_intensity_histogram(target, printed_intensity, loss.thresholds)
+
+# ╔═╡ b9636f03-e822-4b7f-8639-ae8ec3bd1d78
+plot_intensity_histogram(target, printed_intensity2, loss.thresholds)
+
+# ╔═╡ d1ee51c2-e30e-4a4e-976d-7322a879d35e
+md"Different projection patterns: $(@bind angle PlutoUI.Slider(axes(patterns, 2), show_value=true, default=0.5))"
+
+# ╔═╡ 9c05b82e-787e-4909-9fcf-4257bd8c9ddc
+simshow(Array(patterns[:,angle,:])[end:-1:begin, :]', cmap=:turbo, set_one=true)
+
+# ╔═╡ 58187e08-9608-415d-ba8a-c4a952bfa2a7
+simshow(Array(patterns2[:,angle,:])[end:-1:begin, :]', cmap=:turbo, set_one=true)
+
+# ╔═╡ 9a9051e1-03bf-4e76-85dc-8b07d35c6ca1
+md"# 5. Compare to non Diffusion
+Propagate the non diffusion patterns with the diffusion model
+"
+
+# ╔═╡ c1bcdafe-e03a-4403-9911-b6084c4cd539
+fwd_diffusion, _ = SwissVAMyKnife._prepare_ray_forward(togoc(target), geometry, diffusion)
+
+# ╔═╡ 2fe8b777-1867-44b3-905f-6f9079d38f1a
+md"Choose threshold for image: $(@bind thresh5 PlutoUI.Slider(0:0.01:1, show_value=true, default=0.7))"
+
+# ╔═╡ 966b4aa1-ddc2-4fd4-98d0-f571ca67c8d1
+md"z slice $(@bind slice3 PlutoUI.Slider(axes(target, 3), show_value=true, default=77))
+
+Intensity distribution ----------- after threshold -------------------- target -------------------------------difference
+"
+
+# ╔═╡ 6da2ab24-8817-4873-8006-bee30ddf4b2e
+[simshow(Array(printend_intensity_without_diff[:, :, slice2]), set_one=false, cmap=:turbo) simshow(ones((size(target, 1), 5))) simshow(thresh5 .< Array(printend_intensity_without_diff[:, :, slice3])) simshow(ones((size(target, 1), 5))) simshow(Array(target[:, :, slice3])) simshow(ones((size(target, 1), 5))) simshow(Array(togoc(target)[:, :, slice3] .!= (thresh5 .< (printend_intensity_without_diff[:, :, slice3]))))]
+
+# ╔═╡ ba5ff89a-f569-4a70-9205-7dbde326df4b
+plot_intensity_histogram(target, printend_intensity_without_diff, loss.thresholds)
+
+# ╔═╡ db4c7a5c-1395-4566-912d-5150503ce1a7
+Δt = 40 / 3 / 3
+
+# ╔═╡ 37320779-2cfd-44e5-a258-6cec54ba1a38
+# ╠═╡ disabled = true
+#=╠═╡
+printend_intensity_without_diff = fwd_diffusion(patterns2);
+ ╠═╡ =#
+
+# ╔═╡ 48fb1a90-d410-40e2-8d5f-9b5714b611f0
+printend_intensity_without_diff = fwd_diffusion(patterns ./ diffusion.N_rotations);
+
+# ╔═╡ Cell order:
+# ╟─8d4ea5a5-3f98-4481-ac16-3fda7ccec3bb
+# ╟─e4d6ebe2-6f2d-43ff-9b6b-109808935aff
+# ╠═c982861a-5a10-4a82-8be8-fa5418d729da
+# ╠═1bac4230-2837-4146-a49b-248097fd0cf3
+# ╠═23a890a3-ae1b-4566-b050-677604a0c5e0
+# ╠═ce68dbde-20c0-4bb6-bbca-9914986d9f63
+# ╠═015d4d1b-8921-41c6-9bc3-a4f43f3586aa
+# ╟─a3077f1e-403b-421b-bf14-ab2307c2369b
+# ╠═b49b0a49-1317-4820-a1a3-51f3bc4cdef3
+# ╠═be1d52e3-c2a2-4cd0-8d10-ab088473bec3
+# ╠═d4395dd0-c784-45db-b80b-516840127f17
+# ╠═9960dd8c-c57f-4e1b-898e-110b3b5b5363
+# ╟─fc67829c-53ed-4b92-a46d-c1216116e14d
+# ╠═c4dfa825-b276-4b8c-811e-3ad3d6a9b129
+# ╠═5b85bcc4-6bdf-4f09-969e-5c56cc236f81
+# ╟─58963b11-5534-40b9-8101-977e6d21a0a4
+# ╠═bcd3affd-a880-4ba3-bcd2-52b45de97b1e
+# ╟─b52d634d-37dc-47c7-8f6c-5e5684e066a2
+# ╠═542d400f-4f63-45f7-bccf-a9370ee5663a
+# ╠═2a3d1e2e-7603-4ee6-93e6-80faddfafc07
+# ╠═5e6ae68e-5dd7-4e26-84ad-f5ef7fd4b970
+# ╠═aeadc55a-6601-403a-b9cd-a3c6639cb15b
+# ╠═43136316-d95d-47e5-bfbb-45443ae615cd
+# ╟─a7d4db4f-94b8-444a-846a-6e2ebfa5f368
+# ╠═00004b83-427a-4704-bf13-c240ce4870fe
+# ╠═e5a8c428-1dca-42df-87f7-3c429f29b61a
+# ╠═985bf921-4783-4f0f-99c8-c08cb772e2f8
+# ╠═5d1b390d-175a-47f5-a8e9-ca8212eca646
+# ╠═0cc85f47-068f-49f9-9701-0fcd9334f178
+# ╟─b48ff258-5163-42b5-9168-0ccab5ab10dc
+# ╟─a5c3f411-42f8-401b-b0b2-380dc9b57bfa
+# ╠═0d665c2b-7e6c-4af4-afeb-cee2db582a02
+# ╟─5f8ae887-5d69-41bc-9601-8894f8557ffd
+# ╠═44b67acf-b31f-4cd1-b675-475a81e8ed85
+# ╠═b9636f03-e822-4b7f-8639-ae8ec3bd1d78
+# ╠═d1ee51c2-e30e-4a4e-976d-7322a879d35e
+# ╠═9c05b82e-787e-4909-9fcf-4257bd8c9ddc
+# ╠═58187e08-9608-415d-ba8a-c4a952bfa2a7
+# ╟─9a9051e1-03bf-4e76-85dc-8b07d35c6ca1
+# ╠═c1bcdafe-e03a-4403-9911-b6084c4cd539
+# ╠═48fb1a90-d410-40e2-8d5f-9b5714b611f0
+# ╠═37320779-2cfd-44e5-a258-6cec54ba1a38
+# ╟─2fe8b777-1867-44b3-905f-6f9079d38f1a
+# ╟─966b4aa1-ddc2-4fd4-98d0-f571ca67c8d1
+# ╟─6da2ab24-8817-4873-8006-bee30ddf4b2e
+# ╠═ba5ff89a-f569-4a70-9205-7dbde326df4b
+# ╠═db4c7a5c-1395-4566-912d-5150503ce1a7

src/ray_optics.jl

@@ -1,6 +1,6 @@
 export ParallelRayOptics, VialRayOptics
 export Polarization, PolarizationParallel, PolarizationPerpendicular, PolarizationRandom
-
+export Diffusion
 
 """
  Polarization
@@ -131,6 +131,14 @@ end
 
 
 
+@with_kw struct Diffusion{T}
+ voxel_size::T=100f-6
+ D::T=1f-10
+ printing_time::T=40f0
+ N_rotations::Int=3
+ diffusion_steps_per_rotation::Int=3
+end
+
 """
  optimize_patterns(target, ps::{VialRayOptics, ParallelRayOptics}, op::GradientBased, loss::LossTarget)
 
@@ -156,12 +164,14 @@ julia> patterns, printed_intensity, res = optimize_patterns(target, VialRayOptic
  ), GradientBased(), LossThreshold())
 ```
 """
-function optimize_patterns(target::AbstractArray{T}, ps::Union{VialRayOptics, ParallelRayOptics}, op::GradientBased, loss::LossTarget) where T
+function optimize_patterns(target::AbstractArray{T}, ps::Union{VialRayOptics, ParallelRayOptics}, diffusion::Union{Nothing, Diffusion},
+ op::GradientBased, loss::LossTarget) where T
  if T == Float64 && target isa CuArray
  @warn "Target seems to be Float64. For CUDA it is recommended to use a Float32 element type"
  end
 
- fwd, pat0 = _prepare_ray_forward(target, ps)
+ # diffusion is either nothing or Diffusion
+ fwd, pat0 = _prepare_ray_forward(target, ps, diffusion)
  # create loss evaluation and gradient function
  fg! = make_fg!(fwd, target, loss)
 
@@ -180,7 +190,56 @@ end
 
 
 
-function _prepare_ray_forward(target::AbstractArray{T}, ps::ParallelRayOptics) where T
+function optimize_patterns(target::AbstractArray{T}, ps::Union{VialRayOptics, ParallelRayOptics},
+ op::GradientBased, loss::LossTarget) where T
+ return optimize_patterns(target, ps, nothing, op, loss)
+end
+
+
+"""
+
+inspired by: https://www.nature.com/articles/s41467-023-39886-4
+"""
+function _prepare_ray_forward(target::AbstractArray{T}, ps::ParallelRayOptics, diff::Diffusion) where T
+ geometry = RadonParallelCircle(size(target, 1), -(size(target,1) -1)÷2:1:(size(target,1) -1)÷2)
+ pat0 = radon(target, ps.angles, μ=ps.μ, geometry=geometry)
+
+ @assert length(ps.angles) % diff.diffusion_steps_per_rotation == 0 "Number of angles must be divisible by diffusion_steps_per_rotation"
+ p = plan_rfft(similar(target), (1,2,3))
+ kernel = similar(target)
+ Δt = diff.printing_time / diff.N_rotations / diff.diffusion_steps_per_rotation
+
+ kernel .= exp.(.- rr2(T, size(target), scale=diff.voxel_size) ./ (4 * T(π) * diff.D * Δt))
+ kernel ./= sum(kernel)
+ kernel_fft = p * ifftshift(kernel, (1,2,3))
+
+ fwd = let angles=ps.angles, μ=ps.μ, geometry=geometry, kernel_fft=kernel_fft, target=target, p=p, pinv=inv(p)
+ function fwd(x)
+ out = 0 .* target
+ f(j) = begin
+ N_angles_d = length(angles) ÷ diff.diffusion_steps_per_rotation
+ angle_range = 1 + (j-1) * N_angles_d : j * N_angles_d
+ return backproject(NNlib.relu.(view(x, :, angle_range, :) ./ length(angles)),
+ angles[angle_range];
+ μ, geometry)
+ end
+
+ backprojections = [f(j) for j in 1:diff.diffusion_steps_per_rotation]
+
+ for i in 1:diff.N_rotations
+ for j in 1:diff.diffusion_steps_per_rotation
+ # FFT based convolution
+ out = (pinv.p * ((p * (out .+ backprojections[j])) .* kernel_fft .* pinv.scale))
+ end
+ end
+ return out
+ end
+ end
+ return fwd, pat0
+end
+
+
+function _prepare_ray_forward(target::AbstractArray{T}, ps::ParallelRayOptics, ::Nothing) where T
  geometry = RadonParallelCircle(size(target, 1), -(size(target,1) -1)÷2:1:(size(target,1) -1)÷2)
  pat0 = radon(target, ps.angles, μ=ps.μ, geometry=geometry)
  fwd = let angles=ps.angles, μ=ps.μ, geometry=geometry
@@ -191,7 +250,7 @@ end
 
 
 
-function _prepare_ray_forward(target::AbstractArray{T}, ps::VialRayOptics) where T
+function _prepare_ray_forward(target::AbstractArray{T}, ps::VialRayOptics, ::Nothing) where T
 
  N = iseven(size(target, 1)) ? T(size(target, 1) - 1) : T(size(target, 1))
  radius_pixel = T(N / 2)